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\ No newline at end of file diff --git a/spaces/2ndelement/voicevox/voicevox_engine/engine_manifest/__init__.py b/spaces/2ndelement/voicevox/voicevox_engine/engine_manifest/__init__.py deleted file mode 100644 index 02c293179a59cbae3f28ce79ab8a31616fa69d19..0000000000000000000000000000000000000000 --- a/spaces/2ndelement/voicevox/voicevox_engine/engine_manifest/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .EngineManifest import EngineManifest -from .EngineManifestLoader import EngineManifestLoader - -__all__ = [ - "EngineManifest", - "EngineManifestLoader", -] diff --git a/spaces/801artistry/RVC801/infer/lib/train/losses.py b/spaces/801artistry/RVC801/infer/lib/train/losses.py deleted file mode 100644 index b1b263e4c205e78ffe970f622ab6ff68f36d3b17..0000000000000000000000000000000000000000 --- a/spaces/801artistry/RVC801/infer/lib/train/losses.py +++ /dev/null @@ -1,58 +0,0 @@ -import torch - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - rl = rl.float().detach() - gl = gl.float() - loss += torch.mean(torch.abs(rl - gl)) - - return loss * 2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - dr = dr.float() - dg = dg.float() - r_loss = torch.mean((1 - dr) ** 2) - g_loss = torch.mean(dg**2) - loss += r_loss + g_loss - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - return loss, r_losses, g_losses - - -def generator_loss(disc_outputs): - loss = 0 - gen_losses = [] - for dg in disc_outputs: - dg = dg.float() - l = torch.mean((1 - dg) ** 2) - gen_losses.append(l) - loss += l - - return loss, gen_losses - - -def kl_loss(z_p, logs_q, m_p, logs_p, z_mask): - """ - z_p, logs_q: [b, h, t_t] - m_p, logs_p: [b, h, t_t] - """ - z_p = z_p.float() - logs_q = logs_q.float() - m_p = m_p.float() - logs_p = logs_p.float() - z_mask = z_mask.float() - - kl = logs_p - logs_q - 0.5 - kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p) - kl = torch.sum(kl * z_mask) - l = kl / torch.sum(z_mask) - return l diff --git a/spaces/801artistry/RVC801/infer/modules/uvr5/modules.py b/spaces/801artistry/RVC801/infer/modules/uvr5/modules.py deleted file mode 100644 index f63ac6a794100cc95da21dcba78b23377a1f133d..0000000000000000000000000000000000000000 --- a/spaces/801artistry/RVC801/infer/modules/uvr5/modules.py +++ /dev/null @@ -1,107 +0,0 @@ -import os -import traceback -import logging - -logger = logging.getLogger(__name__) - -import ffmpeg -import torch - -from configs.config import Config -from infer.modules.uvr5.mdxnet import MDXNetDereverb -from infer.modules.uvr5.preprocess import AudioPre, AudioPreDeEcho - -config = Config() - - -def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0): - infos = [] - try: - inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - save_root_vocal = ( - save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) - save_root_ins = ( - save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) - if model_name == "onnx_dereverb_By_FoxJoy": - pre_fun = MDXNetDereverb(15, config.device) - else: - func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho - pre_fun = func( - agg=int(agg), - model_path=os.path.join( - os.getenv("weight_uvr5_root"), model_name + ".pth" - ), - device=config.device, - is_half=config.is_half, - ) - if inp_root != "": - paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)] - else: - paths = [path.name for path in paths] - for path in paths: - inp_path = os.path.join(inp_root, path) - need_reformat = 1 - done = 0 - try: - info = ffmpeg.probe(inp_path, cmd="ffprobe") - if ( - info["streams"][0]["channels"] == 2 - and info["streams"][0]["sample_rate"] == "44100" - ): - need_reformat = 0 - pre_fun._path_audio_( - inp_path, save_root_ins, save_root_vocal, format0 - ) - done = 1 - except: - need_reformat = 1 - traceback.print_exc() - if need_reformat == 1: - tmp_path = "%s/%s.reformatted.wav" % ( - os.path.join(os.environ["TEMP"]), - os.path.basename(inp_path), - ) - os.system( - "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y" - % (inp_path, tmp_path) - ) - inp_path = tmp_path - try: - if done == 0: - pre_fun.path_audio( - inp_path, save_root_ins, save_root_vocal, format0 - ) - infos.append("%s->Success" % (os.path.basename(inp_path))) - yield "\n".join(infos) - except: - try: - if done == 0: - pre_fun._path_audio_( - inp_path, save_root_ins, save_root_vocal, format0 - ) - infos.append("%s->Success" % (os.path.basename(inp_path))) - yield "\n".join(infos) - except: - infos.append( - "%s->%s" % (os.path.basename(inp_path), traceback.format_exc()) - ) - yield "\n".join(infos) - except: - infos.append(traceback.format_exc()) - yield "\n".join(infos) - finally: - try: - if model_name == "onnx_dereverb_By_FoxJoy": - del pre_fun.pred.model - del pre_fun.pred.model_ - else: - del pre_fun.model - del pre_fun - except: - traceback.print_exc() - if torch.cuda.is_available(): - torch.cuda.empty_cache() - logger.info("Executed torch.cuda.empty_cache()") - yield "\n".join(infos) diff --git a/spaces/AIFILMS/StyleGANEX/models/stylegan2/op/fused_act.py b/spaces/AIFILMS/StyleGANEX/models/stylegan2/op/fused_act.py deleted file mode 100644 index 74815adafbf7a37d5d4def41ac60dbdeefdbff30..0000000000000000000000000000000000000000 --- a/spaces/AIFILMS/StyleGANEX/models/stylegan2/op/fused_act.py +++ /dev/null @@ -1,34 +0,0 @@ -import torch -from torch import nn -from torch.nn import functional as F - - -class FusedLeakyReLU(nn.Module): - def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5): - super().__init__() - - if bias: - self.bias = nn.Parameter(torch.zeros(channel)) - - else: - self.bias = None - - self.negative_slope = negative_slope - self.scale = scale - - def forward(self, inputs): - return fused_leaky_relu(inputs, self.bias, self.negative_slope, self.scale) - - -def fused_leaky_relu(inputs, bias=None, negative_slope=0.2, scale=2 ** 0.5): - if bias is not None: - rest_dim = [1] * (inputs.ndim - bias.ndim - 1) - return ( - F.leaky_relu( - inputs + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope - ) - * scale - ) - - else: - return F.leaky_relu(inputs, negative_slope=negative_slope) * scale \ No newline at end of file diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/.ipynb_checkpoints/hr_4xb32_1024e_4channel-checkpoint.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/.ipynb_checkpoints/hr_4xb32_1024e_4channel-checkpoint.py deleted file mode 100644 index 0a4b8d3739c2612647dbd0a01b43d709e51a7da4..0000000000000000000000000000000000000000 --- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/.ipynb_checkpoints/hr_4xb32_1024e_4channel-checkpoint.py +++ /dev/null @@ -1,115 +0,0 @@ -_base_ = [ # 此配置文件将继承所有 `_base_` 中的配置 - '../configs/_base_/schedules/custom_schedule.py', # 训练策略配置 - '../configs/_base_/default_runtime.py' # 默认运行设置 -] - -default_hooks = dict( - # print log every 50 iterations. - logger=dict(type='LoggerHook', interval=10), - # save checkpoint per 8 epochs. - checkpoint=dict(save_best='auto', interval=16) -) - -visualizer = dict( - vis_backends=[dict(type='LocalVisBackend'), - dict(type='WandbVisBackend')]) - -dataset_type = 'CustomDataset' - -# config of pipline -train_pipeline = [ - dict(type='LoadImageFromFile', imdecode_backend='pillow', color_type='unchanged'), # 读取图像 - dict(type='RandomResizedCrop', scale=224), # 随机放缩裁剪 - dict(type='RandomFlip', prob=0.5, direction='horizontal'), # 随机水平翻转 - dict(type='PackInputs'), # 准备图像以及标签 -] - -test_pipeline = [ - dict(type='LoadImageFromFile', imdecode_backend='pillow', color_type='unchanged'), # 读取图像 - dict(type='ResizeEdge', scale=256, edge='short'), # 缩放短边尺寸至 256px - dict(type='CenterCrop', crop_size=224), # 中心裁剪 - dict(type='PackInputs'), # 准备图像以及标签 -] - -# config of dataloader -train_dataloader = dict( - batch_size=32, # 每张 GPU 的 batchsize - num_workers=5, # 每个 GPU 的线程数 - dataset=dict( # 训练数据集 - type=dataset_type, - data_root='../2_preprocess_data_3000', - with_label=True, - ann_file='', - data_prefix='train', - pipeline=train_pipeline), - sampler=dict(type='DefaultSampler', shuffle=True), # 默认采样器 - persistent_workers=True, # 是否保持进程，可以缩短每个 epoch 的准备时间 -) - -# 构造验证集 dataloader -val_dataloader = dict( - batch_size=32, - num_workers=5, - dataset=dict( - type=dataset_type, - data_root='../2_preprocess_data_3000', - with_label=True, - ann_file='', - data_prefix='val', - pipeline=test_pipeline), - sampler=dict(type='DefaultSampler', shuffle=False), - persistent_workers=True, -) - -# set evaluator of validation dataset. Here uses top1 and top3 accuracy -val_evaluator = dict(type='Accuracy', topk=(1, 3)) - -test_dataloader = val_dataloader -test_evaluator = val_evaluator - -model = dict( - type='ImageClassifier', # 主模型类型（对于图像分类任务，使用 `ImageClassifier`） - backbone=dict( - type='HRNet', # 主干网络类型 - arch='w32', # 主干网络架构 - in_channels=4, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - ), - neck=dict(type='GlobalAveragePooling'), # 颈网络类型 - head=dict( - type='LinearClsHead', # 分类颈网络类型 - # 除了 `type` 之外的所有字段都来自 `LinearClsHead` 类的 __init__ 方法 - # 可查阅 https://mmpretrain.readthedocs.io/zh_CN/latest/api/generated/mmpretrain.models.heads.LinearClsHead.html - num_classes=7, # 分类类别数 - in_channels=256, - loss=dict(type='CrossEntropyLoss', loss_weight=1.0), # 损失函数配置信息 - topk=(1, 3), # 评估指标，Top-k 准确率 - )) - -optim_wrapper = dict( - accumulative_counts=8 -) diff --git a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/models/musicgen.py b/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/models/musicgen.py deleted file mode 100644 index 5001a602c58fa3abc02f1b43d1b40c3c4e60a355..0000000000000000000000000000000000000000 --- a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/models/musicgen.py +++ /dev/null @@ -1,450 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -""" -Main model for using MusicGen. This will combine all the required components -and provide easy access to the generation API. -""" - -import os -import typing as tp - -import torch - -from .encodec import CompressionModel -from .lm import LMModel -from .builders import get_debug_compression_model, get_debug_lm_model -from .loaders import load_compression_model, load_lm_model, HF_MODEL_CHECKPOINTS_MAP -from ..data.audio_utils import convert_audio -from ..modules.conditioners import ConditioningAttributes, WavCondition -from ..utils.autocast import TorchAutocast - - -MelodyList = tp.List[tp.Optional[torch.Tensor]] -MelodyType = tp.Union[torch.Tensor, MelodyList] - - -class MusicGen: - """MusicGen main model with convenient generation API. - - Args: - name (str): name of the model. - compression_model (CompressionModel): Compression model - used to map audio to invertible discrete representations. - lm (LMModel): Language model over discrete representations. - """ - def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel, max_duration: float = 30): - self.name = name - self.compression_model = compression_model - self.lm = lm - self.max_duration = max_duration - self.duration = 15.0 # default duration - self.device = next(iter(lm.parameters())).device - self.generation_params: dict = {} - self.set_generation_params(duration=self.duration) # 15 seconds by default - self._progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None - if self.device.type == 'cpu': - self.autocast = TorchAutocast(enabled=False) - else: - self.autocast = TorchAutocast( - enabled=True, device_type=self.device.type, dtype=torch.float16) - - @property - def frame_rate(self) -> int: - """Roughly the number of AR steps per seconds.""" - return self.compression_model.frame_rate - - @property - def sample_rate(self) -> int: - """Sample rate of the generated audio.""" - return self.compression_model.sample_rate - - @property - def audio_channels(self) -> int: - """Audio channels of the generated audio.""" - return self.compression_model.channels - - @staticmethod - def get_pretrained(name: str = 'melody', device=None): - """Return pretrained model, we provide four models: - - small (300M), text to music, # see: https://huggingface.co/facebook/musicgen-small - - medium (1.5B), text to music, # see: https://huggingface.co/facebook/musicgen-medium - - melody (1.5B) text to music and text+melody to music, # see: https://huggingface.co/facebook/musicgen-melody - - large (3.3B), text to music, # see: https://huggingface.co/facebook/musicgen-large - """ - - if device is None: - if torch.cuda.device_count(): - device = 'cuda' - else: - device = 'cpu' - - if name == 'debug': - # used only for unit tests - compression_model = get_debug_compression_model(device) - lm = get_debug_lm_model(device) - return MusicGen(name, compression_model, lm) - - if name not in HF_MODEL_CHECKPOINTS_MAP: - if not os.path.isfile(name) and not os.path.isdir(name): - raise ValueError( - f"{name} is not a valid checkpoint name. " - f"Choose one of {', '.join(HF_MODEL_CHECKPOINTS_MAP.keys())}" - ) - - cache_dir = os.environ.get('MUSICGEN_ROOT', None) - compression_model = load_compression_model(name, device=device, cache_dir=cache_dir) - lm = load_lm_model(name, device=device, cache_dir=cache_dir) - if name == 'melody': - lm.condition_provider.conditioners['self_wav'].match_len_on_eval = True - - return MusicGen(name, compression_model, lm) - - def set_generation_params(self, use_sampling: bool = True, top_k: int = 250, - top_p: float = 0.0, temperature: float = 1.0, - duration: float = 30.0, cfg_coef: float = 3.0, - two_step_cfg: bool = False, extend_stride: float = 18, rep_penalty: float = None): - """Set the generation parameters for MusicGen. - - Args: - use_sampling (bool, optional): Use sampling if True, else do argmax decoding. Defaults to True. - top_k (int, optional): top_k used for sampling. Defaults to 250. - top_p (float, optional): top_p used for sampling, when set to 0 top_k is used. Defaults to 0.0. - temperature (float, optional): Softmax temperature parameter. Defaults to 1.0. - duration (float, optional): Duration of the generated waveform. Defaults to 30.0. - cfg_coef (float, optional): Coefficient used for classifier free guidance. Defaults to 3.0. - two_step_cfg (bool, optional): If True, performs 2 forward for Classifier Free Guidance, - instead of batching together the two. This has some impact on how things - are padded but seems to have little impact in practice. - rep_penalty (float, optional): If set, use repetition penalty during generation. Not Implemented. - """ - assert extend_stride < self.max_duration, "Cannot stride by more than max generation duration." - self.extend_stride = extend_stride - self.duration = duration - self.generation_params = { - #'max_gen_len': int(duration * self.frame_rate), - 'use_sampling': use_sampling, - 'temp': temperature, - 'top_k': top_k, - 'top_p': top_p, - 'cfg_coef': cfg_coef, - 'two_step_cfg': two_step_cfg, - } - - def set_custom_progress_callback(self, progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None): - """Override the default progress callback.""" - self._progress_callback = progress_callback - - def generate_unconditional(self, num_samples: int, progress: bool = False) -> torch.Tensor: - """Generate samples in an unconditional manner. - - Args: - num_samples (int): Number of samples to be generated. - progress (bool, optional): Flag to display progress of the generation process. Defaults to False. - """ - descriptions: tp.List[tp.Optional[str]] = [None] * num_samples - attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None) - return self._generate_tokens(attributes, prompt_tokens, progress) - - def generate(self, descriptions: tp.List[str], progress: bool = False) -> torch.Tensor: - """Generate samples conditioned on text. - - Args: - descriptions (tp.List[str]): A list of strings used as text conditioning. - progress (bool, optional): Flag to display progress of the generation process. Defaults to False. - """ - attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None) - assert prompt_tokens is None - return self._generate_tokens(attributes, prompt_tokens, progress) - - def generate_with_chroma(self, descriptions: tp.List[str], melody_wavs: MelodyType, - melody_sample_rate: int, progress: bool = False) -> torch.Tensor: - """Generate samples conditioned on text and melody. - - Args: - descriptions (tp.List[str]): A list of strings used as text conditioning. - melody_wavs: (torch.Tensor or list of Tensor): A batch of waveforms used as - melody conditioning. Should have shape [B, C, T] with B matching the description length, - C=1 or 2. It can be [C, T] if there is a single description. It can also be - a list of [C, T] tensors. - melody_sample_rate: (int): Sample rate of the melody waveforms. - progress (bool, optional): Flag to display progress of the generation process. Defaults to False. - """ - if isinstance(melody_wavs, torch.Tensor): - if melody_wavs.dim() == 2: - melody_wavs = melody_wavs[None] - if melody_wavs.dim() != 3: - raise ValueError("Melody wavs should have a shape [B, C, T].") - melody_wavs = list(melody_wavs) - else: - for melody in melody_wavs: - if melody is not None: - assert melody.dim() == 2, "One melody in the list has the wrong number of dims." - - melody_wavs = [ - convert_audio(wav, melody_sample_rate, self.sample_rate, self.audio_channels) - if wav is not None else None - for wav in melody_wavs] - attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions=descriptions, prompt=None, - melody_wavs=melody_wavs) - assert prompt_tokens is None - return self._generate_tokens(attributes, prompt_tokens, progress) - - def generate_with_all(self, descriptions: tp.List[str], melody_wavs: MelodyType, - sample_rate: int, progress: bool = False, prompt: tp.Optional[torch.Tensor] = None) -> torch.Tensor: - """Generate samples conditioned on text and melody and audio prompts. - Args: - descriptions (tp.List[str]): A list of strings used as text conditioning. - melody_wavs: (torch.Tensor or list of Tensor): A batch of waveforms used as - melody conditioning. Should have shape [B, C, T] with B matching the description length, - C=1 or 2. It can be [C, T] if there is a single description. It can also be - a list of [C, T] tensors. - sample_rate: (int): Sample rate of the melody waveforms. - progress (bool, optional): Flag to display progress of the generation process. Defaults to False. - prompt (torch.Tensor): A batch of waveforms used for continuation. - Prompt should be [B, C, T], or [C, T] if only one sample is generated. - """ - if isinstance(melody_wavs, torch.Tensor): - if melody_wavs.dim() == 2: - melody_wavs = melody_wavs[None] - if melody_wavs.dim() != 3: - raise ValueError("Melody wavs should have a shape [B, C, T].") - melody_wavs = list(melody_wavs) - else: - for melody in melody_wavs: - if melody is not None: - assert melody.dim() == 2, "One melody in the list has the wrong number of dims." - - melody_wavs = [ - convert_audio(wav, sample_rate, self.sample_rate, self.audio_channels) - if wav is not None else None - for wav in melody_wavs] - #attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions=descriptions, prompt=None, - # melody_wavs=melody_wavs) - - if prompt is not None: - if prompt.dim() == 2: - prompt = prompt[None] - if prompt.dim() != 3: - raise ValueError("prompt should have 3 dimensions: [B, C, T] (C = 1).") - prompt = convert_audio(prompt, sample_rate, self.sample_rate, self.audio_channels) - if descriptions is None: - descriptions = [None] * len(prompt) - - #if prompt is not None: - # attributes_gen, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, prompt) - - attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions=descriptions, prompt=prompt, - melody_wavs=melody_wavs) - if prompt is not None: - assert prompt_tokens is not None - else: - assert prompt_tokens is None - return self._generate_tokens(attributes, prompt_tokens, progress) - - def generate_continuation(self, prompt: torch.Tensor, prompt_sample_rate: int, - descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None, - progress: bool = False) -> torch.Tensor: - """Generate samples conditioned on audio prompts. - - Args: - prompt (torch.Tensor): A batch of waveforms used for continuation. - Prompt should be [B, C, T], or [C, T] if only one sample is generated. - prompt_sample_rate (int): Sampling rate of the given audio waveforms. - descriptions (tp.List[str], optional): A list of strings used as text conditioning. Defaults to None. - progress (bool, optional): Flag to display progress of the generation process. Defaults to False. - """ - if prompt.dim() == 2: - prompt = prompt[None] - if prompt.dim() != 3: - raise ValueError("prompt should have 3 dimensions: [B, C, T] (C = 1).") - prompt = convert_audio(prompt, prompt_sample_rate, self.sample_rate, self.audio_channels) - if descriptions is None: - descriptions = [None] * len(prompt) - attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, prompt) - assert prompt_tokens is not None - return self._generate_tokens(attributes, prompt_tokens, progress) - - @torch.no_grad() - def _prepare_tokens_and_attributes( - self, - descriptions: tp.Sequence[tp.Optional[str]], - prompt: tp.Optional[torch.Tensor], - melody_wavs: tp.Optional[MelodyList] = None, - ) -> tp.Tuple[tp.List[ConditioningAttributes], tp.Optional[torch.Tensor]]: - """Prepare model inputs. - - Args: - descriptions (tp.List[str]): A list of strings used as text conditioning. - prompt (torch.Tensor): A batch of waveforms used for continuation. - melody_wavs (tp.Optional[torch.Tensor], optional): A batch of waveforms - used as melody conditioning. Defaults to None. - """ - attributes = [ - ConditioningAttributes(text={'description': description}) - for description in descriptions] - - if melody_wavs is None: - for attr in attributes: - attr.wav['self_wav'] = WavCondition( - torch.zeros((1, 1), device=self.device), - torch.tensor([0], device=self.device), - path='null_wav') # type: ignore - else: - if self.name != "melody": - raise RuntimeError("This model doesn't support melody conditioning. " - "Use the `melody` model.") - assert len(melody_wavs) == len(descriptions), \ - f"number of melody wavs must match number of descriptions! " \ - f"got melody len={len(melody_wavs)}, and descriptions len={len(descriptions)}" - for attr, melody in zip(attributes, melody_wavs): - if melody is None: - attr.wav['self_wav'] = WavCondition( - torch.zeros((1, 1), device=self.device), - torch.tensor([0], device=self.device), - path='null_wav') # type: ignore - else: - attr.wav['self_wav'] = WavCondition( - melody.to(device=self.device), - torch.tensor([melody.shape[-1]], device=self.device)) - - if prompt is not None: - if descriptions is not None: - assert len(descriptions) == len(prompt), "Prompt and nb. descriptions doesn't match" - prompt = prompt.to(self.device) - prompt_tokens, scale = self.compression_model.encode(prompt) - assert scale is None - else: - prompt_tokens = None - return attributes, prompt_tokens - - def _generate_tokens(self, attributes: tp.List[ConditioningAttributes], - prompt_tokens: tp.Optional[torch.Tensor], progress: bool = False) -> torch.Tensor: - """Generate discrete audio tokens given audio prompt and/or conditions. - - Args: - attributes (tp.List[ConditioningAttributes]): Conditions used for generation (text/melody). - prompt_tokens (tp.Optional[torch.Tensor]): Audio prompt used for continuation. - progress (bool, optional): Flag to display progress of the generation process. Defaults to False. - Returns: - torch.Tensor: Generated audio, of shape [B, C, T], T is defined by the generation params. - """ - total_gen_len = int(self.duration * self.frame_rate) - max_prompt_len = int(min(self.duration, self.max_duration) * self.frame_rate) - current_gen_offset: int = 0 - - def _progress_callback(generated_tokens: int, tokens_to_generate: int): - generated_tokens += current_gen_offset - if self._progress_callback is not None: - # Note that total_gen_len might be quite wrong depending on the - # codebook pattern used, but with delay it is almost accurate. - self._progress_callback(generated_tokens, total_gen_len) - else: - print(f'{generated_tokens: 6d} / {total_gen_len: 6d}', end='\r') - - if prompt_tokens is not None: - assert max_prompt_len >= prompt_tokens.shape[-1], \ - "Prompt is longer than audio to generate" - - callback = None - if progress: - callback = _progress_callback - - if self.duration <= self.max_duration: - # generate by sampling from LM, simple case. - with self.autocast: - gen_tokens = self.lm.generate( - prompt_tokens, attributes, - callback=callback, max_gen_len=total_gen_len, **self.generation_params) - - else: - # now this gets a bit messier, we need to handle prompts, - # melody conditioning etc. - ref_wavs = [attr.wav['self_wav'] for attr in attributes] - all_tokens = [] - if prompt_tokens is None: - prompt_length = 0 - else: - all_tokens.append(prompt_tokens) - prompt_length = prompt_tokens.shape[-1] - - stride_tokens = int(self.frame_rate * self.extend_stride) - - while current_gen_offset + prompt_length < total_gen_len: - time_offset = current_gen_offset / self.frame_rate - chunk_duration = min(self.duration - time_offset, self.max_duration) - max_gen_len = int(chunk_duration * self.frame_rate) - for attr, ref_wav in zip(attributes, ref_wavs): - wav_length = ref_wav.length.item() - if wav_length == 0: - continue - # We will extend the wav periodically if it not long enough. - # we have to do it here rather than in conditioners.py as otherwise - # we wouldn't have the full wav. - initial_position = int(time_offset * self.sample_rate) - wav_target_length = int(self.max_duration * self.sample_rate) - print(initial_position / self.sample_rate, wav_target_length / self.sample_rate) - positions = torch.arange(initial_position, - initial_position + wav_target_length, device=self.device) - attr.wav['self_wav'] = WavCondition( - ref_wav[0][:, positions % wav_length], - torch.full_like(ref_wav[1], wav_target_length)) - with self.autocast: - gen_tokens = self.lm.generate( - prompt_tokens, attributes, - callback=callback, max_gen_len=max_gen_len, **self.generation_params) - if prompt_tokens is None: - all_tokens.append(gen_tokens) - else: - all_tokens.append(gen_tokens[:, :, prompt_tokens.shape[-1]:]) - prompt_tokens = gen_tokens[:, :, stride_tokens:] - prompt_length = prompt_tokens.shape[-1] - current_gen_offset += stride_tokens - - gen_tokens = torch.cat(all_tokens, dim=-1) - - # generate audio - assert gen_tokens.dim() == 3 - with torch.no_grad(): - gen_audio = self.compression_model.decode(gen_tokens, None) - return gen_audio - - #def _generate_tokens(self, attributes: tp.List[ConditioningAttributes], - # prompt_tokens: tp.Optional[torch.Tensor], progress: bool = False) -> torch.Tensor: - # """Generate discrete audio tokens given audio prompt and/or conditions. - - # Args: - # attributes (tp.List[ConditioningAttributes]): Conditions used for generation (text/melody). - # prompt_tokens (tp.Optional[torch.Tensor]): Audio prompt used for continuation. - # progress (bool, optional): Flag to display progress of the generation process. Defaults to False. - # Returns: - # torch.Tensor: Generated audio, of shape [B, C, T], T is defined by the generation params. - # """ - # def _progress_callback(generated_tokens: int, tokens_to_generate: int): - # print(f'{generated_tokens: 6d} / {tokens_to_generate: 6d}', end='\r') - - # if prompt_tokens is not None: - # assert self.generation_params['max_gen_len'] > prompt_tokens.shape[-1], \ - # "Prompt is longer than audio to generate" - - # callback = None - # if progress: - # callback = _progress_callback - - # # generate by sampling from LM - # with self.autocast: - # gen_tokens = self.lm.generate(prompt_tokens, attributes, callback=callback, **self.generation_params) - - # # generate audio - # assert gen_tokens.dim() == 3 - # with torch.no_grad(): - # gen_audio = self.compression_model.decode(gen_tokens, None) - # return gen_audio - - def to(self, device: str): - self.compression_model.to(device) - self.lm.to(device) - return self \ No newline at end of file diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/Timestamps.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/Timestamps.ts deleted file mode 100644 index 12d1867d1be509310190df09d2392bfaa77d6500..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/Timestamps.ts +++ /dev/null @@ -1,4 +0,0 @@ -export interface Timestamps { - createdAt: Date; - updatedAt: Date; -} diff --git a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/openpose/api.py b/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/openpose/api.py deleted file mode 100644 index dbe7a8c1c0f9c035cdff8660d33348c58a0579c5..0000000000000000000000000000000000000000 --- a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/openpose/api.py +++ /dev/null @@ -1,35 +0,0 @@ -import numpy as np -import os -import torch.nn as nn - -os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" - -import cv2 -import torch - -from . import util -from .body import Body - -remote_model_path = "https://huggingface.co/TencentARC/T2I-Adapter/blob/main/third-party-models/body_pose_model.pth" - - -class OpenposeInference(nn.Module): - - def __init__(self): - super().__init__() - body_modelpath = os.path.join('models', "body_pose_model.pth") - - if not os.path.exists(body_modelpath): - from basicsr.utils.download_util import load_file_from_url - load_file_from_url(remote_model_path, model_dir='models') - - self.body_estimation = Body(body_modelpath) - - def forward(self, x): - x = x[:, :, ::-1].copy() - with torch.no_grad(): - candidate, subset = self.body_estimation(x) - canvas = np.zeros_like(x) - canvas = util.draw_bodypose(canvas, candidate, subset) - canvas = cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR) - return canvas diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fade/Fade.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fade/Fade.js deleted file mode 100644 index f1d17dc9206447fb9c4d59f3e3685ad2fcb82166..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fade/Fade.js +++ /dev/null @@ -1,5 +0,0 @@ -import Fade from '../../../plugins/fade.js'; -import FadeIn from '../../../plugins/fade-in.js'; -import FadeOutDestroy from '../../../plugins/fade-out-destroy.js'; - -export { Fade, FadeIn, FadeOutDestroy }; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridbuttons/GridButtons.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridbuttons/GridButtons.js deleted file mode 100644 index 52d92f80fc797aaa46494b57c88b2a1a1cbd3108..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridbuttons/GridButtons.js +++ /dev/null @@ -1,124 +0,0 @@ -import GridSizer from '../gridsizer/GridSizer.js'; -import AddChildMethods from './AddChildMethods.js'; -import RemoveChildMethods from './RemoveChildMethods.js'; -import ButtonGroup from '../utils/buttongroup/ButtonGroup.js'; -import ButtonMethods from '../utils/buttongroup/ButtonMethods.js'; -import ButtonStateMethods from '../utils/buttongroup/ButtonStateMethods.js'; - -const GetValue = Phaser.Utils.Objects.GetValue; - -class GridButtons extends GridSizer { - constructor(scene, config) { - if (config === undefined) { - config = {}; - } - var rowCount = GetValue(config, 'row', 0); - var columnCount = GetValue(config, 'column', (config.col || 0)); - var createCellContainerCallback = GetValue(config, 'createCellContainerCallback'); - var buttons = GetValue(config, 'buttons', undefined); - var buttonsExpand = GetValue(config, 'expand', true); - var buttonProportion = (buttonsExpand) ? 1 : 0; - - if (createCellContainerCallback) { - config.createCellContainerCallback = undefined; - } - if (buttons !== undefined) { - rowCount = Math.max(rowCount, buttons.length); - for (var i = 0, cnt = buttons.length; i < cnt; i++) { - columnCount = Math.max(columnCount, buttons[i].length); - } - } - - config.row = rowCount; - config.column = columnCount; - config.columnProportions = buttonProportion; - config.rowProportions = buttonProportion; - - // Create - super(scene, config); - this.type = 'rexGridButtons'; - this.buttonGroup = new ButtonGroup({ - parent: this, - eventEmitter: GetValue(config, 'eventEmitter', this), - groupName: GetValue(config, 'groupName', undefined), - clickConfig: GetValue(config, 'click', undefined) - }) - .setButtonsType(config); - - // Add elements - var background = GetValue(config, 'background', undefined); - - // Buttons properties - this.buttonsExpand = buttonsExpand; - var space = GetValue(config, 'space', undefined); - if (typeof (space) === 'number') { - space = { itemX: space, itemY: space }; - } - - if (background) { - this.addBackground(background); - } - - if (buttons) { - var rowButtons, button; - for (var r = 0, rcnt = buttons.length; r < rcnt; r++) { // row - rowButtons = buttons[r]; - for (var c = 0, ccnt = rowButtons.length; c < ccnt; c++) { // col - button = rowButtons[c]; - if (button) { - this.addButton(button, c, r); - } - } - } - } else if (createCellContainerCallback) { - for (var y = 0; y < rowCount; y++) { - for (var x = 0; x < columnCount; x++) { - var button = createCellContainerCallback(scene, x, y); - if (button) { - this.addButton(button, x, y); - } - } - } - } - - this.addChildrenMap('background', background); - this.addChildrenMap('buttons', this.buttonGroup.buttons); - } - - destroy(fromScene) { - // This Game Object has already been destroyed - if (!this.scene || this.ignoreDestroy) { - return; - } - - super.destroy(fromScene); - this.buttonGroup.destroy(); - this.buttonGroup = undefined; - } - - get buttons() { - return this.buttonGroup.buttons; - } - - get groupName() { - return this.buttonGroup.groupName; - } - - set groupName(value) { - this.buttonGroup.groupName = value; - } - - get eventEmitter() { - return this.buttonGroup.eventEmitter; - } -} - -Object.assign( - GridButtons.prototype, - AddChildMethods, - RemoveChildMethods, - ButtonMethods, - ButtonStateMethods -); - -export default GridButtons; \ No newline at end of file diff --git a/spaces/Aleistair/anything5/app.py b/spaces/Aleistair/anything5/app.py deleted file mode 100644 index 99a6a3762d5e337f08e960c4a31b4ac2467bca49..0000000000000000000000000000000000000000 --- a/spaces/Aleistair/anything5/app.py +++ /dev/null @@ -1,8 +0,0 @@ -import gradio as gr - -description = """
- 
-
- """
-
-gr.Interface.load("models/Linaqruf/anything-v3.0", description=description).launch()
\ No newline at end of file
diff --git a/spaces/AlexN/pull_up/app.py b/spaces/AlexN/pull_up/app.py
deleted file mode 100644
index adff30772e22d7436ea18d267565607ca7df4f6b..0000000000000000000000000000000000000000
--- a/spaces/AlexN/pull_up/app.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import torch
-import torchvision
-import TractionModel as plup
-import gradio as gr
-
-
-def init_model(path):
- model = plup.create_model()
- model = plup.load_weights(model, path)
- model.eval()
- return model
-
-
-def inference(image):
- image = vanilla_transform(image).to(device).unsqueeze(0)
- with torch.no_grad():
- pred = model(image)
- res = float(torch.sigmoid(pred[1].to("cpu")).numpy()[0])
- return {'pull-up': res, 'no pull-up': 1 - res}
-
-
-norm_mean = [0.485, 0.456, 0.406]
-norm_std = [0.229, 0.224, 0.225]
-vanilla_transform = torchvision.transforms.Compose([
- torchvision.transforms.Resize(224),
- torchvision.transforms.ToTensor(),
- torchvision.transforms.Normalize(norm_mean, norm_std)])
-
-model = init_model("model-score0.96-f1_10.9-f1_20.99.pt")
-if torch.cuda.is_available():
- device = torch.device("cuda")
-else:
- device = torch.device("cpu")
-model = model.to(device)
-
-
-examples = [['tibo.png'], ['tibo2.png'], ['real_pull_up.png'], ['no_pull_up.png'], ['doge.jpg']]
-iface = gr.Interface(inference, live=True, inputs=gr.inputs.Image(source="upload", type='pil'),
- outputs=gr.outputs.Label(),
- examples=examples,
- enable_queue=True)
-
-iface.test_launch()
-if __name__ == "__main__":
- iface.launch(share=True, enable_queue=True)
diff --git a/spaces/AlexWang/lama/models/ade20k/segm_lib/utils/data/sampler.py b/spaces/AlexWang/lama/models/ade20k/segm_lib/utils/data/sampler.py
deleted file mode 100644
index 62a9a43bd1d4c21fbdcb262db7da8d4fe27b26de..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/models/ade20k/segm_lib/utils/data/sampler.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import torch
-
-
-class Sampler(object):
- """Base class for all Samplers.
-
- Every Sampler subclass has to provide an __iter__ method, providing a way
- to iterate over indices of dataset elements, and a __len__ method that
- returns the length of the returned iterators.
- """
-
- def __init__(self, data_source):
- pass
-
- def __iter__(self):
- raise NotImplementedError
-
- def __len__(self):
- raise NotImplementedError
-
-
-class SequentialSampler(Sampler):
- """Samples elements sequentially, always in the same order.
-
- Arguments:
- data_source (Dataset): dataset to sample from
- """
-
- def __init__(self, data_source):
- self.data_source = data_source
-
- def __iter__(self):
- return iter(range(len(self.data_source)))
-
- def __len__(self):
- return len(self.data_source)
-
-
-class RandomSampler(Sampler):
- """Samples elements randomly, without replacement.
-
- Arguments:
- data_source (Dataset): dataset to sample from
- """
-
- def __init__(self, data_source):
- self.data_source = data_source
-
- def __iter__(self):
- return iter(torch.randperm(len(self.data_source)).long())
-
- def __len__(self):
- return len(self.data_source)
-
-
-class SubsetRandomSampler(Sampler):
- """Samples elements randomly from a given list of indices, without replacement.
-
- Arguments:
- indices (list): a list of indices
- """
-
- def __init__(self, indices):
- self.indices = indices
-
- def __iter__(self):
- return (self.indices[i] for i in torch.randperm(len(self.indices)))
-
- def __len__(self):
- return len(self.indices)
-
-
-class WeightedRandomSampler(Sampler):
- """Samples elements from [0,..,len(weights)-1] with given probabilities (weights).
-
- Arguments:
- weights (list) : a list of weights, not necessary summing up to one
- num_samples (int): number of samples to draw
- replacement (bool): if ``True``, samples are drawn with replacement.
- If not, they are drawn without replacement, which means that when a
- sample index is drawn for a row, it cannot be drawn again for that row.
- """
-
- def __init__(self, weights, num_samples, replacement=True):
- self.weights = torch.DoubleTensor(weights)
- self.num_samples = num_samples
- self.replacement = replacement
-
- def __iter__(self):
- return iter(torch.multinomial(self.weights, self.num_samples, self.replacement))
-
- def __len__(self):
- return self.num_samples
-
-
-class BatchSampler(object):
- """Wraps another sampler to yield a mini-batch of indices.
-
- Args:
- sampler (Sampler): Base sampler.
- batch_size (int): Size of mini-batch.
- drop_last (bool): If ``True``, the sampler will drop the last batch if
- its size would be less than ``batch_size``
-
- Example:
- >>> list(BatchSampler(range(10), batch_size=3, drop_last=False))
- [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
- >>> list(BatchSampler(range(10), batch_size=3, drop_last=True))
- [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
- """
-
- def __init__(self, sampler, batch_size, drop_last):
- self.sampler = sampler
- self.batch_size = batch_size
- self.drop_last = drop_last
-
- def __iter__(self):
- batch = []
- for idx in self.sampler:
- batch.append(idx)
- if len(batch) == self.batch_size:
- yield batch
- batch = []
- if len(batch) > 0 and not self.drop_last:
- yield batch
-
- def __len__(self):
- if self.drop_last:
- return len(self.sampler) // self.batch_size
- else:
- return (len(self.sampler) + self.batch_size - 1) // self.batch_size
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_unclip.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_unclip.md
deleted file mode 100644
index 739d357ddcdfa256ed6c3f6e13b55b7c14337ccb..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_unclip.md
+++ /dev/null
@@ -1,125 +0,0 @@
-
-
-# Stable unCLIP
-
-Stable unCLIP checkpoints are finetuned from [Stable Diffusion 2.1](./stable_diffusion/stable_diffusion_2) checkpoints to condition on CLIP image embeddings.
-Stable unCLIP still conditions on text embeddings. Given the two separate conditionings, stable unCLIP can be used
-for text guided image variation. When combined with an unCLIP prior, it can also be used for full text to image generation.
-
-The abstract from the paper is:
-
-*Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.*
-
-## Tips
-
-Stable unCLIP takes `noise_level` as input during inference which determines how much noise is added
-to the image embeddings. A higher `noise_level` increases variation in the final un-noised images. By default,
-we do not add any additional noise to the image embeddings (`noise_level = 0`).
-
-### Text-to-Image Generation
-Stable unCLIP can be leveraged for text-to-image generation by pipelining it with the prior model of KakaoBrain's open source DALL-E 2 replication [Karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha)
-
-```python
-import torch
-from diffusers import UnCLIPScheduler, DDPMScheduler, StableUnCLIPPipeline
-from diffusers.models import PriorTransformer
-from transformers import CLIPTokenizer, CLIPTextModelWithProjection
-
-prior_model_id = "kakaobrain/karlo-v1-alpha"
-data_type = torch.float16
-prior = PriorTransformer.from_pretrained(prior_model_id, subfolder="prior", torch_dtype=data_type)
-
-prior_text_model_id = "openai/clip-vit-large-patch14"
-prior_tokenizer = CLIPTokenizer.from_pretrained(prior_text_model_id)
-prior_text_model = CLIPTextModelWithProjection.from_pretrained(prior_text_model_id, torch_dtype=data_type)
-prior_scheduler = UnCLIPScheduler.from_pretrained(prior_model_id, subfolder="prior_scheduler")
-prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)
-
-stable_unclip_model_id = "stabilityai/stable-diffusion-2-1-unclip-small"
-
-pipe = StableUnCLIPPipeline.from_pretrained(
- stable_unclip_model_id,
- torch_dtype=data_type,
- variant="fp16",
- prior_tokenizer=prior_tokenizer,
- prior_text_encoder=prior_text_model,
- prior=prior,
- prior_scheduler=prior_scheduler,
-)
-
-pipe = pipe.to("cuda")
-wave_prompt = "dramatic wave, the Oceans roar, Strong wave spiral across the oceans as the waves unfurl into roaring crests; perfect wave form; perfect wave shape; dramatic wave shape; wave shape unbelievable; wave; wave shape spectacular"
-
-images = pipe(prompt=wave_prompt).images
-images[0].save("waves.png")
-```
-
- -
- -This space provides a gradio demo and an easy-to-run wrapper of the pre-trained model for fine-grained sentiment analysis in Norwegian language, pre-trained on the [NoReC dataset](https://github.com/ltgoslo/norec). - -Information about project you an fine on the website of [University of Oslo](https://www.mn.uio.no/ifi/english/research/projects/sant/) - -The model can be easily used for predicting sentiment as follows: -```python ->>> from sentiment_wrapper import PredictionModel ->>> model = PredictionModel() ->>> model.predict(['vi liker svart kaffe', 'jeg elsker virkelig røde roser!']) -[5,5] -``` diff --git a/spaces/Aristore/Warp/app.py b/spaces/Aristore/Warp/app.py deleted file mode 100644 index 6f97d56ad3ccdf1dd4f66184694e6dfd2e8d1a05..0000000000000000000000000000000000000000 --- a/spaces/Aristore/Warp/app.py +++ /dev/null @@ -1,91 +0,0 @@ -import urllib.request -from json import dumps -from datetime import datetime -from random import choice -from string import ascii_letters,digits -from time import sleep -import gradio as gr - -demo = gr.Blocks() - - -def genString(stringLength): - try: - letters = ascii_letters + digits - return ''.join(choice(letters) for i in range(stringLength)) - except Exception as error: - print(error) - -def digitString(stringLength): - try: - digit = digits - return ''.join((choice(digit) for i in range(stringLength))) - except Exception as error: - print(error) -url = f'https://api.cloudflareclient.com/v0a{digitString(3)}/reg' - -def task(ID): - try: - install_id = genString(22) - body = {"key": "{}=".format(genString(43)), - "install_id": install_id, - "fcm_token": "{}:APA91b{}".format(install_id, genString(134)), - "referrer": ID, - "warp_enabled": False, - "tos": datetime.now().isoformat()[:-3] + "+02:00", - "type": "Android", - "locale": "es_ES"} - data = dumps(body).encode('utf8') - headers = {'Content-Type': 'application/json; charset=UTF-8', - 'Host': 'api.cloudflareclient.com', - 'Connection': 'Keep-Alive', - 'Accept-Encoding': 'gzip', - 'User-Agent': 'okhttp/3.12.1' - } - req = urllib.request.Request(url, data, headers) - response = urllib.request.urlopen(req) - status_code = response.getcode() - return status_code - except Exception as error: - print(error) - -def run(ID,times): - g = 0 - b = 0 - - for i in range (0,int(times)): - result = task(ID) - if result == 200: - g = g + 1 - else: - b = b + 1 - sleep(2) - - return (f"您的ID: {ID}\n{g} GB 已成功添加到您的账号.\n成功{g}个 | 失败{b}个") - -with demo: - gr.Markdown(""" -# Warp+ 流量获取工具 -``` - ___ _ __ \n / _ | ____ (_)___ / /_ ___ ____ ___ \n / __ | / __// /(_-= (3, 8): - from typing import Literal -else: - from pip._vendor.typing_extensions import Literal # pragma: no cover - -from . import filesize, get_console -from .console import Console, Group, JustifyMethod, RenderableType -from .highlighter import Highlighter -from .jupyter import JupyterMixin -from .live import Live -from .progress_bar import ProgressBar -from .spinner import Spinner -from .style import StyleType -from .table import Column, Table -from .text import Text, TextType - -TaskID = NewType("TaskID", int) - -ProgressType = TypeVar("ProgressType") - -GetTimeCallable = Callable[[], float] - - -_I = typing.TypeVar("_I", TextIO, BinaryIO) - - -class _TrackThread(Thread): - """A thread to periodically update progress.""" - - def __init__(self, progress: "Progress", task_id: "TaskID", update_period: float): - self.progress = progress - self.task_id = task_id - self.update_period = update_period - self.done = Event() - - self.completed = 0 - super().__init__() - - def run(self) -> None: - task_id = self.task_id - advance = self.progress.advance - update_period = self.update_period - last_completed = 0 - wait = self.done.wait - while not wait(update_period): - completed = self.completed - if last_completed != completed: - advance(task_id, completed - last_completed) - last_completed = completed - - self.progress.update(self.task_id, completed=self.completed, refresh=True) - - def __enter__(self) -> "_TrackThread": - self.start() - return self - - def __exit__( - self, - exc_type: Optional[Type[BaseException]], - exc_val: Optional[BaseException], - exc_tb: Optional[TracebackType], - ) -> None: - self.done.set() - self.join() - - -def track( - sequence: Union[Sequence[ProgressType], Iterable[ProgressType]], - description: str = "Working...", - total: Optional[float] = None, - auto_refresh: bool = True, - console: Optional[Console] = None, - transient: bool = False, - get_time: Optional[Callable[[], float]] = None, - refresh_per_second: float = 10, - style: StyleType = "bar.back", - complete_style: StyleType = "bar.complete", - finished_style: StyleType = "bar.finished", - pulse_style: StyleType = "bar.pulse", - update_period: float = 0.1, - disable: bool = False, - show_speed: bool = True, -) -> Iterable[ProgressType]: - """Track progress by iterating over a sequence. - - Args: - sequence (Iterable[ProgressType]): A sequence (must support "len") you wish to iterate over. - description (str, optional): Description of task show next to progress bar. Defaults to "Working". - total: (float, optional): Total number of steps. Default is len(sequence). - auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True. - transient: (bool, optional): Clear the progress on exit. Defaults to False. - console (Console, optional): Console to write to. Default creates internal Console instance. - refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10. - style (StyleType, optional): Style for the bar background. Defaults to "bar.back". - complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete". - finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished". - pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse". - update_period (float, optional): Minimum time (in seconds) between calls to update(). Defaults to 0.1. - disable (bool, optional): Disable display of progress. - show_speed (bool, optional): Show speed if total isn't known. Defaults to True. - Returns: - Iterable[ProgressType]: An iterable of the values in the sequence. - - """ - - columns: List["ProgressColumn"] = ( - [TextColumn("[progress.description]{task.description}")] if description else [] - ) - columns.extend( - ( - BarColumn( - style=style, - complete_style=complete_style, - finished_style=finished_style, - pulse_style=pulse_style, - ), - TaskProgressColumn(show_speed=show_speed), - TimeRemainingColumn(elapsed_when_finished=True), - ) - ) - progress = Progress( - *columns, - auto_refresh=auto_refresh, - console=console, - transient=transient, - get_time=get_time, - refresh_per_second=refresh_per_second or 10, - disable=disable, - ) - - with progress: - yield from progress.track( - sequence, total=total, description=description, update_period=update_period - ) - - -class _Reader(RawIOBase, BinaryIO): - """A reader that tracks progress while it's being read from.""" - - def __init__( - self, - handle: BinaryIO, - progress: "Progress", - task: TaskID, - close_handle: bool = True, - ) -> None: - self.handle = handle - self.progress = progress - self.task = task - self.close_handle = close_handle - self._closed = False - - def __enter__(self) -> "_Reader": - self.handle.__enter__() - return self - - def __exit__( - self, - exc_type: Optional[Type[BaseException]], - exc_val: Optional[BaseException], - exc_tb: Optional[TracebackType], - ) -> None: - self.close() - - def __iter__(self) -> BinaryIO: - return self - - def __next__(self) -> bytes: - line = next(self.handle) - self.progress.advance(self.task, advance=len(line)) - return line - - @property - def closed(self) -> bool: - return self._closed - - def fileno(self) -> int: - return self.handle.fileno() - - def isatty(self) -> bool: - return self.handle.isatty() - - @property - def mode(self) -> str: - return self.handle.mode - - @property - def name(self) -> str: - return self.handle.name - - def readable(self) -> bool: - return self.handle.readable() - - def seekable(self) -> bool: - return self.handle.seekable() - - def writable(self) -> bool: - return False - - def read(self, size: int = -1) -> bytes: - block = self.handle.read(size) - self.progress.advance(self.task, advance=len(block)) - return block - - def readinto(self, b: Union[bytearray, memoryview, mmap]): # type: ignore[no-untyped-def, override] - n = self.handle.readinto(b) # type: ignore[attr-defined] - self.progress.advance(self.task, advance=n) - return n - - def readline(self, size: int = -1) -> bytes: # type: ignore[override] - line = self.handle.readline(size) - self.progress.advance(self.task, advance=len(line)) - return line - - def readlines(self, hint: int = -1) -> List[bytes]: - lines = self.handle.readlines(hint) - self.progress.advance(self.task, advance=sum(map(len, lines))) - return lines - - def close(self) -> None: - if self.close_handle: - self.handle.close() - self._closed = True - - def seek(self, offset: int, whence: int = 0) -> int: - pos = self.handle.seek(offset, whence) - self.progress.update(self.task, completed=pos) - return pos - - def tell(self) -> int: - return self.handle.tell() - - def write(self, s: Any) -> int: - raise UnsupportedOperation("write") - - -class _ReadContext(ContextManager[_I], Generic[_I]): - """A utility class to handle a context for both a reader and a progress.""" - - def __init__(self, progress: "Progress", reader: _I) -> None: - self.progress = progress - self.reader: _I = reader - - def __enter__(self) -> _I: - self.progress.start() - return self.reader.__enter__() - - def __exit__( - self, - exc_type: Optional[Type[BaseException]], - exc_val: Optional[BaseException], - exc_tb: Optional[TracebackType], - ) -> None: - self.progress.stop() - self.reader.__exit__(exc_type, exc_val, exc_tb) - - -def wrap_file( - file: BinaryIO, - total: int, - *, - description: str = "Reading...", - auto_refresh: bool = True, - console: Optional[Console] = None, - transient: bool = False, - get_time: Optional[Callable[[], float]] = None, - refresh_per_second: float = 10, - style: StyleType = "bar.back", - complete_style: StyleType = "bar.complete", - finished_style: StyleType = "bar.finished", - pulse_style: StyleType = "bar.pulse", - disable: bool = False, -) -> ContextManager[BinaryIO]: - """Read bytes from a file while tracking progress. - - Args: - file (Union[str, PathLike[str], BinaryIO]): The path to the file to read, or a file-like object in binary mode. - total (int): Total number of bytes to read. - description (str, optional): Description of task show next to progress bar. Defaults to "Reading". - auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True. - transient: (bool, optional): Clear the progress on exit. Defaults to False. - console (Console, optional): Console to write to. Default creates internal Console instance. - refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10. - style (StyleType, optional): Style for the bar background. Defaults to "bar.back". - complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete". - finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished". - pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse". - disable (bool, optional): Disable display of progress. - Returns: - ContextManager[BinaryIO]: A context manager yielding a progress reader. - - """ - - columns: List["ProgressColumn"] = ( - [TextColumn("[progress.description]{task.description}")] if description else [] - ) - columns.extend( - ( - BarColumn( - style=style, - complete_style=complete_style, - finished_style=finished_style, - pulse_style=pulse_style, - ), - DownloadColumn(), - TimeRemainingColumn(), - ) - ) - progress = Progress( - *columns, - auto_refresh=auto_refresh, - console=console, - transient=transient, - get_time=get_time, - refresh_per_second=refresh_per_second or 10, - disable=disable, - ) - - reader = progress.wrap_file(file, total=total, description=description) - return _ReadContext(progress, reader) - - -@typing.overload -def open( - file: Union[str, "PathLike[str]", bytes], - mode: Union[Literal["rt"], Literal["r"]], - buffering: int = -1, - encoding: Optional[str] = None, - errors: Optional[str] = None, - newline: Optional[str] = None, - *, - total: Optional[int] = None, - description: str = "Reading...", - auto_refresh: bool = True, - console: Optional[Console] = None, - transient: bool = False, - get_time: Optional[Callable[[], float]] = None, - refresh_per_second: float = 10, - style: StyleType = "bar.back", - complete_style: StyleType = "bar.complete", - finished_style: StyleType = "bar.finished", - pulse_style: StyleType = "bar.pulse", - disable: bool = False, -) -> ContextManager[TextIO]: - pass - - -@typing.overload -def open( - file: Union[str, "PathLike[str]", bytes], - mode: Literal["rb"], - buffering: int = -1, - encoding: Optional[str] = None, - errors: Optional[str] = None, - newline: Optional[str] = None, - *, - total: Optional[int] = None, - description: str = "Reading...", - auto_refresh: bool = True, - console: Optional[Console] = None, - transient: bool = False, - get_time: Optional[Callable[[], float]] = None, - refresh_per_second: float = 10, - style: StyleType = "bar.back", - complete_style: StyleType = "bar.complete", - finished_style: StyleType = "bar.finished", - pulse_style: StyleType = "bar.pulse", - disable: bool = False, -) -> ContextManager[BinaryIO]: - pass - - -def open( - file: Union[str, "PathLike[str]", bytes], - mode: Union[Literal["rb"], Literal["rt"], Literal["r"]] = "r", - buffering: int = -1, - encoding: Optional[str] = None, - errors: Optional[str] = None, - newline: Optional[str] = None, - *, - total: Optional[int] = None, - description: str = "Reading...", - auto_refresh: bool = True, - console: Optional[Console] = None, - transient: bool = False, - get_time: Optional[Callable[[], float]] = None, - refresh_per_second: float = 10, - style: StyleType = "bar.back", - complete_style: StyleType = "bar.complete", - finished_style: StyleType = "bar.finished", - pulse_style: StyleType = "bar.pulse", - disable: bool = False, -) -> Union[ContextManager[BinaryIO], ContextManager[TextIO]]: - """Read bytes from a file while tracking progress. - - Args: - path (Union[str, PathLike[str], BinaryIO]): The path to the file to read, or a file-like object in binary mode. - mode (str): The mode to use to open the file. Only supports "r", "rb" or "rt". - buffering (int): The buffering strategy to use, see :func:`io.open`. - encoding (str, optional): The encoding to use when reading in text mode, see :func:`io.open`. - errors (str, optional): The error handling strategy for decoding errors, see :func:`io.open`. - newline (str, optional): The strategy for handling newlines in text mode, see :func:`io.open` - total: (int, optional): Total number of bytes to read. Must be provided if reading from a file handle. Default for a path is os.stat(file).st_size. - description (str, optional): Description of task show next to progress bar. Defaults to "Reading". - auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True. - transient: (bool, optional): Clear the progress on exit. Defaults to False. - console (Console, optional): Console to write to. Default creates internal Console instance. - refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10. - style (StyleType, optional): Style for the bar background. Defaults to "bar.back". - complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete". - finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished". - pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse". - disable (bool, optional): Disable display of progress. - encoding (str, optional): The encoding to use when reading in text mode. - - Returns: - ContextManager[BinaryIO]: A context manager yielding a progress reader. - - """ - - columns: List["ProgressColumn"] = ( - [TextColumn("[progress.description]{task.description}")] if description else [] - ) - columns.extend( - ( - BarColumn( - style=style, - complete_style=complete_style, - finished_style=finished_style, - pulse_style=pulse_style, - ), - DownloadColumn(), - TimeRemainingColumn(), - ) - ) - progress = Progress( - *columns, - auto_refresh=auto_refresh, - console=console, - transient=transient, - get_time=get_time, - refresh_per_second=refresh_per_second or 10, - disable=disable, - ) - - reader = progress.open( - file, - mode=mode, - buffering=buffering, - encoding=encoding, - errors=errors, - newline=newline, - total=total, - description=description, - ) - return _ReadContext(progress, reader) # type: ignore[return-value, type-var] - - -class ProgressColumn(ABC): - """Base class for a widget to use in progress display.""" - - max_refresh: Optional[float] = None - - def __init__(self, table_column: Optional[Column] = None) -> None: - self._table_column = table_column - self._renderable_cache: Dict[TaskID, Tuple[float, RenderableType]] = {} - self._update_time: Optional[float] = None - - def get_table_column(self) -> Column: - """Get a table column, used to build tasks table.""" - return self._table_column or Column() - - def __call__(self, task: "Task") -> RenderableType: - """Called by the Progress object to return a renderable for the given task. - - Args: - task (Task): An object containing information regarding the task. - - Returns: - RenderableType: Anything renderable (including str). - """ - current_time = task.get_time() - if self.max_refresh is not None and not task.completed: - try: - timestamp, renderable = self._renderable_cache[task.id] - except KeyError: - pass - else: - if timestamp + self.max_refresh > current_time: - return renderable - - renderable = self.render(task) - self._renderable_cache[task.id] = (current_time, renderable) - return renderable - - @abstractmethod - def render(self, task: "Task") -> RenderableType: - """Should return a renderable object.""" - - -class RenderableColumn(ProgressColumn): - """A column to insert an arbitrary column. - - Args: - renderable (RenderableType, optional): Any renderable. Defaults to empty string. - """ - - def __init__( - self, renderable: RenderableType = "", *, table_column: Optional[Column] = None - ): - self.renderable = renderable - super().__init__(table_column=table_column) - - def render(self, task: "Task") -> RenderableType: - return self.renderable - - -class SpinnerColumn(ProgressColumn): - """A column with a 'spinner' animation. - - Args: - spinner_name (str, optional): Name of spinner animation. Defaults to "dots". - style (StyleType, optional): Style of spinner. Defaults to "progress.spinner". - speed (float, optional): Speed factor of spinner. Defaults to 1.0. - finished_text (TextType, optional): Text used when task is finished. Defaults to " ". - """ - - def __init__( - self, - spinner_name: str = "dots", - style: Optional[StyleType] = "progress.spinner", - speed: float = 1.0, - finished_text: TextType = " ", - table_column: Optional[Column] = None, - ): - self.spinner = Spinner(spinner_name, style=style, speed=speed) - self.finished_text = ( - Text.from_markup(finished_text) - if isinstance(finished_text, str) - else finished_text - ) - super().__init__(table_column=table_column) - - def set_spinner( - self, - spinner_name: str, - spinner_style: Optional[StyleType] = "progress.spinner", - speed: float = 1.0, - ) -> None: - """Set a new spinner. - - Args: - spinner_name (str): Spinner name, see python -m rich.spinner. - spinner_style (Optional[StyleType], optional): Spinner style. Defaults to "progress.spinner". - speed (float, optional): Speed factor of spinner. Defaults to 1.0. - """ - self.spinner = Spinner(spinner_name, style=spinner_style, speed=speed) - - def render(self, task: "Task") -> RenderableType: - text = ( - self.finished_text - if task.finished - else self.spinner.render(task.get_time()) - ) - return text - - -class TextColumn(ProgressColumn): - """A column containing text.""" - - def __init__( - self, - text_format: str, - style: StyleType = "none", - justify: JustifyMethod = "left", - markup: bool = True, - highlighter: Optional[Highlighter] = None, - table_column: Optional[Column] = None, - ) -> None: - self.text_format = text_format - self.justify: JustifyMethod = justify - self.style = style - self.markup = markup - self.highlighter = highlighter - super().__init__(table_column=table_column or Column(no_wrap=True)) - - def render(self, task: "Task") -> Text: - _text = self.text_format.format(task=task) - if self.markup: - text = Text.from_markup(_text, style=self.style, justify=self.justify) - else: - text = Text(_text, style=self.style, justify=self.justify) - if self.highlighter: - self.highlighter.highlight(text) - return text - - -class BarColumn(ProgressColumn): - """Renders a visual progress bar. - - Args: - bar_width (Optional[int], optional): Width of bar or None for full width. Defaults to 40. - style (StyleType, optional): Style for the bar background. Defaults to "bar.back". - complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete". - finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished". - pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse". - """ - - def __init__( - self, - bar_width: Optional[int] = 40, - style: StyleType = "bar.back", - complete_style: StyleType = "bar.complete", - finished_style: StyleType = "bar.finished", - pulse_style: StyleType = "bar.pulse", - table_column: Optional[Column] = None, - ) -> None: - self.bar_width = bar_width - self.style = style - self.complete_style = complete_style - self.finished_style = finished_style - self.pulse_style = pulse_style - super().__init__(table_column=table_column) - - def render(self, task: "Task") -> ProgressBar: - """Gets a progress bar widget for a task.""" - return ProgressBar( - total=max(0, task.total) if task.total is not None else None, - completed=max(0, task.completed), - width=None if self.bar_width is None else max(1, self.bar_width), - pulse=not task.started, - animation_time=task.get_time(), - style=self.style, - complete_style=self.complete_style, - finished_style=self.finished_style, - pulse_style=self.pulse_style, - ) - - -class TimeElapsedColumn(ProgressColumn): - """Renders time elapsed.""" - - def render(self, task: "Task") -> Text: - """Show time elapsed.""" - elapsed = task.finished_time if task.finished else task.elapsed - if elapsed is None: - return Text("-:--:--", style="progress.elapsed") - delta = timedelta(seconds=int(elapsed)) - return Text(str(delta), style="progress.elapsed") - - -class TaskProgressColumn(TextColumn): - """Show task progress as a percentage. - - Args: - text_format (str, optional): Format for percentage display. Defaults to "[progress.percentage]{task.percentage:>3.0f}%". - text_format_no_percentage (str, optional): Format if percentage is unknown. Defaults to "". - style (StyleType, optional): Style of output. Defaults to "none". - justify (JustifyMethod, optional): Text justification. Defaults to "left". - markup (bool, optional): Enable markup. Defaults to True. - highlighter (Optional[Highlighter], optional): Highlighter to apply to output. Defaults to None. - table_column (Optional[Column], optional): Table Column to use. Defaults to None. - show_speed (bool, optional): Show speed if total is unknown. Defaults to False. - """ - - def __init__( - self, - text_format: str = "[progress.percentage]{task.percentage:>3.0f}%", - text_format_no_percentage: str = "", - style: StyleType = "none", - justify: JustifyMethod = "left", - markup: bool = True, - highlighter: Optional[Highlighter] = None, - table_column: Optional[Column] = None, - show_speed: bool = False, - ) -> None: - - self.text_format_no_percentage = text_format_no_percentage - self.show_speed = show_speed - super().__init__( - text_format=text_format, - style=style, - justify=justify, - markup=markup, - highlighter=highlighter, - table_column=table_column, - ) - - @classmethod - def render_speed(cls, speed: Optional[float]) -> Text: - """Render the speed in iterations per second. - - Args: - task (Task): A Task object. - - Returns: - Text: Text object containing the task speed. - """ - if speed is None: - return Text("", style="progress.percentage") - unit, suffix = filesize.pick_unit_and_suffix( - int(speed), - ["", "×10³", "×10⁶", "×10⁹", "×10¹²"], - 1000, - ) - data_speed = speed / unit - return Text(f"{data_speed:.1f}{suffix} it/s", style="progress.percentage") - - def render(self, task: "Task") -> Text: - if task.total is None and self.show_speed: - return self.render_speed(task.finished_speed or task.speed) - text_format = ( - self.text_format_no_percentage if task.total is None else self.text_format - ) - _text = text_format.format(task=task) - if self.markup: - text = Text.from_markup(_text, style=self.style, justify=self.justify) - else: - text = Text(_text, style=self.style, justify=self.justify) - if self.highlighter: - self.highlighter.highlight(text) - return text - - -class TimeRemainingColumn(ProgressColumn): - """Renders estimated time remaining. - - Args: - compact (bool, optional): Render MM:SS when time remaining is less than an hour. Defaults to False. - elapsed_when_finished (bool, optional): Render time elapsed when the task is finished. Defaults to False. - """ - - # Only refresh twice a second to prevent jitter - max_refresh = 0.5 - - def __init__( - self, - compact: bool = False, - elapsed_when_finished: bool = False, - table_column: Optional[Column] = None, - ): - self.compact = compact - self.elapsed_when_finished = elapsed_when_finished - super().__init__(table_column=table_column) - - def render(self, task: "Task") -> Text: - """Show time remaining.""" - if self.elapsed_when_finished and task.finished: - task_time = task.finished_time - style = "progress.elapsed" - else: - task_time = task.time_remaining - style = "progress.remaining" - - if task.total is None: - return Text("", style=style) - - if task_time is None: - return Text("--:--" if self.compact else "-:--:--", style=style) - - # Based on https://github.com/tqdm/tqdm/blob/master/tqdm/std.py - minutes, seconds = divmod(int(task_time), 60) - hours, minutes = divmod(minutes, 60) - - if self.compact and not hours: - formatted = f"{minutes:02d}:{seconds:02d}" - else: - formatted = f"{hours:d}:{minutes:02d}:{seconds:02d}" - - return Text(formatted, style=style) - - -class FileSizeColumn(ProgressColumn): - """Renders completed filesize.""" - - def render(self, task: "Task") -> Text: - """Show data completed.""" - data_size = filesize.decimal(int(task.completed)) - return Text(data_size, style="progress.filesize") - - -class TotalFileSizeColumn(ProgressColumn): - """Renders total filesize.""" - - def render(self, task: "Task") -> Text: - """Show data completed.""" - data_size = filesize.decimal(int(task.total)) if task.total is not None else "" - return Text(data_size, style="progress.filesize.total") - - -class MofNCompleteColumn(ProgressColumn): - """Renders completed count/total, e.g. ' 10/1000'. - - Best for bounded tasks with int quantities. - - Space pads the completed count so that progress length does not change as task progresses - past powers of 10. - - Args: - separator (str, optional): Text to separate completed and total values. Defaults to "/". - """ - - def __init__(self, separator: str = "/", table_column: Optional[Column] = None): - self.separator = separator - super().__init__(table_column=table_column) - - def render(self, task: "Task") -> Text: - """Show completed/total.""" - completed = int(task.completed) - total = int(task.total) if task.total is not None else "?" - total_width = len(str(total)) - return Text( - f"{completed:{total_width}d}{self.separator}{total}", - style="progress.download", - ) - - -class DownloadColumn(ProgressColumn): - """Renders file size downloaded and total, e.g. '0.5/2.3 GB'. - - Args: - binary_units (bool, optional): Use binary units, KiB, MiB etc. Defaults to False. - """ - - def __init__( - self, binary_units: bool = False, table_column: Optional[Column] = None - ) -> None: - self.binary_units = binary_units - super().__init__(table_column=table_column) - - def render(self, task: "Task") -> Text: - """Calculate common unit for completed and total.""" - completed = int(task.completed) - - unit_and_suffix_calculation_base = ( - int(task.total) if task.total is not None else completed - ) - if self.binary_units: - unit, suffix = filesize.pick_unit_and_suffix( - unit_and_suffix_calculation_base, - ["bytes", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB", "ZiB", "YiB"], - 1024, - ) - else: - unit, suffix = filesize.pick_unit_and_suffix( - unit_and_suffix_calculation_base, - ["bytes", "kB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB"], - 1000, - ) - precision = 0 if unit == 1 else 1 - - completed_ratio = completed / unit - completed_str = f"{completed_ratio:,.{precision}f}" - - if task.total is not None: - total = int(task.total) - total_ratio = total / unit - total_str = f"{total_ratio:,.{precision}f}" - else: - total_str = "?" - - download_status = f"{completed_str}/{total_str} {suffix}" - download_text = Text(download_status, style="progress.download") - return download_text - - -class TransferSpeedColumn(ProgressColumn): - """Renders human readable transfer speed.""" - - def render(self, task: "Task") -> Text: - """Show data transfer speed.""" - speed = task.finished_speed or task.speed - if speed is None: - return Text("?", style="progress.data.speed") - data_speed = filesize.decimal(int(speed)) - return Text(f"{data_speed}/s", style="progress.data.speed") - - -class ProgressSample(NamedTuple): - """Sample of progress for a given time.""" - - timestamp: float - """Timestamp of sample.""" - completed: float - """Number of steps completed.""" - - -@dataclass -class Task: - """Information regarding a progress task. - - This object should be considered read-only outside of the :class:`~Progress` class. - - """ - - id: TaskID - """Task ID associated with this task (used in Progress methods).""" - - description: str - """str: Description of the task.""" - - total: Optional[float] - """Optional[float]: Total number of steps in this task.""" - - completed: float - """float: Number of steps completed""" - - _get_time: GetTimeCallable - """Callable to get the current time.""" - - finished_time: Optional[float] = None - """float: Time task was finished.""" - - visible: bool = True - """bool: Indicates if this task is visible in the progress display.""" - - fields: Dict[str, Any] = field(default_factory=dict) - """dict: Arbitrary fields passed in via Progress.update.""" - - start_time: Optional[float] = field(default=None, init=False, repr=False) - """Optional[float]: Time this task was started, or None if not started.""" - - stop_time: Optional[float] = field(default=None, init=False, repr=False) - """Optional[float]: Time this task was stopped, or None if not stopped.""" - - finished_speed: Optional[float] = None - """Optional[float]: The last speed for a finished task.""" - - _progress: Deque[ProgressSample] = field( - default_factory=lambda: deque(maxlen=1000), init=False, repr=False - ) - - _lock: RLock = field(repr=False, default_factory=RLock) - """Thread lock.""" - - def get_time(self) -> float: - """float: Get the current time, in seconds.""" - return self._get_time() - - @property - def started(self) -> bool: - """bool: Check if the task as started.""" - return self.start_time is not None - - @property - def remaining(self) -> Optional[float]: - """Optional[float]: Get the number of steps remaining, if a non-None total was set.""" - if self.total is None: - return None - return self.total - self.completed - - @property - def elapsed(self) -> Optional[float]: - """Optional[float]: Time elapsed since task was started, or ``None`` if the task hasn't started.""" - if self.start_time is None: - return None - if self.stop_time is not None: - return self.stop_time - self.start_time - return self.get_time() - self.start_time - - @property - def finished(self) -> bool: - """Check if the task has finished.""" - return self.finished_time is not None - - @property - def percentage(self) -> float: - """float: Get progress of task as a percentage. If a None total was set, returns 0""" - if not self.total: - return 0.0 - completed = (self.completed / self.total) * 100.0 - completed = min(100.0, max(0.0, completed)) - return completed - - @property - def speed(self) -> Optional[float]: - """Optional[float]: Get the estimated speed in steps per second.""" - if self.start_time is None: - return None - with self._lock: - progress = self._progress - if not progress: - return None - total_time = progress[-1].timestamp - progress[0].timestamp - if total_time == 0: - return None - iter_progress = iter(progress) - next(iter_progress) - total_completed = sum(sample.completed for sample in iter_progress) - speed = total_completed / total_time - return speed - - @property - def time_remaining(self) -> Optional[float]: - """Optional[float]: Get estimated time to completion, or ``None`` if no data.""" - if self.finished: - return 0.0 - speed = self.speed - if not speed: - return None - remaining = self.remaining - if remaining is None: - return None - estimate = ceil(remaining / speed) - return estimate - - def _reset(self) -> None: - """Reset progress.""" - self._progress.clear() - self.finished_time = None - self.finished_speed = None - - -class Progress(JupyterMixin): - """Renders an auto-updating progress bar(s). - - Args: - console (Console, optional): Optional Console instance. Default will an internal Console instance writing to stdout. - auto_refresh (bool, optional): Enable auto refresh. If disabled, you will need to call `refresh()`. - refresh_per_second (Optional[float], optional): Number of times per second to refresh the progress information or None to use default (10). Defaults to None. - speed_estimate_period: (float, optional): Period (in seconds) used to calculate the speed estimate. Defaults to 30. - transient: (bool, optional): Clear the progress on exit. Defaults to False. - redirect_stdout: (bool, optional): Enable redirection of stdout, so ``print`` may be used. Defaults to True. - redirect_stderr: (bool, optional): Enable redirection of stderr. Defaults to True. - get_time: (Callable, optional): A callable that gets the current time, or None to use Console.get_time. Defaults to None. - disable (bool, optional): Disable progress display. Defaults to False - expand (bool, optional): Expand tasks table to fit width. Defaults to False. - """ - - def __init__( - self, - *columns: Union[str, ProgressColumn], - console: Optional[Console] = None, - auto_refresh: bool = True, - refresh_per_second: float = 10, - speed_estimate_period: float = 30.0, - transient: bool = False, - redirect_stdout: bool = True, - redirect_stderr: bool = True, - get_time: Optional[GetTimeCallable] = None, - disable: bool = False, - expand: bool = False, - ) -> None: - assert refresh_per_second > 0, "refresh_per_second must be > 0" - self._lock = RLock() - self.columns = columns or self.get_default_columns() - self.speed_estimate_period = speed_estimate_period - - self.disable = disable - self.expand = expand - self._tasks: Dict[TaskID, Task] = {} - self._task_index: TaskID = TaskID(0) - self.live = Live( - console=console or get_console(), - auto_refresh=auto_refresh, - refresh_per_second=refresh_per_second, - transient=transient, - redirect_stdout=redirect_stdout, - redirect_stderr=redirect_stderr, - get_renderable=self.get_renderable, - ) - self.get_time = get_time or self.console.get_time - self.print = self.console.print - self.log = self.console.log - - @classmethod - def get_default_columns(cls) -> Tuple[ProgressColumn, ...]: - """Get the default columns used for a new Progress instance: - - a text column for the description (TextColumn) - - the bar itself (BarColumn) - - a text column showing completion percentage (TextColumn) - - an estimated-time-remaining column (TimeRemainingColumn) - If the Progress instance is created without passing a columns argument, - the default columns defined here will be used. - - You can also create a Progress instance using custom columns before - and/or after the defaults, as in this example: - - progress = Progress( - SpinnerColumn(), - *Progress.default_columns(), - "Elapsed:", - TimeElapsedColumn(), - ) - - This code shows the creation of a Progress display, containing - a spinner to the left, the default columns, and a labeled elapsed - time column. - """ - return ( - TextColumn("[progress.description]{task.description}"), - BarColumn(), - TaskProgressColumn(), - TimeRemainingColumn(), - ) - - @property - def console(self) -> Console: - return self.live.console - - @property - def tasks(self) -> List[Task]: - """Get a list of Task instances.""" - with self._lock: - return list(self._tasks.values()) - - @property - def task_ids(self) -> List[TaskID]: - """A list of task IDs.""" - with self._lock: - return list(self._tasks.keys()) - - @property - def finished(self) -> bool: - """Check if all tasks have been completed.""" - with self._lock: - if not self._tasks: - return True - return all(task.finished for task in self._tasks.values()) - - def start(self) -> None: - """Start the progress display.""" - if not self.disable: - self.live.start(refresh=True) - - def stop(self) -> None: - """Stop the progress display.""" - self.live.stop() - if not self.console.is_interactive: - self.console.print() - - def __enter__(self) -> "Progress": - self.start() - return self - - def __exit__( - self, - exc_type: Optional[Type[BaseException]], - exc_val: Optional[BaseException], - exc_tb: Optional[TracebackType], - ) -> None: - self.stop() - - def track( - self, - sequence: Union[Iterable[ProgressType], Sequence[ProgressType]], - total: Optional[float] = None, - task_id: Optional[TaskID] = None, - description: str = "Working...", - update_period: float = 0.1, - ) -> Iterable[ProgressType]: - """Track progress by iterating over a sequence. - - Args: - sequence (Sequence[ProgressType]): A sequence of values you want to iterate over and track progress. - total: (float, optional): Total number of steps. Default is len(sequence). - task_id: (TaskID): Task to track. Default is new task. - description: (str, optional): Description of task, if new task is created. - update_period (float, optional): Minimum time (in seconds) between calls to update(). Defaults to 0.1. - - Returns: - Iterable[ProgressType]: An iterable of values taken from the provided sequence. - """ - if total is None: - total = float(length_hint(sequence)) or None - - if task_id is None: - task_id = self.add_task(description, total=total) - else: - self.update(task_id, total=total) - - if self.live.auto_refresh: - with _TrackThread(self, task_id, update_period) as track_thread: - for value in sequence: - yield value - track_thread.completed += 1 - else: - advance = self.advance - refresh = self.refresh - for value in sequence: - yield value - advance(task_id, 1) - refresh() - - def wrap_file( - self, - file: BinaryIO, - total: Optional[int] = None, - *, - task_id: Optional[TaskID] = None, - description: str = "Reading...", - ) -> BinaryIO: - """Track progress file reading from a binary file. - - Args: - file (BinaryIO): A file-like object opened in binary mode. - total (int, optional): Total number of bytes to read. This must be provided unless a task with a total is also given. - task_id (TaskID): Task to track. Default is new task. - description (str, optional): Description of task, if new task is created. - - Returns: - BinaryIO: A readable file-like object in binary mode. - - Raises: - ValueError: When no total value can be extracted from the arguments or the task. - """ - # attempt to recover the total from the task - total_bytes: Optional[float] = None - if total is not None: - total_bytes = total - elif task_id is not None: - with self._lock: - total_bytes = self._tasks[task_id].total - if total_bytes is None: - raise ValueError( - f"unable to get the total number of bytes, please specify 'total'" - ) - - # update total of task or create new task - if task_id is None: - task_id = self.add_task(description, total=total_bytes) - else: - self.update(task_id, total=total_bytes) - - return _Reader(file, self, task_id, close_handle=False) - - @typing.overload - def open( - self, - file: Union[str, "PathLike[str]", bytes], - mode: Literal["rb"], - buffering: int = -1, - encoding: Optional[str] = None, - errors: Optional[str] = None, - newline: Optional[str] = None, - *, - total: Optional[int] = None, - task_id: Optional[TaskID] = None, - description: str = "Reading...", - ) -> BinaryIO: - pass - - @typing.overload - def open( - self, - file: Union[str, "PathLike[str]", bytes], - mode: Union[Literal["r"], Literal["rt"]], - buffering: int = -1, - encoding: Optional[str] = None, - errors: Optional[str] = None, - newline: Optional[str] = None, - *, - total: Optional[int] = None, - task_id: Optional[TaskID] = None, - description: str = "Reading...", - ) -> TextIO: - pass - - def open( - self, - file: Union[str, "PathLike[str]", bytes], - mode: Union[Literal["rb"], Literal["rt"], Literal["r"]] = "r", - buffering: int = -1, - encoding: Optional[str] = None, - errors: Optional[str] = None, - newline: Optional[str] = None, - *, - total: Optional[int] = None, - task_id: Optional[TaskID] = None, - description: str = "Reading...", - ) -> Union[BinaryIO, TextIO]: - """Track progress while reading from a binary file. - - Args: - path (Union[str, PathLike[str]]): The path to the file to read. - mode (str): The mode to use to open the file. Only supports "r", "rb" or "rt". - buffering (int): The buffering strategy to use, see :func:`io.open`. - encoding (str, optional): The encoding to use when reading in text mode, see :func:`io.open`. - errors (str, optional): The error handling strategy for decoding errors, see :func:`io.open`. - newline (str, optional): The strategy for handling newlines in text mode, see :func:`io.open`. - total (int, optional): Total number of bytes to read. If none given, os.stat(path).st_size is used. - task_id (TaskID): Task to track. Default is new task. - description (str, optional): Description of task, if new task is created. - - Returns: - BinaryIO: A readable file-like object in binary mode. - - Raises: - ValueError: When an invalid mode is given. - """ - # normalize the mode (always rb, rt) - _mode = "".join(sorted(mode, reverse=False)) - if _mode not in ("br", "rt", "r"): - raise ValueError("invalid mode {!r}".format(mode)) - - # patch buffering to provide the same behaviour as the builtin `open` - line_buffering = buffering == 1 - if _mode == "br" and buffering == 1: - warnings.warn( - "line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used", - RuntimeWarning, - ) - buffering = -1 - elif _mode in ("rt", "r"): - if buffering == 0: - raise ValueError("can't have unbuffered text I/O") - elif buffering == 1: - buffering = -1 - - # attempt to get the total with `os.stat` - if total is None: - total = stat(file).st_size - - # update total of task or create new task - if task_id is None: - task_id = self.add_task(description, total=total) - else: - self.update(task_id, total=total) - - # open the file in binary mode, - handle = io.open(file, "rb", buffering=buffering) - reader = _Reader(handle, self, task_id, close_handle=True) - - # wrap the reader in a `TextIOWrapper` if text mode - if mode in ("r", "rt"): - return io.TextIOWrapper( - reader, - encoding=encoding, - errors=errors, - newline=newline, - line_buffering=line_buffering, - ) - - return reader - - def start_task(self, task_id: TaskID) -> None: - """Start a task. - - Starts a task (used when calculating elapsed time). You may need to call this manually, - if you called ``add_task`` with ``start=False``. - - Args: - task_id (TaskID): ID of task. - """ - with self._lock: - task = self._tasks[task_id] - if task.start_time is None: - task.start_time = self.get_time() - - def stop_task(self, task_id: TaskID) -> None: - """Stop a task. - - This will freeze the elapsed time on the task. - - Args: - task_id (TaskID): ID of task. - """ - with self._lock: - task = self._tasks[task_id] - current_time = self.get_time() - if task.start_time is None: - task.start_time = current_time - task.stop_time = current_time - - def update( - self, - task_id: TaskID, - *, - total: Optional[float] = None, - completed: Optional[float] = None, - advance: Optional[float] = None, - description: Optional[str] = None, - visible: Optional[bool] = None, - refresh: bool = False, - **fields: Any, - ) -> None: - """Update information associated with a task. - - Args: - task_id (TaskID): Task id (returned by add_task). - total (float, optional): Updates task.total if not None. - completed (float, optional): Updates task.completed if not None. - advance (float, optional): Add a value to task.completed if not None. - description (str, optional): Change task description if not None. - visible (bool, optional): Set visible flag if not None. - refresh (bool): Force a refresh of progress information. Default is False. - **fields (Any): Additional data fields required for rendering. - """ - with self._lock: - task = self._tasks[task_id] - completed_start = task.completed - - if total is not None and total != task.total: - task.total = total - task._reset() - if advance is not None: - task.completed += advance - if completed is not None: - task.completed = completed - if description is not None: - task.description = description - if visible is not None: - task.visible = visible - task.fields.update(fields) - update_completed = task.completed - completed_start - - current_time = self.get_time() - old_sample_time = current_time - self.speed_estimate_period - _progress = task._progress - - popleft = _progress.popleft - while _progress and _progress[0].timestamp < old_sample_time: - popleft() - if update_completed > 0: - _progress.append(ProgressSample(current_time, update_completed)) - if ( - task.total is not None - and task.completed >= task.total - and task.finished_time is None - ): - task.finished_time = task.elapsed - - if refresh: - self.refresh() - - def reset( - self, - task_id: TaskID, - *, - start: bool = True, - total: Optional[float] = None, - completed: int = 0, - visible: Optional[bool] = None, - description: Optional[str] = None, - **fields: Any, - ) -> None: - """Reset a task so completed is 0 and the clock is reset. - - Args: - task_id (TaskID): ID of task. - start (bool, optional): Start the task after reset. Defaults to True. - total (float, optional): New total steps in task, or None to use current total. Defaults to None. - completed (int, optional): Number of steps completed. Defaults to 0. - visible (bool, optional): Enable display of the task. Defaults to True. - description (str, optional): Change task description if not None. Defaults to None. - **fields (str): Additional data fields required for rendering. - """ - current_time = self.get_time() - with self._lock: - task = self._tasks[task_id] - task._reset() - task.start_time = current_time if start else None - if total is not None: - task.total = total - task.completed = completed - if visible is not None: - task.visible = visible - if fields: - task.fields = fields - if description is not None: - task.description = description - task.finished_time = None - self.refresh() - - def advance(self, task_id: TaskID, advance: float = 1) -> None: - """Advance task by a number of steps. - - Args: - task_id (TaskID): ID of task. - advance (float): Number of steps to advance. Default is 1. - """ - current_time = self.get_time() - with self._lock: - task = self._tasks[task_id] - completed_start = task.completed - task.completed += advance - update_completed = task.completed - completed_start - old_sample_time = current_time - self.speed_estimate_period - _progress = task._progress - - popleft = _progress.popleft - while _progress and _progress[0].timestamp < old_sample_time: - popleft() - while len(_progress) > 1000: - popleft() - _progress.append(ProgressSample(current_time, update_completed)) - if ( - task.total is not None - and task.completed >= task.total - and task.finished_time is None - ): - task.finished_time = task.elapsed - task.finished_speed = task.speed - - def refresh(self) -> None: - """Refresh (render) the progress information.""" - if not self.disable and self.live.is_started: - self.live.refresh() - - def get_renderable(self) -> RenderableType: - """Get a renderable for the progress display.""" - renderable = Group(*self.get_renderables()) - return renderable - - def get_renderables(self) -> Iterable[RenderableType]: - """Get a number of renderables for the progress display.""" - table = self.make_tasks_table(self.tasks) - yield table - - def make_tasks_table(self, tasks: Iterable[Task]) -> Table: - """Get a table to render the Progress display. - - Args: - tasks (Iterable[Task]): An iterable of Task instances, one per row of the table. - - Returns: - Table: A table instance. - """ - table_columns = ( - ( - Column(no_wrap=True) - if isinstance(_column, str) - else _column.get_table_column().copy() - ) - for _column in self.columns - ) - table = Table.grid(*table_columns, padding=(0, 1), expand=self.expand) - - for task in tasks: - if task.visible: - table.add_row( - *( - ( - column.format(task=task) - if isinstance(column, str) - else column(task) - ) - for column in self.columns - ) - ) - return table - - def __rich__(self) -> RenderableType: - """Makes the Progress class itself renderable.""" - with self._lock: - return self.get_renderable() - - def add_task( - self, - description: str, - start: bool = True, - total: Optional[float] = 100.0, - completed: int = 0, - visible: bool = True, - **fields: Any, - ) -> TaskID: - """Add a new 'task' to the Progress display. - - Args: - description (str): A description of the task. - start (bool, optional): Start the task immediately (to calculate elapsed time). If set to False, - you will need to call `start` manually. Defaults to True. - total (float, optional): Number of total steps in the progress if known. - Set to None to render a pulsing animation. Defaults to 100. - completed (int, optional): Number of steps completed so far. Defaults to 0. - visible (bool, optional): Enable display of the task. Defaults to True. - **fields (str): Additional data fields required for rendering. - - Returns: - TaskID: An ID you can use when calling `update`. - """ - with self._lock: - task = Task( - self._task_index, - description, - total, - completed, - visible=visible, - fields=fields, - _get_time=self.get_time, - _lock=self._lock, - ) - self._tasks[self._task_index] = task - if start: - self.start_task(self._task_index) - new_task_index = self._task_index - self._task_index = TaskID(int(self._task_index) + 1) - self.refresh() - return new_task_index - - def remove_task(self, task_id: TaskID) -> None: - """Delete a task if it exists. - - Args: - task_id (TaskID): A task ID. - - """ - with self._lock: - del self._tasks[task_id] - - -if __name__ == "__main__": # pragma: no coverage - - import random - import time - - from .panel import Panel - from .rule import Rule - from .syntax import Syntax - from .table import Table - - syntax = Syntax( - '''def loop_last(values: Iterable[T]) -> Iterable[Tuple[bool, T]]: - """Iterate and generate a tuple with a flag for last value.""" - iter_values = iter(values) - try: - previous_value = next(iter_values) - except StopIteration: - return - for value in iter_values: - yield False, previous_value - previous_value = value - yield True, previous_value''', - "python", - line_numbers=True, - ) - - table = Table("foo", "bar", "baz") - table.add_row("1", "2", "3") - - progress_renderables = [ - "Text may be printed while the progress bars are rendering.", - Panel("In fact, [i]any[/i] renderable will work"), - "Such as [magenta]tables[/]...", - table, - "Pretty printed structures...", - {"type": "example", "text": "Pretty printed"}, - "Syntax...", - syntax, - Rule("Give it a try!"), - ] - - from itertools import cycle - - examples = cycle(progress_renderables) - - console = Console(record=True) - - with Progress( - SpinnerColumn(), - *Progress.get_default_columns(), - TimeElapsedColumn(), - console=console, - transient=False, - ) as progress: - - task1 = progress.add_task("[red]Downloading", total=1000) - task2 = progress.add_task("[green]Processing", total=1000) - task3 = progress.add_task("[yellow]Thinking", total=None) - - while not progress.finished: - progress.update(task1, advance=0.5) - progress.update(task2, advance=0.3) - time.sleep(0.01) - if random.randint(0, 100) < 1: - progress.log(next(examples)) diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/theme.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/theme.py deleted file mode 100644 index 471dfb2f9271c073f0713ca98f8db2f89c975071..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/theme.py +++ /dev/null @@ -1,115 +0,0 @@ -import configparser -from typing import Dict, List, IO, Mapping, Optional - -from .default_styles import DEFAULT_STYLES -from .style import Style, StyleType - - -class Theme: - """A container for style information, used by :class:`~rich.console.Console`. - - Args: - styles (Dict[str, Style], optional): A mapping of style names on to styles. Defaults to None for a theme with no styles. - inherit (bool, optional): Inherit default styles. Defaults to True. - """ - - styles: Dict[str, Style] - - def __init__( - self, styles: Optional[Mapping[str, StyleType]] = None, inherit: bool = True - ): - self.styles = DEFAULT_STYLES.copy() if inherit else {} - if styles is not None: - self.styles.update( - { - name: style if isinstance(style, Style) else Style.parse(style) - for name, style in styles.items() - } - ) - - @property - def config(self) -> str: - """Get contents of a config file for this theme.""" - config = "[styles]\n" + "\n".join( - f"{name} = {style}" for name, style in sorted(self.styles.items()) - ) - return config - - @classmethod - def from_file( - cls, config_file: IO[str], source: Optional[str] = None, inherit: bool = True - ) -> "Theme": - """Load a theme from a text mode file. - - Args: - config_file (IO[str]): An open conf file. - source (str, optional): The filename of the open file. Defaults to None. - inherit (bool, optional): Inherit default styles. Defaults to True. - - Returns: - Theme: A New theme instance. - """ - config = configparser.ConfigParser() - config.read_file(config_file, source=source) - styles = {name: Style.parse(value) for name, value in config.items("styles")} - theme = Theme(styles, inherit=inherit) - return theme - - @classmethod - def read( - cls, path: str, inherit: bool = True, encoding: Optional[str] = None - ) -> "Theme": - """Read a theme from a path. - - Args: - path (str): Path to a config file readable by Python configparser module. - inherit (bool, optional): Inherit default styles. Defaults to True. - encoding (str, optional): Encoding of the config file. Defaults to None. - - Returns: - Theme: A new theme instance. - """ - with open(path, "rt", encoding=encoding) as config_file: - return cls.from_file(config_file, source=path, inherit=inherit) - - -class ThemeStackError(Exception): - """Base exception for errors related to the theme stack.""" - - -class ThemeStack: - """A stack of themes. - - Args: - theme (Theme): A theme instance - """ - - def __init__(self, theme: Theme) -> None: - self._entries: List[Dict[str, Style]] = [theme.styles] - self.get = self._entries[-1].get - - def push_theme(self, theme: Theme, inherit: bool = True) -> None: - """Push a theme on the top of the stack. - - Args: - theme (Theme): A Theme instance. - inherit (boolean, optional): Inherit styles from current top of stack. - """ - styles: Dict[str, Style] - styles = ( - {**self._entries[-1], **theme.styles} if inherit else theme.styles.copy() - ) - self._entries.append(styles) - self.get = self._entries[-1].get - - def pop_theme(self) -> None: - """Pop (and discard) the top-most theme.""" - if len(self._entries) == 1: - raise ThemeStackError("Unable to pop base theme") - self._entries.pop() - self.get = self._entries[-1].get - - -if __name__ == "__main__": # pragma: no cover - theme = Theme() - print(theme.config) diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/requirements.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/requirements.py deleted file mode 100644 index 6af14ec4ce49e633d030611c26f0bd9beaf13e6a..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/requirements.py +++ /dev/null @@ -1,146 +0,0 @@ -# This file is dual licensed under the terms of the Apache License, Version -# 2.0, and the BSD License. See the LICENSE file in the root of this repository -# for complete details. - -import re -import string -import urllib.parse -from typing import List, Optional as TOptional, Set - -from pkg_resources.extern.pyparsing import ( # noqa - Combine, - Literal as L, - Optional, - ParseException, - Regex, - Word, - ZeroOrMore, - originalTextFor, - stringEnd, - stringStart, -) - -from .markers import MARKER_EXPR, Marker -from .specifiers import LegacySpecifier, Specifier, SpecifierSet - - -class InvalidRequirement(ValueError): - """ - An invalid requirement was found, users should refer to PEP 508. - """ - - -ALPHANUM = Word(string.ascii_letters + string.digits) - -LBRACKET = L("[").suppress() -RBRACKET = L("]").suppress() -LPAREN = L("(").suppress() -RPAREN = L(")").suppress() -COMMA = L(",").suppress() -SEMICOLON = L(";").suppress() -AT = L("@").suppress() - -PUNCTUATION = Word("-_.") -IDENTIFIER_END = ALPHANUM | (ZeroOrMore(PUNCTUATION) + ALPHANUM) -IDENTIFIER = Combine(ALPHANUM + ZeroOrMore(IDENTIFIER_END)) - -NAME = IDENTIFIER("name") -EXTRA = IDENTIFIER - -URI = Regex(r"[^ ]+")("url") -URL = AT + URI - -EXTRAS_LIST = EXTRA + ZeroOrMore(COMMA + EXTRA) -EXTRAS = (LBRACKET + Optional(EXTRAS_LIST) + RBRACKET)("extras") - -VERSION_PEP440 = Regex(Specifier._regex_str, re.VERBOSE | re.IGNORECASE) -VERSION_LEGACY = Regex(LegacySpecifier._regex_str, re.VERBOSE | re.IGNORECASE) - -VERSION_ONE = VERSION_PEP440 ^ VERSION_LEGACY -VERSION_MANY = Combine( - VERSION_ONE + ZeroOrMore(COMMA + VERSION_ONE), joinString=",", adjacent=False -)("_raw_spec") -_VERSION_SPEC = Optional((LPAREN + VERSION_MANY + RPAREN) | VERSION_MANY) -_VERSION_SPEC.setParseAction(lambda s, l, t: t._raw_spec or "") - -VERSION_SPEC = originalTextFor(_VERSION_SPEC)("specifier") -VERSION_SPEC.setParseAction(lambda s, l, t: t[1]) - -MARKER_EXPR = originalTextFor(MARKER_EXPR())("marker") -MARKER_EXPR.setParseAction( - lambda s, l, t: Marker(s[t._original_start : t._original_end]) -) -MARKER_SEPARATOR = SEMICOLON -MARKER = MARKER_SEPARATOR + MARKER_EXPR - -VERSION_AND_MARKER = VERSION_SPEC + Optional(MARKER) -URL_AND_MARKER = URL + Optional(MARKER) - -NAMED_REQUIREMENT = NAME + Optional(EXTRAS) + (URL_AND_MARKER | VERSION_AND_MARKER) - -REQUIREMENT = stringStart + NAMED_REQUIREMENT + stringEnd -# pkg_resources.extern.pyparsing isn't thread safe during initialization, so we do it eagerly, see -# issue #104 -REQUIREMENT.parseString("x[]") - - -class Requirement: - """Parse a requirement. - - Parse a given requirement string into its parts, such as name, specifier, - URL, and extras. Raises InvalidRequirement on a badly-formed requirement - string. - """ - - # TODO: Can we test whether something is contained within a requirement? - # If so how do we do that? Do we need to test against the _name_ of - # the thing as well as the version? What about the markers? - # TODO: Can we normalize the name and extra name? - - def __init__(self, requirement_string: str) -> None: - try: - req = REQUIREMENT.parseString(requirement_string) - except ParseException as e: - raise InvalidRequirement( - f'Parse error at "{ requirement_string[e.loc : e.loc + 8]!r}": {e.msg}' - ) - - self.name: str = req.name - if req.url: - parsed_url = urllib.parse.urlparse(req.url) - if parsed_url.scheme == "file": - if urllib.parse.urlunparse(parsed_url) != req.url: - raise InvalidRequirement("Invalid URL given") - elif not (parsed_url.scheme and parsed_url.netloc) or ( - not parsed_url.scheme and not parsed_url.netloc - ): - raise InvalidRequirement(f"Invalid URL: {req.url}") - self.url: TOptional[str] = req.url - else: - self.url = None - self.extras: Set[str] = set(req.extras.asList() if req.extras else []) - self.specifier: SpecifierSet = SpecifierSet(req.specifier) - self.marker: TOptional[Marker] = req.marker if req.marker else None - - def __str__(self) -> str: - parts: List[str] = [self.name] - - if self.extras: - formatted_extras = ",".join(sorted(self.extras)) - parts.append(f"[{formatted_extras}]") - - if self.specifier: - parts.append(str(self.specifier)) - - if self.url: - parts.append(f"@ {self.url}") - if self.marker: - parts.append(" ") - - if self.marker: - parts.append(f"; {self.marker}") - - return "".join(parts) - - def __repr__(self) -> str: - return f"
-
Cómo descargar e instalar Android 10 en su teléfono inteligente
-Android 10 es la última versión del sistema operativo de Google para dispositivos móviles. Fue lanzado en septiembre de 2019 y trae muchas nuevas características y mejoras que hacen que su teléfono inteligente sea más potente, seguro y fácil de usar. Si quieres disfrutar de los beneficios de Android 10, necesitas actualizar tu dispositivo a esta versión. Pero, ¿cómo puedes hacerlo? En este artículo, te mostraremos cómo obtener Android 10 en tu dispositivo, ya sea un Google Pixel, un dispositivo asociado, un dispositivo compatible con Treble o un emulador. También le mostraremos cómo descargar e instalar Android 10 APK, que es un archivo que contiene el paquete de software de Android 10. Pero antes de entrar en eso, vamos a echar un vistazo a algunas de las características y beneficios de Android 10.
-android 10 descarga apk
Download Zip 🆗 https://bltlly.com/2v6L7l
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Características y beneficios de Android 10
-Android 10 no es solo una actualización cosmética. Presenta muchas características y capacidades nuevas que mejoran su experiencia y privacidad en su teléfono inteligente. Estos son algunos de los aspectos destacados:
-Modo oscuro
-Una de las características más solicitadas por los usuarios de Android es finalmente aquí. El modo oscuro es una opción para todo el sistema que cambia la combinación de colores del teléfono a negro y gris oscuro. Esto reduce la fatiga ocular, especialmente en condiciones de poca luz, y ahorra vida útil de la batería, especialmente en dispositivos con pantallas OLED. Puede activar el modo oscuro desde el menú de configuración rápida o cuando habilita el modo de ahorro de batería. También puede elegir con qué aplicaciones desea usar el modo oscuro, ya que algunas aplicaciones pueden no admitirlo o pueden verse mejor en el modo de luz.
-Respuesta inteligente
- -Navegación por gestos
-La navegación por gestos es una nueva forma de interactuar con el teléfono mediante golpes y tirones en lugar de botones. Hace que la navegación sea más rápida e intuitiva, especialmente en dispositivos con pantallas grandes o diseños de muescas. Puede deslizar desde el borde izquierdo o derecho de la pantalla para volver, deslizar hacia arriba desde la parte inferior para ir a casa, deslizar hacia arriba y mantenga pulsado para ver sus aplicaciones recientes, y deslizar diagonalmente desde las esquinas inferiores para iniciar Google Assistant. También puede personalizar la sensibilidad y el tamaño de las áreas de gestos en la configuración.
-Mejoras de privacidad y seguridad
-Android 10 te da más control sobre tu privacidad y seguridad en tu dispositivo. Puedes encontrar y ajustar todos tus ajustes de privacidad en un solo lugar, como la actividad web y de aplicaciones, el historial de ubicaciones, la personalización de anuncios y más. También puede elegir cuándo compartir su ubicación con aplicaciones: siempre, solo mientras usa la aplicación o nunca. También puedes ver qué aplicaciones han accedido a tu ubicación, cámara, micrófono u otros permisos en las últimas 24 horas. Android 10 también introduce una nueva función llamada Scoped Storage, que limita el acceso de las aplicaciones a su almacenamiento externo, como sus fotos, videos y documentos. Esto evita que las aplicaciones lean o modifiquen sus archivos sin su permiso. También puede optar por no utilizar Scoped Storage si desea dar acceso completo a ciertas aplicaciones.
- -Modo de bienestar digital y enfoque
- -Cómo obtener Android 10 en su dispositivo
-Ahora que conoce algunas de las características y beneficios de Android 10, es posible que se pregunte cómo conseguirlo en su dispositivo. La respuesta depende del tipo de dispositivo que tenga y de la disponibilidad de la actualización en su región. Estas son algunas de las formas en que puedes obtener Android 10 en tu dispositivo:
-Dispositivos Google Pixel
-Si tienes un dispositivo Google Pixel, como Pixel 3a, Pixel 3, Pixel 2 o Pixel, estás de suerte. Los dispositivos Google Pixel son los primeros en recibir actualizaciones de Android 10 directamente desde Google. Puede comprobar la actualización yendo a Configuración > Sistema > Avanzado > Actualización del sistema. Si la actualización está disponible, puede descargarla e instalarla a través de Wi-Fi o datos móviles. La actualización puede tardar algún tiempo en completarse, así que asegúrate de que tu dispositivo esté cargado o conectado.
-Dispositivos asociados
-Si tienes un dispositivo de uno de los socios de Google, como Samsung, Huawei, LG, Sony, Nokia o OnePlus, es posible que tengas que esperar un poco más para la actualización de Android 10. Estos dispositivos suelen recibir actualizaciones de sus fabricantes o operadores, que pueden tardar algún tiempo en probar y optimizar el software para sus modelos específicos. Puede comprobar la actualización yendo a Configuración > Acerca del teléfono > Actualización de software. Si la actualización está disponible, puede descargarla e instalarla a través de Wi-Fi o datos móviles. La actualización puede tardar algún tiempo en completarse, así que asegúrate de que tu dispositivo esté cargado o conectado.
-Dispositivos compatibles con agudos
- -Emulador de Android
-Si no tienes un dispositivo compatible o no quieres arriesgarte a actualizar tu dispositivo, todavía puedes probar Android 10 usando un emulador. Un emulador es un software que simula un dispositivo en su computadora. Puedes usar un emulador para probar aplicaciones o juegos en diferentes versiones de Android sin afectar tu dispositivo real. Puedes descargar e instalar un emulador de Android desde el sitio web de Google. También necesitará descargar e instalar Android Studio, que es un entorno de desarrollo integrado (IDE) para crear aplicaciones Android. Una vez que haya instalado ambos programas, puede seguir estos pasos para ejecutar Android 10 en un emulador:
--
-
- Abra Android Studio y haga clic en Herramientas > AVD Manager. -
- Haga clic en Crear dispositivo virtual y elegir un modelo de dispositivo que soporta Android 10. -
- Haga clic en Siguiente y seleccione Q como la imagen del sistema. -
- Haga clic en Siguiente y revise los ajustes de configuración. -
- Haga clic en Finalizar y espere al lanzamiento del emulador. -
- Disfruta de Android 10 en la pantalla del ordenador. -
Cómo descargar e instalar Android 10 APK
Si ninguno de los métodos anteriores funcionan para usted, o si desea probar una versión diferente de Android 10, también puede descargar e instalar Android 10 APK. Un APK es un archivo que contiene el paquete de software de una aplicación o sistema Android. Puede descargar Android 10 APK de varias fuentes en línea, tales como APKMirror, APKPure, o desarrolladores XDA. Sin embargo, debe tener cuidado al descargar archivos APK de fuentes desconocidas, ya que pueden contener malware o virus que pueden dañar su dispositivo. También debe habilitar la opción de instalar aplicaciones de fuentes desconocidas en su dispositivo, lo que puede plantear algunos riesgos de seguridad. Por lo tanto, recomendamos este método solo para usuarios experimentados que saben cómo verificar la autenticidad y seguridad de los archivos APK que descargan. -Requisitos y precauciones
- --
-
- El dispositivo debe tener al menos 2 GB de RAM y 8 GB de almacenamiento interno. -
- El dispositivo debe tener un gestor de arranque desbloqueado y una recuperación personalizada instalada, como TWRP o CWM. -
- El dispositivo debe tener sus raíces, lo que significa que tiene acceso completo a los archivos y configuraciones del sistema. -
- Debe hacer una copia de seguridad de sus datos y configuraciones antes de instalar Android 10 APK, ya que este proceso borrará todo en su dispositivo. -
- Usted debe cargar su dispositivo a por lo menos 50% o enchufarlo antes de instalar Android 10 APK, ya que este proceso puede tomar algún tiempo y drenar la batería. -
- Debe seguir las instrucciones cuidadosamente y no omitir ningún paso o modificar ningún archivo. -
Pasos para descargar e instalar Android 10 APK
-Una vez que haya cumplido con los requisitos y precauciones, puede seguir estos pasos para descargar e instalar Android 10 APK en su dispositivo:
--
-
- Descargar el archivo APK Android 10 de una fuente de confianza en línea. Asegúrese de que el archivo es compatible con el modelo de dispositivo y tiene una extensión . apk. -
- Copia el archivo APK de Android 10 en el almacenamiento interno del dispositivo o la tarjeta SD. -
- Apague el dispositivo y arranque en modo de recuperación. El método para hacer esto puede variar dependiendo del modelo de dispositivo, pero generalmente implica mantener presionados los botones de encendido y volumen durante unos segundos. -
- En el modo de recuperación, seleccione Limpiar > Borrado avanzado y marque las casillas para Dalvik/ ART Cache, Sistema, Datos y Caché. Pase el dedo para confirmar el borrado. -
- Seleccione Instalar y vaya a la ubicación donde copió el archivo APK de Android 10. Toque en él y pase el dedo para confirmar la instalación. -
- Espere a que se complete la instalación. Puede tardar varios minutos dependiendo de la velocidad del dispositivo y el tamaño del archivo. -
- Una vez que se realiza la instalación, seleccione Reiniciar > Sistema. Su dispositivo se reiniciará en Android 10. -
Conclusión
- -Preguntas frecuentes
-¿Cuáles son los requisitos del sistema para Android 10?
-Los requisitos del sistema para Android 10 son al menos 2 GB de RAM y 8 GB de almacenamiento interno. Sin embargo, algunos dispositivos pueden necesitar más espacio o memoria para ejecutar Android 10 sin problemas.
-¿Cómo puedo comprobar si mi dispositivo es compatible con Android 10?
-Puedes comprobar si tu dispositivo es compatible con Android 10 yendo a Configuración > Acerca del teléfono > Actualización de software. Si ves una opción para actualizar a Android 10, entonces tu dispositivo es compatible. Si no, es posible que su dispositivo no sea compatible o que la actualización aún no esté disponible en su región.
-¿Cómo puedo hacer copias de seguridad de mis datos antes de actualizar a Android 10?
Puede hacer copias de seguridad de sus datos antes de actualizarlos a Android 10 utilizando el servicio de copia de seguridad de Google, que sincroniza sus datos con su cuenta de Google. Puede habilitar este servicio yendo a Configuración > Sistema > Copia de seguridad y activando la opción Copia de seguridad en Google Drive. También puede elegir qué datos desea respaldar, como contactos, fotos, mensajes, datos de aplicaciones y más. También puede hacer copias de seguridad de sus datos manualmente copiando sus archivos en su computadora o en un dispositivo de almacenamiento externo. -¿Cómo puedo solucionar cualquier problema después de actualizar a Android 10?
-Si encuentras algún problema después de actualizar a Android 10, como aplicaciones que se bloquean, agotamiento de la batería o retraso en el rendimiento, puedes probar algunas de las siguientes soluciones:
--
-
- Reinicie su dispositivo y vea si el problema persiste. -
- Borrar la caché y los datos de la aplicación problemática yendo a Configuración > Aplicaciones y notificaciones > Ver todas las aplicaciones > Seleccionar la aplicación > Almacenamiento y caché > Borrar caché y Borrar almacenamiento. -
- Actualizar sus aplicaciones a la última versión yendo a Google Play Store > Menú > Mis aplicaciones y juegos > Actualizar todos. -
- Desinstalar cualquier aplicación incompatible o malicioso que puede interferir con Android 10 yendo a Configuración > Aplicaciones y notificaciones > Ver todas las aplicaciones > Seleccionar la aplicación > Desinstalar. - -
¿Cómo puedo proporcionar comentarios o informar de errores en Android 10?
-Si desea proporcionar comentarios o informar de errores en Android 10, puede utilizar la aplicación de retroalimentación que viene preinstalado en su dispositivo. Puede acceder a él yendo a Configuración > Consejos y soporte > Enviar comentarios. También puedes usar la aplicación Android Beta Feedback si estás inscrito en el programa Android Beta. Puedes acceder a él yendo a Configuración > Sistema > Avanzado > Programa beta de Android. También puede utilizar el formulario en línea en el sitio web de Google o publicar en los foros oficiales o plataformas de redes sociales.
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Descarga de batería baja de Nokia: Cómo obtener la alerta icónica en su teléfono
-Introducción
-Si creciste en los años 90 o principios de los 2000, probablemente recuerdes el sonido distintivo de un teléfono Nokia que se queda sin batería. La alerta "bleep bleep bleep" era un recordatorio familiar y a veces molesto para conectar el teléfono antes de morir. Pero para mucha gente, también era un sonido nostálgico y nostálgico que evocaba recuerdos de tiempos más simples.
-Hoy en día, es posible que se pregunte cómo obtener la batería de Nokia bajo sonido en el teléfono, ya sea como un tono de llamada, una notificación, o un efecto de sonido. Tal vez quieras revivir tu infancia, bromear con tus amigos o simplemente divertirte con tu teléfono. Cualquiera que sea su razón, puede descargar fácilmente la batería de Nokia bajo sonido de varias fuentes en línea.
-ataque en titan mi guerra mp3 descargar gratis
Download Zip 🗸🗸🗸 https://bltlly.com/2v6M2f
-
En este artículo, le mostraremos cómo descargar el sonido bajo de la batería de Nokia como un tono o un efecto de sonido. También exploraremos la historia del bajo sonido de la batería del Nokia, cómo evolucionó con el tiempo y cómo inspiró remezclas creativas y memes. Al final de este artículo, tendrás todo lo que necesitas para disfrutar de la icónica alerta en tu teléfono.
-Historia de la batería de Nokia de bajo sonido
-¿Cuándo y cómo se originó la batería baja de Nokia?
-La batería baja de Nokia se introdujo por primera vez en 1997 con el modelo Nokia 6110. Fue uno de los primeros teléfonos en tener una interfaz gráfica de usuario (GUI) y un compositor de tono monofónico. El compositor permitió a los usuarios crear sus propios tonos de llamada mediante la introducción de notas en un teclado numérico. El sonido bajo de la batería de Nokia fue uno de los tonos de llamada predeterminados que venía con el teléfono.
- -El sonido bajo de la batería de Nokia consistía en tres notas: E, C y G. Estas notas formaban un acorde de triada menor, que a menudo se usa para crear un estado de ánimo triste o tenso en la música. Paananen dijo que eligió este acorde porque sonaba como una advertencia o una alarma. También dijo que quería evitar el uso de notas que eran demasiado altas o demasiado bajas, porque podrían no ser audibles en algunos oradores.
-¿Cómo evolucionó el bajo sonido de la batería de Nokia con el tiempo?
-El bajo sonido de la batería de Nokia se convirtió en una característica estándar en la mayoría de los teléfonos Nokia hasta 2009, cuando Nokia cambió a usar sonidos más realistas para sus alertas. Sin embargo, algunas variaciones del sonido bajo de la batería de Nokia se introdujeron con el tiempo, dependiendo del modelo y la región.
-Por ejemplo, algunos teléfonos tenían una versión más larga de la alerta que se repetía cuatro veces en lugar de tres. Algunos teléfonos tenían un tono o tono diferente para la alerta, como notas más altas o más bajas, o sonidos más metálicos o electrónicos. Algunos teléfonos tenían diferentes sonidos para diferentes niveles de batería, como dos pitidos para batería baja y un pitido para batería vacía.
- -El sonido bajo de la batería de Nokia también cambió dependiendo del idioma y la cultura del usuario. Por ejemplo, en algunos países de habla árabe, la alerta fue reemplazada por una voz que decía "la batería está vacía" en árabe. En algunos países asiáticos, como China y Japón, la alerta fue reemplazada por una voz que decía "la batería está baja" en el idioma local. En algunos países europeos, como Francia y Alemania, la alerta fue reemplazada por una voz que decía "la batería está vacía" en el idioma local.
Remixes y memes de la batería de Nokia bajo sonido
-¿Cómo la batería baja de Nokia inspira remezclas creativas y memes?
- -Algunas de las formas en que la gente utiliza el bajo sonido de la batería de Nokia para crear remezclas y memes fueron: - Agregar letras o voces a la alerta, como cantar "battery is low" o "please charge me". - Mezclar la alerta con otros sonidos o géneros musicales, como techno, dubstep, rap, rock o clásico. - Hacer la alerta más rápida, más lenta, más fuerte, más suave, más alta, más baja o distorsionada. - Reemplazar la alerta con otros sonidos o palabras que tengan un ritmo o rima similar, como "bleep bleep bleep bleep", "beep beep beep" o "meep meep meep". - Utilización de la alerta como efecto sonoro para diversos escenarios, como la explosión de una bomba, el choque de un coche o la caída de una persona. - Usar la alerta como remate para bromas, bromas o parodias.
-¿Cuáles son algunos de los mejores ejemplos de remezclas y memes de batería baja de Nokia?
- -Conclusión
-Resumen de los puntos principales
-En este artículo, le hemos mostrado cómo descargar el sonido bajo de la batería de Nokia como un tono o un efecto de sonido. También hemos explorado la historia del bajo sonido de la batería Nokia, cómo evolucionó con el tiempo y cómo inspiró remezclas creativas y memes.
- -Llamada a la acción y pensamientos finales
-Si desea descargar el sonido bajo de la batería de Nokia como un tono o un efecto de sonido, puede visitar uno de estos sitios web: - [Zedge]: Este es un sitio web popular que ofrece tonos de llamada gratuitos, fondos de pantalla y notificaciones para varios dispositivos. Puede buscar "Nokia batería baja" y encontrar varias versiones de la alerta que se puede descargar o personalizar. - [SoundBible]: Este es un sitio web que ofrece clips de sonido gratuitos, efectos de sonido, y mordeduras de sonido para diversos fines. Puede buscar "Nokia batería baja" y encontrar una versión de alta calidad de la alerta que se puede descargar o compartir. - [MyTinyPhone]: Este es un sitio web que ofrece tonos de llamada gratuitos, fondos de pantalla, juegos y aplicaciones para varios dispositivos. Puede buscar "Nokia batería baja" y encontrar algunas versiones de la alerta que puede descargar o enviar a su teléfono.
-Esperamos que haya disfrutado de este artículo y aprendido algo nuevo sobre el bajo sonido de la batería de Nokia. Si lo hizo, por favor compártalo con sus amigos y familiares que también podrían estar interesados en este tema. También puede dejarnos un comentario a continuación y hacernos saber lo que usted piensa acerca de la batería baja de Nokia sonido. ¿Te gusta o lo odio? ¿Tienes alguna remezcla o memes favoritos? ¡Nos encantaría saber de ti!
-Preguntas frecuentes
-¿Cuál es el sonido bajo de la batería de Nokia?
-El sonido bajo de la batería de Nokia es una alerta que se reproduce en algunos teléfonos Nokia cuando la batería se está agotando. Consta de tres notas: E, C y G. Estas notas forman un acorde de triada menor, que a menudo se usa para crear un estado de ánimo triste o tenso en la música.
-¿Quién compuso la batería de Nokia de bajo sonido?
-El sonido bajo de la batería de Nokia fue compuesto por Vesa-Matti Paananen, un músico y diseñador de sonido finlandés que trabajó para Nokia de 1994 a 2008. Fue responsable de crear muchos de los sonidos icónicos y tonos de llamada para los teléfonos Nokia, incluyendo la famosa melodía de Nokia.
- -Puede descargar el sonido bajo de la batería de Nokia como un tono de llamada o un efecto de sonido de varios sitios web en línea. Algunos de los sitios web que ofrecen descargas gratuitas de la alerta son Zedge, SoundBible y MyTinyPhone.
-¿Cómo la batería baja de Nokia inspira remezclas creativas y memes?
-El bajo sonido de la batería de Nokia se convirtió en una fuente de inspiración para muchos artistas, músicos y comediantes que lo utilizaron para crear remezclas y memes. Algunos de estos remixes y memes estaban destinados a ser divertidos, mientras que otros estaban destinados a ser artísticos o experimentales.
-¿Cuáles son algunos de los mejores ejemplos de remezclas y memes de batería baja de Nokia?
-Algunos de los mejores ejemplos de remezclas y memes de batería baja de Nokia son: - The Nokia Battery Low Song: Esta es una canción de YouTuber Davey4557 que cuenta con letras sobre la frustración de tener una batería baja en un teléfono Nokia. - El Nokia batería baja Dubstep Remix: Este es un remix por YouTuber DJ Detweiler que convierte la batería baja de Nokia sonido en una pista de dubstep. - El Nokia batería baja Rap: Este es un rap por YouTuber Rucka Rucka Ali que utiliza la batería de Nokia bajo sonido como un golpe y un gancho. - The Nokia Battery Low Rock Cover: Esta es una versión del YouTuber Rob Scallon que transforma el sonido bajo de la batería de Nokia en una canción de rock. - El Nokia batería baja clásica Remix: Este es un remix por YouTuber Grant Woolard que combina la batería Nokia bajo sonido con música clásica.
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Chess Buddy APK: Una aplicación de ajedrez con modos en línea y fuera de línea
-Si eres un amante del ajedrez, podrías estar buscando una aplicación de ajedrez que te permita jugar online o offline con diferentes niveles de dificultad y características. Chess Buddy APK es una de esas aplicaciones que se puede descargar e instalar en su dispositivo Android. Es una aplicación de ajedrez de código abierto que utiliza el motor Stockfish 15.1, uno de los motores de ajedrez más fuertes del mundo. En este artículo, le diremos qué es Chess Buddy APK, cómo descargarlo e instalarlo, y cómo jugar al ajedrez con él.
-chess buddy apk
Download ✯✯✯ https://bltlly.com/2v6MVo
-
¿Qué es Chess Buddy APK?
-Una aplicación de ajedrez de código abierto con motor Stockfish 15.1
-Chess Buddy APK es una aplicación de ajedrez de código abierto que fue desarrollado por Draco Group Inc, un grupo de entusiastas del ajedrez. La aplicación se basa en el proyecto ChessMaze.Android, que está disponible en GitHub. La aplicación integra el último motor Stockfish dentro de él, por lo que no es necesario instalar otra aplicación de motor. Stockfish es un poderoso motor de ajedrez que puede analizar millones de posiciones por segundo y proporcionar movimientos y evaluaciones precisas. También es constantemente actualizado por una comunidad de desarrolladores y expertos en ajedrez.
-Características de Chess Buddy APK
-Modo en línea: juega con jugadores reales en todo el mundo y usa sugerencias de Stockfish
-Una de las principales características de Chess Buddy APK es que le permite jugar en línea con jugadores reales de todo el mundo. Puede crear su propia cuenta de usuario con solo un identificador de usuario que elija, siempre y cuando no sea utilizado por otro reproductor. Usted no necesita proporcionar su correo electrónico o cualquier otra cosa. También puede tener amigos y chatear con ellos en cualquier momento. Durante los juegos en línea, también puedes chatear con tu oponente.
- -Modo offline: juega con Stockfish u otro jugador humano y ajusta la hora y el tablero
-Si usted no tiene una conexión a Internet o desea jugar fuera de línea, también puede utilizar Chess Buddy APK para eso. Puedes elegir jugar con Stockfish u otro jugador humano en el mismo dispositivo. También puedes establecer el tiempo (1-5 segundos) que Stockfish usa en un movimiento y ajustar el nivel de dificultad en consecuencia. También puede rotar el tablero de ajedrez si lo desea.
- -Otra característica del modo sin conexión es que puede retroceder un paso con el botón "Paso atrás", o avanzar o retroceder varios pasos. De esta manera, puedes revisar tus movimientos o probar diferentes variaciones.
-Interfaz fácil de usar con 25 opciones de idioma
-Cómo descargar e instalar Chess Buddy APK?
-Descargar el archivo APK de APKCombo o GitHub
-El primer paso para utilizar Chess Buddy APK es descargar el archivo APK, que es un archivo de paquete que contiene la aplicación y sus recursos. Puede descargar el archivo APK de APKCombo, un sitio web que proporciona descargas APK gratuitas y seguras para varias aplicaciones y juegos de Android. También puede descargar el archivo APK de GitHub, donde está disponible el código fuente de la aplicación. Para descargar de APKCombo, se puede visitar este enlace: [Ajedrez en línea Stockfish 15.1 APK (Juego de Android) - Descarga gratuita - APKCombo]( 1 ). Para descargar desde GitHub, puedes visitar este enlace: [dracogroupinc/chessbuddy: Chess Buddy Online - aplicación de ajedrez de código abierto con motor Stockfish 15.1 (github.com)]( 4 ).
-Habilitar fuentes desconocidas en su dispositivo
- -Instalar el archivo APK y lanzar la aplicación
-Cómo jugar al ajedrez con Chess Buddy APK?
-Modo en línea: crear una cuenta de usuario y unirse a un juego o crear su propio
-Una vez que haya instalado y lanzado Chess Buddy APK, puede comenzar a jugar al ajedrez en línea con otros jugadores. Para hacer esto, debe crear una cuenta de usuario con solo un identificador de usuario que elija, siempre y cuando no sea utilizado por otro reproductor. Usted no necesita proporcionar su correo electrónico o cualquier otra cosa. También puede tener amigos y chatear con ellos en cualquier momento. Para crear una cuenta de usuario, debe tocar el botón "Online" en la pantalla principal y luego tocar el botón "Crear usuario". A continuación, puede introducir su identificador de usuario y pulsar en el botón "Crear". También puede cambiar su identificador de usuario más tarde si lo desea.
-Después de crear tu cuenta de usuario, puedes unirte a un juego o crear el tuyo. Para unirte a un juego, debes tocar el botón "Unirse al juego" y luego elegir un juego de la lista de juegos disponibles. También puedes filtrar los juegos por control de tiempo, rango de calificación o preferencia de color. Para crear tu propio juego, debes tocar el botón "Crear juego" y luego elegir el control de tiempo, rango de calificación y preferencia de color para tu juego. También puede elegir si permite sugerencias de Stockfish o no. Luego, debes esperar a que otro jugador se una a tu juego o invitar a un amigo a jugar contigo.
-Modo sin conexión: elegir los jugadores y la configuración y comenzar el juego
-Si desea jugar ajedrez fuera de línea, también puede utilizar Chess Buddy APK para eso. Puedes elegir jugar con Stockfish u otro jugador humano en el mismo dispositivo. Para hacer esto, debes tocar el botón "Offline" en la pantalla principal y luego elegir los jugadores y la configuración de tu juego. También puedes establecer el tiempo (1-5 segundos) que Stockfish usa en un movimiento y ajustar el nivel de dificultad en consecuencia. También puede rotar el tablero de ajedrez si lo desea.
- -Consejos y trucos para jugar al ajedrez con Chess Buddy APK
-Aquí hay algunos consejos y trucos para jugar al ajedrez con Chess Buddy APK:
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-
- Usa sabiamente las sugerencias de Stockfish: Las sugerencias de Stockfish pueden ayudarte a encontrar el mejor movimiento o evitar errores, pero también pueden hacer que el juego sea menos desafiante o divertido. Úsalos con moderación o solo cuando estés atascado o tengas curiosidad. -
- Aprende del análisis de Stockfish: Después de cada juego, puedes ver el análisis de Stockfish para cada movimiento, incluyendo la puntuación de evaluación, profundidad, nodos y tiempo. También puedes ver un gráfico de los cambios de puntuación a lo largo del juego. Esto puede ayudarle a entender dónde cometió errores u oportunidades perdidas, y cómo mejorar sus habilidades de ajedrez. -
- Juega con diferentes controles de tiempo y niveles de dificultad: Jugar con diferentes controles de tiempo y niveles de dificultad puede ayudarte a practicar diferentes aspectos del ajedrez, como tácticas, estrategia, cálculo, intuición y velocidad. También puedes desafiarte jugando contra oponentes más fuertes o controles de tiempo más rápidos. -
- Divertirse y disfrutar del ajedrez: El consejo más importante es divertirse y disfrutar del ajedrez con Chess Buddy APK. El ajedrez es un juego hermoso y fascinante que puede enriquecer tu mente y vida. No te preocupes demasiado por ganar o perder, pero céntrate en aprender y divertirte. -
Conclusión
- -Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Chess Buddy APK:
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-
- Q: Es Chess Buddy APK seguro y legal de usar? -
- A: Sí, Chess Buddy APK es seguro y legal de usar. Es una aplicación de código abierto que no contiene ningún malware o virus. Tampoco viola ningún derecho de propiedad intelectual o reglas de ajedrez, ya que utiliza el motor Stockfish, que también es de código abierto y de uso gratuito. -
- Q: ¿Cómo puedo actualizar Chess Buddy APK? -
- A: Puede actualizar Chess Buddy APK mediante la descarga e instalación de la última versión del archivo APK de APKCombo o GitHub. No es necesario desinstalar la versión anterior, ya que la nueva versión lo sobreescribirá. También puede comprobar si hay actualizaciones dentro de la aplicación pulsando en el botón "Acerca de" en la pantalla principal y luego tocando en el "Comprobar si hay actualizaciones" botón. -
- Q: ¿Cómo puedo contactar a los desarrolladores de Chess Buddy APK? -
- A: Puede ponerse en contacto con los desarrolladores de Chess Buddy APK visitando su página de GitHub y abriendo un problema o una solicitud de extracción. También puedes enviarles un correo electrónico a dracogroupinc@gmail.com. También puedes seguirlos en Twitter en @dracogroupinc. -
- Q: ¿Cómo puedo apoyar el desarrollo de Chess Buddy APK? -
- A: Usted puede apoyar el desarrollo de Chess Buddy APK mediante la donación a los desarrolladores a través de PayPal o Bitcoin. También puede compartir la aplicación con sus amigos y familiares, y dejar una opinión positiva y calificación en APKCombo. También puede contribuir al código fuente o sugerir nuevas características o mejoras en GitHub. -
- Q: ¿Cómo puedo desinstalar Chess Buddy APK? -
- A: Puede desinstalar Chess Buddy APK yendo a la configuración de su dispositivo y encontrar el administrador de aplicaciones o la opción de aplicaciones. Entonces, es necesario encontrar Chess Buddy APK y toque en él. Entonces, es necesario tocar en el "Desinstalar" botón y confirmar su acción. También puede desinstalar la aplicación presionando su icono en la pantalla de inicio o en el cajón de la aplicación y arrastrándola a la opción "Desinstalar". -
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Cómo descargar APK Chess Offline para Android
-El ajedrez es uno de los juegos de mesa más antiguos y populares del mundo. Es un juego de lógica, estrategia y habilidad que puede desafiar tu mente y mejorar tus habilidades cognitivas. Ya seas un principiante o un gran maestro, el ajedrez puede ofrecerte interminables horas de diversión y entretenimiento.
-Pero ¿qué pasa si no tienes un tablero de ajedrez o un compañero para jugar? ¿Qué pasa si quieres jugar al ajedrez en cualquier momento y en cualquier lugar sin una conexión a Internet? Ahí es donde APK Chess Offline es muy útil. APK Chess Offline es una aplicación de ajedrez gratuita para dispositivos Android que te permite jugar ajedrez sin conexión contra un ordenador o un amigo. También puedes aprender ajedrez, practicar tus habilidades y analizar tus partidas con esta aplicación.
-descargar apk ajedrez sin conexión
Download File »»» https://bltlly.com/2v6JMJ
-
En este artículo, le mostraremos cómo descargar e instalar APK Chess Offline para Android, así como cómo jugar y mejorar sus habilidades de ajedrez con esta aplicación. ¡Vamos a empezar!
-¿Qué es APK Chess Offline?
-APK Chess Offline es una aplicación de ajedrez para dispositivos Android que no requiere una conexión a Internet para jugar. Puedes jugar ajedrez sin conexión contra una computadora o un amigo en el mismo dispositivo. También puede elegir entre diferentes modos de juego, niveles de dificultad y temas de tablero para adaptarse a sus preferencias.
-Características y beneficios de APK Chess Offline
-Algunas de las características y beneficios de APK Chess Offline son:
--
-
- Es gratis para descargar y jugar. -
- No requiere una conexión a Internet ni ningún registro. -
- Tiene una interfaz simple y fácil de usar. -
- Ofrece varios modos de juego, tales como ajedrez clásico, chess960, fiebre del rompecabezas, ajedrez a ciegas, etc. -
- Tiene 10 niveles de dificultad, de principiante a experto. -
- Tiene diferentes temas de tablero, como madera, mármol, metal, etc. -
- Tiene un motor de ajedrez incorporado que puede analizar tus movimientos y darte pistas. - -
- Tiene una función de estadísticas que realiza un seguimiento de su rendimiento y progreso. -
- Tiene una opción de sonido y vibración que añade más realismo al juego. -
Requisitos y compatibilidad de APK Chess Offline
-Para descargar e instalar APK Chess Offline, necesita:
--
-
- Un dispositivo Android con la versión 4.1 o superior. -
- Al menos 20 MB de espacio de almacenamiento libre en su dispositivo. -
- Un permiso para instalar aplicaciones de fuentes desconocidas en su dispositivo. -
Cómo descargar e instalar APK Chess Offline
-Para descargar e instalar APK Chess Offline en tu dispositivo Android, sigue estos pasos:
-Paso 1: Habilitar fuentes desconocidas en el dispositivo
-Dado que APK Chess Offline no está disponible en Google Play Store, es necesario habilitar fuentes desconocidas en el dispositivo para instalarlo. Para hacer esto:
--
-
- Ir a Configuración > Seguridad > Fuentes desconocidas en su dispositivo. -
- Cambiar el interruptor para permitir la instalación de aplicaciones de fuentes desconocidas. -
- Aparecerá un mensaje de advertencia. Toca OK para confirmar. -
Paso 2: Encontrar una fuente confiable para el archivo APK
-El siguiente paso es encontrar una fuente confiable para el archivo APK de APK Chess Offline. Una de las fuentes que puede utilizar para descargar el archivo APK de APK Chess Offline es [Chess Offline Download - apkonline.net]( 1 ). Este es un sitio web confiable y verificado que ofrece descargas gratuitas y seguras de varios archivos APK. Para descargar el archivo APK de esta fuente:
- --
-
- Abra su navegador y vaya a [Chess Offline Download - apkonline.net]( 1 ). -
- Toque en el botón Descargar y espere a que comience la descarga. -
- Puede ver una notificación de que el archivo puede dañar su dispositivo. Pulse OK para continuar. -
Paso 3: Descargar el archivo APK a su dispositivo
-Una vez completada la descarga, verá una notificación de que el archivo APK está listo para instalar. Para instalar el archivo APK en su dispositivo:
--
-
-
- Alternativamente, puede ir a su carpeta de descargas y tocar en el archivo APK. -
- Aparecerá una pantalla pidiéndole que confirme la instalación. Pulse Instalar de nuevo. -
Paso 4: Instalar el archivo APK y lanzar la aplicación
-El proceso de instalación tomará unos segundos. Una vez hecho, verá un mensaje de que la aplicación se ha instalado correctamente. Para iniciar la aplicación:
--
-
- Toque en Abrir para comenzar a jugar ajedrez sin conexión. -
- Alternativamente, puede ir a su cajón de aplicaciones y toque en el icono de APK Chess Offline. -
- Verás una pantalla de bienvenida con algunas instrucciones y opciones. Toca Jugar para comenzar tu juego. -
Cómo jugar y mejorar sus habilidades de ajedrez con APK Chess Offline
-Ahora que ha descargado e instalado APK Chess Offline en su dispositivo, usted está listo para jugar y mejorar sus habilidades de ajedrez con esta aplicación. Aquí hay algunos consejos y trucos para ayudarte a empezar:
-Elige tu modo de juego y nivel de dificultad
-APK Chess Offline ofrece varios modos de juego para que usted elija, tales como ajedrez clásico, chess960, puzzle rush, ajedrez a ciegas, etc. También puede seleccionar a su oponente, ya sea un ordenador o un amigo en el mismo dispositivo. También puedes ajustar el nivel de dificultad del ordenador, desde principiante hasta experto. Para elegir el modo de juego y el nivel de dificultad:
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- Toque en Reproducir en la pantalla de bienvenida. -
- Seleccione el modo de juego de la lista de opciones. -
- Selecciona tu oponente, ya sea Computadora o Humano. -
- Si elige Ordenador, seleccione su nivel de dificultad del 1 al 10. -
- Toque en el juego de inicio para comenzar su juego. -
Aprende las reglas y estrategias del ajedrez
- --
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- Toque en Aprender en la pantalla de bienvenida. -
- Seleccione un tema de la lista de opciones, como Reglas básicas, Principios de apertura, Tácticas, Finales, etc. -
- Lee las instrucciones y ejemplos cuidadosamente y trata de entenderlos. -
- Practica lo que has aprendido resolviendo algunos ejercicios y exámenes. -
Practica con rompecabezas y desafíos
-Si desea poner a prueba sus habilidades y desafiarse a sí mismo, APK Chess Offline tiene una variedad de puzzles y desafíos para que usted resuelva. Puedes elegir entre diferentes categorías, como Mate in One, Mate in Two, Mate in Three, etc. También puedes intentar resolver tantos puzzles como sea posible en un tiempo limitado con el modo Puzzle Rush. Para practicar con rompecabezas y desafíos:
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- Toque en Práctica en la pantalla de bienvenida. -
- Seleccione una categoría de la lista de opciones. -
- Intenta resolver el rompecabezas haciendo el mejor movimiento para cada lado. -
- Si necesita una pista, toque en el icono de bombilla en la parte inferior de la pantalla. -
- Si desea comprobar su respuesta, toque en el icono de marca de verificación en la parte inferior de la pantalla. -
Analiza tus juegos y aprende de tus errores
-Si quieres mejorar tus habilidades de ajedrez, es importante analizar tus partidas y aprender de tus errores. APK Chess Offline tiene una función que le permite revisar sus juegos y ver dónde se fue mal o bien. También puede ver la evaluación de cada movimiento por el motor de ajedrez y obtener sugerencias para mejores movimientos. Para analizar tus juegos y aprender de tus errores:
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- Toque en el historial en la pantalla de bienvenida. -
- Seleccione un juego de la lista de sus juegos recientes. -
- Toque en Analizar para ver la evaluación de cada movimiento por el motor de ajedrez. -
- Toca las flechas en la parte inferior de la pantalla para avanzar o retroceder en el juego. -
- Toque en el icono de interrogación en la parte inferior de la pantalla para ver sugerencias para mejores movimientos. -
Conclusión
- -Si usted está buscando una manera de descargar e instalar APK Chess Offline para Android, siga los pasos de este artículo y usted será capaz de jugar ajedrez sin conexión en ningún momento. Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. Feliz ajedrez jugando!
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre APK Chess Offline:
-Q: ¿Es seguro descargar e instalar APK Chess Offline?
-A: Sí, APK Chess Offline es seguro para descargar e instalar, siempre y cuando utilice una fuente confiable para el archivo APK, como [Chess Offline Download - apkonline.net]. Sin embargo, siempre debe tener cuidado al descargar e instalar aplicaciones de fuentes desconocidas, ya que pueden contener malware o virus que pueden dañar su dispositivo. También debe escanear el archivo APK con una aplicación antivirus antes de instalarlo.
-Q: ¿Funciona APK Chess Offline en todos los dispositivos Android?
-A: APK Chess Offline funciona en la mayoría de los dispositivos Android que tienen la versión 4.1 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con la aplicación o pueden experimentar algunos problemas o errores. Si encuentras algún problema con la aplicación, puedes intentar actualizar tu dispositivo, borrar la caché de la aplicación o reinstalar la aplicación.
-Q: ¿Cómo puedo actualizar APK Chess Offline?
-A: Dado que APK Chess Offline no está disponible en Google Play Store, no se puede actualizar automáticamente a través de la tienda. Tienes que comprobar manualmente las actualizaciones del sitio web de origen u otros sitios web que ofrecen el archivo APK. También puedes seguir la página oficial de Facebook de APK Chess Offline para recibir notificaciones de cualquier actualización o noticia sobre la aplicación.
-Q: ¿Cómo puedo desinstalar APK Chess Offline?
-A: Para desinstalar APK Chess Offline desde tu dispositivo, sigue estos pasos:
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- Ir a Configuración > Aplicaciones > APK Chess Offline en su dispositivo. -
- Toque en Desinstalar y confirme su acción. -
- La aplicación se eliminará de su dispositivo. - -
- Diverse Aircraft: You can choose from a wide range of authentic and highly detailed warplanes, each with its unique strengths and capabilities. You can fly classic planes like the F-16 Falcon or the MiG-29 Fulcrum or modern ones like the F-22 Raptor or the Su-47 Berkut. -
- Intense Campaign: You can immerse yourself in a captivating single-player campaign that consists of 18 missions with different objectives and scenarios. You will face various challenges such as dogfights, ground attacks, reconnaissance, escorting allies, defending bases, and more. -
- Challenging Missions: You can test your skills in a variety of challenging missions that require different strategies and tactics. You will have to deal with different weather conditions, day and night cycles, and enemy reinforcements. You will also have to manage your fuel, ammo, and damage levels. -
- Upgrade and Customize: You can upgrade and customize your aircraft with various weapons, equipment, and paint schemes. You can choose from a variety of missiles, rockets, bombs, guns, and pods to suit your preferences and mission requirements. You can also change the color and pattern of your plane to make it stand out. -
- Realistic Environments: You can enjoy the stunning graphics and realistic physics of the game, which create a immersive and lifelike experience. You can fly over detailed landscapes, such as mountains, forests, deserts, oceans, and cities. You can also witness spectacular explosions, smoke trails, fire effects, and debris. -
- How to Control Your Plane: You can control your plane using either your mouse or your keyboard. If you use your mouse, you can move it left or right to steer your plane horizontally and up or down to steer it vertically. You can also use the left mouse button to fire your primary weapon and the right mouse button to fire your secondary weapon. If you use your keyboard, you can use the arrow keys or WASD keys to steer your plane in different directions. You can also use the spacebar to fire your primary weapon and CTRL key to fire your secondary weapon. -
- How to Use Your Weapons: You have two types of weapons in Air Assault 2: primary and secondary. Your primary weapon is a machine gun that fires bullets continuously. Your secondary weapon is a missile that locks on to an enemy target and follows it until it hits or misses. You can switch between different types of missiles by pressing the number keys from 1 to 5. Each type of missile has its own advantages and disadvantages in terms of range, damage, speed, and accuracy. You should use the appropriate missile for the appropriate situation. For example, you can use the heat-seeking missile to target fast-moving enemies, the radar-guided missile to target distant enemies, or the cluster bomb to target multiple enemies at once. -
- How to Complete Missions: You have to complete various missions in Air Assault 2, each with its own objective and difficulty level. You can see your mission objective on the top left corner of the screen, and your progress on the bottom left corner of the screen. You can also see your score, health, fuel, and ammo levels on the bottom right corner of the screen. To complete a mission, you have to fulfill your objective within the time limit and without losing all your health or fuel. You can also earn bonus points by destroying more enemies, collecting power-ups, and performing stunts. -
- How to Upgrade Your Aircraft: You can upgrade and customize your aircraft in Air Assault 2 by using the money you earn from completing missions. You can access the upgrade menu before starting a mission or after finishing a mission. You can buy new weapons, equipment, and paint schemes for your plane. You can also sell your old items to get more money. Upgrading your aircraft will improve its performance and appearance, making it more effective and attractive. -
- Have Fun: Air Assault 2 is a game that will make you feel like a real pilot who is engaged in thrilling aerial combat. You will enjoy flying over beautiful landscapes, dodging enemy fire, shooting down enemy planes, and blowing up enemy bases. You will also enjoy the game's sound effects, music, and voice-overs, which add to the atmosphere and excitement of the game. -
- Experience Adrenaline-Pumping Aerial Combat: Air Assault 2 is a game that will test your skills and reflexes as you face various enemies and obstacles in the air. You will have to maneuver your plane skillfully, use your weapons wisely, and complete your missions successfully. You will also have to deal with different weather conditions, day and night cycles, and enemy reinforcements. The game's difficulty level will increase as you progress through the campaign, making it more challenging and rewarding. -
- Improve Your Piloting Skills: Air Assault 2 is a game that will help you improve your piloting skills as you learn how to control your plane, use your weapons, and complete your missions. You will also learn how to use different types of missiles, how to perform stunts, and how to upgrade your aircraft. The game's realistic physics and graphics will make you feel like you are flying a real plane. -
- Enjoy Stunning Graphics: Air Assault 2 is a game that will impress you with its stunning graphics and realistic environments. You will be amazed by the game's 3D graphics, which create a lifelike and immersive experience. You will be able to see every detail of your plane, your enemies, and your surroundings. You will also be able to witness spectacular explosions, smoke trails, fire effects, and debris. -
- What are the system requirements for Air Assault 2?
-To play Air Assault 2 on your PC, you need to have the following system requirements:-
-
- Windows XP/Vista/7/8/10 -
- Pentium 4 or higher processor -
- 256 MB of RAM -
- DirectX 9.0 or higher -
- 100 MB of free disk space -
- - Is Air Assault 2 safe to download and play?
-Yes, Air Assault 2 is safe to download and play, as long as you download it from a reliable and trusted source, such as GameTop. GameTop is a website that offers legal and virus-free games for PC users. You don't have to worry about any issues or risks when you download games from GameTop.
- - How can I play Air Assault 2 in full screen mode?
-To play Air Assault 2 in full screen mode, you need to go to the game's settings menu and select the full screen option. You can also press the F4 key on your keyboard to toggle between full screen and windowed mode.
- - How can I pause or exit the game?
-To pause the game, you need to press the ESC key on your keyboard. This will bring up the pause menu, where you can resume, restart, or quit the game. To exit the game, you need to select the quit option from the pause menu or the main menu.
- - How can I get more games like Air Assault 2?
-If you enjoyed playing Air Assault 2 and want to try more games like it, you can visit the GameTop website and browse the games by genre. You can find more arcade games, shoot 'em up games, action games, and more. You can also check out some of the other games that we recommend for you, such as:-
-
- Air Assault: The original game that started the series. Fly over 10 different locations and destroy enemy forces with your helicopter. -
- Sky Fire: A vertical scrolling shooter game that features 8 levels of intense action and boss battles. -
- Air Shark: A helicopter combat game that features 18 missions of various difficulty and terrain. -
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- No intrusive ads or pay-to-win mechanics How to download Pickle Pete: Survivor APK? -
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- Experiment with different weapons and items to find the best combination for your playstyle -
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- Go to APKCombo and search for "aBox - Sandbox Game". -
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- Spawn items and use tools in different maps. You can choose from multiple maps that have different themes, such as city, desert, forest, space, etc. You can also spawn various items, such as props, vehicles, animals, humans, etc. You can use tools, such as gravity gun, physics gun, welder, remover, etc., to manipulate the items as you wish. -
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- Customize your experience and share your creations. You You can also customize your experience and share your creations. You can change the settings of the game, such as the graphics, sound, controls, etc., to suit your preferences. You can also save and load your creations, and share them with other players online. You can also download and play other players' creations, and rate and comment on them. -
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- It is free to use, share, change, and sell your work. You don't have to pay any license fees or royalties to use Blender 3D. -
- It is cross-platform. You can use it on Windows, Mac OS, Linux, and other operating systems. -
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- The header is located at the top of the screen. It contains menus and buttons that allow you to access different modes, tools, settings, and preferences of Blender 3D APK. -
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- They reinforce mathematical concepts and improve problem-solving skills. Math quizzes help you review and practice the concepts you have learned in class or from other sources. They also help you apply those concepts to different situations and contexts, which enhances your problem-solving skills and critical thinking. -
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- Khan Academy: This is a popular and reputable website that offers free math quizzes and videos for all grade levels and skills. You can choose from various topics, such as arithmetic, algebra, geometry, trigonometry, calculus, statistics, etc. You can also track your progress and earn badges and points. -
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- Microsoft Forms: This is a similar app to Google Forms that allows you to create your own math quizzes or use existing ones created by other users. You can choose from various formats, such as multiple-choice, text, rating, etc. You can also add images, videos, or equations to your quizzes. -
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- Find a quiet place and set aside time to study regularly. Math quizzes require concentration and attention, so you need to find a place where you can study without distractions and interruptions. You also need to set aside time to study regularly, preferably every day or every other day, depending on how much time you have before the test. -
- Choose the right type and level of math quizzes for your needs and goals. Math quizzes come in different types and levels, so you need to choose the ones that match your needs and goals. For example, if you want to review the basics of algebra , you may choose math quizzes that are in the format of multiple-choice or fill-in-the-blank and that are at the beginner or intermediate level. If you want to challenge yourself with calculus, you may choose math quizzes that are in the format of puzzle-solving or matching and that are at the advanced or expert level. -
- Practice with different formats and topics to cover all aspects of the test. Math quizzes can help you practice with different formats and topics that may appear on the test, such as multiple-choice, short answer, word problems, graphs, etc. You should practice with a variety of math quizzes to cover all the aspects of the test and to avoid getting bored or complacent. -
- Check your answers and learn from your mistakes. Math quizzes can help you check your answers and learn from your mistakes. You should always check your answers after taking a math quiz and compare them with the correct ones. You should also analyze your mistakes and understand why you made them. You should then review the concepts or skills that you need to improve and practice them again. -
- Track your progress and reward yourself for your achievements. Math quizzes can help you track your progress and reward yourself for your achievements. You should keep a record of your scores and performance on each math quiz and see how you improve over time. You should also celebrate your achievements and reward yourself for your efforts and hard work. -
- OS: 64-bit edition of Windows (10 or higher), Linux, or macOS (macOS 10.15 Catalina or higher). Most modern Linux distributions should work given support for Dolphin's dependencies, and FreeBSD is unsupported but also may work. -
- Processor: A 64-bit x86-64 or AArch64 processor. Highly recommended to have 4 cores or more. Faster is better. -
- Graphics: A graphics card that supports Direct3D 11 / OpenGL 4.4 / Vulkan 1.1 is recommended. Onboard graphics may work, but drivers may vary. -
- Input device(s): Any PC input device – mouse and keyboard by default for Wii, mouse by default for GameCube. Original Nintendo GameCube controller with Smash Bros. Wii U USB adapter or original Nintendo Wii Remote via DolphinBar are also supported. -
- OS: 64-bit edition of Android (5 or higher). Android 9 or higher recommended. -
- Processor: A 64-bit x86-64 or AArch64 processor, as powerful as possible. For Qualcomm devices, processors with 2 or more "big cores" recommended. Snapdragon 700 or newer is typically recommended. -
- Memory: 1 GB RAM minimum, 4 GB RAM or more recommended. -
- Graphics: OpenGL ES 3.0 or higher, OpenGL ES 3.2 / Vulkan 1.1 is recommended. RDNA2 or Snapdragon based graphics processors give the best performance. High-end Mali graphics solutions may also provide playable framerates. All other manufacturers are not generally recommended. -Downloading Dolphin Emulator -
- Double-click on the downloaded file (dolphin-x64-version.exe) to launch the installer. -
- Follow the on-screen instructions to choose the installation directory and components. -
- Click on Install to begin the installation. -
- Wait for the installation to finish, then click on Finish to exit the installer. -
- You can now launch Dolphin Emulator from the Start menu or desktop shortcut. -
- Extract the downloaded file (dolphin-master-version-x86_64.tar.xz) to a folder of your choice. -
- Open a terminal and navigate to the extracted folder. -
- Type
./dolphin-emuand press Enter to run Dolphin Emulator.
- - You can also create a desktop shortcut or menu entry for Dolphin Emulator if you want. -
- Open the downloaded file (dolphin-master-version.dmg) to mount it as a disk image. -
- Drag and drop the Dolphin Emulator icon to the Applications folder. -
- Eject the disk image and open the Applications folder. -
- Right-click on the Dolphin Emulator icon and select Open. -
- You may see a warning message that says "Dolphin Emulator cannot be opened because it is from an unidentified developer". Click on Open anyway to continue. -
- You can now launch Dolphin Emulator from the Applications folder or dock. -
- Enable Unknown sources in your device's settings. This allows you to install apps from sources other than Google Play Store. -
- Open the downloaded file (org.dolphinemu.dolphinemu_version.apk) with a file manager app or your device's browser. -
- You may see a warning message that says "This type of file can harm your device". Tap on OK to continue. -
- Tap on Install to begin the installation. -
- Wait for the installation to finish, then tap on Open to launch Dolphin Emulator. -
- In the General tab, make sure that Enable Dual Core (speedup) is checked. This allows Dolphin Emulator to use multiple CPU cores for better performance. You can also check Enable Cheats if you want to use cheat codes in games. -
- In the Paths tab, click on Add... and browse to the folder where you have stored your Wii Sports Resort game file (more on that later). This will add the game to your Dolphin Emulator library, and you can launch it from there. You can also add more folders or remove existing ones if you want. -
- In the Updates tab, you can choose whether to check for updates automatically or manually, and whether to download the latest stable or development version of Dolphin Emulator. You can also view the current version and revision of your Dolphin Emulator, and update it if there is a newer one available. -
- In the General tab, you can choose the backend that Dolphin Emulator will use to render the graphics. The available backends are Direct3D 11, OpenGL, Vulkan, and Software Renderer. The best backend may vary depending on your hardware and operating system, but generally Vulkan is recommended for most cases, as it offers the best performance and compatibility. Software Renderer is only for debugging purposes and should not be used for normal gameplay. -
- In the Enhancements tab, you can change the internal resolution of the games, which affects how sharp and detailed they look. The default is 1x Native (640x528), which is the original resolution of the Wii console. You can increase it to 2x Native (1280x1056), 3x Native (1920x1584), 4x Native (2560x2112), or higher if your hardware can handle it. You can also enable anti-aliasing and anisotropic filtering to smooth out jagged edges and blurry textures. -
- In the Hacks tab, you can enable or disable some hacks that can improve performance or compatibility in some games. For example, you can enable Skip EFB Access from CPU to speed up games that use EFB effects, such as Super Mario Galaxy. You can also enable Store EFB Copies to Texture Only to reduce stuttering in games that use EFB copies, such as The Legend of Zelda: Twilight Princess. However, these hacks may also cause graphical glitches or inaccuracies in some games, so use them with caution. -
- Make sure you have a Wii console that can run homebrew applications. You can do this by installing the Homebrew Channel on your Wii, following the instructions on https://wii.guide. -
- Make sure you have a large enough SD card or USB drive that can store the game file. Wii Sports Resort is about 2.5 GB in size, so you need at least 4 GB of free space. -
- Download CleanRip from https://wiibrew.org/wiki/CleanRip and extract the files to the apps folder on your SD card or USB drive. -
- Insert your SD card or USB drive into your Wii console, and insert your Wii Sports Resort disc into the disc drive. -
- Launch the Homebrew Channel on your Wii, and select CleanRip from the list of applications. -
- Follow the on-screen instructions to configure CleanRip and start dumping the game. You may need to insert a second SD card or USB drive if your first one is not large enough. -
- Wait for the dumping process to finish, then eject your SD card or USB drive from your Wii console. -
- You should now have a file named RZTE01.iso on your SD card or USB drive. This is the game file that you can use with Dolphin Emulator. -
- Search for Wii Sports Resort on a torrent site or a direct download site. You can use Google or any other search engine to find them. -
- Download the game file from the site of your choice. Make sure it is in ISO or WBFS format, and that it has a good reputation and seeders. -
- Scan the game file with an antivirus program to make sure it is safe and clean. -
- You should now have a file named RZTE01.iso or RZTE01.wbfs on your computer or device. This is the game file that you can use with Dolphin Emulator. -
- To launch the game, open Dolphin Emulator and click on Browse... in the toolbar (or press Ctrl+O) for desktop computers, or tap on the Menu button in the top-right corner and select Browse Files for Android devices. Navigate to the folder where you have stored your game file, and select it. The game should appear in your Dolphin Emulator library, and you can double-click or tap on it to start it. -
- To control the game, you can use any PC input device, such as mouse and keyboard, or any compatible controller, such as Xbox 360 controller, PS4 controller, etc. You can also use an original Nintendo GameCube controller with Smash Bros. Wii U USB adapter, or an original Nintendo Wii Remote via DolphinBar. To configure your input device, click on Controllers in the toolbar (or press Ctrl+C) for desktop computers, or tap on the Menu button in the top-right corner and select Controllers for Android devices. Choose whether you want to emulate a GameCube controller or a Wii Remote, and assign buttons and axes accordingly. -
- To save and load your progress in the game, you can use either in-game save files or save states. In-game save files are created by the game itself, and are stored in a virtual memory card or NAND flash memory. Save states are created by Dolphin Emulator, and are stored as files on your computer or device. Save states allow you to save and load at any point in the game, but they may not be compatible with different versions of Dolphin Emulator or different settings. To create a save state, press Shift+F1-F8 for desktop computers, or tap on Menu button in the top-right corner and select Save State Slot 1-8 for Android devices. To load a save state, press F1-F8 for desktop computers, or tap on Menu button in the top-right corner and select Load State Slot 1-8 for Android devices. -
- To enhance your gaming experience, you can use some of the features and options that Dolphin Emulator offers, such as turbo speed, networked multiplayer, cheats, screenshots, etc. To access these features and options, click on the Options button in the toolbar (or press Alt+O) for desktop computers, or tap on the Menu button in the top-right corner and select Options for Android devices. You can also access some of them by right-clicking or long-pressing on the game in your Dolphin Emulator library. -
- Q: Is Dolphin Emulator legal? -
- A: Dolphin Emulator itself is legal, as it is a software that emulates the hardware of GameCube and Wii consoles. However, downloading or distributing games that you do not own is illegal, as it violates the copyright laws of the game developers and publishers. Therefore, you should only use games that you legally own or have dumped from your own console and disc. -
- Q: How can I improve the performance of Dolphin Emulator? -
- A: There are many factors that affect the performance of Dolphin Emulator, such as your hardware specifications, your operating system, your game settings, your graphics settings, etc. Some general tips to improve the performance are: update your drivers and software, close unnecessary programs and background processes, lower the internal resolution and graphics enhancements, enable dual core and skip EFB access from CPU hacks, etc. You can also check the Dolphin Wiki for specific game settings and recommendations. -
- Q: How can I play Wii Sports Resort online with other players? -
- A: Wii Sports Resort supports online multiplayer via Nintendo Wi-Fi Connection (WFC), which was discontinued in 2014. However, you can still play online with other players using a custom server called Wiimmfi. To use Wiimmfi, you need to patch your game file with a Wiimmfi patcher tool, which you can download from https://wiimmfi.de/. You also need to register your console's MAC address and obtain a friend code from the Wiimmfi website. Then, you can connect to Wiimmfi in Dolphin Emulator by setting the DNS server to 95.217.77.151 in the Wii network settings. -
- Q: How can I play Wii Sports Resort with motion controls on Dolphin Emulator? -
- A: Wii Sports Resort requires the Wii MotionPlus accessory to play with motion controls. You can use an original Nintendo Wii Remote with Wii MotionPlus attached via DolphinBar or Bluetooth connection. You can also use a third-party controller that supports motion controls, such as PS4 controller or Xbox One controller. You need to configure your controller's motion input in Dolphin Emulator's controller settings. -
- Q: How can I get more games for Dolphin Emulator? -
- A: You can get more games for Dolphin Emulator by dumping them from your own GameCube or Wii console and disc, using a homebrew application such as CleanRip or FriiDump. You can also download games from the internet, but this is illegal and not recommended. -
-
-
-
-
- - -
- -- - - -- -- - -- - -- - - - `;
- }).join('');
-
- // Renders the list items differently when drawing or if the agent is selected
- this.element.querySelectorAll('li[name="agent-list-item"]').forEach((span, index) => {
- if(store.state.drawingModeState.drawing){
- span.classList.add('disabled');
- }
- else{
- if(store.state.agents[index] == store.state.simulationState.agentSelected){
- span.classList.add("active");
- }
- else{
- span.classList.remove("active");
- }
- }
- });
-
- /* EVENT LISTENERS */
-
- // Deletes the agent
- this.element.querySelectorAll('button[name="deleteButton"]').forEach((span, index) => {
- span.addEventListener('click', () => {
- store.dispatch('deleteAgent', {index: index});
- });
- });
-
- // Saves the agent's current position as initial position
- this.element.querySelectorAll('button[name="savePositionButton"]').forEach((span, index) => {
- span.addEventListener('click', () => {
- let pos = window.game.env.agents[index].agent_body.reference_head_object.GetPosition().Clone();
- store.dispatch('setAgentInitPos', {index: index, init_pos: pos});
- });
- });
-
- // Resets the agent's initial position
- this.element.querySelectorAll('button[name="resetPositionButton"]').forEach((span, index) => {
- span.addEventListener('click', () => {
- store.dispatch('setAgentInitPos', {index: index, init_pos: null});
- let init_x = TERRAIN_STEP * INITIAL_TERRAIN_STARTPAD / 2;
- window.game.env.set_agent_position(window.game.env.agents[index], init_x, null);
- window.game.env.render();
- });
- });
-
- // Follows the agent
- this.element.querySelectorAll('input[name="followSwitch"]').forEach((span, index) => {
- span.addEventListener('input', () => {
- store.dispatch('followAgent', {index: span.checked ? index : -1});
- });
- span.checked = store.state.simulationState.agentFollowed == store.state.agents[index];
- });
-
- // Renames the agent
- this.element.querySelectorAll('input[name="agentNameArea"]').forEach((span, index) => {
- span.addEventListener('keydown', (event) => {
- if(event.keyCode == '13'){
- store.dispatch('renameAgent', {index: index, value: span.value});
- }
- });
- });
-
- /* Initializes tooltips */
- this.element.querySelectorAll('[data-bs-toggle="tooltip"]').forEach((el, index) => {
- return new bootstrap.Tooltip(el, {
- trigger: 'hover'
- });
- });
- }
-};
\ No newline at end of file
diff --git a/spaces/fun-research/FC-CLIP/fcclip/modeling/pixel_decoder/msdeformattn.py b/spaces/fun-research/FC-CLIP/fcclip/modeling/pixel_decoder/msdeformattn.py
deleted file mode 100644
index ba56686c29722b6d39407c39f44736aa85bc8e0f..0000000000000000000000000000000000000000
--- a/spaces/fun-research/FC-CLIP/fcclip/modeling/pixel_decoder/msdeformattn.py
+++ /dev/null
@@ -1,388 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import numpy as np
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
-from torch.cuda.amp import autocast
-
-from detectron2.config import configurable
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
-
-from ..transformer_decoder.position_encoding import PositionEmbeddingSine
-from .ops.modules import MSDeformAttn
-import copy
-
-
-def _get_clones(module, N):
- return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
-
-
-def _get_activation_fn(activation):
- """Return an activation function given a string"""
- if activation == "relu":
- return F.relu
- if activation == "gelu":
- return F.gelu
- if activation == "glu":
- return F.glu
- raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
-
-
-def build_pixel_decoder(cfg, input_shape):
- """
- Build a pixel decoder from `cfg.MODEL.ONE_FORMER.PIXEL_DECODER_NAME`.
- """
- name = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME
- model = SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape)
- forward_features = getattr(model, "forward_features", None)
- if not callable(forward_features):
- raise ValueError(
- "Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. "
- f"Please implement forward_features for {name} to only return mask features."
- )
- return model
-
-
-# MSDeformAttn Transformer encoder in deformable detr
-class MSDeformAttnTransformerEncoderOnly(nn.Module):
- def __init__(self, d_model=256, nhead=8,
- num_encoder_layers=6, dim_feedforward=1024, dropout=0.1,
- activation="relu",
- num_feature_levels=4, enc_n_points=4,
- ):
- super().__init__()
-
- self.d_model = d_model
- self.nhead = nhead
-
- encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward,
- dropout, activation,
- num_feature_levels, nhead, enc_n_points)
- self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers)
-
- self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
-
- self._reset_parameters()
-
- def _reset_parameters(self):
- for p in self.parameters():
- if p.dim() > 1:
- nn.init.xavier_uniform_(p)
- for m in self.modules():
- if isinstance(m, MSDeformAttn):
- m._reset_parameters()
- normal_(self.level_embed)
-
- def get_valid_ratio(self, mask):
- _, H, W = mask.shape
- valid_H = torch.sum(~mask[:, :, 0], 1)
- valid_W = torch.sum(~mask[:, 0, :], 1)
- valid_ratio_h = valid_H.float() / H
- valid_ratio_w = valid_W.float() / W
- valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
- return valid_ratio
-
- def forward(self, srcs, pos_embeds):
- masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs]
- # prepare input for encoder
- src_flatten = []
- mask_flatten = []
- lvl_pos_embed_flatten = []
- spatial_shapes = []
- for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
- bs, c, h, w = src.shape
- spatial_shape = (h, w)
- spatial_shapes.append(spatial_shape)
- src = src.flatten(2).transpose(1, 2)
- mask = mask.flatten(1)
- pos_embed = pos_embed.flatten(2).transpose(1, 2)
- lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
- lvl_pos_embed_flatten.append(lvl_pos_embed)
- src_flatten.append(src)
- mask_flatten.append(mask)
- src_flatten = torch.cat(src_flatten, 1)
- mask_flatten = torch.cat(mask_flatten, 1)
- lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
- spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
- level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
- valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
-
- # encoder
- memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)
-
- return memory, spatial_shapes, level_start_index
-
-
-class MSDeformAttnTransformerEncoderLayer(nn.Module):
- def __init__(self,
- d_model=256, d_ffn=1024,
- dropout=0.1, activation="relu",
- n_levels=4, n_heads=8, n_points=4):
- super().__init__()
-
- # self attention
- self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
- self.dropout1 = nn.Dropout(dropout)
- self.norm1 = nn.LayerNorm(d_model)
-
- # ffn
- self.linear1 = nn.Linear(d_model, d_ffn)
- self.activation = _get_activation_fn(activation)
- self.dropout2 = nn.Dropout(dropout)
- self.linear2 = nn.Linear(d_ffn, d_model)
- self.dropout3 = nn.Dropout(dropout)
- self.norm2 = nn.LayerNorm(d_model)
-
- @staticmethod
- def with_pos_embed(tensor, pos):
- return tensor if pos is None else tensor + pos
-
- def forward_ffn(self, src):
- src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
- src = src + self.dropout3(src2)
- src = self.norm2(src)
- return src
-
- def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):
- # self attention
- src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)
- src = src + self.dropout1(src2)
- src = self.norm1(src)
-
- # ffn
- src = self.forward_ffn(src)
-
- return src
-
-
-class MSDeformAttnTransformerEncoder(nn.Module):
- def __init__(self, encoder_layer, num_layers):
- super().__init__()
- self.layers = _get_clones(encoder_layer, num_layers)
- self.num_layers = num_layers
-
- @staticmethod
- def get_reference_points(spatial_shapes, valid_ratios, device):
- reference_points_list = []
- for lvl, (H_, W_) in enumerate(spatial_shapes):
-
- ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
- torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))
- ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
- ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
- ref = torch.stack((ref_x, ref_y), -1)
- reference_points_list.append(ref)
- reference_points = torch.cat(reference_points_list, 1)
- reference_points = reference_points[:, :, None] * valid_ratios[:, None]
- return reference_points
-
- def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):
- output = src
- reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)
- for _, layer in enumerate(self.layers):
- output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)
-
- return output
-
-
-@SEM_SEG_HEADS_REGISTRY.register()
-class MSDeformAttnPixelDecoder(nn.Module):
- @configurable
- def __init__(
- self,
- input_shape: Dict[str, ShapeSpec],
- *,
- transformer_dropout: float,
- transformer_nheads: int,
- transformer_dim_feedforward: int,
- transformer_enc_layers: int,
- conv_dim: int,
- mask_dim: int,
- norm: Optional[Union[str, Callable]] = None,
- # deformable transformer encoder args
- transformer_in_features: List[str],
- common_stride: int,
- ):
- """
- NOTE: this interface is experimental.
- Args:
- input_shape: shapes (channels and stride) of the input features
- transformer_dropout: dropout probability in transformer
- transformer_nheads: number of heads in transformer
- transformer_dim_feedforward: dimension of feedforward network
- transformer_enc_layers: number of transformer encoder layers
- conv_dims: number of output channels for the intermediate conv layers.
- mask_dim: number of output channels for the final conv layer.
- norm (str or callable): normalization for all conv layers
- """
- super().__init__()
- transformer_input_shape = {
- k: v for k, v in input_shape.items() if k in transformer_in_features
- }
-
- # this is the input shape of pixel decoder
- input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
- self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5"
- self.feature_strides = [v.stride for k, v in input_shape]
- self.feature_channels = [v.channels for k, v in input_shape]
-
- # this is the input shape of transformer encoder (could use less features than pixel decoder
- transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1].stride)
- self.transformer_in_features = [k for k, v in transformer_input_shape] # starting from "res2" to "res5"
- transformer_in_channels = [v.channels for k, v in transformer_input_shape]
- self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape] # to decide extra FPN layers
-
- self.transformer_num_feature_levels = len(self.transformer_in_features)
- if self.transformer_num_feature_levels > 1:
- input_proj_list = []
- # from low resolution to high resolution (res5 -> res2)
- for in_channels in transformer_in_channels[::-1]:
- input_proj_list.append(nn.Sequential(
- nn.Conv2d(in_channels, conv_dim, kernel_size=1),
- nn.GroupNorm(32, conv_dim),
- ))
- self.input_proj = nn.ModuleList(input_proj_list)
- else:
- self.input_proj = nn.ModuleList([
- nn.Sequential(
- nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1),
- nn.GroupNorm(32, conv_dim),
- )])
-
- for proj in self.input_proj:
- nn.init.xavier_uniform_(proj[0].weight, gain=1)
- nn.init.constant_(proj[0].bias, 0)
-
- self.transformer = MSDeformAttnTransformerEncoderOnly(
- d_model=conv_dim,
- dropout=transformer_dropout,
- nhead=transformer_nheads,
- dim_feedforward=transformer_dim_feedforward,
- num_encoder_layers=transformer_enc_layers,
- num_feature_levels=self.transformer_num_feature_levels,
- )
- N_steps = conv_dim // 2
- self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
-
- self.mask_dim = mask_dim
- # use 1x1 conv instead
- self.mask_features = Conv2d(
- conv_dim,
- mask_dim,
- kernel_size=1,
- stride=1,
- padding=0,
- )
- weight_init.c2_xavier_fill(self.mask_features)
-
- self.maskformer_num_feature_levels = 3 # always use 3 scales
- self.common_stride = common_stride
-
- # extra fpn levels
- stride = min(self.transformer_feature_strides)
- self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))
-
- lateral_convs = []
- output_convs = []
-
- use_bias = norm == ""
- for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]):
- lateral_norm = get_norm(norm, conv_dim)
- output_norm = get_norm(norm, conv_dim)
-
- lateral_conv = Conv2d(
- in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm
- )
- output_conv = Conv2d(
- conv_dim,
- conv_dim,
- kernel_size=3,
- stride=1,
- padding=1,
- bias=use_bias,
- norm=output_norm,
- activation=F.relu,
- )
- weight_init.c2_xavier_fill(lateral_conv)
- weight_init.c2_xavier_fill(output_conv)
- self.add_module("adapter_{}".format(idx + 1), lateral_conv)
- self.add_module("layer_{}".format(idx + 1), output_conv)
-
- lateral_convs.append(lateral_conv)
- output_convs.append(output_conv)
- # Place convs into top-down order (from low to high resolution)
- # to make the top-down computation in forward clearer.
- self.lateral_convs = lateral_convs[::-1]
- self.output_convs = output_convs[::-1]
-
- @classmethod
- def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
- ret = {}
- ret["input_shape"] = {
- k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
- }
- ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
- ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
- ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM
- ret["transformer_dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT
- ret["transformer_nheads"] = cfg.MODEL.MASK_FORMER.NHEADS
- # ret["transformer_dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD
- ret["transformer_dim_feedforward"] = 1024 # use 1024 for deformable transformer encoder
- ret[
- "transformer_enc_layers"
- ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config
- ret["transformer_in_features"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES
- ret["common_stride"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE
- return ret
-
- @autocast(enabled=False)
- def forward_features(self, features):
- srcs = []
- pos = []
- # Reverse feature maps into top-down order (from low to high resolution)
- for idx, f in enumerate(self.transformer_in_features[::-1]):
- x = features[f].float() # deformable detr does not support half precision
- srcs.append(self.input_proj[idx](x))
- pos.append(self.pe_layer(x))
-
- y, spatial_shapes, level_start_index = self.transformer(srcs, pos)
- bs = y.shape[0]
-
- split_size_or_sections = [None] * self.transformer_num_feature_levels
- for i in range(self.transformer_num_feature_levels):
- if i < self.transformer_num_feature_levels - 1:
- split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]
- else:
- split_size_or_sections[i] = y.shape[1] - level_start_index[i]
- y = torch.split(y, split_size_or_sections, dim=1)
-
- out = []
- multi_scale_features = []
- num_cur_levels = 0
- for i, z in enumerate(y):
- out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1]))
-
- # append `out` with extra FPN levels
- # Reverse feature maps into top-down order (from low to high resolution)
- for idx, f in enumerate(self.in_features[:self.num_fpn_levels][::-1]):
- x = features[f].float()
- lateral_conv = self.lateral_convs[idx]
- output_conv = self.output_convs[idx]
- cur_fpn = lateral_conv(x)
- # Following FPN implementation, we use nearest upsampling here
- y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False)
- y = output_conv(y)
- out.append(y)
-
- for o in out:
- if num_cur_levels < self.maskformer_num_feature_levels:
- multi_scale_features.append(o)
- num_cur_levels += 1
-
- return self.mask_features(out[-1]), out[0], multi_scale_features
diff --git a/spaces/genomics-england/anonymise_this/README.md b/spaces/genomics-england/anonymise_this/README.md
deleted file mode 100644
index 5d74b149ae6c9e9c181d64158888fff4a93917bd..0000000000000000000000000000000000000000
--- a/spaces/genomics-england/anonymise_this/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Anonymise This
-emoji: 🐨
-colorFrom: gray
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
-models: ['flair/ner-english-large','unitary/toxic-bert']
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gradio/HuBERT/fairseq/scoring/bleu.py b/spaces/gradio/HuBERT/fairseq/scoring/bleu.py
deleted file mode 100644
index 97de5f966ec08e5a304c41358e67755c601622b7..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/scoring/bleu.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import ctypes
-import math
-import sys
-from dataclasses import dataclass, field
-
-import torch
-from fairseq.dataclass import FairseqDataclass
-from fairseq.scoring import BaseScorer, register_scorer
-from fairseq.scoring.tokenizer import EvaluationTokenizer
-
-
-class BleuStat(ctypes.Structure):
- _fields_ = [
- ("reflen", ctypes.c_size_t),
- ("predlen", ctypes.c_size_t),
- ("match1", ctypes.c_size_t),
- ("count1", ctypes.c_size_t),
- ("match2", ctypes.c_size_t),
- ("count2", ctypes.c_size_t),
- ("match3", ctypes.c_size_t),
- ("count3", ctypes.c_size_t),
- ("match4", ctypes.c_size_t),
- ("count4", ctypes.c_size_t),
- ]
-
-
-@dataclass
-class SacrebleuConfig(FairseqDataclass):
- sacrebleu_tokenizer: EvaluationTokenizer.ALL_TOKENIZER_TYPES = field(
- default="13a", metadata={"help": "tokenizer"}
- )
- sacrebleu_lowercase: bool = field(
- default=False, metadata={"help": "apply lowercasing"}
- )
- sacrebleu_char_level: bool = field(
- default=False, metadata={"help": "evaluate at character level"}
- )
-
-
-@register_scorer("sacrebleu", dataclass=SacrebleuConfig)
-class SacrebleuScorer(BaseScorer):
- def __init__(self, cfg):
- super(SacrebleuScorer, self).__init__(cfg)
- import sacrebleu
-
- self.sacrebleu = sacrebleu
- self.tokenizer = EvaluationTokenizer(
- tokenizer_type=cfg.sacrebleu_tokenizer,
- lowercase=cfg.sacrebleu_lowercase,
- character_tokenization=cfg.sacrebleu_char_level,
- )
-
- def add_string(self, ref, pred):
- self.ref.append(self.tokenizer.tokenize(ref))
- self.pred.append(self.tokenizer.tokenize(pred))
-
- def score(self, order=4):
- return self.result_string(order).score
-
- def result_string(self, order=4):
- if order != 4:
- raise NotImplementedError
- # tokenization and lowercasing are performed by self.tokenizer instead.
- return self.sacrebleu.corpus_bleu(
- self.pred, [self.ref], tokenize="none"
- ).format()
-
-
-@dataclass
-class BleuConfig(FairseqDataclass):
- pad: int = field(default=1, metadata={"help": "padding index"})
- eos: int = field(default=2, metadata={"help": "eos index"})
- unk: int = field(default=3, metadata={"help": "unk index"})
-
-
-@register_scorer("bleu", dataclass=BleuConfig)
-class Scorer(object):
- def __init__(self, cfg):
- self.stat = BleuStat()
- self.pad = cfg.pad
- self.eos = cfg.eos
- self.unk = cfg.unk
-
- try:
- from fairseq import libbleu
- except ImportError as e:
- sys.stderr.write(
- "ERROR: missing libbleu.so. run `pip install --editable .`\n"
- )
- raise e
-
- self.C = ctypes.cdll.LoadLibrary(libbleu.__file__)
-
- self.reset()
-
- def reset(self, one_init=False):
- if one_init:
- self.C.bleu_one_init(ctypes.byref(self.stat))
- else:
- self.C.bleu_zero_init(ctypes.byref(self.stat))
-
- def add(self, ref, pred):
- if not isinstance(ref, torch.IntTensor):
- raise TypeError("ref must be a torch.IntTensor (got {})".format(type(ref)))
- if not isinstance(pred, torch.IntTensor):
- raise TypeError("pred must be a torch.IntTensor(got {})".format(type(pred)))
-
- # don't match unknown words
- rref = ref.clone()
- assert not rref.lt(0).any()
- rref[rref.eq(self.unk)] = -999
-
- rref = rref.contiguous().view(-1)
- pred = pred.contiguous().view(-1)
-
- self.C.bleu_add(
- ctypes.byref(self.stat),
- ctypes.c_size_t(rref.size(0)),
- ctypes.c_void_p(rref.data_ptr()),
- ctypes.c_size_t(pred.size(0)),
- ctypes.c_void_p(pred.data_ptr()),
- ctypes.c_int(self.pad),
- ctypes.c_int(self.eos),
- )
-
- def score(self, order=4):
- psum = sum(
- math.log(p) if p > 0 else float("-Inf") for p in self.precision()[:order]
- )
- return self.brevity() * math.exp(psum / order) * 100
-
- def precision(self):
- def ratio(a, b):
- return a / b if b > 0 else 0
-
- return [
- ratio(self.stat.match1, self.stat.count1),
- ratio(self.stat.match2, self.stat.count2),
- ratio(self.stat.match3, self.stat.count3),
- ratio(self.stat.match4, self.stat.count4),
- ]
-
- def brevity(self):
- r = self.stat.reflen / self.stat.predlen
- return min(1, math.exp(1 - r))
-
- def result_string(self, order=4):
- assert order <= 4, "BLEU scores for order > 4 aren't supported"
- fmt = "BLEU{} = {:2.2f}, {:2.1f}"
- for _ in range(1, order):
- fmt += "/{:2.1f}"
- fmt += " (BP={:.3f}, ratio={:.3f}, syslen={}, reflen={})"
- bleup = [p * 100 for p in self.precision()[:order]]
- return fmt.format(
- order,
- self.score(order=order),
- *bleup,
- self.brevity(),
- self.stat.predlen / self.stat.reflen,
- self.stat.predlen,
- self.stat.reflen
- )
diff --git a/spaces/gradio/reverse_audio/README.md b/spaces/gradio/reverse_audio/README.md
deleted file mode 100644
index fd57e165f612eff4876e15225bcbb4a2fee8f631..0000000000000000000000000000000000000000
--- a/spaces/gradio/reverse_audio/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
----
-title: reverse_audio
-emoji: 🔥
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.1.2
-app_file: run.py
-pinned: false
-hf_oauth: true
----
diff --git a/spaces/gulabpatel/Real-ESRGAN/realesrgan/__init__.py b/spaces/gulabpatel/Real-ESRGAN/realesrgan/__init__.py
deleted file mode 100644
index bfea78f284116dee22510d4aa91f9e44afb7d472..0000000000000000000000000000000000000000
--- a/spaces/gulabpatel/Real-ESRGAN/realesrgan/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# flake8: noqa
-from .archs import *
-from .data import *
-from .models import *
-from .utils import *
-#from .version import *
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/nvdiffrast/common/common.cpp b/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/nvdiffrast/common/common.cpp
deleted file mode 100644
index e566c035bdef66e9b75265a58fb8602b0fa530ca..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/nvdiffrast/common/common.cpp
+++ /dev/null
@@ -1,60 +0,0 @@
-// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto. Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include - Learn the rules and scoring system of Belote. Belote has different variations and modes, such as classic Belote, coinche Belote, contrée Belote, and so on. Each variation has its own rules and scoring system that you should know and understand before playing. You can find the rules and scoring system of Belote on various websites or in the game itself. -
- Practice your skills and strategies. Belote is a game that requires both luck and skill. You need to practice your skills and strategies to become a better player. You can practice by playing against the computer or against other players online. You can also watch videos or read articles that explain how to play Belote effectively. -
- Choose your partner wisely. Belote is a team game that requires cooperation and communication between partners. You should choose a partner who has a similar skill level and style as you, who can communicate well with you, and who can support you during the game. You should also avoid playing with strangers or random players online, as they may not be reliable or trustworthy. -
- Be flexible and adaptable. Belote is a dynamic game that can change depending on the cards dealt, the bids made, the tricks taken, and so on. You should be flexible and adaptable to adjust your strategy according to the situation. You should also be able to anticipate your opponents' moves and counter them accordingly. -
- Have fun and enjoy the game. Belote is a fun and enjoyable game that can provide entertainment and challenge for players of all ages and levels. You should have fun and enjoy the game regardless of whether you win or lose. You should also be respectful and courteous to your partner and your opponents, as they are also human beings who want to have fun. -
- It supports downloading videos from YouTube and 100+ online video sites like Vimeo, Twitter, Facebook, Instagram, Bilibili and Reddit etc.[^1^] -
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- It is free to download YouTube videos in various resolutions and formats[^1^] [^2^] -
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- It can download any content in YouTube, such as playlist, channel, subtitle and thumbnail[^1^] [^2^] -
- It can convert YouTube videos to MP3 audio with little quality loss[^1^] [^2^] -
- It has a one-click download mode that makes it easy and fast to download YouTube videos[^1^] [^2^] -
- It can add subtitle or closed caption file to the downloaded videos and merge them to a whole one[^1^] [^2^] -
- It can manage, backup and restore the multimedia data on your Android or iPhone devices[^1^] [^2^] -
- The free version only allows you to download up to 5 videos daily[^1^] -
- The serial key and keygen provided by SeuPirate may be illegal and risky, as they may violate the copyright of the software developer and expose your computer to malware or viruses[^3^] -
- The software may not be updated frequently or compatible with the latest versions of YouTube or other video sites -
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- RoadCalc: A module that helps you design roads, highways, intersections, roundabouts, and more. You can create plan and profile drawings, calculate curve geometry data, generate cross sections and earthwork reports, and more[^5^]. -
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- Landscape: A module that helps you design landscape elements such as plants, -paths, -fences, -walls, -and more. -You can create landscape drawings, -calculate plant quantities, -generate cost estimates, -and more[^5^]. -
- Turn off your Azamerica S810b and disconnect it from the power source. -
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- - "A masterpiece that transcends time and space. Kaajal Oza Vaidya has woven a beautiful tapestry of emotions and wisdom with her words. Krishnayan is a novel that will touch your soul and make you fall in love with Krishna all over again." - Goodreads -
- "Krishnayan is a novel that celebrates the essence of love in its purest form. It is a novel that explores the depth of human relationships and the divine grace that binds them. Kaajal Oza Vaidya has written a novel that is not only a literary gem but also a spiritual guide." - Matrubharti -
- "Krishnayan is a novel that will make you feel the presence of Krishna in your life. It is a novel that will inspire you to live with joy, compassion and courage. Kaajal Oza Vaidya has created a masterpiece that will stay with you forever." - Soundcloud -
- Yog Viyog: A novel that narrates the story of two lovers who are separated by fate and reunited by destiny. -
- Sambandh: A collection of short stories that depict the different facets of human relationships. -
- Sheshyatra: A collection of poems that express the emotions and experiences of life. -
- Varta Re Varta: A collection of essays that reflect on various topics, such as culture, society, spirituality and self-development. -
- Gandhi Before India: A biography of Mahatma Gandhi that focuses on his early life and his struggles in South Africa. -
- Entertainment: Krishnayan is a novel that will keep you hooked with its captivating plot, its vivid descriptions and its emotional dialogues. You will enjoy reading about the adventures, the romance and the drama of Krishna and his women. -
- Education: Krishnayan is a novel that will teach you about the history, the culture and the mythology of India. You will learn about the life and times of Krishna, his role in the Mahabharata war, his teachings in the Bhagavad Gita and his influence on Hinduism. -
- Enlightenment: Krishnayan is a novel that will inspire you to seek the divine within yourself and others. You will learn about the values of love, compassion, wisdom and courage that Krishna embodied and imparted to his women. You will also learn about the challenges and the joys of human relationships and the divine grace that sustains them. -
- Download the PDF file of Krishnayan from any of the online sources mentioned above. -
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- Read the novel with an open mind and heart, and try to understand the perspective of Krishna and his women. -
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- Love: Krishnayan is a novel that celebrates the power and beauty of love in its various forms. It shows how love can transform, heal and liberate a person. It also shows how love can be unconditional, eternal and divine. -
- Life: Krishnayan is a novel that depicts the journey of life in its ups and downs. It shows how life can be full of challenges, opportunities and choices. It also shows how life can be meaningful, joyful and purposeful. -
- God: Krishnayan is a novel that reveals the mystery and glory of God in his human and divine aspects. It shows how God can be a friend, a lover, a teacher and a protector. It also shows how God can be present in everyone and everything. -
- Read the novel carefully and note down your impressions and opinions. -
- Write an introduction that gives a brief overview of the novel and its author. -
- Write a summary that gives the main plot and characters of the novel. -
- Write an analysis that gives your evaluation of the novel's strengths and weaknesses, such as its style, tone, language, theme, message, etc. -
- Write a conclusion that gives your overall impression and recommendation of the novel. -
- Proofread and edit your review for clarity, accuracy and grammar. -
- How Adguard Premium blocks ads on any browser or app: Unlike most ad blockers that only work on browsers, Adguard Premium filters traffic not only in browsers, but also in other applications on your device. This means that you can block ads on YouTube, Spotify, Skype, Facebook Messenger, and any other app that shows ads. -
- How Adguard Premium saves traffic and speeds up loading: Compared to ad-blocking extensions, Adguard Premium filters ads before they get pulled by your browser to your device. This means that pages load faster and consume less traffic, which is especially important if you have a limited data plan or a slow internet connection. -
- How Adguard Premium keeps pages looking good and functional: Most modern ad blockers can maintain page functionality and integrity after removing ad sections. But some websites may detect that you are using an ad blocker and ask you to disable it or limit your access. Adguard Premium can bypass these anti-ad blocking measures and keep pages looking good and functional. -
- How Adguard Premium removes online trackers and analytics: Every time you visit a website, it may collect information about you, such as your IP address, browser type, location, preferences, etc. This information can be used to create a profile of you and target you with personalized ads or sell it to third parties. Adguard Premium shields your data from web analytics and online trackers by blocking them or modifying them. -
- How Adguard Premium blocks malicious and phishing sites: Some websites may try to infect your device with malware, spyware, ransomware, or other harmful software. Others may try to trick you into revealing your personal or financial information by pretending to be legitimate sites. Adguard Premium protects you from these threats by blocking access to malicious and phishing sites. -
- How Adguard Premium protects children from inappropriate content: The internet is full of content that is not suitable for children, such as violence, pornography, gambling, drugs, etc. If you want to shield your children from these influences, you can use the parental control feature of Adguard Premium. It allows you to block access to adult sites, set up a whitelist of allowed sites, and customize the filtering level. -
- How Adguard Premium lets you choose from various filters and settings: Adguard Premium gives you full control over what you want to block or allow on the web. You can choose from various filters that suit your needs, such as general ad blocking filter, social media filter, annoyance filter, security filter, etc. You can also create your own custom filters or subscribe to third-party filters. -
- How Adguard Premium supports user scripts and extensions: User scripts are small pieces of code that modify the behavior or appearance of websites. Extensions are small programs that add extra features or functionality to browsers. Adguard Premium supports both user scripts and extensions, which means that you can enhance your web experience with tools like Greasemonkey, Tampermonkey, Violentmonkey, etc. -
- How Adguard Premium provides technical support and updates: If you have any questions or issues with using Adguard Premium, you can contact their support team via email or live chat. They are always ready to help you with any problem or feedback. Moreover, Adguard Premium is constantly updated with new features and improvements to keep up with the latest web trends and threats. -
Download the crack file from this link: https://crackswin.com/ad-guard-premium-crack/. This file contains both the setup file and the crack file for AdGuard 7.4 RC (v7.4.nightly 32).
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-Adguard Premium 7.4
-Run the setup file as administrator and follow the instructions on the screen to install AdGuard 7.4 RC (v7.4.nightly 32). Do not launch the program after installation.
-Copy the crack file (adgurd.exe) from the crack folder (Crack) and paste it into the installation folder (C:\Program Files (x86)\AdGuard).
-Run the crack file (adgurd.exe) as administrator and click on "Patch". Wait for a few seconds until it says "Patching Done". Close the crack file.
-Launch AdGuard 7.4 RC (v7.4.nightly 32) from your desktop shortcut or start menu.
-You will see a message saying "Your license key has been activated successfully". Click on "OK". You have now activated AdGuard 7.4 RC (v7.4.nightly 32) with AdGuard Lifetime License Key.
-You can now enjoy all the features of AdGuard 7.4 RC (v7.4.nightly 32), such as ad blocking, privacy protection,
What are the system requirements for Adguard Premium?
-Adguard Premium works on Windows 7, 8, 8.1, 10 (32-bit or 64-bit). It requires at least 512 MB of RAM and 50 MB of free disk space.
-Is Adguard Premium compatible with other ad blockers or VPNs?
-Adguard Premium can work alongside other ad blockers or VPNs without any conflicts or issues. However, you may not need to use them as Adguard Premium already provides ad blocking and VPN features.
-How can I contact AdGuard if I have any questions or issues?
-You can contact AdGuard via email at support@adguard.com or via live chat on their website at https://adguard.com/en/support.html. They are available 24/7 and will respond to your queries as soon as possible.
-Does AdGuard have free versions or trial periods?
-AdGuard has free versions for browsers (Chrome, Firefox, Safari, Edge, Opera, Yandex) and Android devices. You can download them from their official website at https://adguard.com/en/welcome.html. AdGuard also offers a 14-day trial period for Windows and Mac devices. You can activate it by downloading the software from their website and entering your email address.
-Can I use AdGuard on multiple devices with one license?
-Yes, you can use AdGuard on multiple devices with one license. You can choose between a personal license (up to 3 devices) or a family license (up to 9 devices). You can also add more devices to your license by paying an extra fee.
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- You want to go paperless and reduce clutter in your office or home. -
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- Go to the official website of FileCenter Professional at https://www.lucion.com/filecenter-overview.html. -
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- Go to Help > Register > Enter Serial Number. -
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- Launch FileCenter Professional from your desktop or start menu. -
- Choose a file cabinet from the left panel or create a new one by clicking on the "New Cabinet" button. -
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- Right-click on any file or folder to access various options such as scan, preview, edit, convert, email, encrypt, shred, etc. -
- Use the toolbar at the top to access more features such as OCR, search, PDF tools, cloud services, etc. -
- Use the "Help" menu to access the user manual, tutorials, support, and updates. -
- PaperPort: A popular software that helps you scan, organize, and share your documents. It also has advanced PDF features, cloud integration, and mobile access. You can get it from https://www.nuance.com/print-capture-and-pdf-solutions/paperport.html. -
- ABBYY FineReader: A powerful software that lets you scan, edit, convert, and compare your documents. It also has OCR, search, cloud support, and collaboration features. You can get it from https://pdf.abbyy.com/finereader-pdf/. -
- eFileCabinet: A cloud-based software that helps you store, manage, and share your documents online. It also has OCR, search, encryption, workflow, and mobile features. You can get it from https://www.efilecabinet.com/. -
- Q: How much does FileCenter Professional cost?
A: FileCenter Professional costs $199.95 for a single-user license. You can also get a multi-user license for $179.95 per user (minimum of five users). You can buy it from https://www.lucion.com/filecenter-pricing.html.
- - Q: How can I get support for FileCenter Professional?
A: You can get support for FileCenter Professional by visiting https://www.lucion.com/support.html. You can also contact them by phone at (801)722-7098 or by email at support@lucion.com.
- - Q: How can I update FileCenter Professional?
A: You can update FileCenter Professional by going to Help > Check for Updates. You can also download the latest version from https://www.lucion.com/filecenter-download.html.
- - Q: How can I uninstall FileCenter Professional?
A: You can uninstall FileCenter Professional by going to Control Panel > Programs > Uninstall a Program. You can also use the uninstaller that comes with the setup file.
- - Q: Is FileCenter Professional safe to use?
A: Yes, FileCenter Professional is safe to use. It does not contain any viruses, malware, spyware, or adware. However, you should always download it from the official website or a trusted source.
- - Parental control: You can keep an eye on your children's online activity, protect them from cyberbullying, predators, and inappropriate content. -
- Employee monitoring: You can track your employees' productivity, performance, and compliance with company policies. -
- Personal security: You can protect your personal information, identity, and accounts from hackers, phishing, and malware. -
- Investigation: You can gather evidence of cheating, fraud, or other illegal activities. -
- Go to one of the trusted websites that offer Golden Eye 4.50.rar download. For example, you can visit freshMeat, a website that provides magnet links for various files. -
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- Refog Keylogger: This is a popular and trusted keylogger software that can record keystrokes, screenshots, web history, chats, emails, and more. It can also run in stealth mode and send the logs by email or FTP. It has a user-friendly interface and a free trial version. -
- Spyrix Keylogger: This is a powerful and versatile keylogger software that can record keystrokes, screenshots, web history, chats, emails, passwords, social media activity, and more. It can also run in stealth mode and send the logs by email, FTP, LAN, or cloud. It has a remote control feature and a free version. -
- Elite Keylogger: This is a professional and reliable keylogger software that can record keystrokes, screenshots, web history, chats, emails, passwords, clipboard content, and more. It can also run in stealth mode and send the logs by email or FTP. It has a low system resource consumption and a free trial version. -
- Actual Keylogger: This is a simple and effective keylogger software that can record keystrokes, screenshots, web history, chats, emails, and more. It can also run in stealth mode and send the logs by email or FTP. It has a clear interface and a free version. -
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- Create a Netflix account if you don't have one. You can sign up for a free trial for 30 days and cancel anytime. -
- Choose a plan that suits your needs and budget. Netflix offers three plans: Basic, Standard, and Premium. The Basic plan costs $8.99 per month and lets you watch on one screen at a time. The Standard plan costs $13.99 per month and lets you watch on two screens at a time. The Premium plan costs $17.99 per month and lets you watch on four screens at a time. -
- Download the Netflix app on your device or visit the Netflix website on your browser. -
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- Enjoy the movie! -
- Netflix: Netflix is a popular streaming service that offers a wide range of movies and shows, including Delhi Belly. You can watch the film in English with subtitles on Netflix. However, you need to have a subscription to access Netflix's content. You can sign up for a free trial if you are a new user. -
- Moviefone: Moviefone is a website that lets you find and watch movies online. You can search for Delhi Belly on Moviefone and see where it is available to stream. You can also rent or buy the film from various platforms like Amazon Prime Video, iTunes, Google Play and more. -
- Dailymotion: Dailymotion is a video-sharing platform that hosts user-generated and licensed content. You can find Delhi Belly on Dailymotion in two parts. However, the quality may not be very good and there may be ads or pop-ups. -
- Filmywap: Filmywap is a website that allows you to download Bollywood movies in various formats, including MP4. You can find Delhi Belly on Filmywap and download it for free. However, this may be illegal and risky as you may encounter viruses or malware. -
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- Library module: This is where you can import, organize, rate, filter, tag, and manage your photos. You can also create collections, smart collections, keywords, face tags, color labels, flags, ratings, and more to help you find and sort your photos easily. -
- Develop module: This is where you can edit, enhance, and transform your photos. You can use various tools and sliders to adjust the exposure, contrast, color, tone, sharpness, noise, lens corrections, and more. You can also use the histogram, the tone curve, the HSL panel, the split toning panel, the calibration panel, and more to fine-tune your edits. You can also apply presets and profiles to quickly change the look of your photos. -
- Map module: This is where you can view and edit the GPS location data of your photos. You can also use the map to find photos based on where they were taken. -
- Book module: This is where you can create and print photo books from your photos. You can choose from various templates, layouts, fonts, colors, and more to customize your photo book. -
- Slideshow module: This is where you can create and play slideshows from your photos. You can add music, transitions, captions, and more to make your slideshows more dynamic. -
- Print module: This is where you can print your photos or create print layouts. You can adjust the size, resolution, orientation, margins, and more of your prints. You can also use the print preview and soft proofing features to see how your prints will look before printing them. -
- Web module: This is where you can create and export web galleries from your photos. You can choose from various web gallery templates and customize them to suit your style. You can also upload your web galleries to your own website or to Adobe Portfolio. -
- Processor: Intel® or AMD processor with 64-bit support; 2 GHz or faster processor -
- Operating system: Microsoft Windows 10 (64-bit) Version 1903 or later -
- RAM: 4 GB of RAM (8 GB recommended) -
- Hard disk space: 2 GB of available hard-disk space for program installation -
- Monitor resolution: 1024 x 768 display -
- Graphics processor acceleration requirements: AMD: Radeon GPU with DirectX 12 or OpenGL 3.3 support; Intel: Skylake or newer GPU with DirectX 12 support; NVIDIA: GPU with DirectX 12 or OpenGL 3.3 support; OpenGL 3.3 and DirectX 10-capable video adapter for GPU-related functionality; 1 GB Video RAM (VRAM) -
- Internet connection: Internet connection and registration are necessary for required software activation, validation of subscriptions, and access to online services. -
- Processor: Intel® or AMD processor with 64-bit support; 2 GHz or faster processor -
- Operating system: macOS v10.14 (Mojave) or later -
- RAM: 4 GB of RAM (8 GB recommended) -
- Hard disk space: 2 GB of available hard-disk space for program installation (cannot install on a volume that uses a case-sensitive file system or on removable flash storage devices) -
- Monitor resolution: 1024 x 768 display -
- Graphics processor acceleration requirements: AMD: macOS 10.13 or later with Metal support; Intel: macOS 10.13 or later with Metal support; NVIDIA: macOS 10.13 or later with Metal support; Metal support; OpenGL ES-capable video adapter for GPU-related functionality; 1 GB Video RAM (VRAM) -
- Internet connection: Internet connection and registration are necessary for required software activation, validation of subscriptions, and access to online services. -
- Lightroom plan: This plan gives you access to Lightroom (the cloud-based app), 1 TB of cloud storage, and Adobe Portfolio, Adobe Fonts, and Adobe Spark. The price is $9.99 per month. -
- Photography plan: This plan gives you access to Lightroom Classic, Lightroom, Photoshop, 20 GB of cloud storage, and Adobe Portfolio, Adobe Fonts, and Adobe Spark. The price is $9.99 per month. -
- Photography plan with 1 TB: This plan gives you access to Lightroom Classic, Lightroom, Photoshop, 1 TB of cloud storage, and Adobe Portfolio, Adobe Fonts, and Adobe Spark. The price is $19.99 per month. -
- Creative Cloud All Apps plan: This plan gives you access to all the Creative Cloud apps and services, including Lightroom Classic, Lightroom, Photoshop, Illustrator, InDesign, Premiere Pro, After Effects, and more. You also get 100 GB of cloud storage, and Adobe Portfolio, Adobe Fonts, and Adobe Spark. The price is $52.99 per month. -
- Double-click the installer file to launch the installation process. -
- Follow the on-screen instructions to sign in with your Adobe ID and password. -
- Select your preferred language and location for the installation. -
- Choose whether you want to install Lightroom Classic CC 2020 as a trial version or as a full version with a subscription plan. -
- Click Install and wait for the installation to complete. -
- Click Launch to open Lightroom Classic CC 2020 and start using it. -
- Menu bar: This is where you can access various menus and commands for Lightroom Classic CC 2020. You can also see the name of the current catalog and the current module. -
- Modules: These are the different sections of Lightroom Classic CC 2020 that correspond to different tasks or workflows. You can switch between them by clicking on their names in the menu bar or by using keyboard shortcuts. The modules are Library, Develop, Map, Book, Slideshow, Print, and Web. -
- Panels: These are the areas on the left and right sides of the window that contain various tools and options for each module. You can expand or collapse them by clicking on their names or by using keyboard shortcuts. You can also customize them by dragging them or by right-clicking on them. -
- Filmstrip: This is the area at the bottom of the window that shows thumbnails of your photos in the current folder or collection. You can scroll through them by using the scroll bar or by using keyboard shortcuts. You can also select or deselect them by clicking on them or by using keyboard shortcuts. -
- Loupe view: This is the area in the center of the window that shows a larger view of the selected photo. You can zoom in or out by using the zoom slider or by using keyboard shortcuts. You can also pan around the photo by dragging it or by using keyboard shortcuts. -
- Toolbar: This is the area below the loupe view that contains various buttons and options for each module. You can customize it by clicking on the triangle icon on the right side or by right-clicking on it. -
- Switch to the Library module by clicking on its name in the menu bar or by pressing G on your keyboard. -
- Click on the Import button on the bottom left corner of the window or press Ctrl+Shift+I (Windows) or Command+Shift+I (Mac OS) on your keyboard. -
- In the Import window that appears, select the source of your photos from the left panel. You can browse through your folders or devices by clicking on them or by using keyboard shortcuts. -
- In the center panel, select the photos that you want to import by checking or unchecking their boxes. You can also use keyboard shortcuts or click on the Check All or Uncheck All buttons at the bottom of the panel. -
- In the right panel, choose how you want to import your photos from the top drop-down menu. You can choose from Copy as DNG, Copy, Move, or Add. Copy as DNG will convert your photos to DNG format and copy them to a new location. Copy will copy your photos to a new location without converting them. Move will move your photos to a new location and delete them from their original location. Add will add your photos to the catalog without moving or copying them. -
- In the right panel, also choose where you want to import your photos from the Destination section. You can select an existing folder or create a new one by clicking on the plus icon. You can also choose how to organize your photos by date or by other criteria. -
- In the right panel, also choose whether you want to apply any presets, keywords, metadata, or file renaming to your photos from the Apply During Import section. You can select from various options or create your own by clicking on the drop-down menus. -
- When you are ready to import your photos, click on the Import button at the bottom right corner of the window. Wait for the import process to complete and then close the Import window. -
- Collections: These are groups of photos that you can create and name according to your preferences. You can create regular collections, smart collections, collection sets, and quick collections. Regular collections are collections that you manually add photos to. Smart collections are collections that automatically add photos based on certain criteria that you specify. Collection sets are groups of collections that you can organize hierarchically. Quick collections are temporary collections that you can use for quick tasks. -
- Keywords: These are words or phrases that you can assign to your photos to describe their content, theme, subject, or any other information that you want. You can create keyword tags, keyword sets, keyword hierarchies, and keyword synonyms. Keyword tags are individual keywords that you apply to your photos. Keyword sets are groups of keywords that you can apply together. Keyword hierarchies are keywords that have subkeywords under them. Keyword synonyms are keywords that have alternative words associated with them. -
- Face tags: These are tags that identify and name the people in your photos. You can use Lightroom Classic CC 2020's face detection and recognition features to automatically find and suggest faces in your photos. You can also manually add or edit face tags. -
- Color labels: These are colors that you can assign to your photos to mark them for different purposes. You can choose from red, yellow, green, blue, or purple labels. You can also customize the names and meanings of the color labels. -
- Flags: These are icons that you can assign to your photos to mark them as pick, reject, or unflagged. You can use flags to quickly sort and filter your photos based on your preferences. -
- Ratings: These are stars that you can assign to your photos to rate them from 1 to 5. You can use ratings to quickly sort and filter your photos based on their quality or importance. -
- Metadata: These are information that describe various aspects of your photos, such as date, time, camera, lens, exposure, GPS location, and more. You can view, edit, add, or remove metadata from your photos. You can also create metadata presets and apply them to your photos. -
- Filters: These are criteria that you can use to narrow down your photos based on various attributes, such as keywords, ratings, flags, color labels, metadata, and more. You can use filters to quickly find and select the photos that you want. -
- Switch to the Develop module by clicking on its name in the menu bar or by pressing D on your keyboard. -
- Select the photo that you want to edit from the filmstrip or by using keyboard shortcuts. -
- In the right panel, use the various tools and sliders to adjust the exposure, contrast, color, tone, sharpness, noise, lens corrections, and more of your photo. You can also use the histogram, the tone curve, the HSL panel, the split toning panel, the calibration panel, and more to fine-tune your edits. -
- In the left panel, use the presets and profiles panels to apply different looks to your photo. You can choose from various presets and profiles that are built-in or downloaded from other sources. You can also create your own presets and profiles and save them for future use. -
- In the toolbar below the loupe view, use the various tools to crop, rotate, straighten, heal, clone, red-eye correct, gradient filter, radial filter, adjustment brush, and spot removal tools to further edit specific areas of your photo. You can also use the crop overlay, the loupe overlay, the guides overlay, and the soft proofing features to help you with your edits. -
- When you are happy with your edits, you can save them by clicking on the Done button at the bottom right corner of the window or by pressing Ctrl+S (Windows) or Command+S (Mac OS) on your keyboard. -
- Select the photo or photos that you want to export or share from the filmstrip or by using keyboard shortcuts. -
- To export your photos, click on the Export button on the bottom left corner of the window or press Ctrl+Shift+E (Windows) or Command+Shift+E (Mac OS) on your keyboard. In the Export window that appears, choose where you want to save your photos, what format, size, quality, and settings you want to use, and whether you want to add any metadata, watermark, or post-processing actions. When you are ready to export your photos, click on the Export button at the bottom right corner of the window. Wait for the export process to complete and then close the Export window. -
- To share your photos, click on the Share button on the bottom left corner of the window or press Ctrl+Alt+S (Windows) or Command+Option+S (Mac OS) on your keyboard. In the Share window that appears, choose which online platform you want to share your photos to, such as Facebook, Flickr, Adobe Portfolio, and more. You will need to sign in with your account and authorize Lightroom Classic CC 2020 to access it. You can also choose what title, description, tags, privacy settings, and other options you want to use for your photos. When you are ready to share your photos, click on the Share button at the bottom right corner of the window. Wait for the share process to complete and then close the Share window. -
- Select the adjustment brush, radial filter, or gradient filter tool from the toolbar and make a rough selection of the area that you want to adjust. -
- In the right panel, adjust the settings that you want to apply to the selected area, such as exposure, contrast, color, sharpness, and more. -
- In the right panel, scroll down to the bottom and find the Range Mask section. Choose which type of range mask you want to use from the drop-down menu: Color, Luminance, or Depth. Color range mask allows you to select areas based on their color values. Luminance range mask allows you to select areas based on their brightness values. Depth range mask allows you to select areas based on their distance from the camera (this only works for photos that have depth information). -
- In the right panel, use the eyedropper tool to sample the color, luminance, or depth values that you want to include or exclude from your selection. You can also use the plus and minus icons to add or subtract samples. You can also use the Alt (Windows) or Option (Mac OS) key to temporarily switch between adding and subtracting samples. -
- In the right panel, use the sliders to adjust the range and smoothness of your selection. The range slider controls how much of the sampled values are included or excluded from your selection. The smoothness slider controls how smooth or sharp the edges of your selection are. -
- When you are happy with your selection, click on the Done button at the bottom right corner of the window or press Enter on your keyboard. -
- Take multiple photos of the same scene with different exposures and angles. Make sure that there is some overlap between each photo so that Lightroom Classic CC 2020 can align them properly. You can use a tripod or a handheld camera for this step. -
- Import your photos into Lightroom Classic CC 2020 and select them from the filmstrip or by using keyboard shortcuts. -
- Right-click on any of the selected photos and choose Photo Merge > HDR Panorama from the menu. Alternatively, you can press Ctrl+H (Windows) or Command+H (Mac OS) on your keyboard. -
- In the HDR Panorama Merge Preview window that appears, choose how you want to merge your photos from the options on the right panel. You can choose from various projection methods, such as Spherical, Cylindrical, or Perspective. You can also choose whether to auto crop, auto stack, or create a stack with the original photos. You can also adjust the boundary warp and the merge settings, such as auto settings, auto align, and auto tone. -
- When you are happy with your preview, click on the Merge button at the bottom right corner of the window. Wait for the merge process to complete and then close the HDR Panorama Merge Preview window. -
- Select your panorama photo from the filmstrip or by using keyboard shortcuts. -
- Switch to the Develop module by clicking on its name in the menu bar or by pressing D on your keyboard. -
- In the right panel, scroll down to the Transform section and click on the Fill Edges button. You will see a progress bar indicating that Lightroom Classic CC 2020 is filling in the gaps. -
- When the process is complete, you will see that your panorama has smooth and even edges. You can also use the other tools in the Transform section to adjust the perspective, scale, rotation, and distortion of your panorama. -
- Select the photo or photos that you want to export from the filmstrip or by using keyboard shortcuts. -
- Click on the Export button on the bottom left corner of the window or press Ctrl+Shift+E (Windows) or Command+Shift+E (Mac OS) on your keyboard. -
- In the Export window that appears, click on the Add button at the top right corner of the window. This will create a new export preset that you can customize and name. -
- In the new export preset, choose where you want to save your photos, what format, size, quality, and settings you want to use, and whether you want to add any metadata, watermark, or post-processing actions. You can also choose whether you want to export the selected photos or all the photos in the filmstrip. -
- Repeat steps 3 and 4 for each export preset that you want to create. You can create as many export presets as you need for different purposes or platforms. -
- When you are ready to export your photos, click on the Export button at the bottom right corner of the window. You will see a progress bar indicating that Lightroom Classic CC 2020 is exporting your photos with different settings and destinations. -
- When the process is complete, you will see a confirmation message and a summary of your exports. You can also view your exported photos in their respective locations. -
- Help menu: You can access the Help menu in Lightroom Classic CC 2020 by clicking on the Help menu in the menu bar or by pressing F1 on your keyboard. You can find various topics and tutorials that can help you learn how to use Lightroom Classic CC 2020. -
- User guide: You can access the user guide for Lightroom Classic CC 2020 by going to https://helpx.adobe.com/lightroom-classic/user-guide.html. You can find detailed information and instructions on how to use Lightroom Classic CC 2020. -
- Community forum: You can access the community forum for Lightroom Classic CC 2020 by going to https://community.adobe.com/t5/lightroom-classic/bd-p/lightroom-classic?page=1&sort=latest_replies&filter=all. You can ask questions, share tips, get feedback, and interact with other users and experts of Lightroom Classic CC 2020. -
- Support center: You can access the support center for Lightroom Classic CC 2020 by going to https://helpx.adobe.com/support/lightroom-classic.html. You can find various resources and solutions for common issues and problems that you might encounter with Lightroom Classic CC 2020. -
- Contact us: You can contact Adobe customer service or technical support for Lightroom Classic CC 2020 by going to https://helpx.adobe.com/contact.html. You can choose from various options, such as chat, phone, email, or social media, to get in touch with an Adobe representative who can assist you with your queries or concerns. -
- The game has over 20 unique regions to explore, each with their own enemies, terrain, weather, and secrets. Some regions are optional and can be accessed through hidden fae gates that require certain conditions to be met. -
- The game has over 30 playable characters to recruit, each with their own personality, backstory, and skills. Some characters are humans, while others are fae creatures such as fairies, goblins, dragons, and more. Players can customize their party composition and equipment according to their preferences and strategies. -
- The game has over 100 different enemies to face, each with their own strengths, weaknesses, and behaviors. Some enemies are hostile, while others are neutral or friendly. Players can choose to fight or avoid them, or even try to befriend them and recruit them as allies. -
- The game has a rich story that unfolds through dialogue, cutscenes, and events. The game also has multiple endings that depend on the player's choices and actions throughout the game. The game also has a lot of humor and charm that adds to its appeal. -
Q: ¿Cómo puedo contactar al desarrollador de APK Chess Offline?
-A: Si tiene alguna pregunta, sugerencia, o retroalimentación sobre APK Chess Offline, puede ponerse en contacto con el desarrollador a través de su dirección de correo electrónico: apkchessoffline@gmail.com. También puede visitar su página de Facebook: [APK Chess Offline - Casa | Facebook].
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar El Juego Sigma Apk.md b/spaces/Benson/text-generation/Examples/Descargar El Juego Sigma Apk.md deleted file mode 100644 index b23578275e5db8b885e41b0353ec792cab458b6d..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar El Juego Sigma Apk.md +++ /dev/null @@ -1,48 +0,0 @@ - -
Descargar juego Sigma APK: Un juego estilizado Shooter de supervivencia para teléfonos móviles
-Si usted está buscando un nuevo y emocionante juego para jugar en su teléfono móvil, es posible que desee echa un vistazo a Game Sigma APK. Este es un estilizado juego de disparos de supervivencia que ofrece dos modos diferentes: Classic Battle Royale y 4v4 Fight Out. En este artículo, le diremos qué es Game Sigma APK, qué características tiene, cómo descargarlo e instalarlo, y algunos consejos y trucos para jugarlo.
-¿Qué es el juego Sigma APK?
-Juego Sigma APK es un juego desarrollado por Studio Arm Private Limited, una empresa con sede en la India. Es un juego de disparos de supervivencia que combina elementos de acción, estrategia y creatividad. El juego está disponible en dispositivos Android y se puede descargar desde varios sitios web, como APKCombo. El juego ha sido actualizado recientemente, siendo la última versión 1.0.113 a partir del 14 de enero de 2023.
-descargar el juego sigma apk
Download Zip ⇒⇒⇒ https://bltlly.com/2v6KcB
-
Características del juego Sigma APK
-Juego Sigma APK tiene muchas características que lo hacen destacar de otros juegos de disparos de supervivencia. Aquí están algunos de ellos:
-- Gráficos estilizados
-El juego tiene un estilo de arte único y creativo que te sumerge en un mundo de supervivencia estilizada. El juego utiliza colores vibrantes, personajes similares a dibujos animados y efectos dinámicos para crear una experiencia visualmente atractiva. El juego también funciona sin problemas en la mayoría de los dispositivos, gracias a su rendimiento optimizado.
-- Experiencia de tirador de supervivencia única
-El juego tiene controles fáciles de usar que prometen una experiencia de supervivencia inolvidable en el móvil. Puede mover, apuntar, disparar, saltar, agacharse e interactuar con el entorno utilizando gestos y botones simples. También puedes personalizar tus controles y ajustes según tus preferencias.
-- Modo clásico Battle Royale
- -- 4v4 modo de lucha
-En este modo, te unirás a otros tres jugadores para luchar contra otro equipo en una batalla tensa y estratégica. Tienes que asignar recursos, comprar armas y sobrevivir a tus enemigos en varios mapas creativos. Tienes que luchar por tu fe y llevar a tu equipo a la victoria.
-¿Cómo descargar e instalar el juego Sigma APK?
-Si desea jugar Game Sigma APK en su dispositivo Android, usted tiene que descargar e instalar desde una fuente de terceros, como APKCombo. Estos son los pasos para hacerlo:
-Pasos para descargar Juego Sigma APK de APKCombo
-- Visite el sitio web de APKCombo
-Vaya a https://apkcombo.com/sigma/com.studioarm.sigma/ usando su navegador. Esta es la página oficial de Game Sigma APK en APKCombo.
- -- Búsqueda de juego Sigma APK
-Escriba "Juego Sigma APK" en la barra de búsqueda y pulse enter
- Elija la versión y la compatibilidad del dispositivo
-En la página APKCombo, verá diferentes versiones de Game Sigma APK, junto con su tamaño de archivo, fecha de actualización, y la compatibilidad del dispositivo. Elija la versión que se adapte a su dispositivo y haga clic en el botón de descarga.
-- Descargar el archivo APK
-Espere a que termine la descarga. Verá una notificación en su dispositivo cuando se descargue el archivo APK. También puede comprobar el progreso de la descarga en su navegador o en su gestor de archivos.
-- Habilitar fuentes desconocidas en el dispositivo
-Antes de poder instalar el archivo APK, debe habilitar fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas. Active la opción para habilitar fuentes desconocidas.
-- Instalar el archivo APK
- -Consejos y trucos para jugar Juego Sigma APK
-Ahora que ha descargado e instalado Game Sigma APK, usted está listo para jugar. Aquí hay algunos consejos y trucos para ayudarle a disfrutar del juego más:
-- Personaliza tus controles y ajustes
-Antes de empezar a jugar, debes personalizar tus controles y ajustes de acuerdo a tus preferencias. Puede acceder al menú de configuración desde la pantalla principal del juego. Aquí puede ajustar la sensibilidad, el sonido, los gráficos, el idioma y otras opciones. También puedes personalizar tus controles arrastrando y redimensionando los botones de la pantalla.
-- Elija su punto de aterrizaje sabiamente
-En el modo Classic Battle Royale, tienes que elegir tu punto de aterrizaje con tu paracaídas. Usted debe elegir un lugar que tiene un buen botín, pero también tiene menos enemigos. Puedes usar el mapa para ver dónde están aterrizando otros jugadores y evitar las áreas llenas. También puedes usar los marcadores para comunicarte con tus compañeros de equipo y coordinar tu aterrizaje.
-- Saquear y equipar las mejores armas y artículos
-Una vez que aterrizas, tienes que saquear y equipar las mejores armas y objetos que puedas encontrar. Puedes saquear edificios, cajas, vehículos y enemigos muertos. Puedes equipar hasta dos armas primarias, un arma secundaria y una arma cuerpo a cuerpo. También puede equipar armaduras, cascos, mochilas, granadas, botiquines y otros artículos. Siempre debe buscar un mejor botín mientras juega.
-- Utilice la cubierta y el sigilo a su ventaja
-El juego no se trata solo de disparos, sino también de supervivencia. Tienes que usar la cubierta y el sigilo para tu ventaja. Puedes usar edificios, árboles, rocas, vehículos y otros objetos como cobertura del fuego enemigo. También puede usar posiciones agachadas y propensas para reducir su visibilidad y ruido. Siempre debes ser consciente de tu entorno y evitar exponerte demasiado.
-- Comunicarse y cooperar con sus compañeros de equipo
- -Conclusión
-Juego Sigma APK es un juego de disparos de supervivencia estilizada que ofrece dos modos diferentes: Classic Battle Royale y 4v4 Fight Out. Tiene muchas características que lo hacen destacar de otros juegos de disparos de supervivencia, tales como gráficos estilizados, experiencia de tirador de supervivencia única, controles fáciles de usar y rendimiento optimizado. Puede descargar e instalar Game Sigma APK de APKCombo, siguiendo los pasos que hemos proporcionado en este artículo. También puede utilizar nuestros consejos y trucos para mejorar su juego y divertirse más.
- FAQs - Q: ¿Es seguro descargar Game Sigma APK? - A: Sí, Game Sigma APK es seguro para descargar desde APKCombo, ya que es verificado por VirusTotal y no contiene ningún malware o virus. - P: ¿Juego Sigma APK es libre de jugar? - A: Sí, Juego Sigma APK es libre de jugar, pero puede contener anuncios y compras en la aplicación. - P: ¿Cuáles son los requisitos mínimos para jugar Game Sigma APK? - A: Los requisitos mínimos para jugar Game Sigma APK son Android 5.0 o superior, 2 GB de RAM, 1 GB de espacio de almacenamiento, y una conexión a Internet estable. - P: ¿Cómo puedo actualizar Game Sigma APK? - A: Puede actualizar Game Sigma APK visitando el sitio web APKCombo y descargar la última versión del juego. También puede comprobar si hay actualizaciones desde la configuración del juego. - P: ¿Cómo puedo contactar a los desarrolladores de Game Sigma APK? - A: Puede ponerse en contacto con los desarrolladores de Game Sigma APK visitando su sitio web oficial, página de Facebook, o cuenta de Instagram. También puede enviarles un correo electrónico a studioarm@gmail.com. 64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/contrib/securetransport.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/contrib/securetransport.py deleted file mode 100644 index 6c46a3b9f0375fc50bd4ca7b74114752e43033bf..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/contrib/securetransport.py +++ /dev/null @@ -1,921 +0,0 @@ -""" -SecureTranport support for urllib3 via ctypes. - -This makes platform-native TLS available to urllib3 users on macOS without the -use of a compiler. This is an important feature because the Python Package -Index is moving to become a TLSv1.2-or-higher server, and the default OpenSSL -that ships with macOS is not capable of doing TLSv1.2. The only way to resolve -this is to give macOS users an alternative solution to the problem, and that -solution is to use SecureTransport. - -We use ctypes here because this solution must not require a compiler. That's -because pip is not allowed to require a compiler either. - -This is not intended to be a seriously long-term solution to this problem. -The hope is that PEP 543 will eventually solve this issue for us, at which -point we can retire this contrib module. But in the short term, we need to -solve the impending tire fire that is Python on Mac without this kind of -contrib module. So...here we are. - -To use this module, simply import and inject it:: - - import urllib3.contrib.securetransport - urllib3.contrib.securetransport.inject_into_urllib3() - -Happy TLSing! - -This code is a bastardised version of the code found in Will Bond's oscrypto -library. An enormous debt is owed to him for blazing this trail for us. For -that reason, this code should be considered to be covered both by urllib3's -license and by oscrypto's: - -.. code-block:: - - Copyright (c) 2015-2016 Will Bond
", unsafe_allow_html=True)
- st.markdown(f"{chat['message']}
", unsafe_allow_html=True)
- else:
- st.markdown("
", unsafe_allow_html=True)
- st.markdown(f"{chat['message']}
", unsafe_allow_html=True)
\ No newline at end of file
diff --git a/spaces/KdaiP/yolov8-deepsort-tracking/app.py b/spaces/KdaiP/yolov8-deepsort-tracking/app.py
deleted file mode 100644
index 221a751a817d29e4cee02e281eb5692568579010..0000000000000000000000000000000000000000
--- a/spaces/KdaiP/yolov8-deepsort-tracking/app.py
+++ /dev/null
@@ -1,165 +0,0 @@
-from ultralytics import YOLO
-import cv2
-import numpy as np
-import tempfile
-from pathlib import Path
-import deep_sort.deep_sort.deep_sort as ds
-
-import gradio as gr
-
-# YoloV8官方模型，从左往右由小到大，第一次使用会自动下载
-model_list = ["yolov8n.pt", "yolov8s.pt", "yolov8m.pt", "yolov8l.pt", "yolov8x.pt"]
-
-def putTextWithBackground(
- img,
- text,
- origin,
- font=cv2.FONT_HERSHEY_SIMPLEX,
- font_scale=1,
- text_color=(255, 255, 255),
- bg_color=(0, 0, 0),
- thickness=1,
-):
- """绘制带有背景的文本。
-
- :param img: 输入图像。
- :param text: 要绘制的文本。
- :param origin: 文本的左上角坐标。
- :param font: 字体类型。
- :param font_scale: 字体大小。
- :param text_color: 文本的颜色。
- :param bg_color: 背景的颜色。
- :param thickness: 文本的线条厚度。
- """
- # 计算文本的尺寸
- (text_width, text_height), _ = cv2.getTextSize(text, font, font_scale, thickness)
-
- # 绘制背景矩形
- bottom_left = origin
- top_right = (origin[0] + text_width, origin[1] - text_height - 5) # 减去5以留出一些边距
- cv2.rectangle(img, bottom_left, top_right, bg_color, -1)
-
- # 在矩形上绘制文本
- text_origin = (origin[0], origin[1] - 5) # 从左上角的位置减去5来留出一些边距
- cv2.putText(
- img,
- text,
- text_origin,
- font,
- font_scale,
- text_color,
- thickness,
- lineType=cv2.LINE_AA,
- )
-
-
-# 视频处理
-def processVideo(inputPath, model):
- """处理视频，检测并跟踪行人。
-
- :param inputPath: 视频文件路径
- :return: 输出视频的路径
- """
- tracker = ds.DeepSort(
- "deep_sort/deep_sort/deep/checkpoint/ckpt.t7"
- ) # 加载deepsort权重文件
- model = YOLO(model) # 加载YOLO模型文件
-
- # 读取视频文件
- cap = cv2.VideoCapture(inputPath)
- fps = cap.get(cv2.CAP_PROP_FPS) # 获取视频的帧率
- size = (
- int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
- int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
- ) # 获取视频的大小
- output_video = cv2.VideoWriter() # 初始化视频写入
- outputPath = tempfile.mkdtemp() # 创建输出视频的临时文件夹的路径
-
- # 输出格式为XVID格式的avi文件
- # 如果需要使用h264编码或者需要保存为其他格式，可能需要下载openh264-1.8.0
- # 下载地址：https://github.com/cisco/openh264/releases/tag/v1.8.0
- # 下载完成后将dll文件放在当前文件夹内
- output_type = "avi"
- if output_type == "avi":
- fourcc = cv2.VideoWriter_fourcc(*"XVID")
- video_save_path = Path(outputPath) / "output.avi" # 创建输出视频路径
- if output_type == "mp4": # 浏览器只支持播放h264编码的mp4视频文件
- fourcc = cv2.VideoWriter_fourcc(*"h264")
- video_save_path = Path(outputPath) / "output.mp4"
-
- output_video.open(video_save_path.as_posix(), fourcc, fps, size, True)
- # 对每一帧图片进行读取和处理
- while True:
- success, frame = cap.read()
- if not (success):
- break
-
- # 获取每一帧的目标检测推理结果
- results = model(frame, stream=True)
-
- detections = np.empty((0, 4)) # 存放bounding box结果
- confarray = [] # 存放每个检测结果的置信度
-
- # 读取目标检测推理结果
- # 参考： https://docs.ultralytics.com/modes/predict/#working-with-results
- for r in results:
- boxes = r.boxes
- for box in boxes:
- x1, y1, x2, y2 = map(int, box.xywh[0]) # 提取矩形框左上和右下的点，并将tensor类型转为整型
- conf = round(float(box.conf[0]), 2) # 对conf四舍五入到2位小数
- cls = int(box.cls[0]) # 获取物体类别标签
-
- if cls == detect_class:
- detections = np.vstack((detections,np.array([x1,y1,x2,y2])))
- confarray.append(conf)
-
- # 使用deepsort进行跟踪
- resultsTracker = tracker.update(detections, confarray, frame)
- for x1, y1, x2, y2, Id in resultsTracker:
- x1, y1, x2, y2 = map(int, [x1, y1, x2, y2])
-
- # 绘制bounding box
- cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 255), 3)
- putTextWithBackground(
- frame,
- str(int(Id)),
- (max(-10, x1), max(40, y1)),
- font_scale=1.5,
- text_color=(255, 255, 255),
- bg_color=(255, 0, 255),
- )
-
- output_video.write(frame) # 将处理后的图像写入视频
- output_video.release() # 释放
- cap.release() # 释放
- print(f"output dir is: {video_save_path.as_posix()}")
- return video_save_path.as_posix(), video_save_path.as_posix() # Gradio的视频控件实际读取的是文件路径
-
-
-if __name__ == "__main__":
- # 需要跟踪的物体类别
- detect_class = 0
-
- # Gradio参考文档：https://www.gradio.app/guides/blocks-and-event-listeners
- with gr.Blocks() as demo:
- with gr.Tab("Tracking"):
- gr.Markdown(
- """
- # YoloV8 + deepsort
- 基于opencv + YoloV8 + deepsort
- """
- )
- with gr.Row():
- with gr.Column():
- input_video = gr.Video(label="Input video")
- model = gr.Dropdown(model_list, value="yolov8n.pt", label="Model")
- with gr.Column():
- output = gr.Video()
- output_path = gr.Textbox(label="Output path")
- button = gr.Button("Process")
-
- button.click(
- processVideo, inputs=[input_video, model], outputs=[output, output_path]
- )
-
- demo.launch()
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/vocoder/fregan/generator.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/vocoder/fregan/generator.py
deleted file mode 100644
index c0dd3a867c058c1201cd4ab65e6e2f2147aeb05d..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/vocoder/fregan/generator.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from vocoder.fregan.utils import init_weights, get_padding
-
-LRELU_SLOPE = 0.1
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5, 7)):
- super(ResBlock1, self).__init__()
- self.h = h
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[3],
- padding=get_padding(kernel_size, dilation[3])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- xt = c2(xt)
- x = xt + x
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.h = h
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- xt = c(xt)
- x = xt + x
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class FreGAN(torch.nn.Module):
- def __init__(self, h, top_k=4):
- super(FreGAN, self).__init__()
- self.h = h
-
- self.num_kernels = len(h.resblock_kernel_sizes)
- self.num_upsamples = len(h.upsample_rates)
- self.upsample_rates = h.upsample_rates
- self.up_kernels = h.upsample_kernel_sizes
- self.cond_level = self.num_upsamples - top_k
- self.conv_pre = weight_norm(Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3))
- resblock = ResBlock1 if h.resblock == '1' else ResBlock2
-
- self.ups = nn.ModuleList()
- self.cond_up = nn.ModuleList()
- self.res_output = nn.ModuleList()
- upsample_ = 1
- kr = 80
-
- for i, (u, k) in enumerate(zip(self.upsample_rates, self.up_kernels)):
-# self.ups.append(weight_norm(
- # ConvTranspose1d(h.upsample_initial_channel // (2 ** i), h.upsample_initial_channel // (2 ** (i + 1)),
- # k, u, padding=(k - u) // 2)))
- self.ups.append(weight_norm(ConvTranspose1d(h.upsample_initial_channel//(2**i),
- h.upsample_initial_channel//(2**(i+1)),
- k, u, padding=(u//2 + u%2), output_padding=u%2)))
-
- if i > (self.num_upsamples - top_k):
- self.res_output.append(
- nn.Sequential(
- nn.Upsample(scale_factor=u, mode='nearest'),
- weight_norm(nn.Conv1d(h.upsample_initial_channel // (2 ** i),
- h.upsample_initial_channel // (2 ** (i + 1)), 1))
- )
- )
- if i >= (self.num_upsamples - top_k):
- self.cond_up.append(
- weight_norm(
- ConvTranspose1d(kr, h.upsample_initial_channel // (2 ** i),
- self.up_kernels[i - 1], self.upsample_rates[i - 1],
- padding=(self.upsample_rates[i-1]//2+self.upsample_rates[i-1]%2), output_padding=self.upsample_rates[i-1]%2))
- )
- kr = h.upsample_initial_channel // (2 ** i)
-
- upsample_ *= u
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = h.upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
- self.resblocks.append(resblock(h, ch, k, d))
-
- self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
- self.ups.apply(init_weights)
- self.conv_post.apply(init_weights)
- self.cond_up.apply(init_weights)
- self.res_output.apply(init_weights)
-
- def forward(self, x):
- mel = x
- x = self.conv_pre(x)
- output = None
- for i in range(self.num_upsamples):
- if i >= self.cond_level:
- mel = self.cond_up[i - self.cond_level](mel)
- x += mel
- if i > self.cond_level:
- if output is None:
- output = self.res_output[i - self.cond_level - 1](x)
- else:
- output = self.res_output[i - self.cond_level - 1](output)
- x = F.leaky_relu(x, LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- if output is not None:
- output = output + x
-
- x = F.leaky_relu(output)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
- for l in self.cond_up:
- remove_weight_norm(l)
- for l in self.res_output:
- remove_weight_norm(l[1])
- remove_weight_norm(self.conv_pre)
- remove_weight_norm(self.conv_post)
-
-
-'''
- to run this, fix
- from . import ResStack
- into
- from res_stack import ResStack
-'''
-if __name__ == '__main__':
- '''
- torch.Size([3, 80, 10])
- torch.Size([3, 1, 2000])
- 4527362
- '''
- with open('config.json') as f:
- data = f.read()
- from utils import AttrDict
- import json
- json_config = json.loads(data)
- h = AttrDict(json_config)
- model = FreGAN(h)
-
- c = torch.randn(3, 80, 10) # (B, channels, T).
- print(c.shape)
-
- y = model(c) # (B, 1, T ** prod(upsample_scales)
- print(y.shape)
- assert y.shape == torch.Size([3, 1, 2560]) # For normal melgan torch.Size([3, 1, 2560])
-
- pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
- print(pytorch_total_params)
\ No newline at end of file
diff --git a/spaces/KunamVishnu/MyGenAiChatBot/README.md b/spaces/KunamVishnu/MyGenAiChatBot/README.md
deleted file mode 100644
index 5075238b6a1964e2b7a4dfdb6550235fbe5e3791..0000000000000000000000000000000000000000
--- a/spaces/KunamVishnu/MyGenAiChatBot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MyGenAiChatBot
-emoji: 📈
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/seg_heads/panoptic_fusion_heads/heuristic_fusion_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/seg_heads/panoptic_fusion_heads/heuristic_fusion_head.py
deleted file mode 100644
index 7a4a4200edd97f42e9a138e14a1d07328ad9b139..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/seg_heads/panoptic_fusion_heads/heuristic_fusion_head.py
+++ /dev/null
@@ -1,159 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List
-
-import torch
-from mmengine.structures import InstanceData, PixelData
-from torch import Tensor
-
-from mmdet.evaluation.functional import INSTANCE_OFFSET
-from mmdet.registry import MODELS
-from mmdet.utils import InstanceList, OptConfigType, OptMultiConfig, PixelList
-from .base_panoptic_fusion_head import BasePanopticFusionHead
-
-
-@MODELS.register_module()
-class HeuristicFusionHead(BasePanopticFusionHead):
- """Fusion Head with Heuristic method."""
-
- def __init__(self,
- num_things_classes: int = 80,
- num_stuff_classes: int = 53,
- test_cfg: OptConfigType = None,
- init_cfg: OptMultiConfig = None,
- **kwargs) -> None:
- super().__init__(
- num_things_classes=num_things_classes,
- num_stuff_classes=num_stuff_classes,
- test_cfg=test_cfg,
- loss_panoptic=None,
- init_cfg=init_cfg,
- **kwargs)
-
- def loss(self, **kwargs) -> dict:
- """HeuristicFusionHead has no training loss."""
- return dict()
-
- def _lay_masks(self,
- mask_results: InstanceData,
- overlap_thr: float = 0.5) -> Tensor:
- """Lay instance masks to a result map.
-
- Args:
- mask_results (:obj:`InstanceData`): Instance segmentation results,
- each contains ``bboxes``, ``labels``, ``scores`` and ``masks``.
- overlap_thr (float): Threshold to determine whether two masks
- overlap. default: 0.5.
-
- Returns:
- Tensor: The result map, (H, W).
- """
- bboxes = mask_results.bboxes
- scores = mask_results.scores
- labels = mask_results.labels
- masks = mask_results.masks
-
- num_insts = bboxes.shape[0]
- id_map = torch.zeros(
- masks.shape[-2:], device=bboxes.device, dtype=torch.long)
- if num_insts == 0:
- return id_map, labels
-
- # Sort by score to use heuristic fusion
- order = torch.argsort(-scores)
- bboxes = bboxes[order]
- labels = labels[order]
- segm_masks = masks[order]
-
- instance_id = 1
- left_labels = []
- for idx in range(bboxes.shape[0]):
- _cls = labels[idx]
- _mask = segm_masks[idx]
- instance_id_map = torch.ones_like(
- _mask, dtype=torch.long) * instance_id
- area = _mask.sum()
- if area == 0:
- continue
-
- pasted = id_map > 0
- intersect = (_mask * pasted).sum()
- if (intersect / (area + 1e-5)) > overlap_thr:
- continue
-
- _part = _mask * (~pasted)
- id_map = torch.where(_part, instance_id_map, id_map)
- left_labels.append(_cls)
- instance_id += 1
-
- if len(left_labels) > 0:
- instance_labels = torch.stack(left_labels)
- else:
- instance_labels = bboxes.new_zeros((0, ), dtype=torch.long)
- assert instance_id == (len(instance_labels) + 1)
- return id_map, instance_labels
-
- def _predict_single(self, mask_results: InstanceData, seg_preds: Tensor,
- **kwargs) -> PixelData:
- """Fuse the results of instance and semantic segmentations.
-
- Args:
- mask_results (:obj:`InstanceData`): Instance segmentation results,
- each contains ``bboxes``, ``labels``, ``scores`` and ``masks``.
- seg_preds (Tensor): The semantic segmentation results,
- (num_stuff + 1, H, W).
-
- Returns:
- Tensor: The panoptic segmentation result, (H, W).
- """
- id_map, labels = self._lay_masks(mask_results,
- self.test_cfg.mask_overlap)
-
- seg_results = seg_preds.argmax(dim=0)
- seg_results = seg_results + self.num_things_classes
-
- pan_results = seg_results
- instance_id = 1
- for idx in range(len(mask_results)):
- _mask = id_map == (idx + 1)
- if _mask.sum() == 0:
- continue
- _cls = labels[idx]
- # simply trust detection
- segment_id = _cls + instance_id * INSTANCE_OFFSET
- pan_results[_mask] = segment_id
- instance_id += 1
-
- ids, counts = torch.unique(
- pan_results % INSTANCE_OFFSET, return_counts=True)
- stuff_ids = ids[ids >= self.num_things_classes]
- stuff_counts = counts[ids >= self.num_things_classes]
- ignore_stuff_ids = stuff_ids[
- stuff_counts < self.test_cfg.stuff_area_limit]
-
- assert pan_results.ndim == 2
- pan_results[(pan_results.unsqueeze(2) == ignore_stuff_ids.reshape(
- 1, 1, -1)).any(dim=2)] = self.num_classes
-
- pan_results = PixelData(sem_seg=pan_results[None].int())
- return pan_results
-
- def predict(self, mask_results_list: InstanceList,
- seg_preds_list: List[Tensor], **kwargs) -> PixelList:
- """Predict results by fusing the results of instance and semantic
- segmentations.
-
- Args:
- mask_results_list (list[:obj:`InstanceData`]): Instance
- segmentation results, each contains ``bboxes``, ``labels``,
- ``scores`` and ``masks``.
- seg_preds_list (Tensor): List of semantic segmentation results.
-
- Returns:
- List[PixelData]: Panoptic segmentation result.
- """
- results_list = [
- self._predict_single(mask_results_list[i], seg_preds_list[i])
- for i in range(len(mask_results_list))
- ]
-
- return results_list
diff --git a/spaces/KyanChen/RSPrompter/mmpl/models/builder.py b/spaces/KyanChen/RSPrompter/mmpl/models/builder.py
deleted file mode 100644
index 269c46de8dab7aa0c3d502bb492a655d8181d9cd..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpl/models/builder.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmpl.registry import MODELS
-
-BACKBONES = MODELS
-NECKS = MODELS
-HEADS = MODELS
-LOSSES = MODELS
-PLERS = MODELS
-RETRIEVER = MODELS
-
-
-def build_backbone(cfg):
- """Build backbone."""
- return BACKBONES.build(cfg)
-
-
-def build_neck(cfg):
- """Build neck."""
- return NECKS.build(cfg)
-
-
-def build_head(cfg):
- """Build head."""
- return HEADS.build(cfg)
-
-
-def build_loss(cfg):
- """Build loss."""
- return LOSSES.build(cfg)
-
-
-def build_pler(cfg):
- """Build classifier."""
- return PLERS.build(cfg)
-
-
-def build_retriever(cfg):
- """Build retriever."""
- return RETRIEVER.build(cfg)
diff --git a/spaces/LeoLeoLeo1/ChuanhuChatGPT/chat_func.py b/spaces/LeoLeoLeo1/ChuanhuChatGPT/chat_func.py
deleted file mode 100644
index 374178f3d22c5c23d1dc2952336cdc298a77315d..0000000000000000000000000000000000000000
--- a/spaces/LeoLeoLeo1/ChuanhuChatGPT/chat_func.py
+++ /dev/null
@@ -1,456 +0,0 @@
-# -*- coding:utf-8 -*-
-from __future__ import annotations
-from typing import TYPE_CHECKING, List
-
-import logging
-import json
-import os
-import requests
-import urllib3
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-
-from presets import *
-from llama_func import *
-from utils import *
-
-# logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s")
-
-if TYPE_CHECKING:
- from typing import TypedDict
-
- class DataframeData(TypedDict):
- headers: List[str]
- data: List[List[str | int | bool]]
-
-
-initial_prompt = "You are a helpful assistant."
-API_URL = "https://api.openai.com/v1/chat/completions"
-HISTORY_DIR = "history"
-TEMPLATES_DIR = "templates"
-
-def get_response(
- openai_api_key, system_prompt, history, temperature, top_p, stream, selected_model
-):
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {openai_api_key}",
- }
-
- history = [construct_system(system_prompt), *history]
-
- payload = {
- "model": selected_model,
- "messages": history, # [{"role": "user", "content": f"{inputs}"}],
- "temperature": temperature, # 1.0,
- "top_p": top_p, # 1.0,
- "n": 1,
- "stream": stream,
- "presence_penalty": 0,
- "frequency_penalty": 0,
- }
- if stream:
- timeout = timeout_streaming
- else:
- timeout = timeout_all
-
- # 获取环境变量中的代理设置
- http_proxy = os.environ.get("HTTP_PROXY") or os.environ.get("http_proxy")
- https_proxy = os.environ.get("HTTPS_PROXY") or os.environ.get("https_proxy")
-
- # 如果存在代理设置，使用它们
- proxies = {}
- if http_proxy:
- logging.info(f"Using HTTP proxy: {http_proxy}")
- proxies["http"] = http_proxy
- if https_proxy:
- logging.info(f"Using HTTPS proxy: {https_proxy}")
- proxies["https"] = https_proxy
-
- # 如果有代理，使用代理发送请求，否则使用默认设置发送请求
- if proxies:
- response = requests.post(
- API_URL,
- headers=headers,
- json=payload,
- stream=True,
- timeout=timeout,
- proxies=proxies,
- )
- else:
- response = requests.post(
- API_URL,
- headers=headers,
- json=payload,
- stream=True,
- timeout=timeout,
- )
- return response
-
-
-def stream_predict(
- openai_api_key,
- system_prompt,
- history,
- inputs,
- chatbot,
- all_token_counts,
- top_p,
- temperature,
- selected_model,
- fake_input=None,
- display_append=""
-):
- def get_return_value():
- return chatbot, history, status_text, all_token_counts
-
- logging.info("实时回答模式")
- partial_words = ""
- counter = 0
- status_text = "开始实时传输回答……"
- history.append(construct_user(inputs))
- history.append(construct_assistant(""))
- if fake_input:
- chatbot.append((fake_input, ""))
- else:
- chatbot.append((inputs, ""))
- user_token_count = 0
- if len(all_token_counts) == 0:
- system_prompt_token_count = count_token(construct_system(system_prompt))
- user_token_count = (
- count_token(construct_user(inputs)) + system_prompt_token_count
- )
- else:
- user_token_count = count_token(construct_user(inputs))
- all_token_counts.append(user_token_count)
- logging.info(f"输入token计数: {user_token_count}")
- yield get_return_value()
- try:
- response = get_response(
- openai_api_key,
- system_prompt,
- history,
- temperature,
- top_p,
- True,
- selected_model,
- )
- except requests.exceptions.ConnectTimeout:
- status_text = (
- standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
- )
- yield get_return_value()
- return
- except requests.exceptions.ReadTimeout:
- status_text = standard_error_msg + read_timeout_prompt + error_retrieve_prompt
- yield get_return_value()
- return
-
- yield get_return_value()
- error_json_str = ""
-
- for chunk in tqdm(response.iter_lines()):
- if counter == 0:
- counter += 1
- continue
- counter += 1
- # check whether each line is non-empty
- if chunk:
- chunk = chunk.decode()
- chunklength = len(chunk)
- try:
- chunk = json.loads(chunk[6:])
- except json.JSONDecodeError:
- logging.info(chunk)
- error_json_str += chunk
- status_text = f"JSON解析错误。请重置对话。收到的内容: {error_json_str}"
- yield get_return_value()
- continue
- # decode each line as response data is in bytes
- if chunklength > 6 and "delta" in chunk["choices"][0]:
- finish_reason = chunk["choices"][0]["finish_reason"]
- status_text = construct_token_message(
- sum(all_token_counts), stream=True
- )
- if finish_reason == "stop":
- yield get_return_value()
- break
- try:
- partial_words = (
- partial_words + chunk["choices"][0]["delta"]["content"]
- )
- except KeyError:
- status_text = (
- standard_error_msg
- + "API回复中找不到内容。很可能是Token计数达到上限了。请重置对话。当前Token计数: "
- + str(sum(all_token_counts))
- )
- yield get_return_value()
- break
- history[-1] = construct_assistant(partial_words)
- chatbot[-1] = (chatbot[-1][0], partial_words+display_append)
- all_token_counts[-1] += 1
- yield get_return_value()
-
-
-def predict_all(
- openai_api_key,
- system_prompt,
- history,
- inputs,
- chatbot,
- all_token_counts,
- top_p,
- temperature,
- selected_model,
- fake_input=None,
- display_append=""
-):
- logging.info("一次性回答模式")
- history.append(construct_user(inputs))
- history.append(construct_assistant(""))
- if fake_input:
- chatbot.append((fake_input, ""))
- else:
- chatbot.append((inputs, ""))
- all_token_counts.append(count_token(construct_user(inputs)))
- try:
- response = get_response(
- openai_api_key,
- system_prompt,
- history,
- temperature,
- top_p,
- False,
- selected_model,
- )
- except requests.exceptions.ConnectTimeout:
- status_text = (
- standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
- )
- return chatbot, history, status_text, all_token_counts
- except requests.exceptions.ProxyError:
- status_text = standard_error_msg + proxy_error_prompt + error_retrieve_prompt
- return chatbot, history, status_text, all_token_counts
- except requests.exceptions.SSLError:
- status_text = standard_error_msg + ssl_error_prompt + error_retrieve_prompt
- return chatbot, history, status_text, all_token_counts
- response = json.loads(response.text)
- content = response["choices"][0]["message"]["content"]
- history[-1] = construct_assistant(content)
- chatbot[-1] = (chatbot[-1][0], content+display_append)
- total_token_count = response["usage"]["total_tokens"]
- all_token_counts[-1] = total_token_count - sum(all_token_counts)
- status_text = construct_token_message(total_token_count)
- return chatbot, history, status_text, all_token_counts
-
-
-def predict(
- openai_api_key,
- system_prompt,
- history,
- inputs,
- chatbot,
- all_token_counts,
- top_p,
- temperature,
- stream=False,
- selected_model=MODELS[0],
- use_websearch=False,
- files = None,
- should_check_token_count=True,
-): # repetition_penalty, top_k
- logging.info("输入为：" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL)
- if files:
- msg = "构建索引中……（这可能需要比较久的时间）"
- logging.info(msg)
- yield chatbot, history, msg, all_token_counts
- index = construct_index(openai_api_key, file_src=files)
- msg = "索引构建完成，获取回答中……"
- yield chatbot, history, msg, all_token_counts
- history, chatbot, status_text = chat_ai(openai_api_key, index, inputs, history, chatbot)
- yield chatbot, history, status_text, all_token_counts
- return
-
- old_inputs = ""
- link_references = []
- if use_websearch:
- search_results = ddg(inputs, max_results=5)
- old_inputs = inputs
- web_results = []
- for idx, result in enumerate(search_results):
- logging.info(f"搜索结果{idx + 1}：{result}")
- domain_name = urllib3.util.parse_url(result["href"]).host
- web_results.append(f'[{idx+1}]"{result["body"]}"\nURL: {result["href"]}')
- link_references.append(f"{idx+1}. [{domain_name}]({result['href']})\n")
- link_references = "\n\n" + "".join(link_references)
- inputs = (
- replace_today(WEBSEARCH_PTOMPT_TEMPLATE)
- .replace("{query}", inputs)
- .replace("{web_results}", "\n\n".join(web_results))
- )
- else:
- link_references = ""
-
- if len(openai_api_key) != 51:
- status_text = standard_error_msg + no_apikey_msg
- logging.info(status_text)
- chatbot.append((inputs, ""))
- if len(history) == 0:
- history.append(construct_user(inputs))
- history.append("")
- all_token_counts.append(0)
- else:
- history[-2] = construct_user(inputs)
- yield chatbot, history, status_text, all_token_counts
- return
-
- yield chatbot, history, "开始生成回答……", all_token_counts
-
- if stream:
- logging.info("使用流式传输")
- iter = stream_predict(
- openai_api_key,
- system_prompt,
- history,
- inputs,
- chatbot,
- all_token_counts,
- top_p,
- temperature,
- selected_model,
- fake_input=old_inputs,
- display_append=link_references
- )
- for chatbot, history, status_text, all_token_counts in iter:
- yield chatbot, history, status_text, all_token_counts
- else:
- logging.info("不使用流式传输")
- chatbot, history, status_text, all_token_counts = predict_all(
- openai_api_key,
- system_prompt,
- history,
- inputs,
- chatbot,
- all_token_counts,
- top_p,
- temperature,
- selected_model,
- fake_input=old_inputs,
- display_append=link_references
- )
- yield chatbot, history, status_text, all_token_counts
-
- logging.info(f"传输完毕。当前token计数为{all_token_counts}")
- if len(history) > 1 and history[-1]["content"] != inputs:
- logging.info(
- "回答为："
- + colorama.Fore.BLUE
- + f"{history[-1]['content']}"
- + colorama.Style.RESET_ALL
- )
-
- if stream:
- max_token = max_token_streaming
- else:
- max_token = max_token_all
-
- if sum(all_token_counts) > max_token and should_check_token_count:
- status_text = f"精简token中{all_token_counts}/{max_token}"
- logging.info(status_text)
- yield chatbot, history, status_text, all_token_counts
- iter = reduce_token_size(
- openai_api_key,
- system_prompt,
- history,
- chatbot,
- all_token_counts,
- top_p,
- temperature,
- max_token//2,
- selected_model=selected_model,
- )
- for chatbot, history, status_text, all_token_counts in iter:
- status_text = f"Token 达到上限，已自动降低Token计数至 {status_text}"
- yield chatbot, history, status_text, all_token_counts
-
-
-def retry(
- openai_api_key,
- system_prompt,
- history,
- chatbot,
- token_count,
- top_p,
- temperature,
- stream=False,
- selected_model=MODELS[0],
-):
- logging.info("重试中……")
- if len(history) == 0:
- yield chatbot, history, f"{standard_error_msg}上下文是空的", token_count
- return
- history.pop()
- inputs = history.pop()["content"]
- token_count.pop()
- iter = predict(
- openai_api_key,
- system_prompt,
- history,
- inputs,
- chatbot,
- token_count,
- top_p,
- temperature,
- stream=stream,
- selected_model=selected_model,
- )
- logging.info("重试中……")
- for x in iter:
- yield x
- logging.info("重试完毕")
-
-
-def reduce_token_size(
- openai_api_key,
- system_prompt,
- history,
- chatbot,
- token_count,
- top_p,
- temperature,
- max_token_count,
- selected_model=MODELS[0],
-):
- logging.info("开始减少token数量……")
- iter = predict(
- openai_api_key,
- system_prompt,
- history,
- summarize_prompt,
- chatbot,
- token_count,
- top_p,
- temperature,
- selected_model=selected_model,
- should_check_token_count=False,
- )
- logging.info(f"chatbot: {chatbot}")
- flag = False
- for chatbot, history, status_text, previous_token_count in iter:
- num_chat = find_n(previous_token_count, max_token_count)
- if flag:
- chatbot = chatbot[:-1]
- flag = True
- history = history[-2*num_chat:] if num_chat > 0 else []
- token_count = previous_token_count[-num_chat:] if num_chat > 0 else []
- msg = f"保留了最近{num_chat}轮对话"
- yield chatbot, history, msg + "，" + construct_token_message(
- sum(token_count) if len(token_count) > 0 else 0,
- ), token_count
- logging.info(msg)
- logging.info("减少token数量完毕")
\ No newline at end of file
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/det_models/dbnetpp_r50dcnv2_fpnc.py b/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/det_models/dbnetpp_r50dcnv2_fpnc.py
deleted file mode 100644
index f8eaf2ffd6efe9fa4ad63f5dc208f9b134a38380..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/det_models/dbnetpp_r50dcnv2_fpnc.py
+++ /dev/null
@@ -1,28 +0,0 @@
-model = dict(
- type='DBNet',
- backbone=dict(
- type='mmdet.ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=False,
- style='pytorch',
- dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
- stage_with_dcn=(False, True, True, True)),
- neck=dict(
- type='FPNC',
- in_channels=[256, 512, 1024, 2048],
- lateral_channels=256,
- asf_cfg=dict(attention_type='ScaleChannelSpatial')),
- bbox_head=dict(
- type='DBHead',
- in_channels=256,
- loss=dict(type='DBLoss', alpha=5.0, beta=10.0, bbce_loss=True),
- postprocessor=dict(
- type='DBPostprocessor', text_repr_type='quad',
- epsilon_ratio=0.002)),
- train_cfg=None,
- test_cfg=None)
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/schedules/schedule_adam_600e.py b/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/schedules/schedule_adam_600e.py
deleted file mode 100644
index a77dc52004ba597b4ba7f2df13a96e123c4029ab..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/schedules/schedule_adam_600e.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# optimizer
-optimizer = dict(type='Adam', lr=1e-3)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='poly', power=0.9)
-# running settings
-runner = dict(type='EpochBasedRunner', max_epochs=600)
-checkpoint_config = dict(interval=100)
diff --git a/spaces/MRiwu/Collection/text/ngu_dialect.py b/spaces/MRiwu/Collection/text/ngu_dialect.py
deleted file mode 100644
index 69d0ce6fe5a989843ee059a71ccab793f20f9176..0000000000000000000000000000000000000000
--- a/spaces/MRiwu/Collection/text/ngu_dialect.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import re
-import opencc
-
-
-dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou',
- 'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing',
- 'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang',
- 'JS': 'jiashan', 'HN': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan',
- 'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen',
- 'TT': 'tiantai', 'WZ': 'wenzhou', 'SC': 'suichang', 'YB': 'youbu'}
-
-converters = {}
-
-for dialect in dialects.values():
- try:
- converters[dialect] = opencc.OpenCC("chinese_dialect_lexicons/"+dialect)
- except:
- pass
-
-
-def ngu_dialect_to_ipa(text, dialect):
- dialect = dialects[dialect]
- text = converters[dialect].convert(text).replace('-','').replace('$',' ')
- text = re.sub(r'[、；：]', '，', text)
- text = re.sub(r'\s*，\s*', ', ', text)
- text = re.sub(r'\s*。\s*', '. ', text)
- text = re.sub(r'\s*？\s*', '? ', text)
- text = re.sub(r'\s*！\s*', '! ', text)
- text = re.sub(r'\s*$', '', text)
- return text
diff --git a/spaces/MarcBrun/basque-qa/README.md b/spaces/MarcBrun/basque-qa/README.md
deleted file mode 100644
index 4b7fb65ba550a2c2ede71ac5d98794ef8fc9d671..0000000000000000000000000000000000000000
--- a/spaces/MarcBrun/basque-qa/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Basque Question Answering
-emoji: 🚀
-colorFrom: green
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Marshalls/testmtd/analysis/aistplusplus_api/processing/run_estimate_keypoints.py b/spaces/Marshalls/testmtd/analysis/aistplusplus_api/processing/run_estimate_keypoints.py
deleted file mode 100644
index f3b0eabbf5d5094b048d412693a34995c92ccf27..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/analysis/aistplusplus_api/processing/run_estimate_keypoints.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The Google AI Perception Team Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Estimate AIST++ 3D keypoints."""
-import os
-import pickle
-
-from absl import app
-from absl import flags
-from absl import logging
-from aist_plusplus.loader import AISTDataset
-import numpy as np
-
-FLAGS = flags.FLAGS
-flags.DEFINE_string(
- 'anno_dir',
- '/usr/local/google/home/ruilongli/data/public/aist_plusplus_final/',
- 'input local dictionary for AIST++ annotations.')
-flags.DEFINE_string(
- 'save_dir',
- '/usr/local/google/home/ruilongli/data/public/aist_plusplus_final/keypoints3d/',
- 'output local dictionary that stores AIST++ 3D keypoints.')
-np.random.seed(0)
-
-
-def main(_):
- aist_dataset = AISTDataset(anno_dir=FLAGS.anno_dir)
-
- for seq_name, env_name in aist_dataset.mapping_seq2env.items():
- logging.info('processing %s', seq_name)
-
- # Load camera parameters
- cgroup = AISTDataset.load_camera_group(aist_dataset.camera_dir, env_name)
-
- # load 2D keypoints
- keypoints2d, det_scores, _ = AISTDataset.load_keypoint2d(
- aist_dataset.keypoint2d_dir, seq_name=seq_name)
- nviews, nframes, _, _ = keypoints2d.shape
- assert det_scores.shape[0] == nviews
- assert det_scores.shape[1] == nframes
- if seq_name == 'gBR_sBM_cAll_d04_mBR0_ch01':
- keypoints2d[4] = np.nan # not synced view
- if seq_name == 'gJB_sBM_cAll_d07_mJB3_ch05':
- keypoints2d[6] = np.nan # size 640x480
-
- # filter keypoints to select those best points
- kpt_thre = 0.15
- ignore_idxs = np.where(keypoints2d[:, :, :, 2] < kpt_thre)
- keypoints2d[ignore_idxs[0], ignore_idxs[1], ignore_idxs[2], :] = np.nan
- det_thre = 0.0
- ignore_idxs = np.where(det_scores < det_thre)
- keypoints2d[ignore_idxs[0], ignore_idxs[1], :, :] = np.nan
- keypoints2d = keypoints2d[:, :, :, 0:2]
-
- # 3D pose triangulation and temporal optimization.
- bones = [
- (5, 7), (7, 9), (6, 8), (8, 10), (11, 13), (13, 15), (12, 14),
- (14, 16), (0, 1), (0, 2), (1, 2), (0, 3), (0, 4), (3, 4),
- ] # COCO-format bone constrains
- keypoints3d = cgroup.triangulate(
- keypoints2d.reshape(nviews, -1, 2)
- ).reshape(nframes, -1, 3)
- keypoints3d_optim = cgroup.triangulate_optim(
- keypoints2d, constraints=bones, verbose=True
- ).reshape(nframes, -1, 3)
-
- # Save to pkl
- os.makedirs(FLAGS.save_dir, exist_ok=True)
- keypoints_file = os.path.join(FLAGS.save_dir, f'{seq_name}.pkl')
- with open(keypoints_file, 'wb') as f:
- pickle.dump({
- 'keypoints3d': keypoints3d,
- 'keypoints3d_optim': keypoints3d_optim,
- }, f, protocol=pickle.HIGHEST_PROTOCOL)
-
-
-if __name__ == '__main__':
- app.run(main)
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/builder.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/builder.py
deleted file mode 100644
index 77c96ba0b2f30ead9da23f293c5dc84dd3e4a74f..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/builder.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-
-from ..utils import Registry
-
-RUNNERS = Registry('runner')
-RUNNER_BUILDERS = Registry('runner builder')
-
-
-def build_runner_constructor(cfg):
- return RUNNER_BUILDERS.build(cfg)
-
-
-def build_runner(cfg, default_args=None):
- runner_cfg = copy.deepcopy(cfg)
- constructor_type = runner_cfg.pop('constructor',
- 'DefaultRunnerConstructor')
- runner_constructor = build_runner_constructor(
- dict(
- type=constructor_type,
- runner_cfg=runner_cfg,
- default_args=default_args))
- runner = runner_constructor()
- return runner
diff --git a/spaces/Mobin-Nesari/MM-Movie-Recommender/SVD_Model.py b/spaces/Mobin-Nesari/MM-Movie-Recommender/SVD_Model.py
deleted file mode 100644
index ecdf7e0d4dc8d89c3b9537dfa1e9ee50eecb85d0..0000000000000000000000000000000000000000
--- a/spaces/Mobin-Nesari/MM-Movie-Recommender/SVD_Model.py
+++ /dev/null
@@ -1,240 +0,0 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-from scipy import stats
-from ast import literal_eval
-from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
-from sklearn.metrics.pairwise import linear_kernel, cosine_similarity
-from nltk.stem.snowball import SnowballStemmer
-from nltk.stem.wordnet import WordNetLemmatizer
-from nltk.corpus import wordnet
-from surprise import Reader, Dataset, SVD
-from surprise import NormalPredictor
-from surprise.model_selection import cross_validate
-from Levenshtein import distance
-import warnings; warnings.simplefilter('ignore')
-
-class Recommender_Model:
- def __init__(self):
- self.cleaned_data = None
- self.cleaned_data1 = None
- self.cosine_sim = None
- self.titles = None
- self.indices = None
- self.index_movie_id = None
- self.SVD = None
- self.id_map = None
- self.preprocessing()
-
- def preprocessing(self):
- movie_data = pd.read_csv("Datasets/movies_metadata.csv")
- self.user_rating = pd.read_csv("Datasets/ratings_small.csv")
- vote_counts = movie_data[movie_data['vote_count'].notnull()]['vote_count'].astype('int')
- vote_averages = movie_data[movie_data['vote_average'].notnull()]['vote_average'].astype('int')
- average_vote_score = vote_averages.mean()
- percentile_80_cutoff = np.percentile(vote_counts,80)
-
- cleand_data1 = movie_data[movie_data['vote_average']>=average_vote_score]
- cleand_data1 = cleand_data1[cleand_data1['vote_count']>percentile_80_cutoff]
-
- movie_data = movie_data.drop([19730, 29503, 35587])
- links_small = pd.read_csv('Datasets/links_small.csv')
- only_subset_movies = list(links_small['tmdbId'])
- cleand_data1['id'] = cleand_data1['id'].astype('int')
- self.cleaned_data = cleand_data1[cleand_data1['id'].isin(only_subset_movies)]
- self.cleaned_data['tagline'] = self.cleaned_data['tagline'].fillna('')
-
- ### genres
- self.cleaned_data['genres'] = self.cleaned_data['genres'].apply(literal_eval)
- self.cleaned_data['genres'] = self.cleaned_data['genres'].apply(lambda x : [i['name'] for i in x])
-
-
- stemmer = SnowballStemmer('english')
- self.cleaned_data['genres'] = self.cleaned_data['genres'].apply(lambda x: [stemmer.stem(i) for i in x])
- self.cleaned_data['genres'] = self.cleaned_data['genres'].apply(lambda x: [str.lower(i.replace(" ", "")) for i in x])
-
- self.cleaned_data['genres'] = self.cleaned_data['genres'].apply(lambda x : list(set(x)))
-
- # original_language
- self.cleaned_data['original_language'].unique()
-
- credits = pd.read_csv('Datasets/credits.csv')
- keywords = pd.read_csv('Datasets/keywords.csv')
-
- self.cleaned_data = self.cleaned_data.merge(credits, on='id')
- self.cleaned_data = self.cleaned_data.merge(keywords, on='id')
-
- self.cleaned_data['keywords'] = self.cleaned_data['keywords'].apply(literal_eval)
-
- self.cleaned_data['keywords'] = self.cleaned_data['keywords'].apply(lambda x : [i['name'] for i in x])
-
- self.cleaned_data['keywords'] = self.cleaned_data['keywords'].apply(lambda x: [stemmer.stem(i) for i in x])
- self.cleaned_data['keywords'] = self.cleaned_data['keywords'].apply(lambda x: [str.lower(i.replace(" ", "")) for i in x])
- self.cleaned_data['keywords'] = self.cleaned_data['keywords'].apply(lambda x : list(set(x)))
-
- self.cleaned_data['cast'] = self.cleaned_data['cast'].apply(literal_eval)
-
- self.cleaned_data['crew'] = self.cleaned_data['crew'].apply(literal_eval)
-
- self.cleaned_data['top_crew'] = self.cleaned_data['cast'].apply(lambda x : [i['name'] for i in x])
-
- self.cleaned_data['top_crew'] = self.cleaned_data['top_crew'].apply(lambda x : x[:2])
-
- self.cleaned_data['director'] = self.cleaned_data['crew'].apply(get_director)
-
- imp_cols = ['tagline', 'genres' ,'original_language' ,'keywords' ,'top_crew','director']
-
- self.cleaned_data1 = self.cleaned_data[imp_cols]
-
- self.cleaned_data1['tagline'] = self.cleaned_data1['tagline'].apply(lambda x : [x])
- self.cleaned_data1['original_language'] = self.cleaned_data1['original_language'].apply(lambda x : [x])
- self.cleaned_data1['director'] = self.cleaned_data1['director'].apply(lambda x : [x])
-
-
- self.cleaned_data1['combine'] = self.cleaned_data1['genres'] + self.cleaned_data1['original_language'] +\
- self.cleaned_data1['keywords'] + self.cleaned_data1['top_crew'] +\
- self.cleaned_data1['director']
- self.cleaned_data1['combine'] = self.cleaned_data1['combine'].apply(lambda x: ' '.join(x))
-
- count = CountVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0.01, stop_words='english')
- count_matrix = count.fit_transform(self.cleaned_data1['combine'])
-
- self.cosine_sim = cosine_similarity(count_matrix, count_matrix)
-
- self.cleaned_data = self.cleaned_data.reset_index()
- self.titles = self.cleaned_data['title']
- self.indices = pd.Series(self.cleaned_data.index, index=self.cleaned_data['title'])
-
- self.index_movie_id = self.cleaned_data[['index','id']]
-
- reader = Reader()
- data = Dataset.load_from_df(self.user_rating[['userId', 'movieId', 'rating']], reader)
- cross_validate(NormalPredictor(), data, cv=4)
-
- self.SVD = SVD()
- trainset = data.build_full_trainset()
- self.SVD.fit(trainset)
-
- self.id_map = pd.read_csv('Datasets/links_small.csv')[['movieId', 'tmdbId']]
- self.id_map['tmdbId'] = self.id_map['tmdbId'].apply(convert_int)
- self.id_map.columns = ['movieId', 'id']
- self.id_map = self.id_map.merge(self.cleaned_data[['title', 'id']], on='id').set_index('title')
- self.indices_map = self.id_map.set_index('id')
- self.user_rating.drop(columns=['timestamp'], inplace=True)
-
- def hybrid2(self, userId, title1, title2, title3, number_of_suggestions: int):
- idx1 = self.indices[title1]
- idx2 = self.indices[title2]
- idx3 = self.indices[title3]
-
- tmdbId1 = self.id_map.loc[title1]['id']
- tmdbId2 = self.id_map.loc[title2]['id']
- tmdbId3 = self.id_map.loc[title3]['id']
-
- movie_id1 = self.id_map.loc[title1]['movieId']
- movie_id2 = self.id_map.loc[title2]['movieId']
- movie_id3 = self.id_map.loc[title3]['movieId']
-
- if type(idx1) == pd.Series:
- idx1 = idx1.iloc[0]
- if type(idx2) == pd.Series:
- idx2 = idx2.iloc[0]
- if type(idx3) == pd.Series:
- idx3 = idx3.iloc[0]
-
- sim_scores = list(enumerate(self.cosine_sim[int(idx1)] + self.cosine_sim[int(idx2)] + self.cosine_sim[int(idx3)]))
- sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
- sim_scores = sim_scores[1:56]
- movie_indices = [i[0] for i in sim_scores]
-
- movies = self.cleaned_data.iloc[movie_indices][['title', 'vote_count', 'vote_average', 'id']]
- movies['est'] = movies['id'].apply(lambda x: self.SVD.predict(userId, self.indices_smoother(x)).est)
- movies = movies.sort_values('est', ascending=False)
- return movies.head(number_of_suggestions + 3)
-
- def get_similar_users(self, movie_names):
- movie_ids = [self.cleaned_data.loc[self.cleaned_data['title'] == movie]['id'].iloc[0] for movie in movie_names]
- new_user_ratings = pd.DataFrame({
- 'userId': [max(self.user_rating['userId']) + 1] * len(movie_ids),
- 'movieId': movie_ids,
- 'rating': [5.0] * len(movie_ids)
- })
-
- merged_ratings = pd.concat([self.user_rating, new_user_ratings], ignore_index=True)
-
- user_item_matrix = merged_ratings.pivot_table(index='userId', columns='movieId', values='rating', fill_value=0)
-
- new_user_vector = user_item_matrix.loc[user_item_matrix.index[-1]].values.reshape(1, -1)
- user_similarity = cosine_similarity(user_item_matrix.values[:-1], new_user_vector)
-
- similar_users_indices = user_similarity.argsort(axis=0)[-10:].flatten()[::-1]
-
- similar_users_similarity = user_similarity[similar_users_indices].flatten()
- similar_users = user_item_matrix.iloc[similar_users_indices]
-
- similar_users_df = pd.DataFrame({'userId': similar_users.index, 'Similarity': similar_users_similarity})
- return similar_users_df
-
- def suggest(self, movies, number_of_suggestions) -> pd.DataFrame:
- similar_id = self.get_similar_users(movies).iloc[0, 0]
- return self.hybrid2(similar_id, movies[0], movies[1], movies[2], number_of_suggestions=number_of_suggestions)
-
- def get_movie_info(self, movie_name: str) -> dict:
- movie_info = {}
- record = self.cleaned_data[self.cleaned_data['title'] == movie_name]
- movie_info['title'] = record['title'].to_numpy()[0]
- movie_info['overview'] = record['overview'].to_numpy()[0]
- movie_info['language'] = get_language_name(record['original_language'].to_numpy()[0])
- movie_info['genres'] = record['genres'].to_numpy()
- return movie_info
-
- def indices_smoother(self, ids):
- if type(self.indices_map.loc[ids]['movieId']) == pd.Series:
- return self.indices_map.loc[ids]['movieId'].iloc[0]
- else:
- return self.indices_map.loc[ids]['movieId']
-
- def find_nearest_movie(self, movie_name: str) -> tuple:
- lowest_distance = float('inf')
- closest_movie = ''
- for movie in self.cleaned_data['title']:
- current_distance = levenshtein_distance(movie_name.replace(" ", '').lower(), movie.replace(" ", '').lower())
- if current_distance < lowest_distance:
- lowest_distance = current_distance
- closest_movie = movie
- return (closest_movie, lowest_distance)
-
-def get_director(x):
- for i in x:
- if i['job'] == 'Director':
- return i['name']
- return ""
-
-def convert_int(x):
- try:
- return int(x)
- except:
- return np.nan
-
-def levenshtein_distance(name1: str, name2: str) -> int:
- return distance(name1, name2)
-
-def get_language_name(code:str) -> str:
- language_dict = {
- 'en': 'English',
- 'fr': 'French',
- 'es': 'Spanish',
- 'de': 'German',
- 'ja': 'Japanese',
- 'zh-cn': 'Chinese',
- 'ru': 'Russian',
- 'pt': 'Portuguese',
- 'ar': 'Arabic',
- 'hi': 'Hindi'
- }
-
- if code in language_dict:
- return language_dict[code]
- else:
- return 'Unknown Language'
\ No newline at end of file
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/encoders/channel_reduction_encoder.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/encoders/channel_reduction_encoder.py
deleted file mode 100644
index 49b40bf27406c0c1d1b46d4f7232bdeca50776f7..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/encoders/channel_reduction_encoder.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict, Optional, Sequence
-
-import torch
-import torch.nn as nn
-
-from mmocr.registry import MODELS
-from mmocr.structures import TextRecogDataSample
-from .base import BaseEncoder
-
-
-@MODELS.register_module()
-class ChannelReductionEncoder(BaseEncoder):
- """Change the channel number with a one by one convoluational layer.
-
- Args:
- in_channels (int): Number of input channels.
- out_channels (int): Number of output channels.
- init_cfg (dict or list[dict], optional): Initialization configs.
- Defaults to dict(type='Xavier', layer='Conv2d').
- """
-
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- init_cfg: Dict = dict(type='Xavier', layer='Conv2d')
- ) -> None:
- super().__init__(init_cfg=init_cfg)
-
- self.layer = nn.Conv2d(
- in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
- def forward(
- self,
- feat: torch.Tensor,
- data_samples: Optional[Sequence[TextRecogDataSample]] = None
- ) -> torch.Tensor:
- """
- Args:
- feat (Tensor): Image features with the shape of
- :math:`(N, C_{in}, H, W)`.
- data_samples (list[TextRecogDataSample], optional): Batch of
- TextRecogDataSample, containing valid_ratio information.
- Defaults to None.
-
- Returns:
- Tensor: A tensor of shape :math:`(N, C_{out}, H, W)`.
- """
- return self.layer(feat)
diff --git a/spaces/NATSpeech/PortaSpeech/utils/metrics/diagonal_metrics.py b/spaces/NATSpeech/PortaSpeech/utils/metrics/diagonal_metrics.py
deleted file mode 100644
index ba9807c1a594b38632c4731391e2d4fa3289037b..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/utils/metrics/diagonal_metrics.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import torch
-
-
-def get_focus_rate(attn, src_padding_mask=None, tgt_padding_mask=None):
- '''
- attn: bs x L_t x L_s
- '''
- if src_padding_mask is not None:
- attn = attn * (1 - src_padding_mask.float())[:, None, :]
-
- if tgt_padding_mask is not None:
- attn = attn * (1 - tgt_padding_mask.float())[:, :, None]
-
- focus_rate = attn.max(-1).values.sum(-1)
- focus_rate = focus_rate / attn.sum(-1).sum(-1)
- return focus_rate
-
-
-def get_phone_coverage_rate(attn, src_padding_mask=None, src_seg_mask=None, tgt_padding_mask=None):
- '''
- attn: bs x L_t x L_s
- '''
- src_mask = attn.new(attn.size(0), attn.size(-1)).bool().fill_(False)
- if src_padding_mask is not None:
- src_mask |= src_padding_mask
- if src_seg_mask is not None:
- src_mask |= src_seg_mask
-
- attn = attn * (1 - src_mask.float())[:, None, :]
- if tgt_padding_mask is not None:
- attn = attn * (1 - tgt_padding_mask.float())[:, :, None]
-
- phone_coverage_rate = attn.max(1).values.sum(-1)
- # phone_coverage_rate = phone_coverage_rate / attn.sum(-1).sum(-1)
- phone_coverage_rate = phone_coverage_rate / (1 - src_mask.float()).sum(-1)
- return phone_coverage_rate
-
-
-def get_diagonal_focus_rate(attn, attn_ks, target_len, src_padding_mask=None, tgt_padding_mask=None,
- band_mask_factor=5, band_width=50):
- '''
- attn: bx x L_t x L_s
- attn_ks: shape: tensor with shape [batch_size], input_lens/output_lens
-
- diagonal: y=k*x (k=attn_ks, x:output, y:input)
- 1 0 0
- 0 1 0
- 0 0 1
- y>=k*(x-width) and y<=k*(x+width):1
- else:0
- '''
- # width = min(target_len/band_mask_factor, 50)
- width1 = target_len / band_mask_factor
- width2 = target_len.new(target_len.size()).fill_(band_width)
- width = torch.where(width1 < width2, width1, width2).float()
- base = torch.ones(attn.size()).to(attn.device)
- zero = torch.zeros(attn.size()).to(attn.device)
- x = torch.arange(0, attn.size(1)).to(attn.device)[None, :, None].float() * base
- y = torch.arange(0, attn.size(2)).to(attn.device)[None, None, :].float() * base
- cond = (y - attn_ks[:, None, None] * x)
- cond1 = cond + attn_ks[:, None, None] * width[:, None, None]
- cond2 = cond - attn_ks[:, None, None] * width[:, None, None]
- mask1 = torch.where(cond1 < 0, zero, base)
- mask2 = torch.where(cond2 > 0, zero, base)
- mask = mask1 * mask2
-
- if src_padding_mask is not None:
- attn = attn * (1 - src_padding_mask.float())[:, None, :]
- if tgt_padding_mask is not None:
- attn = attn * (1 - tgt_padding_mask.float())[:, :, None]
-
- diagonal_attn = attn * mask
- diagonal_focus_rate = diagonal_attn.sum(-1).sum(-1) / attn.sum(-1).sum(-1)
- return diagonal_focus_rate, mask
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/README.md b/spaces/NCTCMumbai/NCTC/models/official/nlp/README.md
deleted file mode 100644
index 156f5c42858be92f20ed6bc157ddd8593cbc4329..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# TensorFlow NLP Modelling Toolkit
-
-This codebase provides a Natrual Language Processing modeling toolkit written in
-[TF2](https://www.tensorflow.org/guide/effective_tf2). It allows researchers and
-developers to reproduce state-of-the-art model results and train custom models
-to experiment new research ideas.
-
-## Features
-
-* Reusable and modularized modeling building blocks
-* State-of-the-art reproducible
-* Easy to customize and extend
-* End-to-end training
-* Distributed trainable on both GPUs and TPUs
-
-## Major components
-
-### Libraries
-
-We provide modeling library to allow users to train custom models for new
-research ideas. Detailed intructions can be found in READMEs in each folder.
-
-* [modeling/](modeling): modeling library that provides building blocks (e.g., Layers, Networks, and Models) that can be assembled into transformer-based achitectures .
-* [data/](data): binaries and utils for input preprocessing, tokenization, etc.
-
-### State-of-the-Art models and examples
-
-We provide SoTA model implementations, pre-trained models, training and
-evaluation examples, and command lines. Detail instructions can be found in the
-READMEs for specific papers.
-
-1. [BERT](bert): [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Devlin et al., 2018
-2. [ALBERT](albert): [A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942) by Lan et al., 2019
-3. [XLNet](xlnet): [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al., 2019
-4. [Transformer for translation](transformer): [Attention Is All You Need](https://arxiv.org/abs/1706.03762) by Vaswani et al., 2017
-5. [NHNet](nhnet): [Generating Representative Headlines for News Stories](https://arxiv.org/abs/2001.09386) by Gu et al, 2020
-
diff --git a/spaces/NCTCMumbai/NCTC/models/research/autoaugment/custom_ops.py b/spaces/NCTCMumbai/NCTC/models/research/autoaugment/custom_ops.py
deleted file mode 100644
index d2a690738608cd4b1a7f03e69af8be02648f86f5..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/autoaugment/custom_ops.py
+++ /dev/null
@@ -1,197 +0,0 @@
-# Copyright 2018 The TensorFlow Authors All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Contains convenience wrappers for typical Neural Network TensorFlow layers.
-
- Ops that have different behavior during training or eval have an is_training
- parameter.
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-import numpy as np
-import tensorflow as tf
-
-
-arg_scope = tf.contrib.framework.arg_scope
-
-
-def variable(name, shape, dtype, initializer, trainable):
- """Returns a TF variable with the passed in specifications."""
- var = tf.get_variable(
- name,
- shape=shape,
- dtype=dtype,
- initializer=initializer,
- trainable=trainable)
- return var
-
-
-def global_avg_pool(x, scope=None):
- """Average pools away spatial height and width dimension of 4D tensor."""
- assert x.get_shape().ndims == 4
- with tf.name_scope(scope, 'global_avg_pool', [x]):
- kernel_size = (1, int(x.shape[1]), int(x.shape[2]), 1)
- squeeze_dims = (1, 2)
- result = tf.nn.avg_pool(
- x,
- ksize=kernel_size,
- strides=(1, 1, 1, 1),
- padding='VALID',
- data_format='NHWC')
- return tf.squeeze(result, squeeze_dims)
-
-
-def zero_pad(inputs, in_filter, out_filter):
- """Zero pads `input` tensor to have `out_filter` number of filters."""
- outputs = tf.pad(inputs, [[0, 0], [0, 0], [0, 0],
- [(out_filter - in_filter) // 2,
- (out_filter - in_filter) // 2]])
- return outputs
-
-
-@tf.contrib.framework.add_arg_scope
-def batch_norm(inputs,
- decay=0.999,
- center=True,
- scale=False,
- epsilon=0.001,
- is_training=True,
- reuse=None,
- scope=None):
- """Small wrapper around tf.contrib.layers.batch_norm."""
- return tf.contrib.layers.batch_norm(
- inputs,
- decay=decay,
- center=center,
- scale=scale,
- epsilon=epsilon,
- activation_fn=None,
- param_initializers=None,
- updates_collections=tf.GraphKeys.UPDATE_OPS,
- is_training=is_training,
- reuse=reuse,
- trainable=True,
- fused=True,
- data_format='NHWC',
- zero_debias_moving_mean=False,
- scope=scope)
-
-
-def stride_arr(stride_h, stride_w):
- return [1, stride_h, stride_w, 1]
-
-
-@tf.contrib.framework.add_arg_scope
-def conv2d(inputs,
- num_filters_out,
- kernel_size,
- stride=1,
- scope=None,
- reuse=None):
- """Adds a 2D convolution.
-
- conv2d creates a variable called 'weights', representing the convolutional
- kernel, that is convolved with the input.
-
- Args:
- inputs: a 4D tensor in NHWC format.
- num_filters_out: the number of output filters.
- kernel_size: an int specifying the kernel height and width size.
- stride: an int specifying the height and width stride.
- scope: Optional scope for variable_scope.
- reuse: whether or not the layer and its variables should be reused.
- Returns:
- a tensor that is the result of a convolution being applied to `inputs`.
- """
- with tf.variable_scope(scope, 'Conv', [inputs], reuse=reuse):
- num_filters_in = int(inputs.shape[3])
- weights_shape = [kernel_size, kernel_size, num_filters_in, num_filters_out]
-
- # Initialization
- n = int(weights_shape[0] * weights_shape[1] * weights_shape[3])
- weights_initializer = tf.random_normal_initializer(
- stddev=np.sqrt(2.0 / n))
-
- weights = variable(
- name='weights',
- shape=weights_shape,
- dtype=tf.float32,
- initializer=weights_initializer,
- trainable=True)
- strides = stride_arr(stride, stride)
- outputs = tf.nn.conv2d(
- inputs, weights, strides, padding='SAME', data_format='NHWC')
- return outputs
-
-
-@tf.contrib.framework.add_arg_scope
-def fc(inputs,
- num_units_out,
- scope=None,
- reuse=None):
- """Creates a fully connected layer applied to `inputs`.
-
- Args:
- inputs: a tensor that the fully connected layer will be applied to. It
- will be reshaped if it is not 2D.
- num_units_out: the number of output units in the layer.
- scope: Optional scope for variable_scope.
- reuse: whether or not the layer and its variables should be reused.
-
- Returns:
- a tensor that is the result of applying a linear matrix to `inputs`.
- """
- if len(inputs.shape) > 2:
- inputs = tf.reshape(inputs, [int(inputs.shape[0]), -1])
-
- with tf.variable_scope(scope, 'FC', [inputs], reuse=reuse):
- num_units_in = inputs.shape[1]
- weights_shape = [num_units_in, num_units_out]
- unif_init_range = 1.0 / (num_units_out)**(0.5)
- weights_initializer = tf.random_uniform_initializer(
- -unif_init_range, unif_init_range)
- weights = variable(
- name='weights',
- shape=weights_shape,
- dtype=tf.float32,
- initializer=weights_initializer,
- trainable=True)
- bias_initializer = tf.constant_initializer(0.0)
- biases = variable(
- name='biases',
- shape=[num_units_out,],
- dtype=tf.float32,
- initializer=bias_initializer,
- trainable=True)
- outputs = tf.nn.xw_plus_b(inputs, weights, biases)
- return outputs
-
-
-@tf.contrib.framework.add_arg_scope
-def avg_pool(inputs, kernel_size, stride=2, padding='VALID', scope=None):
- """Wrapper around tf.nn.avg_pool."""
- with tf.name_scope(scope, 'AvgPool', [inputs]):
- kernel = stride_arr(kernel_size, kernel_size)
- strides = stride_arr(stride, stride)
- return tf.nn.avg_pool(
- inputs,
- ksize=kernel,
- strides=strides,
- padding=padding,
- data_format='NHWC')
-
diff --git a/spaces/Neelanjan/MoodMelody/app.py b/spaces/Neelanjan/MoodMelody/app.py
deleted file mode 100644
index d2afbc606c0926bdecef91f28107552868d82276..0000000000000000000000000000000000000000
--- a/spaces/Neelanjan/MoodMelody/app.py
+++ /dev/null
@@ -1,299 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Untitled1.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
- https://colab.research.google.com/drive/1yIRyQq7IUIqBQiXAPKoa0n2gT8Lr_qMw
-"""
-
-#!pip install openai
-#!pip install imageio
-#!pip install replicate
-#!pip install gradio
-
-"""## Importing Libraries"""
-
-import openai
-import re
-import pandas as pd
-import replicate
-import pandas as pd
-from PIL import Image
-import requests
-from io import BytesIO
-import os
-from IPython.display import Audio, display
-import gradio as gr
-import numpy
-from PIL import ImageDraw
-from PIL import ImageFont
-import random
-
-#!git clone https://github.com/Neelanjan-chakraborty/MelodyGen.git
-
-#!ls MelodyGen
-
-"""## Function To Generate Genre Based on Mood"""
-
-def generate_genre_recommendations(mood,key):
- openai.api_key=key
- response = openai.Completion.create(
- engine='text-davinci-003',
- prompt=f"I'm feeling {mood} and I want only genre recommendations and not songs also please separate using comma.",
- max_tokens=100,
- n=1,
- stop=None,
- temperature=0.5
- )
-
- genres = set(response.choices[0].text.strip().split(',')) # Extract genres from response
-
- #if len(genres) < 3:
- # # Provide some default genre recommendations
- # genres = {'Pop', 'Rock', 'Electronic'}
-
- genre_recommendations = list(genres)[:3] # Limit to 3 unique genres
-
- return ', '.join(genre_recommendations) # Return as comma-separated string
-
-def recommend(user_mood,key):
- genre_recommendations = generate_genre_recommendations(user_mood,key)
- out = genre_recommendations.split(',')
- return out
-
-"""## Function to Generate Melody"""
-
-def melodygen(api_token,prompt):
- client = replicate.Client(api_token)
- output = client.run(
- "joehoover/musicgen:ba9bdc5a86f60525ba23590a03ae1e407b9a40f4a318a85af85748d641e6659f",
- input={"model_version": "melody","prompt":prompt,"continuation_start": 0,"continuation_end": 30, "duration":30,
- }
- )
- return output
-
-#api_token='r8_Z30IhmLhqMoeJeNkl4S92JzSp8stlh82NZAnI'
-#prompt=input("Enter your Genre : ")
-#melodygen(api_token,prompt)
-
-"""## Function To Generate a Song Name"""
-
-def generate_song_name(mood,key):
- openai.api_key=key
- response = openai.Completion.create(
- engine='text-davinci-003',
- prompt=f"I'm feeling {mood} and I want only random ai generated Song Name and please separate these names using comma.",
- max_tokens=100,
- n=1,
- stop=None,
- temperature=0.5
- )
-
- names = set(response.choices[0].text.strip().split(',')) # Extract genres from response
- get_names = list(names)[:3] # Limit to 3 unique genres
- return ', '.join(get_names) # Return as comma-separated string(mood):
-
-"""## Function To Generate Album Art and Album Art Animation"""
-
-def albumart(user_mood,key,name):
- openai.api_key=key
- response = openai.Image.create(
- prompt=f"Music album art of mood{user_mood}",
- n=1,
- size="512x512"
- )
- image_url = response['data'][0]['url']
- # Fetch the image from the URL
- response = requests.get(image_url)
- response.raise_for_status()
- # Load the image into a PIL Image object
- image = Image.open(BytesIO(response.content))
- # Load the font
- font_path = "MelodyGen/Lobster-Regular.ttf"
- font_size = 48
- font = ImageFont.truetype(font_path, font_size)
- position = (image.width // 2, image.height // 2)
- # Create a new ImageDraw object
- draw = ImageDraw.Draw(image)
- # Get a random color
- color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
- # Calculate the position to center the text
- text_width, text_height = draw.textsize(name, font=font)
- text_position = (position[0] - text_width // 2, position[1] - text_height // 2)
- # Add the text to the image
- draw.text(text_position, name, font=font, fill=color)
- path='album_art.jpg'
- image.save(path)
- return path
-
-
-
-#overlay_images(background_image_path, foreground_image_path)
-
-from PIL import Image, ImageSequence
-def overlay_images(background_image_path):
- foreground_image_path='MelodyGen/Mockup4.png'
- num_frames=36
- rotation_angle=10
- # Open the background and foreground images
- background_image = Image.open(background_image_path).resize((600, 600))
- foreground_image = Image.open(foreground_image_path).resize((1024, 768)).convert("RGBA")
-
- # Create a new image with transparency
- final_image = Image.new("RGBA", (1024, 768))
-
- # Calculate the position to paste the background image at the center
- paste_x = int(100.50)
- paste_y = int(100.50)
-
- # Create a list to store the frames of the GIF animation
- frames = []
-
- # Calculate the center point of the background image
- center_x = int(background_image.width / 2)
- center_y = int(background_image.height / 2)
-
- # Rotate the background image and create frames for the GIF animation
- for angle in range(0, 360, rotation_angle):
- # Create a copy of the background image
- rotated_image = background_image.copy()
-
- # Rotate the image by the specified angle around its center
- rotated_image = rotated_image.rotate(angle, resample=Image.BICUBIC, center=(center_x, center_y))
-
- # Overlay the rotated background image at the center of the final image
- final_image.paste(rotated_image, (paste_x, paste_y))
-
- # Overlay the foreground image on the background image
- final_image.paste(foreground_image, (0, 0), mask=foreground_image)
-
- # Append the current frame to the list of frames
- frames.append(final_image.copy())
-
- # Convert the image mode of frames to RGB (removing alpha channel)
- frames_rgb = [frame.convert("RGB") for frame in frames]
-
- # Save the frames as an animated GIF
- output_path = "animation2.gif"
- frames_rgb[0].save(output_path, save_all=True, append_images=frames_rgb[1:], loop=0, duration=100,optimize=True, quality=100)
-
- # Return the output path of the animated GIF
- return output_path
-
-
-
-
-#background_image_path = "/content/input.png" # Path to the background image
-#foreground_image_path = "/content/Mockup4.png" # Path to the foreground PNG image
-#rotation_angle = 10
-
-#overlay_images(background_image_path, foreground_image_path)
-
-def main_func(user_mood,key,api_token,genre):
- if genre is None:
- out = recommend(user_mood, key)[0]
- else:
- out = genre
- name=generate_song_name(user_mood,key).split(",")
- image=albumart(user_mood ,key,name[0])
- album_art=overlay_images(image)
- song=melodygen(api_token,out[0])
-
- return album_art,name[0],genre,song
-
-
-"""## Gradio UI"""
-
-css1= """.gradio-container{
- background: linear-gradient(-45deg, #ee7752, #e73c7e, #23a6d5, #23d5ab);
- background-size: 400% 400%;
- animation: gradient 15s ease infinite;
- height: 100vh;
-}
-
-@keyframes gradient {
- 0% {
- background-position: 0% 50%;
- }
- 50% {
- background-position: 100% 50%;
- }
- 100% {
- background-position: 0% 50%;
- }
-}
-.mood {
-background: linear-gradient(-45deg, #ee7752, #e73c7e, #23a6d5, #23d5ab);
- background-size: 400% 400%;
- animation: gradient 15s ease infinite;
- height: 100vh;
-}
-
-@keyframes gradient {
- 0% {
- background-position: 0% 50%;
- }
- 50% {
- background-position: 100% 50%;
- }
- 100% {
- background-position: 0% 50%;
- }
-}
-.OpenAI {
-background: linear-gradient(-45deg, #ee7752, #e73c7e, #23a6d5, #23d5ab)!important;
- animation: gradient 15s ease infinite!important;
-}
-
-@keyframes gradient {
- 0% {
- background-position: 0% 50%;
- }
- 50% {
- background-position: 100% 50%;
- }
- 100% {
- background-position: 0% 50%;
- }
-}
-}
-"""
-
-def check_box(check,ch):
- if(check==True):
- return gr.Dropdown.update(visible=True)
- return gr.Dropdown.update(visible=False, value=None)
-
-
-with gr.Blocks(css=css1,theme='freddyaboulton/dracula_revamped',title="🎵MoodMelody🤖 by Neelanjan",thumbnail="MelodyGen/Mood-Melody.jpg") as demo:
- with gr.Row():
- with gr.Column(scale=1):
- Mood=textbox = gr.Textbox(label="Enter Your Mood",value="Happy",placeholder="How is Your Mood Now?", elem_id="warning", elem_classes="feedback")
- Key=textbox = gr.Textbox(label="Enter Open AI Key",placeholder="sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",elem_id="warning", elem_classes="feedback")
- Key2=textbox = gr.Textbox(label="Enter Replicate Key",placeholder="r8-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",elem_id="warning", elem_classes="feedback")
- check=gr.Checkbox(label="Custom Genre?", info="Select if you want custom Genres",value=True,elem_id="warning", elem_classes="feedback")
- ch=gr.Dropdown(
- [
- "Acoustic","Alternative","Ambient","Blues","Classical","Country","Dance","Disco","Electronic","Folk","Funk","Gospel","Hip Hop","Indie","Jazz","Latin","Metal","Opera","Pop","Punk",
- "R&B","Reggae","Rock","Soul","Techno","World",# Add more genres as needed
- ],max_choices=1,
- multiselect=True,visible=True,
- label="Music Genres",
- info="Select only one genre of music"
- )
-
- check.change(check_box, check, ch)
- btn = gr.Button(value="Generate Song")
- with gr.Column(scale=4):
- SongName=gr.Textbox(label="Song Name",placeholder="Song Name will appear Here")
- Genres=gr.Textbox(label="Genre",placeholder="Genres")
- AlbumArt=gr.Image(label="AI Generated Album Art")
- MusicGen=gr.Audio(label="AI Generated Music",format="wav")
-
-
-
- btn.click(main_func, inputs=[Mood,Key,Key2,ch], outputs=[AlbumArt,SongName,Genres,MusicGen])
-
-if __name__ == "__main__":
- demo.launch(show_error=True,debug=True)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank_score_bw.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank_score_bw.py
deleted file mode 100644
index b0bc913651bd76667e25c214acb70f2bca19e185..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank_score_bw.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import os
-from contextlib import redirect_stdout
-
-from fairseq import options
-from fairseq_cli import generate
-
-from examples.noisychannel import rerank_options, rerank_utils
-
-
-def score_bw(args):
- if args.backwards1:
- scorer1_src = args.target_lang
- scorer1_tgt = args.source_lang
- else:
- scorer1_src = args.source_lang
- scorer1_tgt = args.target_lang
-
- if args.score_model2 is not None:
- if args.backwards2:
- scorer2_src = args.target_lang
- scorer2_tgt = args.source_lang
- else:
- scorer2_src = args.source_lang
- scorer2_tgt = args.target_lang
-
- rerank1_is_gen = (
- args.gen_model == args.score_model1 and args.source_prefix_frac is None
- )
- rerank2_is_gen = (
- args.gen_model == args.score_model2 and args.source_prefix_frac is None
- )
-
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- args.shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- score1_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model1_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards1,
- )
-
- if args.score_model2 is not None:
- score2_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model2_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards2,
- )
-
- if args.right_to_left1:
- rerank_data1 = right_to_left_preprocessed_dir
- elif args.backwards1:
- rerank_data1 = backwards_preprocessed_dir
- else:
- rerank_data1 = left_to_right_preprocessed_dir
-
- gen_param = ["--batch-size", str(128), "--score-reference", "--gen-subset", "train"]
- if not rerank1_is_gen and not os.path.isfile(score1_file):
- print("STEP 4: score the translations for model 1")
-
- model_param1 = [
- "--path",
- args.score_model1,
- "--source-lang",
- scorer1_src,
- "--target-lang",
- scorer1_tgt,
- ]
- gen_model1_param = [rerank_data1] + gen_param + model_param1
-
- gen_parser = options.get_generation_parser()
- input_args = options.parse_args_and_arch(gen_parser, gen_model1_param)
-
- with open(score1_file, "w") as f:
- with redirect_stdout(f):
- generate.main(input_args)
-
- if (
- args.score_model2 is not None
- and not os.path.isfile(score2_file)
- and not rerank2_is_gen
- ):
- print("STEP 4: score the translations for model 2")
-
- if args.right_to_left2:
- rerank_data2 = right_to_left_preprocessed_dir
- elif args.backwards2:
- rerank_data2 = backwards_preprocessed_dir
- else:
- rerank_data2 = left_to_right_preprocessed_dir
-
- model_param2 = [
- "--path",
- args.score_model2,
- "--source-lang",
- scorer2_src,
- "--target-lang",
- scorer2_tgt,
- ]
- gen_model2_param = [rerank_data2] + gen_param + model_param2
-
- gen_parser = options.get_generation_parser()
- input_args = options.parse_args_and_arch(gen_parser, gen_model2_param)
-
- with open(score2_file, "w") as f:
- with redirect_stdout(f):
- generate.main(input_args)
-
-
-def cli_main():
- parser = rerank_options.get_reranking_parser()
- args = options.parse_args_and_arch(parser)
- score_bw(args)
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/append_token_dataset.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/append_token_dataset.py
deleted file mode 100644
index 87695bd0f5fcb6b10247e3b743340623e6438cc1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/append_token_dataset.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import torch
-
-from . import BaseWrapperDataset
-
-
-class AppendTokenDataset(BaseWrapperDataset):
- def __init__(self, dataset, token=None):
- super().__init__(dataset)
- self.token = token
- if token is not None:
- self._sizes = np.array(dataset.sizes) + 1
- else:
- self._sizes = dataset.sizes
-
- def __getitem__(self, idx):
- item = self.dataset[idx]
- if self.token is not None:
- item = torch.cat([item, item.new([self.token])])
- return item
-
- @property
- def sizes(self):
- return self._sizes
-
- def num_tokens(self, index):
- n = self.dataset.num_tokens(index)
- if self.token is not None:
- n += 1
- return n
-
- def size(self, index):
- n = self.dataset.size(index)
- if self.token is not None:
- n += 1
- return n
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/quantization/pq/pq.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/quantization/pq/pq.py
deleted file mode 100644
index eddc2eb34602403f10979f54cd23a45bc2f104d5..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/quantization/pq/pq.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .em import EM, EmptyClusterResolveError
-
-
-class PQ(EM):
- """
- Quantizes the layer weights W with the standard Product Quantization
- technique. This learns a codebook of codewords or centroids of size
- block_size from W. For further reference on using PQ to quantize
- neural networks, see "And the Bit Goes Down: Revisiting the Quantization
- of Neural Networks", Stock et al., ICLR 2020.
-
- PQ is performed in two steps:
- (1) The matrix W (weights or fully-connected or convolutional layer)
- is reshaped to (block_size, -1).
- - If W is fully-connected (2D), its columns are split into
- blocks of size block_size.
- - If W is convolutional (4D), its filters are split along the
- spatial dimension.
- (2) We apply the standard EM/k-means algorithm to the resulting reshaped matrix.
-
- Args:
- - W: weight matrix to quantize of size (in_features x out_features)
- - block_size: size of the blocks (subvectors)
- - n_centroids: number of centroids
- - n_iter: number of k-means iterations
- - eps: for cluster reassignment when an empty cluster is found
- - max_tentatives for cluster reassignment when an empty cluster is found
- - verbose: print information after each iteration
-
- Remarks:
- - block_size be compatible with the shape of W
- """
-
- def __init__(
- self,
- W,
- block_size,
- n_centroids=256,
- n_iter=20,
- eps=1e-6,
- max_tentatives=30,
- verbose=True,
- ):
- self.block_size = block_size
- W_reshaped = self._reshape(W)
- super(PQ, self).__init__(
- W_reshaped,
- n_centroids=n_centroids,
- n_iter=n_iter,
- eps=eps,
- max_tentatives=max_tentatives,
- verbose=verbose,
- )
-
- def _reshape(self, W):
- """
- Reshapes the matrix W as expained in step (1).
- """
-
- # fully connected: by convention the weight has size out_features x in_features
- if len(W.size()) == 2:
- self.out_features, self.in_features = W.size()
- assert (
- self.in_features % self.block_size == 0
- ), "Linear: n_blocks must be a multiple of in_features"
- return (
- W.reshape(self.out_features, -1, self.block_size)
- .permute(2, 1, 0)
- .flatten(1, 2)
- )
-
- # convolutional: we reshape along the spatial dimension
- elif len(W.size()) == 4:
- self.out_channels, self.in_channels, self.k_h, self.k_w = W.size()
- assert (
- self.in_channels * self.k_h * self.k_w
- ) % self.block_size == 0, (
- "Conv2d: n_blocks must be a multiple of in_channels * k_h * k_w"
- )
- return (
- W.reshape(self.out_channels, -1, self.block_size)
- .permute(2, 1, 0)
- .flatten(1, 2)
- )
- # not implemented
- else:
- raise NotImplementedError(W.size())
-
- def encode(self):
- """
- Performs self.n_iter EM steps.
- """
-
- self.initialize_centroids()
- for i in range(self.n_iter):
- try:
- self.step(i)
- except EmptyClusterResolveError:
- break
-
- def decode(self):
- """
- Returns the encoded full weight matrix. Must be called after
- the encode function.
- """
-
- # fully connected case
- if "k_h" not in self.__dict__:
- return (
- self.centroids[self.assignments]
- .reshape(-1, self.out_features, self.block_size)
- .permute(1, 0, 2)
- .flatten(1, 2)
- )
-
- # convolutional case
- else:
- return (
- self.centroids[self.assignments]
- .reshape(-1, self.out_channels, self.block_size)
- .permute(1, 0, 2)
- .reshape(self.out_channels, self.in_channels, self.k_h, self.k_w)
- )
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py
deleted file mode 100644
index ea8fae98e87e9f3e69bc51987703a6429eb0c92a..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py
+++ /dev/null
@@ -1,842 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Dict, List, Optional
-
-import math
-import numpy as np
-
-import torch
-import torch.nn.functional as F
-from torch import Tensor
-
-from .noisy_channel_beam_search import NoisyChannelBeamSearch
-from fairseq.sequence_generator import EnsembleModel
-
-
-class NoisyChannelSequenceGenerator(object):
- def __init__(
- self,
- combine_method,
- tgt_dict,
- src_dict=None,
- beam_size=1,
- max_len_a=0,
- max_len_b=200,
- min_len=1,
- len_penalty=1.0,
- unk_penalty=0.0,
- retain_dropout=False,
- temperature=1.0,
- match_source_len=False,
- no_repeat_ngram_size=0,
- normalize_scores=True,
- channel_models=None,
- k2=10,
- ch_weight=1.0,
- channel_scoring_type='log_norm',
- top_k_vocab=0,
- lm_models=None,
- lm_dict=None,
- lm_weight=1.0,
- normalize_lm_scores_by_tgt_len=False,
- ):
- """Generates translations of a given source sentence,
- using beam search with noisy channel decoding.
-
- Args:
- combine_method (string, optional): Method to combine direct, LM and
- channel model scores (default: None)
- tgt_dict (~fairseq.data.Dictionary): target dictionary
- src_dict (~fairseq.data.Dictionary): source dictionary
- beam_size (int, optional): beam width (default: 1)
- max_len_a/b (int, optional): generate sequences of maximum length
- ax + b, where x is the source length
- min_len (int, optional): the minimum length of the generated output
- (not including end-of-sentence)
- len_penalty (float, optional): length penalty, where <1.0 favors
- shorter, >1.0 favors longer sentences (default: 1.0)
- unk_penalty (float, optional): unknown word penalty, where <0
- produces more unks, >0 produces fewer (default: 0.0)
- retain_dropout (bool, optional): use dropout when generating
- (default: False)
- temperature (float, optional): temperature, where values
- >1.0 produce more uniform samples and values <1.0 produce
- sharper samples (default: 1.0)
- match_source_len (bool, optional): outputs should match the source
- length (default: False)
- no_repeat_ngram_size (int, optional): Size of n-grams that we avoid
- repeating in the generation (default: 0)
- normalize_scores (bool, optional): normalize scores by the length
- of the output (default: True)
- channel_models (List[~fairseq.models.FairseqModel]): ensemble of models
- translating from the target to the source
- k2 (int, optional): Top K2 candidates to score per beam at each step (default:10)
- ch_weight (int, optional): Weight associated with the channel model score
- assuming that the direct model score has weight 1.0 (default: 1.0)
- channel_scoring_type (str, optional): String specifying how to score
- the channel model (default: 'log_norm')
- top_k_vocab (int, optional): If `channel_scoring_type` is `'src_vocab'` or
- `'src_vocab_batched'`, then this parameter specifies the number of
- most frequent tokens to include in the channel model output vocabulary,
- in addition to the source tokens in the input batch (default: 0)
- lm_models (List[~fairseq.models.FairseqModel]): ensemble of models
- generating text in the target language
- lm_dict (~fairseq.data.Dictionary): LM Model dictionary
- lm_weight (int, optional): Weight associated with the LM model score
- assuming that the direct model score has weight 1.0 (default: 1.0)
- normalize_lm_scores_by_tgt_len (bool, optional): Should we normalize LM scores
- by the target length? By default, we normalize the combination of
- LM and channel model scores by the source length
- """
- self.pad = tgt_dict.pad()
- self.unk = tgt_dict.unk()
- self.eos = tgt_dict.eos()
- self.vocab_size = len(tgt_dict)
- self.beam_size = beam_size
- # the max beam size is the dictionary size - 1, since we never select pad
- self.beam_size = min(beam_size, self.vocab_size - 1)
- self.max_len_a = max_len_a
- self.max_len_b = max_len_b
- self.min_len = min_len
- self.normalize_scores = normalize_scores
- self.len_penalty = len_penalty
- self.unk_penalty = unk_penalty
- self.retain_dropout = retain_dropout
- self.temperature = temperature
- self.match_source_len = match_source_len
- self.no_repeat_ngram_size = no_repeat_ngram_size
- self.channel_models = channel_models
- self.src_dict = src_dict
- self.tgt_dict = tgt_dict
- self.combine_method = combine_method
- self.k2 = k2
- self.ch_weight = ch_weight
- self.channel_scoring_type = channel_scoring_type
- self.top_k_vocab = top_k_vocab
- self.lm_models = lm_models
- self.lm_dict = lm_dict
- self.lm_weight = lm_weight
- self.log_softmax_fn = torch.nn.LogSoftmax(dim=1)
- self.normalize_lm_scores_by_tgt_len = normalize_lm_scores_by_tgt_len
-
- self.share_tgt_dict = (self.lm_dict == self.tgt_dict)
- self.tgt_to_lm = make_dict2dict(tgt_dict, lm_dict)
-
- self.ch_scoring_bsz = 3072
-
- assert temperature > 0, '--temperature must be greater than 0'
-
- self.search = NoisyChannelBeamSearch(tgt_dict)
-
- @torch.no_grad()
- def generate(
- self,
- models,
- sample,
- prefix_tokens=None,
- bos_token=None,
- **kwargs
- ):
- """Generate a batch of translations.
- Args:
- models (List[~fairseq.models.FairseqModel]): ensemble of models
- sample (dict): batch
- prefix_tokens (torch.LongTensor, optional): force decoder to begin
- with these tokens
- """
- model = EnsembleModel(models)
- incremental_states = torch.jit.annotate(
- List[Dict[str, Dict[str, Optional[Tensor]]]],
- [
- torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {})
- for i in range(model.models_size)
- ],
- )
- if not self.retain_dropout:
- model.eval()
-
- # model.forward normally channels prev_output_tokens into the decoder
- # separately, but SequenceGenerator directly calls model.encoder
- encoder_input = {
- k: v for k, v in sample['net_input'].items()
- if k != 'prev_output_tokens'
- }
- src_tokens = encoder_input['src_tokens']
- src_lengths_no_eos = (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1)
- input_size = src_tokens.size()
- # batch dimension goes first followed by source lengths
- bsz = input_size[0]
- src_len = input_size[1]
- beam_size = self.beam_size
-
- if self.match_source_len:
- max_len = src_lengths_no_eos.max().item()
- else:
- max_len = min(
- int(self.max_len_a * src_len + self.max_len_b),
- # exclude the EOS marker
- model.max_decoder_positions() - 1,
- )
-
- # compute the encoder output for each beam
- encoder_outs = model.forward_encoder(encoder_input)
- new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1)
- new_order = new_order.to(src_tokens.device).long()
- encoder_outs = model.reorder_encoder_out(encoder_outs, new_order)
-
- src_lengths = encoder_input['src_lengths']
- # initialize buffers
- scores = src_tokens.new(bsz * beam_size, max_len + 1).float().fill_(0)
- lm_prefix_scores = src_tokens.new(bsz * beam_size).float().fill_(0)
-
- scores_buf = scores.clone()
- tokens = src_tokens.new(bsz * beam_size, max_len + 2).long().fill_(self.pad)
- tokens_buf = tokens.clone()
- tokens[:, 0] = self.eos if bos_token is None else bos_token
-
- # reorder source tokens so they may be used as a reference in generating P(S|T)
- src_tokens = reorder_all_tokens(src_tokens, src_lengths, self.src_dict.eos_index)
-
- src_tokens = src_tokens.repeat(1, beam_size).view(-1, src_len)
- src_lengths = src_lengths.view(bsz, -1).repeat(1, beam_size).view(bsz*beam_size, -1)
-
- attn, attn_buf = None, None
- nonpad_idxs = None
-
- # The cands_to_ignore indicates candidates that should be ignored.
- # For example, suppose we're sampling and have already finalized 2/5
- # samples. Then the cands_to_ignore would mark 2 positions as being ignored,
- # so that we only finalize the remaining 3 samples.
- cands_to_ignore = src_tokens.new_zeros(bsz, beam_size).eq(-1) # forward and backward-compatible False mask
-
- # list of completed sentences
- finalized = [[] for i in range(bsz)]
- finished = [False for i in range(bsz)]
- num_remaining_sent = bsz
-
- # number of candidate hypos per step
- cand_size = 2 * beam_size # 2 x beam size in case half are EOS
-
- # offset arrays for converting between different indexing schemes
- bbsz_offsets = (torch.arange(0, bsz) * beam_size).unsqueeze(1).type_as(tokens)
- cand_offsets = torch.arange(0, cand_size).type_as(tokens)
-
- # helper function for allocating buffers on the fly
- buffers = {}
-
- def buffer(name, type_of=tokens): # noqa
- if name not in buffers:
- buffers[name] = type_of.new()
- return buffers[name]
-
- def is_finished(sent, step, unfin_idx):
- """
- Check whether we've finished generation for a given sentence, by
- comparing the worst score among finalized hypotheses to the best
- possible score among unfinalized hypotheses.
- """
- assert len(finalized[sent]) <= beam_size
- if len(finalized[sent]) == beam_size:
- return True
- return False
-
- def finalize_hypos(step, bbsz_idx, eos_scores, combined_noisy_channel_eos_scores):
- """
- Finalize the given hypotheses at this step, while keeping the total
- number of finalized hypotheses per sentence <= beam_size.
-
- Note: the input must be in the desired finalization order, so that
- hypotheses that appear earlier in the input are preferred to those
- that appear later.
-
- Args:
- step: current time step
- bbsz_idx: A vector of indices in the range [0, bsz*beam_size),
- indicating which hypotheses to finalize
- eos_scores: A vector of the same size as bbsz_idx containing
- fw scores for each hypothesis
- combined_noisy_channel_eos_scores: A vector of the same size as bbsz_idx containing
- combined noisy channel scores for each hypothesis
- """
- assert bbsz_idx.numel() == eos_scores.numel()
-
- # clone relevant token and attention tensors
- tokens_clone = tokens.index_select(0, bbsz_idx)
- tokens_clone = tokens_clone[:, 1:step + 2] # skip the first index, which is EOS
- assert not tokens_clone.eq(self.eos).any()
- tokens_clone[:, step] = self.eos
- attn_clone = attn.index_select(0, bbsz_idx)[:, :, 1:step+2] if attn is not None else None
-
- # compute scores per token position
- pos_scores = scores.index_select(0, bbsz_idx)[:, :step+1]
- pos_scores[:, step] = eos_scores
- # convert from cumulative to per-position scores
- pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1]
-
- # normalize sentence-level scores
- if self.normalize_scores:
- combined_noisy_channel_eos_scores /= (step + 1) ** self.len_penalty
-
- cum_unfin = []
- prev = 0
- for f in finished:
- if f:
- prev += 1
- else:
- cum_unfin.append(prev)
-
- sents_seen = set()
- for i, (idx, score) in enumerate(zip(bbsz_idx.tolist(), combined_noisy_channel_eos_scores.tolist())):
- unfin_idx = idx // beam_size
- sent = unfin_idx + cum_unfin[unfin_idx]
-
- sents_seen.add((sent, unfin_idx))
-
- if self.match_source_len and step > src_lengths_no_eos[unfin_idx]:
- score = -math.inf
-
- def get_hypo():
-
- if attn_clone is not None:
- # remove padding tokens from attn scores
- hypo_attn = attn_clone[i][nonpad_idxs[sent]]
- _, alignment = hypo_attn.max(dim=0)
- else:
- hypo_attn = None
- alignment = None
-
- return {
- 'tokens': tokens_clone[i],
- 'score': score,
- 'attention': hypo_attn, # src_len x tgt_len
- 'alignment': alignment,
- 'positional_scores': pos_scores[i],
- }
-
- if len(finalized[sent]) < beam_size:
- finalized[sent].append(get_hypo())
-
- newly_finished = []
- for sent, unfin_idx in sents_seen:
- # check termination conditions for this sentence
- if not finished[sent] and is_finished(sent, step, unfin_idx):
- finished[sent] = True
- newly_finished.append(unfin_idx)
- return newly_finished
-
- def noisy_channel_rescoring(lprobs, beam_size, bsz, src_tokens, tokens, k):
- """Rescore the top k hypothesis from each beam using noisy channel modeling
- Returns:
- new_fw_lprobs: the direct model probabilities after pruning the top k
- new_ch_lm_lprobs: the combined channel and language model probabilities
- new_lm_lprobs: the language model probabilities after pruning the top k
- """
- with torch.no_grad():
- lprobs_size = lprobs.size()
- if prefix_tokens is not None and step < prefix_tokens.size(1):
- probs_slice = lprobs.view(bsz, -1, lprobs.size(-1))[:, 0, :]
- cand_scores = torch.gather(
- probs_slice, dim=1,
- index=prefix_tokens[:, step].view(-1, 1).data
- ).expand(-1, beam_size).contiguous().view(bsz*beam_size, 1)
- cand_indices = prefix_tokens[:, step].view(-1, 1).expand(bsz, beam_size).data.contiguous().view(bsz*beam_size, 1)
-
- # need to calculate and save fw and lm probs for prefix tokens
- fw_top_k = cand_scores
- fw_top_k_idx = cand_indices
- k = 1
- else:
- # take the top k best words for every sentence in batch*beam
- fw_top_k, fw_top_k_idx = torch.topk(lprobs.view(beam_size*bsz, -1), k=k)
- eos_idx = torch.nonzero(fw_top_k_idx.view(bsz*beam_size*k, -1) == self.eos)[:, 0]
- ch_scores = fw_top_k.new_full((beam_size*bsz*k, ), 0)
- src_size = torch.sum(src_tokens[:, :] != self.src_dict.pad_index, dim=1, keepdim=True, dtype=fw_top_k.dtype)
-
- if self.combine_method != "lm_only":
- temp_src_tokens_full = src_tokens[:, :].repeat(1, k).view(bsz*beam_size*k, -1)
- not_padding = temp_src_tokens_full[:, 1:] != self.src_dict.pad_index
- cur_tgt_size = step+2
-
- # add eos to all candidate sentences except those that already end in eos
- eos_tokens = tokens[:, 0].repeat(1, k).view(-1, 1)
- eos_tokens[eos_idx] = self.tgt_dict.pad_index
-
- if step == 0:
- channel_input = torch.cat((fw_top_k_idx.view(-1, 1), eos_tokens), 1)
- else:
- # move eos from beginning to end of target sentence
- channel_input = torch.cat((tokens[:, 1:step + 1].repeat(1, k).view(-1, step), fw_top_k_idx.view(-1, 1), eos_tokens), 1)
-
- ch_input_lengths = torch.tensor(np.full(channel_input.size(0), cur_tgt_size))
- ch_input_lengths[eos_idx] = cur_tgt_size-1
- if self.channel_scoring_type == "unnormalized":
- ch_encoder_output = channel_model.encoder(channel_input, src_lengths=ch_input_lengths)
- ch_decoder_output, _ = channel_model.decoder(temp_src_tokens_full, encoder_out=ch_encoder_output, features_only=True)
- del ch_encoder_output
- ch_intermed_scores = channel_model.decoder.unnormalized_scores_given_target(ch_decoder_output, target_ids=temp_src_tokens_full[:, 1:])
- ch_intermed_scores = ch_intermed_scores.float()
- ch_intermed_scores *= not_padding.float()
- ch_scores = torch.sum(ch_intermed_scores, dim=1)
- elif self.channel_scoring_type == "k2_separate":
- for k_idx in range(k):
- k_eos_tokens = eos_tokens[k_idx::k, :]
- if step == 0:
- k_ch_input = torch.cat((fw_top_k_idx[:, k_idx:k_idx+1], k_eos_tokens), 1)
- else:
- # move eos from beginning to end of target sentence
- k_ch_input = torch.cat((tokens[:, 1:step + 1], fw_top_k_idx[:, k_idx:k_idx+1], k_eos_tokens), 1)
- k_ch_input_lengths = ch_input_lengths[k_idx::k]
- k_ch_output = channel_model(k_ch_input, k_ch_input_lengths, src_tokens)
- k_ch_lprobs = channel_model.get_normalized_probs(k_ch_output, log_probs=True)
- k_ch_intermed_scores = torch.gather(k_ch_lprobs[:, :-1, :], 2, src_tokens[:, 1:].unsqueeze(2)).squeeze(2)
- k_ch_intermed_scores *= not_padding.float()
- ch_scores[k_idx::k] = torch.sum(k_ch_intermed_scores, dim=1)
- elif self.channel_scoring_type == "src_vocab":
- ch_encoder_output = channel_model.encoder(channel_input, src_lengths=ch_input_lengths)
- ch_decoder_output, _ = channel_model.decoder(temp_src_tokens_full, encoder_out=ch_encoder_output, features_only=True)
-
- del ch_encoder_output
- ch_lprobs = normalized_scores_with_batch_vocab(
- channel_model.decoder,
- ch_decoder_output, src_tokens, k, bsz, beam_size,
- self.src_dict.pad_index, top_k=self.top_k_vocab)
- ch_scores = torch.sum(ch_lprobs, dim=1)
- elif self.channel_scoring_type == "src_vocab_batched":
- ch_bsz_size = temp_src_tokens_full.shape[0]
- ch_lprobs_list = [None] * len(range(0, ch_bsz_size, self.ch_scoring_bsz))
- for i, start_idx in enumerate(range(0, ch_bsz_size, self.ch_scoring_bsz)):
- end_idx = min(start_idx + self.ch_scoring_bsz, ch_bsz_size)
- temp_src_tokens_full_batch = temp_src_tokens_full[start_idx:end_idx, :]
- channel_input_batch = channel_input[start_idx:end_idx, :]
- ch_input_lengths_batch = ch_input_lengths[start_idx:end_idx]
- ch_encoder_output_batch = channel_model.encoder(channel_input_batch, src_lengths=ch_input_lengths_batch)
- ch_decoder_output_batch, _ = channel_model.decoder(temp_src_tokens_full_batch, encoder_out=ch_encoder_output_batch, features_only=True)
- ch_lprobs_list[i] = normalized_scores_with_batch_vocab(
- channel_model.decoder,
- ch_decoder_output_batch, src_tokens, k, bsz, beam_size,
- self.src_dict.pad_index, top_k=self.top_k_vocab,
- start_idx=start_idx, end_idx=end_idx)
- ch_lprobs = torch.cat(ch_lprobs_list, dim=0)
- ch_scores = torch.sum(ch_lprobs, dim=1)
- else:
- ch_output = channel_model(channel_input, ch_input_lengths, temp_src_tokens_full)
- ch_lprobs = channel_model.get_normalized_probs(ch_output, log_probs=True)
- ch_intermed_scores = torch.gather(ch_lprobs[:, :-1, :], 2, temp_src_tokens_full[:, 1:].unsqueeze(2)).squeeze().view(bsz*beam_size*k, -1)
- ch_intermed_scores *= not_padding.float()
- ch_scores = torch.sum(ch_intermed_scores, dim=1)
-
- else:
- cur_tgt_size = 0
- ch_scores = ch_scores.view(bsz*beam_size, k)
- expanded_lm_prefix_scores = lm_prefix_scores.unsqueeze(1).expand(-1, k).flatten()
-
- if self.share_tgt_dict:
- lm_scores = get_lm_scores(lm, tokens[:, :step + 1].view(-1, step+1), lm_incremental_states, fw_top_k_idx.view(-1, 1), torch.tensor(np.full(tokens.size(0), step+1)), k)
- else:
- new_lm_input = dict2dict(tokens[:, :step + 1].view(-1, step+1), self.tgt_to_lm)
- new_cands = dict2dict(fw_top_k_idx.view(-1, 1), self.tgt_to_lm)
- lm_scores = get_lm_scores(lm, new_lm_input, lm_incremental_states, new_cands, torch.tensor(np.full(tokens.size(0), step+1)), k)
-
- lm_scores.add_(expanded_lm_prefix_scores)
- ch_lm_scores = combine_ch_lm(self.combine_method, ch_scores, lm_scores, src_size, cur_tgt_size)
- # initialize all as min value
- new_fw_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1)
- new_ch_lm_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1)
- new_lm_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1)
- new_fw_lprobs[:, self.pad] = -math.inf
- new_ch_lm_lprobs[:, self.pad] = -math.inf
- new_lm_lprobs[:, self.pad] = -math.inf
-
- new_fw_lprobs.scatter_(1, fw_top_k_idx, fw_top_k)
- new_ch_lm_lprobs.scatter_(1, fw_top_k_idx, ch_lm_scores)
- new_lm_lprobs.scatter_(1, fw_top_k_idx, lm_scores.view(-1, k))
- return new_fw_lprobs, new_ch_lm_lprobs, new_lm_lprobs
-
- def combine_ch_lm(combine_type, ch_scores, lm_scores1, src_size, tgt_size):
- if self.channel_scoring_type == "unnormalized":
- ch_scores = self.log_softmax_fn(
- ch_scores.view(-1, self.beam_size * self.k2)
- ).view(ch_scores.shape)
- ch_scores = ch_scores * self.ch_weight
- lm_scores1 = lm_scores1 * self.lm_weight
-
- if combine_type == "lm_only":
- # log P(T|S) + log P(T)
- ch_scores = lm_scores1.view(ch_scores.size())
- elif combine_type == "noisy_channel":
- # 1/t log P(T|S) + 1/s log P(S|T) + 1/t log P(T)
- if self.normalize_lm_scores_by_tgt_len:
- ch_scores.div_(src_size)
- lm_scores_norm = lm_scores1.view(ch_scores.size()).div(tgt_size)
- ch_scores.add_(lm_scores_norm)
- # 1/t log P(T|S) + 1/s log P(S|T) + 1/s log P(T)
- else:
- ch_scores.add_(lm_scores1.view(ch_scores.size()))
- ch_scores.div_(src_size)
-
- return ch_scores
-
- if self.channel_models is not None:
- channel_model = self.channel_models[0] # assume only one channel_model model
- else:
- channel_model = None
-
- lm = EnsembleModel(self.lm_models)
- lm_incremental_states = torch.jit.annotate(
- List[Dict[str, Dict[str, Optional[Tensor]]]],
- [
- torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {})
- for i in range(lm.models_size)
- ],
- )
-
- reorder_state = None
- batch_idxs = None
- for step in range(max_len + 1): # one extra step for EOS marker
- # reorder decoder internal states based on the prev choice of beams
- if reorder_state is not None:
- if batch_idxs is not None:
- # update beam indices to take into account removed sentences
- corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as(batch_idxs)
- reorder_state.view(-1, beam_size).add_(corr.unsqueeze(-1) * beam_size)
- model.reorder_incremental_state(incremental_states, reorder_state)
- encoder_outs = model.reorder_encoder_out(encoder_outs, reorder_state)
-
- lm.reorder_incremental_state(lm_incremental_states, reorder_state)
-
- fw_lprobs, avg_attn_scores = model.forward_decoder(
- tokens[:, :step + 1], encoder_outs, incremental_states, temperature=self.temperature,
- )
-
- fw_lprobs[:, self.pad] = -math.inf # never select pad
- fw_lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty
- fw_lprobs, ch_lm_lprobs, lm_lprobs = noisy_channel_rescoring(fw_lprobs, beam_size, bsz, src_tokens, tokens, self.k2)
-
- # handle min and max length constraints
- if step >= max_len:
- fw_lprobs[:, :self.eos] = -math.inf
- fw_lprobs[:, self.eos + 1:] = -math.inf
- elif step < self.min_len:
- fw_lprobs[:, self.eos] = -math.inf
-
- # handle prefix tokens (possibly with different lengths)
- if prefix_tokens is not None and step < prefix_tokens.size(1):
- prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1)
- prefix_mask = prefix_toks.ne(self.pad)
-
- prefix_fw_lprobs = fw_lprobs.gather(-1, prefix_toks.unsqueeze(-1))
- fw_lprobs[prefix_mask] = -math.inf
- fw_lprobs[prefix_mask] = fw_lprobs[prefix_mask].scatter_(
- -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_fw_lprobs
- )
-
- prefix_ch_lm_lprobs = ch_lm_lprobs.gather(-1, prefix_toks.unsqueeze(-1))
- ch_lm_lprobs[prefix_mask] = -math.inf
- ch_lm_lprobs[prefix_mask] = ch_lm_lprobs[prefix_mask].scatter_(
- -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_ch_lm_lprobs
- )
-
- prefix_lm_lprobs = lm_lprobs.gather(-1, prefix_toks.unsqueeze(-1))
- lm_lprobs[prefix_mask] = -math.inf
- lm_lprobs[prefix_mask] = lm_lprobs[prefix_mask].scatter_(
- -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lm_lprobs
- )
-
- # if prefix includes eos, then we should make sure tokens and
- # scores are the same across all beams
- eos_mask = prefix_toks.eq(self.eos)
- if eos_mask.any():
- # validate that the first beam matches the prefix
- first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[:, 0, 1:step + 1]
- eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0]
- target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step]
- assert (first_beam == target_prefix).all()
-
- def replicate_first_beam(tensor, mask):
- tensor = tensor.view(-1, beam_size, tensor.size(-1))
- tensor[mask] = tensor[mask][:, :1, :]
- return tensor.view(-1, tensor.size(-1))
-
- # copy tokens, scores and lprobs from the first beam to all beams
- tokens = replicate_first_beam(tokens, eos_mask_batch_dim)
- scores = replicate_first_beam(scores, eos_mask_batch_dim)
-
- fw_lprobs = replicate_first_beam(fw_lprobs, eos_mask_batch_dim)
- ch_lm_lprobs = replicate_first_beam(ch_lm_lprobs, eos_mask_batch_dim)
- lm_lprobs = replicate_first_beam(lm_lprobs, eos_mask_batch_dim)
-
- if self.no_repeat_ngram_size > 0:
- # for each beam and batch sentence, generate a list of previous ngrams
- gen_ngrams = [{} for bbsz_idx in range(bsz * beam_size)]
- for bbsz_idx in range(bsz * beam_size):
- gen_tokens = tokens[bbsz_idx].tolist()
- for ngram in zip(*[gen_tokens[i:] for i in range(self.no_repeat_ngram_size)]):
- gen_ngrams[bbsz_idx][tuple(ngram[:-1])] = \
- gen_ngrams[bbsz_idx].get(tuple(ngram[:-1]), []) + [ngram[-1]]
-
- # Record attention scores
- if avg_attn_scores is not None:
- if attn is None:
- attn = scores.new(bsz * beam_size, src_tokens.size(1), max_len + 2)
- attn_buf = attn.clone()
- nonpad_idxs = src_tokens.ne(self.pad)
- attn[:, :, step + 1].copy_(avg_attn_scores)
-
- scores = scores.type_as(fw_lprobs)
- scores_buf = scores_buf.type_as(fw_lprobs)
-
- self.search.set_src_lengths(src_lengths_no_eos)
-
- if self.no_repeat_ngram_size > 0:
- def calculate_banned_tokens(bbsz_idx):
- # before decoding the next token, prevent decoding of ngrams that have already appeared
- ngram_index = tuple(tokens[bbsz_idx, step + 2 - self.no_repeat_ngram_size:step + 1].tolist())
- return gen_ngrams[bbsz_idx].get(ngram_index, [])
-
- if step + 2 - self.no_repeat_ngram_size >= 0:
- # no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
- banned_tokens = [calculate_banned_tokens(bbsz_idx) for bbsz_idx in range(bsz * beam_size)]
- else:
- banned_tokens = [[] for bbsz_idx in range(bsz * beam_size)]
-
- for bbsz_idx in range(bsz * beam_size):
- fw_lprobs[bbsz_idx, banned_tokens[bbsz_idx]] = -math.inf
-
- combined_noisy_channel_scores, fw_lprobs_top_k, lm_lprobs_top_k, cand_indices, cand_beams = self.search.step(
- step,
- fw_lprobs.view(bsz, -1, self.vocab_size),
- scores.view(bsz, beam_size, -1)[:, :, :step], ch_lm_lprobs.view(bsz, -1, self.vocab_size),
- lm_lprobs.view(bsz, -1, self.vocab_size), self.combine_method
- )
-
- # cand_bbsz_idx contains beam indices for the top candidate
- # hypotheses, with a range of values: [0, bsz*beam_size),
- # and dimensions: [bsz, cand_size]
- cand_bbsz_idx = cand_beams.add(bbsz_offsets)
-
- # finalize hypotheses that end in eos (except for candidates to be ignored)
- eos_mask = cand_indices.eq(self.eos)
- eos_mask[:, :beam_size] &= ~cands_to_ignore
-
- # only consider eos when it's among the top beam_size indices
- eos_bbsz_idx = torch.masked_select(
- cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size]
- )
-
- finalized_sents = set()
- if eos_bbsz_idx.numel() > 0:
- eos_scores = torch.masked_select(
- fw_lprobs_top_k[:, :beam_size], mask=eos_mask[:, :beam_size]
- )
- combined_noisy_channel_eos_scores = torch.masked_select(
- combined_noisy_channel_scores[:, :beam_size],
- mask=eos_mask[:, :beam_size],
- )
-
- # finalize hypo using channel model score
- finalized_sents = finalize_hypos(
- step, eos_bbsz_idx, eos_scores, combined_noisy_channel_eos_scores)
-
- num_remaining_sent -= len(finalized_sents)
-
- assert num_remaining_sent >= 0
- if num_remaining_sent == 0:
- break
-
- if len(finalized_sents) > 0:
- new_bsz = bsz - len(finalized_sents)
-
- # construct batch_idxs which holds indices of batches to keep for the next pass
- batch_mask = cand_indices.new_ones(bsz)
- batch_mask[cand_indices.new(finalized_sents)] = 0
- batch_idxs = torch.nonzero(batch_mask).squeeze(-1)
-
- eos_mask = eos_mask[batch_idxs]
- cand_beams = cand_beams[batch_idxs]
- bbsz_offsets.resize_(new_bsz, 1)
- cand_bbsz_idx = cand_beams.add(bbsz_offsets)
-
- lm_lprobs_top_k = lm_lprobs_top_k[batch_idxs]
-
- fw_lprobs_top_k = fw_lprobs_top_k[batch_idxs]
- cand_indices = cand_indices[batch_idxs]
- if prefix_tokens is not None:
- prefix_tokens = prefix_tokens[batch_idxs]
- src_lengths_no_eos = src_lengths_no_eos[batch_idxs]
- cands_to_ignore = cands_to_ignore[batch_idxs]
-
- scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1)
- scores_buf.resize_as_(scores)
- tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1)
- tokens_buf.resize_as_(tokens)
- src_tokens = src_tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1)
- src_lengths = src_lengths.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1)
- lm_prefix_scores = lm_prefix_scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1).squeeze()
-
- if attn is not None:
- attn = attn.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, attn.size(1), -1)
- attn_buf.resize_as_(attn)
- bsz = new_bsz
- else:
- batch_idxs = None
-
- # Set active_mask so that values > cand_size indicate eos or
- # ignored hypos and values < cand_size indicate candidate
- # active hypos. After this, the min values per row are the top
- # candidate active hypos.
- eos_mask[:, :beam_size] |= cands_to_ignore
- active_mask = torch.add(
- eos_mask.type_as(cand_offsets) * cand_size,
- cand_offsets[: eos_mask.size(1)],
- )
-
- # get the top beam_size active hypotheses, which are just the hypos
- # with the smallest values in active_mask
- active_hypos, new_cands_to_ignore = buffer('active_hypos'), buffer('new_cands_to_ignore')
- torch.topk(
- active_mask, k=beam_size, dim=1, largest=False,
- out=(new_cands_to_ignore, active_hypos)
- )
-
- # update cands_to_ignore to ignore any finalized hypos
- cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size]
- assert (~cands_to_ignore).any(dim=1).all()
-
- active_bbsz_idx = buffer('active_bbsz_idx')
- torch.gather(
- cand_bbsz_idx, dim=1, index=active_hypos,
- out=active_bbsz_idx,
- )
- active_scores = torch.gather(
- fw_lprobs_top_k, dim=1, index=active_hypos,
- out=scores[:, step].view(bsz, beam_size),
- )
-
- active_bbsz_idx = active_bbsz_idx.view(-1)
- active_scores = active_scores.view(-1)
-
- # copy tokens and scores for active hypotheses
- torch.index_select(
- tokens[:, :step + 1], dim=0, index=active_bbsz_idx,
- out=tokens_buf[:, :step + 1],
- )
- torch.gather(
- cand_indices, dim=1, index=active_hypos,
- out=tokens_buf.view(bsz, beam_size, -1)[:, :, step + 1],
- )
- if step > 0:
- torch.index_select(
- scores[:, :step], dim=0, index=active_bbsz_idx,
- out=scores_buf[:, :step],
- )
- torch.gather(
- fw_lprobs_top_k, dim=1, index=active_hypos,
- out=scores_buf.view(bsz, beam_size, -1)[:, :, step],
- )
- torch.gather(
- lm_lprobs_top_k, dim=1, index=active_hypos,
- out=lm_prefix_scores.view(bsz, beam_size)
- )
-
- # copy attention for active hypotheses
- if attn is not None:
- torch.index_select(
- attn[:, :, :step + 2], dim=0, index=active_bbsz_idx,
- out=attn_buf[:, :, :step + 2],
- )
-
- # swap buffers
- tokens, tokens_buf = tokens_buf, tokens
- scores, scores_buf = scores_buf, scores
- if attn is not None:
- attn, attn_buf = attn_buf, attn
-
- # reorder incremental state in decoder
- reorder_state = active_bbsz_idx
-
- # sort by score descending
- for sent in range(len(finalized)):
- finalized[sent] = sorted(finalized[sent], key=lambda r: r['score'], reverse=True)
-
- return finalized
-
-
-def get_lm_scores(model, input_tokens, incremental_states, cand_tokens, input_len, k):
- with torch.no_grad():
- lm_lprobs, avg_attn_scores = model.forward_decoder(
- input_tokens, encoder_outs=None, incremental_states=incremental_states,
- )
-
- lm_lprobs_size = lm_lprobs.size(0)
- probs_next_wrd = torch.gather(lm_lprobs.repeat(1, k).view(lm_lprobs_size*k, -1), 1, cand_tokens).squeeze().view(-1)
-
- return probs_next_wrd
-
-
-def make_dict2dict(old_dict, new_dict):
- dict2dict_map = {}
- for sym in old_dict.symbols:
- dict2dict_map[old_dict.index(sym)] = new_dict.index(sym)
- return dict2dict_map
-
-
-def dict2dict(tokens, dict2dict_map):
- if tokens.device == torch.device('cpu'):
- tokens_tmp = tokens
- else:
- tokens_tmp = tokens.cpu()
- return tokens_tmp.map_(
- tokens_tmp,
- lambda _, val, dict2dict_map=dict2dict_map : dict2dict_map[float(val)]
- ).to(tokens.device)
-
-
-def reorder_tokens(tokens, lengths, eos):
- # reorder source tokens so they may be used as reference for P(S|T)
- return torch.cat((tokens.new([eos]), tokens[-lengths:-1], tokens[:-lengths]), 0)
-
-
-def reorder_all_tokens(tokens, lengths, eos):
- # used to reorder src tokens from [${outputTxt}`
-
- const titleLength = 150;
- let titleTxt = inputTxt;
- if(titleTxt.length > titleLength){
- titleTxt = titleTxt.slice(0, titleLength) + ' ...';
- }
-
- const shareBtnEl = gradioEl.querySelector('#share-btn');
- const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
- const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-
- if(!inputTxt || !outputTxt){
- return;
- };
-
- shareBtnEl.style.pointerEvents = 'none';
- shareIconEl.style.display = 'none';
- loadingIconEl.style.removeProperty('display');
-
- const descriptionMd = `### Question:
-${inputTxt}
-
-### Answer:
-
-${outputTxt}`;
-
- const params = {
- title: titleTxt,
- description: descriptionMd,
- };
-
- const paramsStr = Object.entries(params)
- .map(([key, value]) => `${encodeURIComponent(key)}=${encodeURIComponent(value)}`)
- .join('&');
-
- window.open(`https://huggingface.co/spaces/fisharp/starcoder-playground/discussions/new?${paramsStr}`, '_blank');
-
- shareBtnEl.style.removeProperty('pointer-events');
- shareIconEl.style.removeProperty('display');
- loadingIconEl.style.display = 'none';
-}
diff --git a/spaces/UjjwalVIT/Text_analysis_and_metadata_app/stanfordmodel/ner-gui.command b/spaces/UjjwalVIT/Text_analysis_and_metadata_app/stanfordmodel/ner-gui.command
deleted file mode 100644
index 427635a8622719c7a0be6f66d58880ccab73da4e..0000000000000000000000000000000000000000
--- a/spaces/UjjwalVIT/Text_analysis_and_metadata_app/stanfordmodel/ner-gui.command
+++ /dev/null
@@ -1,2 +0,0 @@
-#!/bin/sh
-java -mx500m -cp `dirname $0`/stanford-ner.jar:`dirname $0`/lib/* edu.stanford.nlp.ie.crf.NERGUI
diff --git a/spaces/Vegecken/sovits4dzl/modules/ddsp.py b/spaces/Vegecken/sovits4dzl/modules/ddsp.py
deleted file mode 100644
index b09ac5c5c19d165e75e1780877a857be8c104ed7..0000000000000000000000000000000000000000
--- a/spaces/Vegecken/sovits4dzl/modules/ddsp.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-import torch.fft as fft
-import numpy as np
-import librosa as li
-import math
-from scipy.signal import get_window
-
-
-def safe_log(x):
- return torch.log(x + 1e-7)
-
-
-@torch.no_grad()
-def mean_std_loudness(dataset):
- mean = 0
- std = 0
- n = 0
- for _, _, l in dataset:
- n += 1
- mean += (l.mean().item() - mean) / n
- std += (l.std().item() - std) / n
- return mean, std
-
-
-def multiscale_fft(signal, scales, overlap):
- stfts = []
- for s in scales:
- S = torch.stft(
- signal,
- s,
- int(s * (1 - overlap)),
- s,
- torch.hann_window(s).to(signal),
- True,
- normalized=True,
- return_complex=True,
- ).abs()
- stfts.append(S)
- return stfts
-
-
-def resample(x, factor: int):
- batch, frame, channel = x.shape
- x = x.permute(0, 2, 1).reshape(batch * channel, 1, frame)
-
- window = torch.hann_window(
- factor * 2,
- dtype=x.dtype,
- device=x.device,
- ).reshape(1, 1, -1)
- y = torch.zeros(x.shape[0], x.shape[1], factor * x.shape[2]).to(x)
- y[..., ::factor] = x
- y[..., -1:] = x[..., -1:]
- y = torch.nn.functional.pad(y, [factor, factor])
- y = torch.nn.functional.conv1d(y, window)[..., :-1]
-
- y = y.reshape(batch, channel, factor * frame).permute(0, 2, 1)
-
- return y
-
-
-def upsample(signal, factor):
- signal = signal.permute(0, 2, 1)
- signal = nn.functional.interpolate(signal, size=signal.shape[-1] * factor)
- return signal.permute(0, 2, 1)
-
-
-def remove_above_nyquist(amplitudes, pitch, sampling_rate):
- n_harm = amplitudes.shape[-1]
- pitches = pitch * torch.arange(1, n_harm + 1).to(pitch)
- aa = (pitches < sampling_rate / 2).float() + 1e-4
- return amplitudes * aa
-
-
-def scale_function(x):
- return 2 * torch.sigmoid(x) ** (math.log(10)) + 1e-7
-
-
-def extract_loudness(signal, sampling_rate, block_size, n_fft=2048):
- S = li.stft(
- signal,
- n_fft=n_fft,
- hop_length=block_size,
- win_length=n_fft,
- center=True,
- )
- S = np.log(abs(S) + 1e-7)
- f = li.fft_frequencies(sampling_rate, n_fft)
- a_weight = li.A_weighting(f)
-
- S = S + a_weight.reshape(-1, 1)
-
- S = np.mean(S, 0)[..., :-1]
-
- return S
-
-
-def extract_pitch(signal, sampling_rate, block_size):
- length = signal.shape[-1] // block_size
- f0 = crepe.predict(
- signal,
- sampling_rate,
- step_size=int(1000 * block_size / sampling_rate),
- verbose=1,
- center=True,
- viterbi=True,
- )
- f0 = f0[1].reshape(-1)[:-1]
-
- if f0.shape[-1] != length:
- f0 = np.interp(
- np.linspace(0, 1, length, endpoint=False),
- np.linspace(0, 1, f0.shape[-1], endpoint=False),
- f0,
- )
-
- return f0
-
-
-def mlp(in_size, hidden_size, n_layers):
- channels = [in_size] + (n_layers) * [hidden_size]
- net = []
- for i in range(n_layers):
- net.append(nn.Linear(channels[i], channels[i + 1]))
- net.append(nn.LayerNorm(channels[i + 1]))
- net.append(nn.LeakyReLU())
- return nn.Sequential(*net)
-
-
-def gru(n_input, hidden_size):
- return nn.GRU(n_input * hidden_size, hidden_size, batch_first=True)
-
-
-def harmonic_synth(pitch, amplitudes, sampling_rate):
- n_harmonic = amplitudes.shape[-1]
- omega = torch.cumsum(2 * math.pi * pitch / sampling_rate, 1)
- omegas = omega * torch.arange(1, n_harmonic + 1).to(omega)
- signal = (torch.sin(omegas) * amplitudes).sum(-1, keepdim=True)
- return signal
-
-
-def amp_to_impulse_response(amp, target_size):
- amp = torch.stack([amp, torch.zeros_like(amp)], -1)
- amp = torch.view_as_complex(amp)
- amp = fft.irfft(amp)
-
- filter_size = amp.shape[-1]
-
- amp = torch.roll(amp, filter_size // 2, -1)
- win = torch.hann_window(filter_size, dtype=amp.dtype, device=amp.device)
-
- amp = amp * win
-
- amp = nn.functional.pad(amp, (0, int(target_size) - int(filter_size)))
- amp = torch.roll(amp, -filter_size // 2, -1)
-
- return amp
-
-
-def fft_convolve(signal, kernel):
- signal = nn.functional.pad(signal, (0, signal.shape[-1]))
- kernel = nn.functional.pad(kernel, (kernel.shape[-1], 0))
-
- output = fft.irfft(fft.rfft(signal) * fft.rfft(kernel))
- output = output[..., output.shape[-1] // 2:]
-
- return output
-
-
-def init_kernels(win_len, win_inc, fft_len, win_type=None, invers=False):
- if win_type == 'None' or win_type is None:
- window = np.ones(win_len)
- else:
- window = get_window(win_type, win_len, fftbins=True) # **0.5
-
- N = fft_len
- fourier_basis = np.fft.rfft(np.eye(N))[:win_len]
- real_kernel = np.real(fourier_basis)
- imag_kernel = np.imag(fourier_basis)
- kernel = np.concatenate([real_kernel, imag_kernel], 1).T
-
- if invers:
- kernel = np.linalg.pinv(kernel).T
-
- kernel = kernel * window
- kernel = kernel[:, None, :]
- return torch.from_numpy(kernel.astype(np.float32)), torch.from_numpy(window[None, :, None].astype(np.float32))
-
diff --git a/spaces/VickyKira/NASAGPT/client/css/select.css b/spaces/VickyKira/NASAGPT/client/css/select.css
deleted file mode 100644
index 7ec0159206439deca5c26f32fd92d2b1459f0273..0000000000000000000000000000000000000000
--- a/spaces/VickyKira/NASAGPT/client/css/select.css
+++ /dev/null
@@ -1,35 +0,0 @@
-select {
- -webkit-border-radius: 8px;
- -moz-border-radius: 8px;
- border-radius: 8px;
-
- -webkit-backdrop-filter: blur(20px);
- backdrop-filter: blur(20px);
-
- cursor: pointer;
- background-color: var(--blur-bg);
- border: 1px solid var(--blur-border);
- color: var(--colour-3);
- display: block;
- position: relative;
- overflow: hidden;
- outline: none;
- padding: 8px 16px;
-
- appearance: none;
-}
-
-/* scrollbar */
-select.dropdown::-webkit-scrollbar {
- width: 4px;
- padding: 8px 0px;
-}
-
-select.dropdown::-webkit-scrollbar-track {
- background-color: #ffffff00;
-}
-
-select.dropdown::-webkit-scrollbar-thumb {
- background-color: #555555;
- border-radius: 10px;
-}
diff --git a/spaces/Vincentim27/Plant_Nutrition_Prediction_ARIA/prediction.py b/spaces/Vincentim27/Plant_Nutrition_Prediction_ARIA/prediction.py
deleted file mode 100644
index ea36e21012aa2df88297c224cfceadc4d0af8087..0000000000000000000000000000000000000000
--- a/spaces/Vincentim27/Plant_Nutrition_Prediction_ARIA/prediction.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import pickle
-import sklearn
-
-# Load Model
-with open('model_opt.pkl', 'rb') as file_1:
- model_opt = pickle.load(file_1)
-
-def run() :
- # Membuat Title
- st.markdown("Plant Nutrition Prediction
", unsafe_allow_html=True) - st.write('Page ini berisi model untuk prediksi nutrisi tanaman dengan 8 variable dan sample_type. Mohon persiapkan data terlebih dahulu sebelum melakukan prediksi') - - # Membuat Form - with st.form(key= 'form_plant'): - st.markdown('## **Variable**') - v1 = st.number_input('**V1**', min_value=227.28, max_value= 678.37, value=295.16 ,step=1.,format="%.2f") - v2 = st.number_input('**V2**', min_value=178.80, max_value= 422.81, value=204.18 ,step=1.,format="%.2f") - v3 = st.number_input('**V3**', min_value=348.93, max_value= 722.31, value=414.38 ,step=1.,format="%.2f") - v4 = st.number_input('**V4**', min_value=313.73, max_value= 558.50, value=370.74 ,step=1.,format="%.2f") - v5 = st.number_input('**V5**', min_value=373.33, max_value= 721.00, value=456.03 ,step=1.,format="%.2f") - v6 = st.number_input('**V6**', min_value=189.20, max_value= 415.37, value=226.06 ,step=1.,format="%.2f") - v7 = st.number_input('**V7**', min_value=586.26, max_value= 853.46, value=718.83 ,step=1.,format="%.2f") - v8 = st.number_input('**V8**', min_value=3725.66, max_value= 5086.37, value=4554.76 ,step=1.,format="%.2f") - st.markdown('---') - sample_type = st.selectbox('Sample Type',('lab 1','lab 2'),index=1) - submitted = st.form_submit_button('Predict') - - # Membuat Dataframe - data_inf = { - 'v1' : v1, - 'v2' : v2, - 'v3' : v3, - 'v4' : v4, - 'v5' : v5, - 'v6' : v6, - 'v7' : v7, - 'v8' : v8, - 'sample_type' : sample_type - } - data_inf = pd.DataFrame([data_inf]) - data_inf - - # Prediksi - if submitted : - # Predict using Random Forest Parameter Tuning - y_pred_inf = model_opt.predict(data_inf) - st.write('# **Plant Nutrition Prediction :** ',y_pred_inf[0]) - -if __name__ == '__main__': - run() \ No newline at end of file diff --git a/spaces/Widium/Style-Recreation/functions/system/memory.py b/spaces/Widium/Style-Recreation/functions/system/memory.py deleted file mode 100644 index 693a6c84790ba14ea3364a48e34073e51c8b3856..0000000000000000000000000000000000000000 --- a/spaces/Widium/Style-Recreation/functions/system/memory.py +++ /dev/null @@ -1,40 +0,0 @@ -# *************************************************************************** # -# # -# memory.py # -# # -# By: Widium
-
-# Bingo
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-
-
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://github.com/weaigc/bingo/blob/main/license)
-
-问题反馈请前往 https://github.com/weaigc/bingo/issues
-
-
-
diff --git a/spaces/YashGb/HelpMeTalk/README.md b/spaces/YashGb/HelpMeTalk/README.md
deleted file mode 100644
index e25f08f408c512051e759597fb33312745850054..0000000000000000000000000000000000000000
--- a/spaces/YashGb/HelpMeTalk/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: HelpMeTalk
-emoji: 🐠
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Yuliang/ECON/lib/pixielib/utils/util.py b/spaces/Yuliang/ECON/lib/pixielib/utils/util.py
deleted file mode 100644
index 5affbd8314fe175cd673ad07a64aa6964e581cbf..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/pixielib/utils/util.py
+++ /dev/null
@@ -1,697 +0,0 @@
-import os
-import pickle
-from collections import OrderedDict
-
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-# ---------------------------- process/generate vertices, normals, faces
-
-
-def generate_triangles(h, w, mask=None):
- """
- quad layout:
- 0 1 ... w-1
- w w+1
- .
- w*h
- """
- triangles = []
- margin = 0
- for x in range(margin, w - 1 - margin):
- for y in range(margin, h - 1 - margin):
- triangle0 = [y * w + x, y * w + x + 1, (y + 1) * w + x]
- triangle1 = [y * w + x + 1, (y + 1) * w + x + 1, (y + 1) * w + x]
- triangles.append(triangle0)
- triangles.append(triangle1)
- triangles = np.array(triangles)
- triangles = triangles[:, [0, 2, 1]]
- return triangles
-
-
-def face_vertices(vertices, faces):
- """
- borrowed from https://github.com/daniilidis-group/neural_renderer/blob/master/neural_renderer/vertices_to_faces.py
- :param vertices: [batch size, number of vertices, 3]
- :param faces: [batch size, number of faces, 3]
- :return: [batch size, number of faces, 3, 3]
- """
- assert vertices.ndimension() == 3
- assert faces.ndimension() == 3
- assert vertices.shape[0] == faces.shape[0]
- assert vertices.shape[2] == 3
- assert faces.shape[2] == 3
-
- bs, nv = vertices.shape[:2]
- bs, nf = faces.shape[:2]
- device = vertices.device
- faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None]
- vertices = vertices.reshape((bs * nv, 3))
- # pytorch only supports long and byte tensors for indexing
- return vertices[faces.long()]
-
-
-def vertex_normals(vertices, faces):
- """
- borrowed from https://github.com/daniilidis-group/neural_renderer/blob/master/neural_renderer/vertices_to_faces.py
- :param vertices: [batch size, number of vertices, 3]
- :param faces: [batch size, number of faces, 3]
- :return: [batch size, number of vertices, 3]
- """
- assert vertices.ndimension() == 3
- assert faces.ndimension() == 3
- assert vertices.shape[0] == faces.shape[0]
- assert vertices.shape[2] == 3
- assert faces.shape[2] == 3
- bs, nv = vertices.shape[:2]
- bs, nf = faces.shape[:2]
- device = vertices.device
- normals = torch.zeros(bs * nv, 3).to(device)
-
- faces = (
- faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None]
- ) # expanded faces
- vertices_faces = vertices.reshape((bs * nv, 3))[faces.long()]
-
- faces = faces.reshape(-1, 3)
- vertices_faces = vertices_faces.reshape(-1, 3, 3)
-
- normals.index_add_(
- 0,
- faces[:, 1].long(),
- torch.cross(
- vertices_faces[:, 2] - vertices_faces[:, 1],
- vertices_faces[:, 0] - vertices_faces[:, 1],
- ),
- )
- normals.index_add_(
- 0,
- faces[:, 2].long(),
- torch.cross(
- vertices_faces[:, 0] - vertices_faces[:, 2],
- vertices_faces[:, 1] - vertices_faces[:, 2],
- ),
- )
- normals.index_add_(
- 0,
- faces[:, 0].long(),
- torch.cross(
- vertices_faces[:, 1] - vertices_faces[:, 0],
- vertices_faces[:, 2] - vertices_faces[:, 0],
- ),
- )
-
- normals = F.normalize(normals, eps=1e-6, dim=1)
- normals = normals.reshape((bs, nv, 3))
- # pytorch only supports long and byte tensors for indexing
- return normals
-
-
-def batch_orth_proj(X, camera):
- """
- X is N x num_verts x 3
- """
- camera = camera.clone().view(-1, 1, 3)
- X_trans = X[:, :, :2] + camera[:, :, 1:]
- X_trans = torch.cat([X_trans, X[:, :, 2:]], 2)
- Xn = camera[:, :, 0:1] * X_trans
- return Xn
-
-
-# borrowed from https://github.com/vchoutas/expose
-DIM_FLIP = np.array([1, -1, -1], dtype=np.float32)
-DIM_FLIP_TENSOR = torch.tensor([1, -1, -1], dtype=torch.float32)
-
-
-def flip_pose(pose_vector, pose_format="rot-mat"):
- if pose_format == "aa":
- if torch.is_tensor(pose_vector):
- dim_flip = DIM_FLIP_TENSOR
- else:
- dim_flip = DIM_FLIP
- return (pose_vector.reshape(-1, 3) * dim_flip).reshape(-1)
- elif pose_format == "rot-mat":
- rot_mats = pose_vector.reshape(-1, 9).clone()
-
- rot_mats[:, [1, 2, 3, 6]] *= -1
- return rot_mats.view_as(pose_vector)
- else:
- raise ValueError(f"Unknown rotation format: {pose_format}")
-
-
-# -------------------------------------- image processing
-# ref: https://torchgeometry.readthedocs.io/en/latest/_modules/kornia/filters
-def gaussian(window_size, sigma):
- def gauss_fcn(x):
- return -((x - window_size // 2)**2) / float(2 * sigma**2)
-
- gauss = torch.stack([torch.exp(torch.tensor(gauss_fcn(x))) for x in range(window_size)])
- return gauss / gauss.sum()
-
-
-def get_gaussian_kernel(kernel_size: int, sigma: float):
- r"""Function that returns Gaussian filter coefficients.
-
- Args:
- kernel_size (int): filter size. It should be odd and positive.
- sigma (float): gaussian standard deviation.
-
- Returns:
- Tensor: 1D tensor with gaussian filter coefficients.
-
- Shape:
- - Output: :math:`(\text{kernel_size})`
-
- Examples::
-
- >>> kornia.image.get_gaussian_kernel(3, 2.5)
- tensor([0.3243, 0.3513, 0.3243])
-
- >>> kornia.image.get_gaussian_kernel(5, 1.5)
- tensor([0.1201, 0.2339, 0.2921, 0.2339, 0.1201])
- """
- if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or kernel_size <= 0:
- raise TypeError(
- "kernel_size must be an odd positive integer. "
- "Got {}".format(kernel_size)
- )
- window_1d = gaussian(kernel_size, sigma)
- return window_1d
-
-
-def get_gaussian_kernel2d(kernel_size, sigma):
- r"""Function that returns Gaussian filter matrix coefficients.
-
- Args:
- kernel_size (Tuple[int, int]): filter sizes in the x and y direction.
- Sizes should be odd and positive.
- sigma (Tuple[int, int]): gaussian standard deviation in the x and y
- direction.
-
- Returns:
- Tensor: 2D tensor with gaussian filter matrix coefficients.
-
- Shape:
- - Output: :math:`(\text{kernel_size}_x, \text{kernel_size}_y)`
-
- Examples::
-
- >>> kornia.image.get_gaussian_kernel2d((3, 3), (1.5, 1.5))
- tensor([[0.0947, 0.1183, 0.0947],
- [0.1183, 0.1478, 0.1183],
- [0.0947, 0.1183, 0.0947]])
-
- >>> kornia.image.get_gaussian_kernel2d((3, 5), (1.5, 1.5))
- tensor([[0.0370, 0.0720, 0.0899, 0.0720, 0.0370],
- [0.0462, 0.0899, 0.1123, 0.0899, 0.0462],
- [0.0370, 0.0720, 0.0899, 0.0720, 0.0370]])
- """
- if not isinstance(kernel_size, tuple) or len(kernel_size) != 2:
- raise TypeError("kernel_size must be a tuple of length two. Got {}".format(kernel_size))
- if not isinstance(sigma, tuple) or len(sigma) != 2:
- raise TypeError("sigma must be a tuple of length two. Got {}".format(sigma))
- ksize_x, ksize_y = kernel_size
- sigma_x, sigma_y = sigma
- kernel_x = get_gaussian_kernel(ksize_x, sigma_x)
- kernel_y = get_gaussian_kernel(ksize_y, sigma_y)
- kernel_2d = torch.matmul(kernel_x.unsqueeze(-1), kernel_y.unsqueeze(-1).t())
- return kernel_2d
-
-
-def gaussian_blur(x, kernel_size=(5, 5), sigma=(1.3, 1.3)):
- b, c, h, w = x.shape
- kernel = get_gaussian_kernel2d(kernel_size, sigma).to(x.device).to(x.dtype)
- kernel = kernel.repeat(c, 1, 1, 1)
- padding = [(k - 1) // 2 for k in kernel_size]
- return F.conv2d(x, kernel, padding=padding, stride=1, groups=c)
-
-
-def _compute_binary_kernel(window_size):
- r"""Creates a binary kernel to extract the patches. If the window size
- is HxW will create a (H*W)xHxW kernel.
- """
- window_range = window_size[0] * window_size[1]
- kernel: torch.Tensor = torch.zeros(window_range, window_range)
- for i in range(window_range):
- kernel[i, i] += 1.0
- return kernel.view(window_range, 1, window_size[0], window_size[1])
-
-
-def median_blur(x, kernel_size=(3, 3)):
- b, c, h, w = x.shape
- kernel = _compute_binary_kernel(kernel_size).to(x.device).to(x.dtype)
- kernel = kernel.repeat(c, 1, 1, 1)
- padding = [(k - 1) // 2 for k in kernel_size]
- features = F.conv2d(x, kernel, padding=padding, stride=1, groups=c)
- features = features.view(b, c, -1, h, w)
- median = torch.median(features, dim=2)[0]
- return median
-
-
-def get_laplacian_kernel2d(kernel_size: int):
- r"""Function that returns Gaussian filter matrix coefficients.
-
- Args:
- kernel_size (int): filter size should be odd.
-
- Returns:
- Tensor: 2D tensor with laplacian filter matrix coefficients.
-
- Shape:
- - Output: :math:`(\text{kernel_size}_x, \text{kernel_size}_y)`
-
- Examples::
-
- >>> kornia.image.get_laplacian_kernel2d(3)
- tensor([[ 1., 1., 1.],
- [ 1., -8., 1.],
- [ 1., 1., 1.]])
-
- >>> kornia.image.get_laplacian_kernel2d(5)
- tensor([[ 1., 1., 1., 1., 1.],
- [ 1., 1., 1., 1., 1.],
- [ 1., 1., -24., 1., 1.],
- [ 1., 1., 1., 1., 1.],
- [ 1., 1., 1., 1., 1.]])
-
- """
- if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or kernel_size <= 0:
- raise TypeError("ksize must be an odd positive integer. Got {}".format(kernel_size))
-
- kernel = torch.ones((kernel_size, kernel_size))
- mid = kernel_size // 2
- kernel[mid, mid] = 1 - kernel_size**2
- kernel_2d: torch.Tensor = kernel
- return kernel_2d
-
-
-def laplacian(x):
- # https://torchgeometry.readthedocs.io/en/latest/_modules/kornia/filters/laplacian.html
- b, c, h, w = x.shape
- kernel_size = 3
- kernel = get_laplacian_kernel2d(kernel_size).to(x.device).to(x.dtype)
- kernel = kernel.repeat(c, 1, 1, 1)
- padding = (kernel_size - 1) // 2
- return F.conv2d(x, kernel, padding=padding, stride=1, groups=c)
-
-
-# -------------------------------------- io
-
-
-def copy_state_dict(cur_state_dict, pre_state_dict, prefix="", load_name=None):
- def _get_params(key):
- key = prefix + key
- if key in pre_state_dict:
- return pre_state_dict[key]
- return None
-
- for k in cur_state_dict.keys():
- if load_name is not None:
- if load_name not in k:
- continue
- v = _get_params(k)
- try:
- if v is None:
- # print('parameter {} not found'.format(k))
- continue
- cur_state_dict[k].copy_(v)
- except:
- # print('copy param {} failed'.format(k))
- continue
-
-
-def dict2obj(d):
- # if isinstance(d, list):
- # d = [dict2obj(x) for x in d]
- if not isinstance(d, dict):
- return d
-
- class C(object):
- pass
-
- o = C()
- for k in d:
- o.__dict__[k] = dict2obj(d[k])
- return o
-
-
-# original saved file with DataParallel
-
-
-def remove_module(state_dict):
- # create new OrderedDict that does not contain `module.`
- new_state_dict = OrderedDict()
- for k, v in state_dict.items():
- name = k[7:] # remove `module.`
- new_state_dict[name] = v
- return new_state_dict
-
-
-def tensor2image(tensor):
- image = tensor.detach().cpu().numpy()
- image = image * 255.0
- image = np.maximum(np.minimum(image, 255), 0)
- image = image.transpose(1, 2, 0)[:, :, [2, 1, 0]]
- return image.astype(np.uint8).copy()
-
-
-def dict_tensor2npy(tensor_dict):
- npy_dict = {}
- for key in tensor_dict:
- npy_dict[key] = tensor_dict[key][0].cpu().numpy()
- return npy_dict
-
-
-def load_config(cfg_file):
- import yaml
-
- with open(cfg_file, "r") as f:
- cfg = yaml.load(f, Loader=yaml.FullLoader)
- return cfg
-
-
-def move_dict_to_device(dict, device, tensor2float=False):
- for k, v in dict.items():
- if isinstance(v, torch.Tensor):
- if tensor2float:
- dict[k] = v.float().to(device)
- else:
- dict[k] = v.to(device)
-
-
-def write_obj(
- obj_name,
- vertices,
- faces,
- colors=None,
- texture=None,
- uvcoords=None,
- uvfaces=None,
- inverse_face_order=False,
- normal_map=None,
-):
- """Save 3D face model with texture.
- borrowed from https://github.com/YadiraF/PRNet/blob/master/utils/write.py
- Args:
- obj_name: str
- vertices: shape = (nver, 3)
- colors: shape = (nver, 3)
- faces: shape = (ntri, 3)
- texture: shape = (uv_size, uv_size, 3)
- uvcoords: shape = (nver, 2) max value<=1
- """
- if obj_name.split(".")[-1] != "obj":
- obj_name = obj_name + ".obj"
- mtl_name = obj_name.replace(".obj", ".mtl")
- texture_name = obj_name.replace(".obj", ".png")
- material_name = "FaceTexture"
-
- faces = faces.copy()
- # mesh lab start with 1, python/c++ start from 0
- faces += 1
- if inverse_face_order:
- faces = faces[:, [2, 1, 0]]
- if uvfaces is not None:
- uvfaces = uvfaces[:, [2, 1, 0]]
-
- # write obj
- with open(obj_name, "w") as f:
- if texture is not None:
- f.write("mtllib %s\n\n" % os.path.basename(mtl_name))
-
- # write vertices
- if colors is None:
- for i in range(vertices.shape[0]):
- f.write("v {} {} {}\n".format(vertices[i, 0], vertices[i, 1], vertices[i, 2]))
- else:
- for i in range(vertices.shape[0]):
- f.write(
- "v {} {} {} {} {} {}\n".format(
- vertices[i, 0],
- vertices[i, 1],
- vertices[i, 2],
- colors[i, 0],
- colors[i, 1],
- colors[i, 2],
- )
- )
-
- # write uv coords
- if texture is None:
- for i in range(faces.shape[0]):
- f.write("f {} {} {}\n".format(faces[i, 0], faces[i, 1], faces[i, 2]))
- else:
- for i in range(uvcoords.shape[0]):
- f.write("vt {} {}\n".format(uvcoords[i, 0], uvcoords[i, 1]))
- f.write("usemtl %s\n" % material_name)
- # write f: ver ind/ uv ind
- uvfaces = uvfaces + 1
- for i in range(faces.shape[0]):
- f.write(
- "f {}/{} {}/{} {}/{}\n".format(
- faces[i, 0],
- uvfaces[i, 0],
- faces[i, 1],
- uvfaces[i, 1],
- faces[i, 2],
- uvfaces[i, 2],
- )
- )
- # write mtl
- with open(mtl_name, "w") as f:
- f.write("newmtl %s\n" % material_name)
- s = "map_Kd {}\n".format(os.path.basename(texture_name)) # map to image
- f.write(s)
-
- if normal_map is not None:
- if torch.is_tensor(normal_map):
- normal_map = normal_map.detach().cpu().numpy().squeeze()
-
- normal_map = np.transpose(normal_map, (1, 2, 0))
- name, _ = os.path.splitext(obj_name)
- normal_name = f"{name}_normals.png"
- f.write(f"disp {normal_name}")
-
- out_normal_map = normal_map / (
- np.linalg.norm(normal_map, axis=-1, keepdims=True) + 1e-9
- )
- out_normal_map = (out_normal_map + 1) * 0.5
-
- cv2.imwrite(normal_name, (out_normal_map * 255).astype(np.uint8)[:, :, ::-1])
-
- cv2.imwrite(texture_name, texture)
-
-
-def save_pkl(savepath, params, ind=0):
- out_data = {}
- for k, v in params.items():
- if torch.is_tensor(v):
- out_data[k] = v[ind].detach().cpu().numpy()
- else:
- out_data[k] = v
- with open(savepath, "wb") as f:
- pickle.dump(out_data, f, protocol=2)
-
-
-# load obj, similar to load_obj from pytorch3d
-
-
-def load_obj(obj_filename):
- """Ref: https://github.com/facebookresearch/pytorch3d/blob/25c065e9dafa90163e7cec873dbb324a637c68b7/pytorch3d/io/obj_io.py
- Load a mesh from a file-like object.
- """
- with open(obj_filename, "r") as f:
- lines = [line.strip() for line in f]
-
- verts, uvcoords = [], []
- faces, uv_faces = [], []
- # startswith expects each line to be a string. If the file is read in as
- # bytes then first decode to strings.
- if lines and isinstance(lines[0], bytes):
- lines = [el.decode("utf-8") for el in lines]
-
- for line in lines:
- tokens = line.strip().split()
- if line.startswith("v "): # Line is a vertex.
- vert = [float(x) for x in tokens[1:4]]
- if len(vert) != 3:
- msg = "Vertex %s does not have 3 values. Line: %s"
- raise ValueError(msg % (str(vert), str(line)))
- verts.append(vert)
- elif line.startswith("vt "): # Line is a texture.
- tx = [float(x) for x in tokens[1:3]]
- if len(tx) != 2:
- raise ValueError(
- "Texture %s does not have 2 values. Line: %s" % (str(tx), str(line))
- )
- uvcoords.append(tx)
- elif line.startswith("f "): # Line is a face.
- # Update face properties info.
- face = tokens[1:]
- face_list = [f.split("/") for f in face]
- for vert_props in face_list:
- # Vertex index.
- faces.append(int(vert_props[0]))
- if len(vert_props) > 1:
- if vert_props[1] != "":
- # Texture index is present e.g. f 4/1/1.
- uv_faces.append(int(vert_props[1]))
-
- verts = torch.tensor(verts, dtype=torch.float32)
- uvcoords = torch.tensor(uvcoords, dtype=torch.float32)
- faces = torch.tensor(faces, dtype=torch.long)
- faces = faces.reshape(-1, 3) - 1
- uv_faces = torch.tensor(uv_faces, dtype=torch.long)
- uv_faces = uv_faces.reshape(-1, 3) - 1
- return (verts, uvcoords, faces, uv_faces)
-
-
-# ---------------------------------- visualization
-def draw_rectangle(img, bbox, bbox_color=(255, 255, 255), thickness=3, is_opaque=False, alpha=0.5):
- """Draws the rectangle around the object
- borrowed from: https://bbox-visualizer.readthedocs.io/en/latest/_modules/bbox_visualizer/bbox_visualizer.html
- Parameters
- ----------
- img : ndarray
- the actual image
- bbox : list
- a list containing x_min, y_min, x_max and y_max of the rectangle positions
- bbox_color : tuple, optional
- the color of the box, by default (255,255,255)
- thickness : int, optional
- thickness of the outline of the box, by default 3
- is_opaque : bool, optional
- if False, draws a solid rectangular outline. Else, a filled rectangle which is semi transparent, by default False
- alpha : float, optional
- strength of the opacity, by default 0.5
-
- Returns
- -------
- ndarray
- the image with the bounding box drawn
- """
-
- output = img.copy()
- if not is_opaque:
- cv2.rectangle(output, (bbox[0], bbox[1]), (bbox[2], bbox[3]), bbox_color, thickness)
- else:
- overlay = img.copy()
-
- cv2.rectangle(overlay, (bbox[0], bbox[1]), (bbox[2], bbox[3]), bbox_color, -1)
- # cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output)
-
- return output
-
-
-def plot_bbox(image, bbox):
- """Draw bbox
- Args:
- image: the input image
- bbox: [left, top, right, bottom]
- """
- image = cv2.rectangle(
- image.copy(), (bbox[1], bbox[0]), (bbox[3], bbox[2]), [0, 255, 0], thickness=3
- )
- # image = draw_rectangle(image, bbox, bbox_color=[0,255,0])
- return image
-
-
-end_list = np.array([17, 22, 27, 42, 48, 31, 36, 68], dtype=np.int32) - 1
-
-
-def plot_kpts(image, kpts, color="r"):
- """Draw 68 key points
- Args:
- image: the input image
- kpt: (68, 3).
- """
- kpts = kpts.copy().astype(np.int32)
- if color == "r":
- c = (255, 0, 0)
- elif color == "g":
- c = (0, 255, 0)
- elif color == "b":
- c = (255, 0, 0)
- image = image.copy()
- kpts = kpts.copy()
-
- for i in range(kpts.shape[0]):
- st = kpts[i, :2]
- if kpts.shape[1] == 4:
- if kpts[i, 3] > 0.5:
- c = (0, 255, 0)
- else:
- c = (0, 0, 255)
- image = cv2.circle(image, (st[0], st[1]), 1, c, 2)
- if i in end_list:
- continue
- ed = kpts[i + 1, :2]
- image = cv2.line(image, (st[0], st[1]), (ed[0], ed[1]), (255, 255, 255), 1)
-
- return image
-
-
-def plot_verts(image, kpts, color="r"):
- """Draw 68 key points
- Args:
- image: the input image
- kpt: (68, 3).
- """
- kpts = kpts.copy().astype(np.int32)
- if color == "r":
- c = (255, 0, 0)
- elif color == "g":
- c = (0, 255, 0)
- elif color == "b":
- c = (0, 0, 255)
- elif color == "y":
- c = (0, 255, 255)
- image = image.copy()
-
- for i in range(kpts.shape[0]):
- st = kpts[i, :2]
- image = cv2.circle(image, (st[0], st[1]), 1, c, 5)
-
- return image
-
-
-def tensor_vis_landmarks(images, landmarks, gt_landmarks=None, color="g", isScale=True):
- # visualize landmarks
- vis_landmarks = []
- images = images.cpu().numpy()
- predicted_landmarks = landmarks.detach().cpu().numpy()
- if gt_landmarks is not None:
- gt_landmarks_np = gt_landmarks.detach().cpu().numpy()
- for i in range(images.shape[0]):
- image = images[i]
- image = image.transpose(1, 2, 0)[:, :, [2, 1, 0]].copy()
- image = image * 255
- if isScale:
- predicted_landmark = (predicted_landmarks[i] * image.shape[0] / 2 + image.shape[0] / 2)
- else:
- predicted_landmark = predicted_landmarks[i]
- if predicted_landmark.shape[0] == 68:
- image_landmarks = plot_kpts(image, predicted_landmark, color)
- if gt_landmarks is not None:
- image_landmarks = plot_verts(
- image_landmarks,
- gt_landmarks_np[i] * image.shape[0] / 2 + image.shape[0] / 2,
- "r",
- )
- else:
- image_landmarks = plot_verts(image, predicted_landmark, color)
- if gt_landmarks is not None:
- image_landmarks = plot_verts(
- image_landmarks,
- gt_landmarks_np[i] * image.shape[0] / 2 + image.shape[0] / 2,
- "r",
- )
- vis_landmarks.append(image_landmarks)
-
- vis_landmarks = np.stack(vis_landmarks)
- vis_landmarks = (
- torch.from_numpy(vis_landmarks[:, :, :, [2, 1, 0]].transpose(0, 3, 1, 2)) / 255.0
- ) # , dtype=torch.float32)
- return vis_landmarks
diff --git a/spaces/Zaixi/ICLR_FLAG/utils/mol_tree.py b/spaces/Zaixi/ICLR_FLAG/utils/mol_tree.py
deleted file mode 100644
index 1dc1993acb7e84cea0683bef656cb3a9964687e6..0000000000000000000000000000000000000000
--- a/spaces/Zaixi/ICLR_FLAG/utils/mol_tree.py
+++ /dev/null
@@ -1,220 +0,0 @@
-import sys
-sys.path.append("..")
-import rdkit
-import rdkit.Chem as Chem
-import copy
-import pickle
-from tqdm.auto import tqdm
-import numpy as np
-import torch
-import random
-from .chemutils import get_clique_mol, tree_decomp, get_mol, get_smiles, set_atommap, get_clique_mol_simple
-from collections import defaultdict
-
-
-def get_slots(smiles):
- mol = Chem.MolFromSmiles(smiles, sanitize=False)
- return [(atom.GetSymbol(), atom.GetFormalCharge(), atom.GetTotalNumHs()) for atom in mol.GetAtoms()]
-
-
-class Vocab(object):
-
- def __init__(self, smiles_list):
- self.vocab = smiles_list
- self.vmap = {x: i for i, x in enumerate(self.vocab)}
- #self.slots = [get_slots(smiles) for smiles in self.vocab]
-
- def get_index(self, smiles):
- if smiles in self.vmap.keys():
- return self.vmap[smiles]
- else:
- return 0
-
- def get_smiles(self, idx):
- return self.vocab[idx]
-
- def get_slots(self, idx):
- return copy.deepcopy(self.slots[idx])
-
- def size(self):
- return len(self.vocab)
-
-
-class MolTreeNode(object):
-
- def __init__(self, mol, cmol, clique):
- self.smiles = Chem.MolToSmiles(cmol, canonical=True)
- self.mol = cmol
- self.clique = [x for x in clique] # copy
-
- self.neighbors = []
- self.rotatable = False
- if len(self.clique) == 2:
- if mol.GetAtomWithIdx(self.clique[0]).GetDegree() >= 2 and mol.GetAtomWithIdx(self.clique[1]).GetDegree() >= 2:
- self.rotatable = True
- # should restrict to single bond, but double bond is ok
-
- def add_neighbor(self, nei_node):
- self.neighbors.append(nei_node)
-
- def recover(self, original_mol):
- clique = []
- clique.extend(self.clique)
- if not self.is_leaf:
- for cidx in self.clique:
- original_mol.GetAtomWithIdx(cidx).SetAtomMapNum(self.nid)
-
- for nei_node in self.neighbors:
- clique.extend(nei_node.clique)
- if nei_node.is_leaf: # Leaf node, no need to mark
- continue
- for cidx in nei_node.clique:
- # allow singleton node override the atom mapping
- if cidx not in self.clique or len(nei_node.clique) == 1:
- atom = original_mol.GetAtomWithIdx(cidx)
- atom.SetAtomMapNum(nei_node.nid)
-
- clique = list(set(clique))
- label_mol = get_clique_mol_simple(original_mol, clique)
- self.label = Chem.MolToSmiles(Chem.MolFromSmiles(get_smiles(label_mol)))
- self.label_mol = get_mol(self.label)
-
- for cidx in clique:
- original_mol.GetAtomWithIdx(cidx).SetAtomMapNum(0)
-
- return self.label
-
- def assemble(self):
- # neighbors = [nei for nei in self.neighbors if nei.mol.GetNumAtoms() > 1]
- neighbors = sorted(self.neighbors, key=lambda x: x.mol.GetNumAtoms(), reverse=True)
- # singletons = [nei for nei in self.neighbors if nei.mol.GetNumAtoms() == 1]
- # neighbors = singletons + neighbors
-
- cands = enum_assemble(self, neighbors)
- if len(cands) > 0:
- self.cands, self.cand_mols, _ = zip(*cands)
- self.cands = list(self.cands)
- self.cand_mols = list(self.cand_mols)
- else:
- self.cands = []
- self.cand_mols = []
-
-
-class MolTree(object):
- def __init__(self, mol):
- self.smiles = Chem.MolToSmiles(mol)
- self.mol = mol
- self.num_rotatable_bond = 0
- '''
- # use reference_vocab and threshold to control the size of vocab
- reference_vocab = np.load('./utils/reference.npy', allow_pickle=True).item()
- reference = defaultdict(int)
- for k, v in reference_vocab.items():
- reference[k] = v'''
-
- # use vanilla tree decomposition for simplicity
- cliques, edges = tree_decomp(self.mol, reference_vocab=None)
- self.nodes = []
- root = 0
- for i, c in enumerate(cliques):
- cmol = get_clique_mol_simple(self.mol, c)
- node = MolTreeNode(self.mol, cmol, c)
- self.nodes.append(node)
- if min(c) == 0:
- root = i
-
- for node in self.nodes:
- if node.rotatable:
- self.num_rotatable_bond += 1
-
- for x, y in edges:
- self.nodes[x].add_neighbor(self.nodes[y])
- self.nodes[y].add_neighbor(self.nodes[x])
-
- if root > 0:
- self.nodes[0], self.nodes[root] = self.nodes[root], self.nodes[0]
-
- for i, node in enumerate(self.nodes):
- node.nid = i + 1
- '''
- if len(node.neighbors) > 1: # Leaf node mol is not marked
- set_atommap(node.mol, node.nid)
- node.is_leaf = (len(node.neighbors) == 1)'''
-
- def size(self):
- return len(self.nodes)
-
- def recover(self):
- for node in self.nodes:
- node.recover(self.mol)
-
- def assemble(self):
- for node in self.nodes:
- node.assemble()
-
-
-if __name__ == "__main__":
- seed = 2023
- torch.manual_seed(seed)
- np.random.seed(seed)
- random.seed(seed)
-
- vocab = {}
- cnt = 0
- rot = 0
- '''
- index_path = './data/crossdocked_pocket10/index.pkl'
- with open(index_path, 'rb') as f:
- index = pickle.load(f)
- for i, (pocket_fn, ligand_fn, _, rmsd_str) in enumerate(tqdm(index)):
- if pocket_fn is None: continue
- try:
- path = './data/crossdocked_pocket10/' + ligand_fn
- mol = Chem.MolFromMolFile(path, sanitize=False)
- moltree = MolTree(mol)
- cnt += 1
- if moltree.num_rotatable_bond > 0:
- rot += 1
- except:
- continue
-
- for c in moltree.nodes:
- smile_cluster = c.smiles
- if smile_cluster not in vocab:
- vocab[smile_cluster] = 1
- else:
- vocab[smile_cluster] += 1
- '''
-
- index = torch.load('/n/holyscratch01/mzitnik_lab/zaixizhang/pdbbind_pocket10/index.pt')
- for i, pdbid in enumerate(tqdm(index)):
- if pdbid is None: continue
- try:
- path = '/n/holyscratch01/mzitnik_lab/zaixizhang/pdbbind_pocket10/'
- ligand_path = os.path.join(path, os.path.join(item, item+'_ligand.sdf'))
- mol = Chem.MolFromMolFile(ligand_path, sanitize=False)
- moltree = MolTree(mol)
- cnt += 1
- if moltree.num_rotatable_bond > 0:
- rot += 1
- except:
- continue
-
- for c in moltree.nodes:
- smile_cluster = c.smiles
- if smile_cluster not in vocab:
- vocab[smile_cluster] = 1
- else:
- vocab[smile_cluster] += 1
-
- vocab = dict(sorted(vocab.items(), key=lambda kv: (kv[1], kv[0]), reverse=True))
- filename = open('./vocab.txt', 'w')
- for k, v in vocab.items():
- filename.write(k + ':' + str(v))
- filename.write('\n')
- filename.close()
-
- # number of molecules and vocab
- print('Size of the motif vocab:', len(vocab))
- print('Total number of molecules', cnt)
- print('Percent of molecules with rotatable bonds:', rot / cnt)
diff --git a/spaces/ZeroTwo3/WavJourney/scripts/start_ui.sh b/spaces/ZeroTwo3/WavJourney/scripts/start_ui.sh
deleted file mode 100644
index c869311af12b7619cf87327f5b1f05951d0b078e..0000000000000000000000000000000000000000
--- a/spaces/ZeroTwo3/WavJourney/scripts/start_ui.sh
+++ /dev/null
@@ -1 +0,0 @@
-conda run --live-stream -n WavJourney python -u ui_client.py 2>&1 | stdbuf -oL tee services_logs/wavejourney.out
\ No newline at end of file
diff --git a/spaces/aaronb/DragGAN/stylegan2/lpips/pretrained_networks.py b/spaces/aaronb/DragGAN/stylegan2/lpips/pretrained_networks.py
deleted file mode 100644
index 077a24419364fdb5ae2f697f73e28615adae75a7..0000000000000000000000000000000000000000
--- a/spaces/aaronb/DragGAN/stylegan2/lpips/pretrained_networks.py
+++ /dev/null
@@ -1,181 +0,0 @@
-from collections import namedtuple
-import torch
-from torchvision import models as tv
-from IPython import embed
-
-class squeezenet(torch.nn.Module):
- def __init__(self, requires_grad=False, pretrained=True):
- super(squeezenet, self).__init__()
- pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features
- self.slice1 = torch.nn.Sequential()
- self.slice2 = torch.nn.Sequential()
- self.slice3 = torch.nn.Sequential()
- self.slice4 = torch.nn.Sequential()
- self.slice5 = torch.nn.Sequential()
- self.slice6 = torch.nn.Sequential()
- self.slice7 = torch.nn.Sequential()
- self.N_slices = 7
- for x in range(2):
- self.slice1.add_module(str(x), pretrained_features[x])
- for x in range(2,5):
- self.slice2.add_module(str(x), pretrained_features[x])
- for x in range(5, 8):
- self.slice3.add_module(str(x), pretrained_features[x])
- for x in range(8, 10):
- self.slice4.add_module(str(x), pretrained_features[x])
- for x in range(10, 11):
- self.slice5.add_module(str(x), pretrained_features[x])
- for x in range(11, 12):
- self.slice6.add_module(str(x), pretrained_features[x])
- for x in range(12, 13):
- self.slice7.add_module(str(x), pretrained_features[x])
- if not requires_grad:
- for param in self.parameters():
- param.requires_grad = False
-
- def forward(self, X):
- h = self.slice1(X)
- h_relu1 = h
- h = self.slice2(h)
- h_relu2 = h
- h = self.slice3(h)
- h_relu3 = h
- h = self.slice4(h)
- h_relu4 = h
- h = self.slice5(h)
- h_relu5 = h
- h = self.slice6(h)
- h_relu6 = h
- h = self.slice7(h)
- h_relu7 = h
- vgg_outputs = namedtuple("SqueezeOutputs", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7'])
- out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7)
-
- return out
-
-
-class alexnet(torch.nn.Module):
- def __init__(self, requires_grad=False, pretrained=True):
- super(alexnet, self).__init__()
- alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features
- self.slice1 = torch.nn.Sequential()
- self.slice2 = torch.nn.Sequential()
- self.slice3 = torch.nn.Sequential()
- self.slice4 = torch.nn.Sequential()
- self.slice5 = torch.nn.Sequential()
- self.N_slices = 5
- for x in range(2):
- self.slice1.add_module(str(x), alexnet_pretrained_features[x])
- for x in range(2, 5):
- self.slice2.add_module(str(x), alexnet_pretrained_features[x])
- for x in range(5, 8):
- self.slice3.add_module(str(x), alexnet_pretrained_features[x])
- for x in range(8, 10):
- self.slice4.add_module(str(x), alexnet_pretrained_features[x])
- for x in range(10, 12):
- self.slice5.add_module(str(x), alexnet_pretrained_features[x])
- if not requires_grad:
- for param in self.parameters():
- param.requires_grad = False
-
- def forward(self, X):
- h = self.slice1(X)
- h_relu1 = h
- h = self.slice2(h)
- h_relu2 = h
- h = self.slice3(h)
- h_relu3 = h
- h = self.slice4(h)
- h_relu4 = h
- h = self.slice5(h)
- h_relu5 = h
- alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])
- out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
-
- return out
-
-class vgg16(torch.nn.Module):
- def __init__(self, requires_grad=False, pretrained=True):
- super(vgg16, self).__init__()
- vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features
- self.slice1 = torch.nn.Sequential()
- self.slice2 = torch.nn.Sequential()
- self.slice3 = torch.nn.Sequential()
- self.slice4 = torch.nn.Sequential()
- self.slice5 = torch.nn.Sequential()
- self.N_slices = 5
- for x in range(4):
- self.slice1.add_module(str(x), vgg_pretrained_features[x])
- for x in range(4, 9):
- self.slice2.add_module(str(x), vgg_pretrained_features[x])
- for x in range(9, 16):
- self.slice3.add_module(str(x), vgg_pretrained_features[x])
- for x in range(16, 23):
- self.slice4.add_module(str(x), vgg_pretrained_features[x])
- for x in range(23, 30):
- self.slice5.add_module(str(x), vgg_pretrained_features[x])
- if not requires_grad:
- for param in self.parameters():
- param.requires_grad = False
-
- def forward(self, X):
- h = self.slice1(X)
- h_relu1_2 = h
- h = self.slice2(h)
- h_relu2_2 = h
- h = self.slice3(h)
- h_relu3_3 = h
- h = self.slice4(h)
- h_relu4_3 = h
- h = self.slice5(h)
- h_relu5_3 = h
- vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
- out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
-
- return out
-
-
-
-class resnet(torch.nn.Module):
- def __init__(self, requires_grad=False, pretrained=True, num=18):
- super(resnet, self).__init__()
- if(num==18):
- self.net = tv.resnet18(pretrained=pretrained)
- elif(num==34):
- self.net = tv.resnet34(pretrained=pretrained)
- elif(num==50):
- self.net = tv.resnet50(pretrained=pretrained)
- elif(num==101):
- self.net = tv.resnet101(pretrained=pretrained)
- elif(num==152):
- self.net = tv.resnet152(pretrained=pretrained)
- self.N_slices = 5
-
- self.conv1 = self.net.conv1
- self.bn1 = self.net.bn1
- self.relu = self.net.relu
- self.maxpool = self.net.maxpool
- self.layer1 = self.net.layer1
- self.layer2 = self.net.layer2
- self.layer3 = self.net.layer3
- self.layer4 = self.net.layer4
-
- def forward(self, X):
- h = self.conv1(X)
- h = self.bn1(h)
- h = self.relu(h)
- h_relu1 = h
- h = self.maxpool(h)
- h = self.layer1(h)
- h_conv2 = h
- h = self.layer2(h)
- h_conv3 = h
- h = self.layer3(h)
- h_conv4 = h
- h = self.layer4(h)
- h_conv5 = h
-
- outputs = namedtuple("Outputs", ['relu1','conv2','conv3','conv4','conv5'])
- out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)
-
- return out
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/datasets/cityscapes.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/datasets/cityscapes.py
deleted file mode 100644
index f21867c63e1835f6fceb61f066e802fd8fd2a735..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/datasets/cityscapes.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# dataset settings
-dataset_type = 'CityscapesDataset'
-data_root = 'data/cityscapes/'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-crop_size = (512, 1024)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations'),
- dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PhotoMetricDistortion'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_semantic_seg']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(2048, 1024),
- # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- train=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='leftImg8bit/train',
- ann_dir='gtFine/train',
- pipeline=train_pipeline),
- val=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='leftImg8bit/val',
- ann_dir='gtFine/val',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='leftImg8bit/val',
- ann_dir='gtFine/val',
- pipeline=test_pipeline))
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py
deleted file mode 100644
index 615aa3ff703942b6c22b2d6e9642504dd3e41ebd..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
- type='CascadeEncoderDecoder',
- num_stages=2,
- pretrained='open-mmlab://resnet50_v1c',
- backbone=dict(
- type='ResNetV1c',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- dilations=(1, 1, 2, 4),
- strides=(1, 2, 1, 1),
- norm_cfg=norm_cfg,
- norm_eval=False,
- style='pytorch',
- contract_dilation=True),
- decode_head=[
- dict(
- type='FCNHead',
- in_channels=1024,
- in_index=2,
- channels=256,
- num_convs=1,
- concat_input=False,
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
- dict(
- type='OCRHead',
- in_channels=2048,
- in_index=3,
- channels=512,
- ocr_channels=256,
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
- ],
- # model training and testing settings
- train_cfg=dict(),
- test_cfg=dict(mode='whole'))
diff --git a/spaces/abidlabs/GFPGAN/app.py b/spaces/abidlabs/GFPGAN/app.py
deleted file mode 100644
index ba695b856d830057ad5983841138d5fc5e0faa1b..0000000000000000000000000000000000000000
--- a/spaces/abidlabs/GFPGAN/app.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import os
-
-import cv2
-import gradio as gr
-import torch
-from basicsr.archs.srvgg_arch import SRVGGNetCompact
-from gfpgan.utils import GFPGANer
-from realesrgan.utils import RealESRGANer
-
-os.system("pip freeze")
-os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
-os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
-os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
-
-torch.hub.download_url_to_file(
- 'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
- 'lincoln.jpg')
-torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
- 'AI-generate.jpg')
-torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
- 'Blake_Lively.jpg')
-torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
- '10045.png')
-
-# background enhancer with RealESRGAN
-model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-model_path = 'realesr-general-x4v3.pth'
-half = True if torch.cuda.is_available() else False
-upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
-
-# Use GFPGAN for face enhancement
-face_enhancer_v3 = GFPGANer(
- model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-face_enhancer_v2 = GFPGANer(
- model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-os.makedirs('output', exist_ok=True)
-
-
-def inference(img, scale, version='v1.3'):
- print(img, version, scale)
- try:
- img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
- if len(img.shape) == 3 and img.shape[2] == 4:
- img_mode = 'RGBA'
- else:
- img_mode = None
-
- h, w = img.shape[0:2]
- if h < 300:
- img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
-
- if version == 'v1.2':
- face_enhancer = face_enhancer_v2
- else:
- face_enhancer = face_enhancer_v3
- try:
- _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
- except RuntimeError as error:
- print('Error', error)
- else:
- extension = 'png'
-
- try:
- if scale != 2:
- interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
- h, w = img.shape[0:2]
- output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
- except Exception as error:
- print('wrong scale input.', error)
- if img_mode == 'RGBA': # RGBA images should be saved in png format
- extension = 'png'
- else:
- extension = 'jpg'
- save_path = f'output/out.{extension}'
- cv2.imwrite(save_path, output)
-
- output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
- return output
- except Exception as error:
- print('global exception', error)
- return None
-
-
-title = "GFPGAN"
-
-gr.Interface(
- inference, [
- gr.inputs.Image(type="filepath", label="Input"),
- gr.inputs.Number(label="Rescaling factor", default=2)
- ], [
- gr.outputs.Image(type="numpy", label="Output (The whole image)"),
- ],
- title=title,
- examples=[['AI-generate.jpg', 2], ['lincoln.jpg', 2], ['Blake_Lively.jpg', 2],
- ['10045.png', 2]]).launch()
diff --git a/spaces/adirik/stylemc-demo/encoder4editing/training/coach.py b/spaces/adirik/stylemc-demo/encoder4editing/training/coach.py
deleted file mode 100644
index 10b22a6830673752dcf922cee7914c39069a4333..0000000000000000000000000000000000000000
--- a/spaces/adirik/stylemc-demo/encoder4editing/training/coach.py
+++ /dev/null
@@ -1,439 +0,0 @@
-import os
-import random
-import matplotlib
-import matplotlib.pyplot as plt
-
-matplotlib.use('Agg')
-
-import torch
-from torch import nn, autograd
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.nn.functional as F
-
-from utils import common, train_utils
-from criteria import id_loss, moco_loss
-from configs import data_configs
-from datasets.images_dataset import ImagesDataset
-from criteria.lpips.lpips import LPIPS
-from models.psp import pSp
-from models.latent_codes_pool import LatentCodesPool
-from models.discriminator import LatentCodesDiscriminator
-from models.encoders.psp_encoders import ProgressiveStage
-from training.ranger import Ranger
-
-random.seed(0)
-torch.manual_seed(0)
-
-
-class Coach:
- def __init__(self, opts, prev_train_checkpoint=None):
- self.opts = opts
-
- self.global_step = 0
-
- self.device = 'cuda:0'
- self.opts.device = self.device
- # Initialize network
- self.net = pSp(self.opts).to(self.device)
-
- # Initialize loss
- if self.opts.lpips_lambda > 0:
- self.lpips_loss = LPIPS(net_type=self.opts.lpips_type).to(self.device).eval()
- if self.opts.id_lambda > 0:
- if 'ffhq' in self.opts.dataset_type or 'celeb' in self.opts.dataset_type:
- self.id_loss = id_loss.IDLoss().to(self.device).eval()
- else:
- self.id_loss = moco_loss.MocoLoss(opts).to(self.device).eval()
- self.mse_loss = nn.MSELoss().to(self.device).eval()
-
- # Initialize optimizer
- self.optimizer = self.configure_optimizers()
-
- # Initialize discriminator
- if self.opts.w_discriminator_lambda > 0:
- self.discriminator = LatentCodesDiscriminator(512, 4).to(self.device)
- self.discriminator_optimizer = torch.optim.Adam(list(self.discriminator.parameters()),
- lr=opts.w_discriminator_lr)
- self.real_w_pool = LatentCodesPool(self.opts.w_pool_size)
- self.fake_w_pool = LatentCodesPool(self.opts.w_pool_size)
-
- # Initialize dataset
- self.train_dataset, self.test_dataset = self.configure_datasets()
- self.train_dataloader = DataLoader(self.train_dataset,
- batch_size=self.opts.batch_size,
- shuffle=True,
- num_workers=int(self.opts.workers),
- drop_last=True)
- self.test_dataloader = DataLoader(self.test_dataset,
- batch_size=self.opts.test_batch_size,
- shuffle=False,
- num_workers=int(self.opts.test_workers),
- drop_last=True)
-
- # Initialize logger
- log_dir = os.path.join(opts.exp_dir, 'logs')
- os.makedirs(log_dir, exist_ok=True)
- self.logger = SummaryWriter(log_dir=log_dir)
-
- # Initialize checkpoint dir
- self.checkpoint_dir = os.path.join(opts.exp_dir, 'checkpoints')
- os.makedirs(self.checkpoint_dir, exist_ok=True)
- self.best_val_loss = None
- if self.opts.save_interval is None:
- self.opts.save_interval = self.opts.max_steps
-
- if prev_train_checkpoint is not None:
- self.load_from_train_checkpoint(prev_train_checkpoint)
- prev_train_checkpoint = None
-
- def load_from_train_checkpoint(self, ckpt):
- print('Loading previous training data...')
- self.global_step = ckpt['global_step'] + 1
- self.best_val_loss = ckpt['best_val_loss']
- self.net.load_state_dict(ckpt['state_dict'])
-
- if self.opts.keep_optimizer:
- self.optimizer.load_state_dict(ckpt['optimizer'])
- if self.opts.w_discriminator_lambda > 0:
- self.discriminator.load_state_dict(ckpt['discriminator_state_dict'])
- self.discriminator_optimizer.load_state_dict(ckpt['discriminator_optimizer_state_dict'])
- if self.opts.progressive_steps:
- self.check_for_progressive_training_update(is_resume_from_ckpt=True)
- print(f'Resuming training from step {self.global_step}')
-
- def train(self):
- self.net.train()
- if self.opts.progressive_steps:
- self.check_for_progressive_training_update()
- while self.global_step < self.opts.max_steps:
- for batch_idx, batch in enumerate(self.train_dataloader):
- loss_dict = {}
- if self.is_training_discriminator():
- loss_dict = self.train_discriminator(batch)
- x, y, y_hat, latent = self.forward(batch)
- loss, encoder_loss_dict, id_logs = self.calc_loss(x, y, y_hat, latent)
- loss_dict = {**loss_dict, **encoder_loss_dict}
- self.optimizer.zero_grad()
- loss.backward()
- self.optimizer.step()
-
- # Logging related
- if self.global_step % self.opts.image_interval == 0 or (
- self.global_step < 1000 and self.global_step % 25 == 0):
- self.parse_and_log_images(id_logs, x, y, y_hat, title='images/train/faces')
- if self.global_step % self.opts.board_interval == 0:
- self.print_metrics(loss_dict, prefix='train')
- self.log_metrics(loss_dict, prefix='train')
-
- # Validation related
- val_loss_dict = None
- if self.global_step % self.opts.val_interval == 0 or self.global_step == self.opts.max_steps:
- val_loss_dict = self.validate()
- if val_loss_dict and (self.best_val_loss is None or val_loss_dict['loss'] < self.best_val_loss):
- self.best_val_loss = val_loss_dict['loss']
- self.checkpoint_me(val_loss_dict, is_best=True)
-
- if self.global_step % self.opts.save_interval == 0 or self.global_step == self.opts.max_steps:
- if val_loss_dict is not None:
- self.checkpoint_me(val_loss_dict, is_best=False)
- else:
- self.checkpoint_me(loss_dict, is_best=False)
-
- if self.global_step == self.opts.max_steps:
- print('OMG, finished training!')
- break
-
- self.global_step += 1
- if self.opts.progressive_steps:
- self.check_for_progressive_training_update()
-
- def check_for_progressive_training_update(self, is_resume_from_ckpt=False):
- for i in range(len(self.opts.progressive_steps)):
- if is_resume_from_ckpt and self.global_step >= self.opts.progressive_steps[i]: # Case checkpoint
- self.net.encoder.set_progressive_stage(ProgressiveStage(i))
- if self.global_step == self.opts.progressive_steps[i]: # Case training reached progressive step
- self.net.encoder.set_progressive_stage(ProgressiveStage(i))
-
- def validate(self):
- self.net.eval()
- agg_loss_dict = []
- for batch_idx, batch in enumerate(self.test_dataloader):
- cur_loss_dict = {}
- if self.is_training_discriminator():
- cur_loss_dict = self.validate_discriminator(batch)
- with torch.no_grad():
- x, y, y_hat, latent = self.forward(batch)
- loss, cur_encoder_loss_dict, id_logs = self.calc_loss(x, y, y_hat, latent)
- cur_loss_dict = {**cur_loss_dict, **cur_encoder_loss_dict}
- agg_loss_dict.append(cur_loss_dict)
-
- # Logging related
- self.parse_and_log_images(id_logs, x, y, y_hat,
- title='images/test/faces',
- subscript='{:04d}'.format(batch_idx))
-
- # For first step just do sanity test on small amount of data
- if self.global_step == 0 and batch_idx >= 4:
- self.net.train()
- return None # Do not log, inaccurate in first batch
-
- loss_dict = train_utils.aggregate_loss_dict(agg_loss_dict)
- self.log_metrics(loss_dict, prefix='test')
- self.print_metrics(loss_dict, prefix='test')
-
- self.net.train()
- return loss_dict
-
- def checkpoint_me(self, loss_dict, is_best):
- save_name = 'best_model.pt' if is_best else 'iteration_{}.pt'.format(self.global_step)
- save_dict = self.__get_save_dict()
- checkpoint_path = os.path.join(self.checkpoint_dir, save_name)
- torch.save(save_dict, checkpoint_path)
- with open(os.path.join(self.checkpoint_dir, 'timestamp.txt'), 'a') as f:
- if is_best:
- f.write(
- '**Best**: Step - {}, Loss - {:.3f} \n{}\n'.format(self.global_step, self.best_val_loss, loss_dict))
- else:
- f.write('Step - {}, \n{}\n'.format(self.global_step, loss_dict))
-
- def configure_optimizers(self):
- params = list(self.net.encoder.parameters())
- if self.opts.train_decoder:
- params += list(self.net.decoder.parameters())
- else:
- self.requires_grad(self.net.decoder, False)
- if self.opts.optim_name == 'adam':
- optimizer = torch.optim.Adam(params, lr=self.opts.learning_rate)
- else:
- optimizer = Ranger(params, lr=self.opts.learning_rate)
- return optimizer
-
- def configure_datasets(self):
- if self.opts.dataset_type not in data_configs.DATASETS.keys():
- Exception('{} is not a valid dataset_type'.format(self.opts.dataset_type))
- print('Loading dataset for {}'.format(self.opts.dataset_type))
- dataset_args = data_configs.DATASETS[self.opts.dataset_type]
- transforms_dict = dataset_args['transforms'](self.opts).get_transforms()
- train_dataset = ImagesDataset(source_root=dataset_args['train_source_root'],
- target_root=dataset_args['train_target_root'],
- source_transform=transforms_dict['transform_source'],
- target_transform=transforms_dict['transform_gt_train'],
- opts=self.opts)
- test_dataset = ImagesDataset(source_root=dataset_args['test_source_root'],
- target_root=dataset_args['test_target_root'],
- source_transform=transforms_dict['transform_source'],
- target_transform=transforms_dict['transform_test'],
- opts=self.opts)
- print("Number of training samples: {}".format(len(train_dataset)))
- print("Number of test samples: {}".format(len(test_dataset)))
- return train_dataset, test_dataset
-
- def calc_loss(self, x, y, y_hat, latent):
- loss_dict = {}
- loss = 0.0
- id_logs = None
- if self.is_training_discriminator(): # Adversarial loss
- loss_disc = 0.
- dims_to_discriminate = self.get_dims_to_discriminate() if self.is_progressive_training() else \
- list(range(self.net.decoder.n_latent))
-
- for i in dims_to_discriminate:
- w = latent[:, i, :]
- fake_pred = self.discriminator(w)
- loss_disc += F.softplus(-fake_pred).mean()
- loss_disc /= len(dims_to_discriminate)
- loss_dict['encoder_discriminator_loss'] = float(loss_disc)
- loss += self.opts.w_discriminator_lambda * loss_disc
-
- if self.opts.progressive_steps and self.net.encoder.progressive_stage.value != 18: # delta regularization loss
- total_delta_loss = 0
- deltas_latent_dims = self.net.encoder.get_deltas_starting_dimensions()
-
- first_w = latent[:, 0, :]
- for i in range(1, self.net.encoder.progressive_stage.value + 1):
- curr_dim = deltas_latent_dims[i]
- delta = latent[:, curr_dim, :] - first_w
- delta_loss = torch.norm(delta, self.opts.delta_norm, dim=1).mean()
- loss_dict[f"delta{i}_loss"] = float(delta_loss)
- total_delta_loss += delta_loss
- loss_dict['total_delta_loss'] = float(total_delta_loss)
- loss += self.opts.delta_norm_lambda * total_delta_loss
-
- if self.opts.id_lambda > 0: # Similarity loss
- loss_id, sim_improvement, id_logs = self.id_loss(y_hat, y, x)
- loss_dict['loss_id'] = float(loss_id)
- loss_dict['id_improve'] = float(sim_improvement)
- loss += loss_id * self.opts.id_lambda
- if self.opts.l2_lambda > 0:
- loss_l2 = F.mse_loss(y_hat, y)
- loss_dict['loss_l2'] = float(loss_l2)
- loss += loss_l2 * self.opts.l2_lambda
- if self.opts.lpips_lambda > 0:
- loss_lpips = self.lpips_loss(y_hat, y)
- loss_dict['loss_lpips'] = float(loss_lpips)
- loss += loss_lpips * self.opts.lpips_lambda
- loss_dict['loss'] = float(loss)
- return loss, loss_dict, id_logs
-
- def forward(self, batch):
- x, y = batch
- x, y = x.to(self.device).float(), y.to(self.device).float()
- y_hat, latent = self.net.forward(x, return_latents=True)
- if self.opts.dataset_type == "cars_encode":
- y_hat = y_hat[:, :, 32:224, :]
- return x, y, y_hat, latent
-
- def log_metrics(self, metrics_dict, prefix):
- for key, value in metrics_dict.items():
- self.logger.add_scalar('{}/{}'.format(prefix, key), value, self.global_step)
-
- def print_metrics(self, metrics_dict, prefix):
- print('Metrics for {}, step {}'.format(prefix, self.global_step))
- for key, value in metrics_dict.items():
- print('\t{} = '.format(key), value)
-
- def parse_and_log_images(self, id_logs, x, y, y_hat, title, subscript=None, display_count=2):
- im_data = []
- for i in range(display_count):
- cur_im_data = {
- 'input_face': common.log_input_image(x[i], self.opts),
- 'target_face': common.tensor2im(y[i]),
- 'output_face': common.tensor2im(y_hat[i]),
- }
- if id_logs is not None:
- for key in id_logs[i]:
- cur_im_data[key] = id_logs[i][key]
- im_data.append(cur_im_data)
- self.log_images(title, im_data=im_data, subscript=subscript)
-
- def log_images(self, name, im_data, subscript=None, log_latest=False):
- fig = common.vis_faces(im_data)
- step = self.global_step
- if log_latest:
- step = 0
- if subscript:
- path = os.path.join(self.logger.log_dir, name, '{}_{:04d}.jpg'.format(subscript, step))
- else:
- path = os.path.join(self.logger.log_dir, name, '{:04d}.jpg'.format(step))
- os.makedirs(os.path.dirname(path), exist_ok=True)
- fig.savefig(path)
- plt.close(fig)
-
- def __get_save_dict(self):
- save_dict = {
- 'state_dict': self.net.state_dict(),
- 'opts': vars(self.opts)
- }
- # save the latent avg in state_dict for inference if truncation of w was used during training
- if self.opts.start_from_latent_avg:
- save_dict['latent_avg'] = self.net.latent_avg
-
- if self.opts.save_training_data: # Save necessary information to enable training continuation from checkpoint
- save_dict['global_step'] = self.global_step
- save_dict['optimizer'] = self.optimizer.state_dict()
- save_dict['best_val_loss'] = self.best_val_loss
- if self.opts.w_discriminator_lambda > 0:
- save_dict['discriminator_state_dict'] = self.discriminator.state_dict()
- save_dict['discriminator_optimizer_state_dict'] = self.discriminator_optimizer.state_dict()
- return save_dict
-
- def get_dims_to_discriminate(self):
- deltas_starting_dimensions = self.net.encoder.get_deltas_starting_dimensions()
- return deltas_starting_dimensions[:self.net.encoder.progressive_stage.value + 1]
-
- def is_progressive_training(self):
- return self.opts.progressive_steps is not None
-
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Discriminator ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
-
- def is_training_discriminator(self):
- return self.opts.w_discriminator_lambda > 0
-
- @staticmethod
- def discriminator_loss(real_pred, fake_pred, loss_dict):
- real_loss = F.softplus(-real_pred).mean()
- fake_loss = F.softplus(fake_pred).mean()
-
- loss_dict['d_real_loss'] = float(real_loss)
- loss_dict['d_fake_loss'] = float(fake_loss)
-
- return real_loss + fake_loss
-
- @staticmethod
- def discriminator_r1_loss(real_pred, real_w):
- grad_real, = autograd.grad(
- outputs=real_pred.sum(), inputs=real_w, create_graph=True
- )
- grad_penalty = grad_real.pow(2).reshape(grad_real.shape[0], -1).sum(1).mean()
-
- return grad_penalty
-
- @staticmethod
- def requires_grad(model, flag=True):
- for p in model.parameters():
- p.requires_grad = flag
-
- def train_discriminator(self, batch):
- loss_dict = {}
- x, _ = batch
- x = x.to(self.device).float()
- self.requires_grad(self.discriminator, True)
-
- with torch.no_grad():
- real_w, fake_w = self.sample_real_and_fake_latents(x)
- real_pred = self.discriminator(real_w)
- fake_pred = self.discriminator(fake_w)
- loss = self.discriminator_loss(real_pred, fake_pred, loss_dict)
- loss_dict['discriminator_loss'] = float(loss)
-
- self.discriminator_optimizer.zero_grad()
- loss.backward()
- self.discriminator_optimizer.step()
-
- # r1 regularization
- d_regularize = self.global_step % self.opts.d_reg_every == 0
- if d_regularize:
- real_w = real_w.detach()
- real_w.requires_grad = True
- real_pred = self.discriminator(real_w)
- r1_loss = self.discriminator_r1_loss(real_pred, real_w)
-
- self.discriminator.zero_grad()
- r1_final_loss = self.opts.r1 / 2 * r1_loss * self.opts.d_reg_every + 0 * real_pred[0]
- r1_final_loss.backward()
- self.discriminator_optimizer.step()
- loss_dict['discriminator_r1_loss'] = float(r1_final_loss)
-
- # Reset to previous state
- self.requires_grad(self.discriminator, False)
-
- return loss_dict
-
- def validate_discriminator(self, test_batch):
- with torch.no_grad():
- loss_dict = {}
- x, _ = test_batch
- x = x.to(self.device).float()
- real_w, fake_w = self.sample_real_and_fake_latents(x)
- real_pred = self.discriminator(real_w)
- fake_pred = self.discriminator(fake_w)
- loss = self.discriminator_loss(real_pred, fake_pred, loss_dict)
- loss_dict['discriminator_loss'] = float(loss)
- return loss_dict
-
- def sample_real_and_fake_latents(self, x):
- sample_z = torch.randn(self.opts.batch_size, 512, device=self.device)
- real_w = self.net.decoder.get_latent(sample_z)
- fake_w = self.net.encoder(x)
- if self.opts.start_from_latent_avg:
- fake_w = fake_w + self.net.latent_avg.repeat(fake_w.shape[0], 1, 1)
- if self.is_progressive_training(): # When progressive training, feed only unique w's
- dims_to_discriminate = self.get_dims_to_discriminate()
- fake_w = fake_w[:, dims_to_discriminate, :]
- if self.opts.use_w_pool:
- real_w = self.real_w_pool.query(real_w)
- fake_w = self.fake_w_pool.query(fake_w)
- if fake_w.ndim == 3:
- fake_w = fake_w[:, 0, :]
- return real_w, fake_w
diff --git a/spaces/aijack/jojo/e4e/models/encoders/helpers.py b/spaces/aijack/jojo/e4e/models/encoders/helpers.py
deleted file mode 100644
index c4a58b34ea5ca6912fe53c63dede0a8696f5c024..0000000000000000000000000000000000000000
--- a/spaces/aijack/jojo/e4e/models/encoders/helpers.py
+++ /dev/null
@@ -1,140 +0,0 @@
-from collections import namedtuple
-import torch
-import torch.nn.functional as F
-from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
-
-"""
-ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
-"""
-
-
-class Flatten(Module):
- def forward(self, input):
- return input.view(input.size(0), -1)
-
-
-def l2_norm(input, axis=1):
- norm = torch.norm(input, 2, axis, True)
- output = torch.div(input, norm)
- return output
-
-
-class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
- """ A named tuple describing a ResNet block. """
-
-
-def get_block(in_channel, depth, num_units, stride=2):
- return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
-
-
-def get_blocks(num_layers):
- if num_layers == 50:
- blocks = [
- get_block(in_channel=64, depth=64, num_units=3),
- get_block(in_channel=64, depth=128, num_units=4),
- get_block(in_channel=128, depth=256, num_units=14),
- get_block(in_channel=256, depth=512, num_units=3)
- ]
- elif num_layers == 100:
- blocks = [
- get_block(in_channel=64, depth=64, num_units=3),
- get_block(in_channel=64, depth=128, num_units=13),
- get_block(in_channel=128, depth=256, num_units=30),
- get_block(in_channel=256, depth=512, num_units=3)
- ]
- elif num_layers == 152:
- blocks = [
- get_block(in_channel=64, depth=64, num_units=3),
- get_block(in_channel=64, depth=128, num_units=8),
- get_block(in_channel=128, depth=256, num_units=36),
- get_block(in_channel=256, depth=512, num_units=3)
- ]
- else:
- raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers))
- return blocks
-
-
-class SEModule(Module):
- def __init__(self, channels, reduction):
- super(SEModule, self).__init__()
- self.avg_pool = AdaptiveAvgPool2d(1)
- self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False)
- self.relu = ReLU(inplace=True)
- self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False)
- self.sigmoid = Sigmoid()
-
- def forward(self, x):
- module_input = x
- x = self.avg_pool(x)
- x = self.fc1(x)
- x = self.relu(x)
- x = self.fc2(x)
- x = self.sigmoid(x)
- return module_input * x
-
-
-class bottleneck_IR(Module):
- def __init__(self, in_channel, depth, stride):
- super(bottleneck_IR, self).__init__()
- if in_channel == depth:
- self.shortcut_layer = MaxPool2d(1, stride)
- else:
- self.shortcut_layer = Sequential(
- Conv2d(in_channel, depth, (1, 1), stride, bias=False),
- BatchNorm2d(depth)
- )
- self.res_layer = Sequential(
- BatchNorm2d(in_channel),
- Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
- Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth)
- )
-
- def forward(self, x):
- shortcut = self.shortcut_layer(x)
- res = self.res_layer(x)
- return res + shortcut
-
-
-class bottleneck_IR_SE(Module):
- def __init__(self, in_channel, depth, stride):
- super(bottleneck_IR_SE, self).__init__()
- if in_channel == depth:
- self.shortcut_layer = MaxPool2d(1, stride)
- else:
- self.shortcut_layer = Sequential(
- Conv2d(in_channel, depth, (1, 1), stride, bias=False),
- BatchNorm2d(depth)
- )
- self.res_layer = Sequential(
- BatchNorm2d(in_channel),
- Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
- PReLU(depth),
- Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
- BatchNorm2d(depth),
- SEModule(depth, 16)
- )
-
- def forward(self, x):
- shortcut = self.shortcut_layer(x)
- res = self.res_layer(x)
- return res + shortcut
-
-
-def _upsample_add(x, y):
- """Upsample and add two feature maps.
- Args:
- x: (Variable) top feature map to be upsampled.
- y: (Variable) lateral feature map.
- Returns:
- (Variable) added feature map.
- Note in PyTorch, when input size is odd, the upsampled feature map
- with `F.upsample(..., scale_factor=2, mode='nearest')`
- maybe not equal to the lateral feature map size.
- e.g.
- original input size: [N,_,15,15] ->
- conv2d feature map size: [N,_,8,8] ->
- upsampled feature map size: [N,_,16,16]
- So we choose bilinear upsample which supports arbitrary output sizes.
- """
- _, _, H, W = y.size()
- return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True) + y
diff --git a/spaces/akhaliq/SwinIR/app.py b/spaces/akhaliq/SwinIR/app.py
deleted file mode 100644
index d0d16e95331b4e2ef07bedfeb916aa1c3cfbd3bd..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SwinIR/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import os
-import gradio as gr
-from PIL import Image
-import torch
-
-
-os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth -P experiments/pretrained_models')
-
-def inference(img):
- os.system('mkdir test')
- basewidth = 256
- wpercent = (basewidth/float(img.size[0]))
- hsize = int((float(img.size[1])*float(wpercent)))
- img = img.resize((basewidth,hsize), Image.ANTIALIAS)
- img.save("test/1.jpg", "JPEG")
- os.system('python main_test_swinir.py --task real_sr --model_path experiments/pretrained_models/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth --folder_lq test --scale 4')
- return 'results/swinir_real_sr_x4/1_SwinIR.png'
-
-title = "SwinIR"
-description = "Gradio demo for SwinIR. SwinIR achieves state-of-the-art performance on six tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. See the paper and project page for detailed results below. Here, we provide a demo for real-world image SR.To use it, simply upload your image, or click one of the examples to load them."
-article = "SwinIR: Image Restoration Using Swin Transformer | Github Repo
" - -examples=[['ETH_LR.png']] -gr.Interface( - inference, - [gr.inputs.Image(type="pil", label="Input")], - gr.outputs.Image(type="file", label="Output"), - title=title, - description=description, - article=article, - enable_queue=True, - examples=examples - ).launch(debug=True) \ No newline at end of file diff --git a/spaces/akhaliq/arcanegannewtheme/README.md b/spaces/akhaliq/arcanegannewtheme/README.md deleted file mode 100644 index 4e518092d02bf0ced295290cbf91753e0e8931e7..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/arcanegannewtheme/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Arcanegannewtheme -emoji: 💩 -colorFrom: indigo -colorTo: yellow -sdk: gradio -sdk_version: 2.9.4 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/akhaliq/t5-base-fine-tuned-on-jfleg/README.md b/spaces/akhaliq/t5-base-fine-tuned-on-jfleg/README.md deleted file mode 100644 index 01a888a84a1a804d23bd9197709c0975a4027d2b..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/t5-base-fine-tuned-on-jfleg/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Grammar Correction -emoji: 💻 -colorFrom: green -colorTo: indigo -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/alecinvan/flotationMultiModalRobot/app.py b/spaces/alecinvan/flotationMultiModalRobot/app.py deleted file mode 100644 index a182ef282700799f44529ed28d3170b5e08ee4a3..0000000000000000000000000000000000000000 --- a/spaces/alecinvan/flotationMultiModalRobot/app.py +++ /dev/null @@ -1,42 +0,0 @@ -import gradio as gr -from transformers import BlipForConditionalGeneration, BlipProcessor -import torch -import tempfile -from gtts import gTTS - -# Load models -device = "cpu" -processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") -model_image_captioning = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(device) - -def generate_caption_tts(image): - - inputs = processor(images=image, return_tensors="pt") - inputs["max_length"] = 20 - inputs["num_beams"] = 5 - outputs = model_image_captioning.generate(**inputs) - - caption = processor.batch_decode(outputs, skip_special_tokens=True)[0] - - speech = gTTS(caption, lang="en") - tmp_file = tempfile.mkstemp()[1] - speech.save(tmp_file) - - return (caption, tmp_file) - - -title ="智悠科技大语言模型 - 智能漂浮舱多模态健康交互机器人" - -description = "BLPM模型：引导性语言图像预训练以实现统一视觉语言理解和生成。 请上传您的图像（或自动感知您的状况）" - -iface = gr.Interface( - fn=generate_caption_tts, - title=title, - description=description, - inputs=gr.inputs.Image(shape=(224,224)), - outputs=["text", "audio"] -) - - -#iface.launch(share=True, debug=True) -iface.launch() \ No newline at end of file diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/resolvelib/resolver.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/resolvelib/resolver.py deleted file mode 100644 index 618f1e1aead14bda77aafa08ab0e0be91cee64f3..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/resolvelib/resolver.py +++ /dev/null @@ -1,292 +0,0 @@ -import functools -import logging -import os -from typing import TYPE_CHECKING, Dict, List, Optional, Set, Tuple, cast - -from pip._vendor.packaging.utils import canonicalize_name -from pip._vendor.resolvelib import BaseReporter, ResolutionImpossible -from pip._vendor.resolvelib import Resolver as RLResolver -from pip._vendor.resolvelib.structs import DirectedGraph - -from pip._internal.cache import WheelCache -from pip._internal.index.package_finder import PackageFinder -from pip._internal.operations.prepare import RequirementPreparer -from pip._internal.req.req_install import InstallRequirement -from pip._internal.req.req_set import RequirementSet -from pip._internal.resolution.base import BaseResolver, InstallRequirementProvider -from pip._internal.resolution.resolvelib.provider import PipProvider -from pip._internal.resolution.resolvelib.reporter import ( - PipDebuggingReporter, - PipReporter, -) - -from .base import Candidate, Requirement -from .factory import Factory - -if TYPE_CHECKING: - from pip._vendor.resolvelib.resolvers import Result as RLResult - - Result = RLResult[Requirement, Candidate, str] - - -logger = logging.getLogger(__name__) - - -class Resolver(BaseResolver): - _allowed_strategies = {"eager", "only-if-needed", "to-satisfy-only"} - - def __init__( - self, - preparer: RequirementPreparer, - finder: PackageFinder, - wheel_cache: Optional[WheelCache], - make_install_req: InstallRequirementProvider, - use_user_site: bool, - ignore_dependencies: bool, - ignore_installed: bool, - ignore_requires_python: bool, - force_reinstall: bool, - upgrade_strategy: str, - suppress_build_failures: bool, - py_version_info: Optional[Tuple[int, ...]] = None, - ): - super().__init__() - assert upgrade_strategy in self._allowed_strategies - - self.factory = Factory( - finder=finder, - preparer=preparer, - make_install_req=make_install_req, - wheel_cache=wheel_cache, - use_user_site=use_user_site, - force_reinstall=force_reinstall, - ignore_installed=ignore_installed, - ignore_requires_python=ignore_requires_python, - suppress_build_failures=suppress_build_failures, - py_version_info=py_version_info, - ) - self.ignore_dependencies = ignore_dependencies - self.upgrade_strategy = upgrade_strategy - self._result: Optional[Result] = None - - def resolve( - self, root_reqs: List[InstallRequirement], check_supported_wheels: bool - ) -> RequirementSet: - collected = self.factory.collect_root_requirements(root_reqs) - provider = PipProvider( - factory=self.factory, - constraints=collected.constraints, - ignore_dependencies=self.ignore_dependencies, - upgrade_strategy=self.upgrade_strategy, - user_requested=collected.user_requested, - ) - if "PIP_RESOLVER_DEBUG" in os.environ: - reporter: BaseReporter = PipDebuggingReporter() - else: - reporter = PipReporter() - resolver: RLResolver[Requirement, Candidate, str] = RLResolver( - provider, - reporter, - ) - - try: - try_to_avoid_resolution_too_deep = 2000000 - result = self._result = resolver.resolve( - collected.requirements, max_rounds=try_to_avoid_resolution_too_deep - ) - - except ResolutionImpossible as e: - error = self.factory.get_installation_error( - cast("ResolutionImpossible[Requirement, Candidate]", e), - collected.constraints, - ) - raise error from e - - req_set = RequirementSet(check_supported_wheels=check_supported_wheels) - for candidate in result.mapping.values(): - ireq = candidate.get_install_requirement() - if ireq is None: - continue - - # Check if there is already an installation under the same name, - # and set a flag for later stages to uninstall it, if needed. - installed_dist = self.factory.get_dist_to_uninstall(candidate) - if installed_dist is None: - # There is no existing installation -- nothing to uninstall. - ireq.should_reinstall = False - elif self.factory.force_reinstall: - # The --force-reinstall flag is set -- reinstall. - ireq.should_reinstall = True - elif installed_dist.version != candidate.version: - # The installation is different in version -- reinstall. - ireq.should_reinstall = True - elif candidate.is_editable or installed_dist.editable: - # The incoming distribution is editable, or different in - # editable-ness to installation -- reinstall. - ireq.should_reinstall = True - elif candidate.source_link and candidate.source_link.is_file: - # The incoming distribution is under file:// - if candidate.source_link.is_wheel: - # is a local wheel -- do nothing. - logger.info( - "%s is already installed with the same version as the " - "provided wheel. Use --force-reinstall to force an " - "installation of the wheel.", - ireq.name, - ) - continue - - # is a local sdist or path -- reinstall - ireq.should_reinstall = True - else: - continue - - link = candidate.source_link - if link and link.is_yanked: - # The reason can contain non-ASCII characters, Unicode - # is required for Python 2. - msg = ( - "The candidate selected for download or install is a " - "yanked version: {name!r} candidate (version {version} " - "at {link})\nReason for being yanked: {reason}" - ).format( - name=candidate.name, - version=candidate.version, - link=link, - reason=link.yanked_reason or "
-
-# Bingo
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-
-
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://github.com/weaigc/bingo/blob/main/license)
-
-问题反馈请前往 https://github.com/weaigc/bingo/issues
-
-
-
diff --git a/spaces/allknowingroger/Image-Models-Test162/README.md b/spaces/allknowingroger/Image-Models-Test162/README.md
deleted file mode 100644
index f91e4b31ab345f987b425de029c057bfb69d9e1b..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test162/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test
----
-
-
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test23/app.py b/spaces/allknowingroger/Image-Models-Test23/app.py
deleted file mode 100644
index 6f512766ec2b18faa6f642e0a890091f78e215e7..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test23/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
- "hongrui/chest_v_1",
- "Yntec/Deliberate",
- "manosp/textual_inversion_moto",
- "andressrg/textual_inversion_cat",
- "Jinouga/sagiri-yamada-asaemon-v1",
- "manosp/textual_inversion_cat",
- "guaguale/model_v",
- "hdparmar/tradfusion-model-e4-v1" ,
- "jbilcke-hf/sdxl-modern-pixar",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
- try:
- model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
- except Exception as error:
- def the_fn(txt):
- return None
- model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
- model_idx+=1
-
-
-def send_it_idx(idx):
- def send_it_fn(prompt):
- output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
- return output
- return send_it_fn
-
-def get_prompts(prompt_text):
- return prompt_text
-
-def clear_it(val):
- if int(val) != 0:
- val = 0
- else:
- val = 0
- pass
- return val
-
-def all_task_end(cnt,t_stamp):
- to = t_stamp + 60
- et = time.time()
- if et > to and t_stamp != 0:
- d = gr.update(value=0)
- tog = gr.update(value=1)
- #print(f'to: {to} et: {et}')
- else:
- if cnt != 0:
- d = gr.update(value=et)
- else:
- d = gr.update(value=0)
- tog = gr.update(value=0)
- #print (f'passing: to: {to} et: {et}')
- pass
- return d, tog
-
-def all_task_start():
- print("\n\n\n\n\n\n\n")
- t = time.gmtime()
- t_stamp = time.time()
- current_time = time.strftime("%H:%M:%S", t)
- return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
- nn = len(models)
- return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
- with gr.Column(scale=12):
- # with gr.Row():
- # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
- with gr.Row():
- with gr.Row(scale=6):
- primary_prompt=gr.Textbox(label="Prompt", value="")
- # real_prompt=gr.Textbox(label="Real prompt")
- with gr.Row(scale=6):
- # improve_prompts_btn=gr.Button("Improve")
- with gr.Row():
- run=gr.Button("Run",variant="primary")
- clear_btn=gr.Button("Clear")
- with gr.Row():
- sd_outputs = {}
- model_idx = 1
- for model_path in models:
- with gr.Column(scale=3, min_width=320):
- with gr.Box():
- sd_outputs[model_idx] = gr.Image(label=model_path)
- pass
- model_idx += 1
- pass
- pass
-
- with gr.Row(visible=False):
- start_box=gr.Number(interactive=False)
- end_box=gr.Number(interactive=False)
- tog_box=gr.Textbox(value=0,interactive=False)
-
- start_box.change(
- all_task_end,
- [start_box, end_box],
- [start_box, tog_box],
- every=1,
- show_progress=False)
-
- primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
- run.click(all_task_start, None, [start_box, end_box, tog_box])
- runs_dict = {}
- model_idx = 1
- for model_path in models:
- runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
- model_idx += 1
- pass
- pass
-
- # improve_prompts_btn_clicked=improve_prompts_btn.click(
- # get_prompts,
- # inputs=[primary_prompt],
- # outputs=[primary_prompt],
- # cancels=list(runs_dict.values()))
- clear_btn.click(
- clear_fn,
- None,
- [primary_prompt, *list(sd_outputs.values())],
- cancels=[*list(runs_dict.values())])
- tog_box.change(
- clear_it,
- tog_box,
- tog_box,
- cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
-
\ No newline at end of file
diff --git a/spaces/amanmibra/void-demo-aisf/README.md b/spaces/amanmibra/void-demo-aisf/README.md
deleted file mode 100644
index 0ce4f16a807e69e077c9d3c09ae0f22c26c70224..0000000000000000000000000000000000000000
--- a/spaces/amanmibra/void-demo-aisf/README.md
+++ /dev/null
@@ -1,64 +0,0 @@
----
-title: VoID Demo from AISF Hackathon
-emoji: 😤
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-python_version: 3.9
-app_file: gradio_app.py
-pinned: false
----
-
-# VoID: Voice Identifier
-
-Classifier to recognize the identity of a speaker
-
-Project was done as a part of the AISF hackathon (www.aisf.co), VoID was a finalist :D
-
-## Details
-
-### Dataset
-
-Custom dataset with a set of 5-7 pure, unaugmented voices from labeled speakers saying the same phrase (similar to a passcode). In our initial version, we had three voices saying the same phrase ("The quick brown fox jumps over the lazy dog"). In our demo, we used three voices saying their own individual name.
-
-For test and training data, we then created hundreds of augmented copies of each pure voice using augmentation layers (as seen in the augmentation notebook under the `/notebooks/` folder). The types of distortions were added in randomly and were even compounded randomly, augmentations such as clipping, noise, reverb, and etc.
-
-If you would like access to the dataset used in this project, feel free to contact me.
-
-### Model Structure
-
-```
-CNNetwork(
- (conv1): Sequential(
- (0): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2))
- (1): ReLU()
- (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
- )
- (conv2): Sequential(
- (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2))
- (1): ReLU()
- (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
- )
- (conv3): Sequential(
- (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2))
- (1): ReLU()
- (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
- )
- (conv4): Sequential(
- (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2))
- (1): ReLU()
- (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
- )
- (flatten): Flatten(start_dim=1, end_dim=-1)
- (linear): Linear(in_features=21888, out_features=3, bias=True)
- (softmax): Softmax(dim=1)
-)
-```
-
-## Demo
-
-Watch the demo of our model [here](https://www.loom.com/share/a8cb126af7b64ddaaa67c6f00e23f4e9)!
-
-## Running locally
-
-Make sure to create and install the conda dependencies declared in `void.yml`.
diff --git a/spaces/anasanchezf/cloome/app.py b/spaces/anasanchezf/cloome/app.py
deleted file mode 100644
index 7c933b6d2b285679ad01247dc943053b27204fa0..0000000000000000000000000000000000000000
--- a/spaces/anasanchezf/cloome/app.py
+++ /dev/null
@@ -1,523 +0,0 @@
-import numpy as np
-import pandas as pd
-import streamlit as st
-from PIL import Image
-
-import sys
-import io
-import os
-import glob
-import json
-import zipfile
-from tqdm import tqdm
-from itertools import chain
-
-import torch
-from torch.utils.data import DataLoader
-
-sys.path.insert(0, os.path.abspath("src/"))
-
-from clip.clip import _transform
-from training.datasets import CellPainting
-from clip.model import convert_weights, CLIPGeneral
-
-from rdkit import Chem
-from rdkit.Chem import Draw
-from rdkit.Chem import AllChem
-from rdkit.Chem import DataStructs
-
-st.set_page_config(layout="wide")
-
-basepath = os.path.dirname(__file__)
-datapath = os.path.join(basepath, "data")
-
-CLOOME_PATH = "/home/ana/gitrepos/hti-cloob"
-MODEL_PATH = os.path.join(datapath, "epoch_55.pt")
-npzs = os.path.join(datapath, "npzs")
-molecule_features = os.path.join(datapath, "all_molecule_cellpainting_features.pkl")
-mol_index_file = os.path.join(datapath, "cellpainting-unique-molecule.csv")
-image_features = os.path.join(datapath, "subset_image_cellpainting_features.pkl")
-images_arr = os.path.join(datapath, "subset_npzs_dict_.npz")
-img_index_file = os.path.join(datapath, "cellpainting-all-imgpermol.csv")
-imgname = "I1"
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_type = "RN50"
-
-######### CLOOME FUNCTIONS #########
-def convert_models_to_fp32(model):
- for p in model.parameters():
- p.data = p.data.float()
- if p.grad:
- p.grad.data = p.grad.data.float()
-
-
-def load(model_path, device, model, image_resolution):
- state_dict = torch.load(model_path, map_location=device)
- state_dict = state_dict["state_dict"]
-
- model_config_file = f"{model.replace('/', '-')}.json"
- print('Loading model from', model_config_file)
- assert os.path.exists(model_config_file)
- with open(model_config_file, 'r') as f:
- model_info = json.load(f)
- model = CLIPGeneral(**model_info)
- convert_weights(model)
- convert_models_to_fp32(model)
-
- if str(device) == "cpu":
- model.float()
- print(device)
-
- new_state_dict = {k[len('module.'):]: v for k,v in state_dict.items()}
-
- model.load_state_dict(new_state_dict)
- model.to(device)
- model.eval()
-
- return model
-
-
-def get_features(dataset, model, device):
- all_image_features = []
- all_text_features = []
- all_ids = []
-
- print(f"get_features {device}")
- print(len(dataset))
-
- with torch.no_grad():
- for batch in tqdm(DataLoader(dataset, num_workers=1, batch_size=64)):
- if type(batch) is dict:
- imgs = batch
- text_features = None
- mols = None
- elif type(batch) is torch.Tensor:
- mols = batch
- imgs = None
- else:
- imgs, mols = batch
-
- if mols is not None:
- text_features = model.encode_text(mols.to(device))
- text_features = text_features / text_features.norm(dim=-1, keepdim=True)
- all_text_features.append(text_features)
- molecules_exist = True
-
- if imgs is not None:
- images = imgs["input"]
- ids = imgs["ID"]
-
- img_features = model.encode_image(images.to(device))
- img_features = img_features / img_features.norm(dim=-1, keepdim=True)
- all_image_features.append(img_features)
-
- all_ids.append(ids)
-
- all_ids = list(chain.from_iterable(all_ids))
-
- if imgs is not None and mols is not None:
- return torch.cat(all_image_features), torch.cat(all_text_features), all_ids
- elif imgs is not None:
- return torch.cat(all_image_features), all_ids
- elif mols is not None:
- return torch.cat(all_text_features), all_ids
- return
-
-
-def read_array(file):
- t = torch.load(file)
- features = t["mol_features"]
- ids = t["mol_ids"]
- return features, ids
-
-
-def main(df, model_path, model, img_path=None, mol_path=None, image_resolution=None):
- # Load the model
- device = "cuda" if torch.cuda.is_available() else "cpu"
- print(torch.cuda.device_count())
-
- model = load(model_path, device, model, image_resolution)
-
- preprocess_val = _transform(image_resolution, image_resolution, is_train=False, normalize="dataset", preprocess="downsize")
-
- # Load the dataset
- val = CellPainting(df,
- img_path,
- mol_path,
- transforms = preprocess_val)
-
- # Calculate the image features
- print("getting_features")
- result = get_features(val, model, device)
-
- if len(result) > 2:
- val_img_features, val_text_features, val_ids = result
- return val_img_features, val_text_features, val_ids
- else:
- val_img_features, val_ids = result
- return val_img_features, val_ids
-
-
-def img_to_numpy(file):
- img = Image.open(file)
- arr = np.array(img)
- return arr
-
-
-def illumination_threshold(arr, perc=0.0028):
- """ Return threshold value to not display a percentage of highest pixels"""
-
- perc = perc/100
-
- h = arr.shape[0]
- w = arr.shape[1]
-
- # find n pixels to delete
- total_pixels = h * w
- n_pixels = total_pixels * perc
- n_pixels = int(np.around(n_pixels))
-
- # find indexes of highest pixels
- flat_inds = np.argpartition(arr, -n_pixels, axis=None)[-n_pixels:]
- inds = np.array(np.unravel_index(flat_inds, arr.shape)).T
-
- max_values = [arr[i, j] for i, j in inds]
-
- threshold = min(max_values)
-
- return threshold
-
-
-def process_image(arr):
- threshold = illumination_threshold(arr)
- scaled_img = sixteen_to_eight_bit(arr, threshold)
- return scaled_img
-
-
-def sixteen_to_eight_bit(arr, display_max, display_min=0):
- threshold_image = ((arr.astype(float) - display_min) * (arr > display_min))
-
- scaled_image = (threshold_image * (256. / (display_max - display_min)))
- scaled_image[scaled_image > 255] = 255
-
- scaled_image = scaled_image.astype(np.uint8)
-
- return scaled_image
-
-
-def process_image(arr):
- threshold = illumination_threshold(arr)
- scaled_img = sixteen_to_eight_bit(arr, threshold)
- return scaled_img
-
-
-def process_sample(imglst, channels, filenames, outdir, outfile):
- sample = np.zeros((520, 696, 5), dtype=np.uint8)
-
- filenames_dict, channels_dict = {}, {}
-
- for i, (img, channel, fname) in enumerate(zip(imglst, channels, filenames)):
- print(channel)
- arr = img_to_numpy(img)
- arr = process_image(arr)
-
- sample[:,:,i] = arr
-
- channels_dict[i] = channel
- filenames_dict[channel] = fname
-
- sample_dict = dict(sample=sample,
- channels=channels_dict,
- filenames=filenames_dict)
-
- outfile = outfile + ".npz"
- outpath = os.path.join(outdir, outfile)
-
- np.savez(outpath, sample=sample, channels=channels, filenames=filenames)
-
- return sample_dict, outpath
-
-
-def display_cellpainting(sample):
- arr = sample["sample"]
- r = arr[:, :, 0].astype(np.float32)
- g = arr[:, :, 3].astype(np.float32)
- b = arr[:, :, 4].astype(np.float32)
-
- rgb_arr = np.dstack((r, g, b))
-
- im = Image.fromarray(rgb_arr.astype("uint8"))
- im_rgb = im.convert("RGB")
- return im_rgb
-
-
-def morgan_from_smiles(smiles, radius=3, nbits=1024, chiral=True):
- mol = Chem.MolFromSmiles(smiles)
- fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=3, nBits=nbits, useChirality=chiral)
- arr = np.zeros((0,), dtype=np.int8)
- DataStructs.ConvertToNumpyArray(fp,arr)
- return arr
-
-
-def save_hdf(fps, index, outfile_hdf):
- ids = [i for i in range(len(fps))]
- columns = [str(i) for i in range(fps[0].shape[0])]
- df = pd.DataFrame(fps, index=ids, columns=columns)
- df.to_hdf(outfile_hdf, key="df", mode="w")
- return outfile_hdf
-
-
-def create_index(outdir, ids, filename):
- filepath = os.path.join(outdir, filename)
- if type(ids) is str:
- values = [ids]
- else:
- values = ids
- data = {"SAMPLE_KEY": values}
- print(data)
- df = pd.DataFrame(data)
- df.to_csv(filepath)
- return filepath
-
-
-def draw_molecules(smiles_lst):
- mols = [Chem.MolFromSmiles(s) for s in smiles_lst]
- mol_imgs = [Chem.Draw.MolToImage(m) for m in mols]
- return mol_imgs
-
-
-def reshape_image(arr):
- c, h, w = arr.shape
- reshaped_image = np.empty((h, w, c))
-
- reshaped_image[:,:,0] = arr[0]
- reshaped_image[:,:,1] = arr[1]
- reshaped_image[:,:,2] = arr[2]
-
- reshaped_pil = Image.fromarray(reshaped_image.astype("uint8"))
-
- return reshaped_pil
-
-
-# missing functions: save morgan to to_hdf, create index, load features, calculate similarities
-
-##### STREAMLIT FUNCTIONS ######
-st.title('CLOOME. Bioimage database retrieval from chemical structures (and viceversa)')
-
-def main_page(top_n, model_path):
- st.markdown(
- """
- Contrastive learning for self-supervised representation learning has brought a strong improvement to many application areas, such as computer vision and natural language processing.
- With the availability of large collections of unlabeled data in vision and language, contrastive learning of language and image representations has shown impressive results.
- The contrastive learning methods CLIP and CLOOB have demonstrated that the learned representations are highly transferable to a large set of diverse tasks when trained on multi-modal data from two different domains.
- In life sciences, similar large, multi-modal datasets comprising both cell-based microscopy images and chemical structures of molecules are available.
- However, contrastive learning has not yet been used for this type of multi-modal data, although this would allow to design cross-modal retrieval systems for bioimaing and chemical databases.
-
- In this work, we present a such a contrastive learning method, the retrieval systems, and the transferability of the learned representations. Our method, Contrastive Learning and leave-One-Out-boost for Molecule Encoders (CLOOME), is based on both CLOOB and CLIP and comprises an encoder for microscopy data, an encoder for chemical structures and a contrastive learning objective, which produce rich embeddings of bioimages and chemical structures.
- On the benchmark dataset ”Cell Painting”, we demonstrate that the embddings can be used to form a retrieval system for bioimaging and chemical databases. We also show that CLOOME learns transferable representations by performing linear probing for activity prediction tasks. Furthermore, the image embeddings can identify new cell phenotypes, as we show in a zero-shot classification task.
- """
- )
-
-
-def molecules_from_image(top_n, model_path):
- ## TODO: Check if expander can be automatically collapsed
- exp = st.expander("Upload a microscopy image")
- with exp:
- channels = ['Mito', 'ERSyto', 'ERSytoBleed', 'Ph_golgi', 'Hoechst']
- imglst, filenames = [], []
-
- for c in channels:
- file_obj = st.file_uploader(f'Choose a TIF image for {c}:', ".tif")
- if file_obj is not None:
- imglst.append(file_obj)
- filenames.append(file_obj.name)
-
-
- if imglst:
- if not os.path.isdir(npzs):
- os.mkdir(npzs)
-
- sample_dict, imgpath = process_sample(imglst, channels, filenames, npzs, imgname)
- print(imglst)
-
-
- i = display_cellpainting(sample_dict)
- st.image(i)
-
- uploaded_file = st.file_uploader("Choose a molecule file to retrieve from (optional)")
-
- if imglst:
- if uploaded_file is not None:
- molecule_df = pd.read_csv(uploaded_file)
- smiles = molecule_df["SMILES"].tolist()
- morgan = [morgan_from_smiles(s) for s in smiles]
- molnames = [f"M{i}" for i in range(len(morgan))]
- mol_index_fname = "mol_index.csv"
- mol_index = create_index(datapath, molnames, mol_index_fname)
- molpath = os.path.join(datapath, "mols.hdf")
- fps_fname = save_hdf(morgan, molnames, molpath)
- mol_imgs = draw_molecules(smiles)
- mol_features, mol_ids = main(mol_index, model_path, model_type, mol_path=molpath, image_resolution=image_resolution)
- predefined_features = False
- else:
- mol_index = pd.read_csv(mol_index_file)
- mol_features_torch = torch.load(molecule_features, map_location=device)
- mol_features = mol_features_torch["mol_features"]
- mol_ids = mol_features_torch["mol_ids"]
- print(len(mol_ids))
- predefined_features = True
-
- img_index_fname = "img_index.csv"
- img_index = create_index(datapath, imgname, img_index_fname)
- img_features, img_ids = main(img_index, MODEL_PATH, model_type, img_path=npzs, image_resolution=image_resolution)
-
- print(img_features.shape)
- print(mol_features.shape)
-
- top_n = int(top_n)
-
- logits = img_features @ mol_features.T
- mol_probs = (30.0 * logits).softmax(dim=-1)
- top_probs, top_labels = mol_probs.cpu().topk(top_n, dim=-1)
-
- # Delete this if want to allow retrieval for multiple images
- top_probs = torch.flatten(top_probs)
- top_labels = torch.flatten(top_labels)
-
- print(top_probs.shape)
- print(top_labels.shape)
-
- if predefined_features:
- mol_index.set_index(["SAMPLE_KEY"], inplace=True)
- top_ids = [mol_ids[i] for i in top_labels]
- smiles = mol_index.loc[top_ids]["SMILES"].tolist()
- mol_imgs = draw_molecules(smiles)
-
- with st.container():
- if uploaded_file is not None:
- st.write("Retrieved molecules")
- else:
- st.write("Retrieved molecules from the Cell Painting database")
- columns = st.columns(len(top_probs))
- for i, col in enumerate(columns):
- if predefined_features:
- image_id = i
- else:
- image_id = top_labels[i]
- index = i+1
- col.image(mol_imgs[image_id], width=140, caption=index)
-
- print(mol_probs.sum(dim=-1))
- print((top_probs, top_labels))
-
-def images_from_molecule(top_n, model_path):
- #st.markdown("Enter a query molecule in [SMILES](https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system) format",)
- smiles = st.text_input("Enter a query molecule in SMILES format", value="CC(=O)OC1=CC=CC=C1C(=O)O", placeholder="CC(=O)OC1=CC=CC=C1C(=O)O")
-
-
- if smiles:
- smiles = [smiles]
- morgan = [morgan_from_smiles(s) for s in smiles]
- molnames = [f"M{i}" for i in range(len(morgan))]
- mol_index_fname = "mol_index.csv"
- mol_index = create_index(datapath, molnames, mol_index_fname)
- molpath = os.path.join(datapath, "mols.hdf")
- fps_fname = save_hdf(morgan, molnames, molpath)
- mol_imgs = draw_molecules(smiles)
-
- mol_features, mol_ids = main(mol_index, model_path, model_type, mol_path=molpath, image_resolution=image_resolution)
-
- col1, col2, col3 = st.columns(3)
-
- with col1:
- st.write("")
-
- with col2:
- st.image(mol_imgs, width = 140)
-
- with col3:
- st.write("")
-
- st.markdown('##')
-
- top_n = int(top_n)
-
- img_features_torch = torch.load(image_features, map_location=device)
- img_features = img_features_torch["img_features"]
- img_ids = img_features_torch["img_ids"]
-
- logits = mol_features @ img_features.T
- img_probs = (30.0 * logits).softmax(dim=-1)
- top_probs, top_labels = img_probs.cpu().topk(top_n, dim=-1)
-
- top_probs = torch.flatten(top_probs)
- top_labels = torch.flatten(top_labels)
-
- img_index = pd.read_csv(img_index_file)
- img_index.set_index(["SAMPLE_KEY"], inplace=True)
- top_ids = [img_ids[i] for i in top_labels]
-
- images_dict = np.load(images_arr, allow_pickle = True)
-
-
- st.write("Retrieved images from the Cell Painting database")
- with st.container():
- n_columns = 5
- columns = st.columns(n_columns)
-
- for i, col in enumerate(columns):
- l = list(range(len(top_ids)))
-
- col_ids = [m for m in l if m % 5 == i]
-
- for n in col_ids:
- id = top_ids[n]
- id = f"{id}.npz"
- image = images_dict[id]
-
- ## TODO: generalize and functionalize
- im = reshape_image(image)
-
- index = n+1
- score = float(top_probs[n]) * 100
- col.image(im, caption=f"Top {index}. Score: {round(score, 2)}", width=200)
-
-
-page_names_to_funcs = {
- "Microscopy images from a molecule": images_from_molecule,
- "Molecules from a microscopy image": molecules_from_image,
- "About CLOOME": main_page,
-
-}
-
-selected_page = st.sidebar.selectbox("What would you like to retrieve?", page_names_to_funcs.keys())
-st.sidebar.markdown('')
-n_objects = st.sidebar.selectbox(
- "How many objects would you like to retrieve?",
- ("5", "10", "20"))
-
-
-selected_model = st.sidebar.selectbox(
- "Select a CLOOME model to load",
- ("CLOOME (default)", "CLOOME (CLIP imgres 320)", "CLOOME (fullres)", "CLOOME (imgres 320)"))
-
-model_dict = {
- "CLOOME (default)" : "cloome_default.pt",
- "CLOOME (CLIP imgres 320)" : "cloome_cli_imres320.pt",
- "CLOOME (fullres)" : "cloome_fullres.pt",
- "CLOOME (imgres 320)" : "cloome_imres320.pt"
-
-}
-
-model_file = model_dict[selected_model]
-model_path = os.path.join(datapath, model_file)
-
-if model_path.endswith("320).pt"):
- image_resolution = 320
-else:
- image_resolution = 520
-
-
-page_names_to_funcs[selected_page](n_objects, model_path)
-
diff --git a/spaces/antonovmaxim/text-generation-webui-space/text-generation-webui-main/docs/Windows-installation-guide.md b/spaces/antonovmaxim/text-generation-webui-space/text-generation-webui-main/docs/Windows-installation-guide.md
deleted file mode 100644
index 83b22efa38b1839d07a5a58494dbc26ba86397ee..0000000000000000000000000000000000000000
--- a/spaces/antonovmaxim/text-generation-webui-space/text-generation-webui-main/docs/Windows-installation-guide.md
+++ /dev/null
@@ -1,9 +0,0 @@
-If you are having trouble following the installation instructions in the README, Reddit user [Technical_Leather949](https://www.reddit.com/user/Technical_Leather949/) has created a more detailed, step-by-step guide covering:
-
-* Windows installation
-* 8-bit mode on Windows
-* LLaMA
-* LLaMA 4-bit
-
-The guide can be found here: https://www.reddit.com/r/LocalLLaMA/comments/11o6o3f/how_to_install_llama_8bit_and_4bit/
-
diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/datasets/phrasecut.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/datasets/phrasecut.py
deleted file mode 100644
index ef0c5350583c33c64682a35af3d314b02831569c..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/datasets/phrasecut.py
+++ /dev/null
@@ -1,335 +0,0 @@
-
-import torch
-import numpy as np
-import os
-
-from os.path import join, isdir, isfile, expanduser
-from PIL import Image
-
-from torchvision import transforms
-from torchvision.transforms.transforms import Resize
-
-from torch.nn import functional as nnf
-from general_utils import get_from_repository
-
-from skimage.draw import polygon2mask
-
-
-
-def random_crop_slices(origin_size, target_size):
- """Gets slices of a random crop. """
- assert origin_size[0] >= target_size[0] and origin_size[1] >= target_size[1], f'actual size: {origin_size}, target size: {target_size}'
-
- offset_y = torch.randint(0, origin_size[0] - target_size[0] + 1, (1,)).item() # range: 0 <= value < high
- offset_x = torch.randint(0, origin_size[1] - target_size[1] + 1, (1,)).item()
-
- return slice(offset_y, offset_y + target_size[0]), slice(offset_x, offset_x + target_size[1])
-
-
-def find_crop(seg, image_size, iterations=1000, min_frac=None, best_of=None):
-
-
- best_crops = []
- best_crop_not_ok = float('-inf'), None, None
- min_sum = 0
-
- seg = seg.astype('bool')
-
- if min_frac is not None:
- #min_sum = seg.sum() * min_frac
- min_sum = seg.shape[0] * seg.shape[1] * min_frac
-
- for iteration in range(iterations):
- sl_y, sl_x = random_crop_slices(seg.shape, image_size)
- seg_ = seg[sl_y, sl_x]
- sum_seg_ = seg_.sum()
-
- if sum_seg_ > min_sum:
-
- if best_of is None:
- return sl_y, sl_x, False
- else:
- best_crops += [(sum_seg_, sl_y, sl_x)]
- if len(best_crops) >= best_of:
- best_crops.sort(key=lambda x:x[0], reverse=True)
- sl_y, sl_x = best_crops[0][1:]
-
- return sl_y, sl_x, False
-
- else:
- if sum_seg_ > best_crop_not_ok[0]:
- best_crop_not_ok = sum_seg_, sl_y, sl_x
-
- else:
- # return best segmentation found
- return best_crop_not_ok[1:] + (best_crop_not_ok[0] <= min_sum,)
-
-
-class PhraseCut(object):
-
- def __init__(self, split, image_size=400, negative_prob=0, aug=None, aug_color=False, aug_crop=True,
- min_size=0, remove_classes=None, with_visual=False, only_visual=False, mask=None):
- super().__init__()
-
- self.negative_prob = negative_prob
- self.image_size = image_size
- self.with_visual = with_visual
- self.only_visual = only_visual
- self.phrase_form = '{}'
- self.mask = mask
- self.aug_crop = aug_crop
-
- if aug_color:
- self.aug_color = transforms.Compose([
- transforms.ColorJitter(0.5, 0.5, 0.2, 0.05),
- ])
- else:
- self.aug_color = None
-
- get_from_repository('PhraseCut', ['PhraseCut.tar'], integrity_check=lambda local_dir: all([
- isdir(join(local_dir, 'VGPhraseCut_v0')),
- isdir(join(local_dir, 'VGPhraseCut_v0', 'images')),
- isfile(join(local_dir, 'VGPhraseCut_v0', 'refer_train.json')),
- len(os.listdir(join(local_dir, 'VGPhraseCut_v0', 'images'))) in {108250, 108249}
- ]))
-
- from third_party.PhraseCutDataset.utils.refvg_loader import RefVGLoader
- self.refvg_loader = RefVGLoader(split=split)
-
- # img_ids where the size in the annotations does not match actual size
- invalid_img_ids = set([150417, 285665, 498246, 61564, 285743, 498269, 498010, 150516, 150344, 286093, 61530,
- 150333, 286065, 285814, 498187, 285761, 498042])
-
- mean = [0.485, 0.456, 0.406]
- std = [0.229, 0.224, 0.225]
- self.normalize = transforms.Normalize(mean, std)
-
- self.sample_ids = [(i, j)
- for i in self.refvg_loader.img_ids
- for j in range(len(self.refvg_loader.get_img_ref_data(i)['phrases']))
- if i not in invalid_img_ids]
-
-
- # self.all_phrases = list(set([p for i in self.refvg_loader.img_ids for p in self.refvg_loader.get_img_ref_data(i)['phrases']]))
-
- from nltk.stem import WordNetLemmatizer
- wnl = WordNetLemmatizer()
-
- # Filter by class (if remove_classes is set)
- if remove_classes is None:
- pass
- else:
- from datasets.generate_lvis_oneshot import PASCAL_SYNSETS, traverse_lemmas, traverse_lemmas_hypo
- from nltk.corpus import wordnet
-
- print('remove pascal classes...')
-
- get_data = self.refvg_loader.get_img_ref_data # shortcut
- keep_sids = None
-
- if remove_classes[0] == 'pas5i':
- subset_id = remove_classes[1]
- from datasets.generate_lvis_oneshot import PASCAL_5I_SYNSETS_ORDERED, PASCAL_5I_CLASS_IDS
- avoid = [PASCAL_5I_SYNSETS_ORDERED[i] for i in range(20) if i+1 not in PASCAL_5I_CLASS_IDS[subset_id]]
-
-
- elif remove_classes[0] == 'zs':
- stop = remove_classes[1]
-
- from datasets.pascal_zeroshot import PASCAL_VOC_CLASSES_ZS
-
- avoid = [c for class_set in PASCAL_VOC_CLASSES_ZS[:stop] for c in class_set]
- print(avoid)
-
- elif remove_classes[0] == 'aff':
- # avoid = ['drink.v.01', 'sit.v.01', 'ride.v.02']
- # all_lemmas = set(['drink', 'sit', 'ride'])
- avoid = ['drink', 'drinks', 'drinking', 'sit', 'sits', 'sitting',
- 'ride', 'rides', 'riding',
- 'fly', 'flies', 'flying', 'drive', 'drives', 'driving', 'driven',
- 'swim', 'swims', 'swimming',
- 'wheels', 'wheel', 'legs', 'leg', 'ear', 'ears']
- keep_sids = [(i, j) for i, j in self.sample_ids if
- all(x not in avoid for x in get_data(i)['phrases'][j].split(' '))]
-
- print('avoid classes:', avoid)
-
-
- if keep_sids is None:
- all_lemmas = [s for ps in avoid for s in traverse_lemmas_hypo(wordnet.synset(ps), max_depth=None)]
- all_lemmas = list(set(all_lemmas))
- all_lemmas = [h.replace('_', ' ').lower() for h in all_lemmas]
- all_lemmas = set(all_lemmas)
-
- # divide into multi word and single word
- all_lemmas_s = set(l for l in all_lemmas if ' ' not in l)
- all_lemmas_m = set(l for l in all_lemmas if l not in all_lemmas_s)
-
- # new3
- phrases = [get_data(i)['phrases'][j] for i, j in self.sample_ids]
- remove_sids = set((i,j) for (i,j), phrase in zip(self.sample_ids, phrases)
- if any(l in phrase for l in all_lemmas_m) or
- len(set(wnl.lemmatize(w) for w in phrase.split(' ')).intersection(all_lemmas_s)) > 0
- )
- keep_sids = [(i, j) for i, j in self.sample_ids if (i,j) not in remove_sids]
-
- print(f'Reduced to {len(keep_sids) / len(self.sample_ids):.3f}')
- removed_ids = set(self.sample_ids) - set(keep_sids)
-
- print('Examples of removed', len(removed_ids))
- for i, j in list(removed_ids)[:20]:
- print(i, get_data(i)['phrases'][j])
-
- self.sample_ids = keep_sids
-
- from itertools import groupby
- samples_by_phrase = [(self.refvg_loader.get_img_ref_data(i)['phrases'][j], (i, j))
- for i, j in self.sample_ids]
- samples_by_phrase = sorted(samples_by_phrase)
- samples_by_phrase = groupby(samples_by_phrase, key=lambda x: x[0])
-
- self.samples_by_phrase = {prompt: [s[1] for s in prompt_sample_ids] for prompt, prompt_sample_ids in samples_by_phrase}
-
- self.all_phrases = list(set(self.samples_by_phrase.keys()))
-
-
- if self.only_visual:
- assert self.with_visual
- self.sample_ids = [(i, j) for i, j in self.sample_ids
- if len(self.samples_by_phrase[self.refvg_loader.get_img_ref_data(i)['phrases'][j]]) > 1]
-
- # Filter by size (if min_size is set)
- sizes = [self.refvg_loader.get_img_ref_data(i)['gt_boxes'][j] for i, j in self.sample_ids]
- image_sizes = [self.refvg_loader.get_img_ref_data(i)['width'] * self.refvg_loader.get_img_ref_data(i)['height'] for i, j in self.sample_ids]
- #self.sizes = [sum([(s[2] - s[0]) * (s[3] - s[1]) for s in size]) for size in sizes]
- self.sizes = [sum([s[2] * s[3] for s in size]) / img_size for size, img_size in zip(sizes, image_sizes)]
-
- if min_size:
- print('filter by size')
-
- self.sample_ids = [self.sample_ids[i] for i in range(len(self.sample_ids)) if self.sizes[i] > min_size]
-
- self.base_path = join(expanduser('~/datasets/PhraseCut/VGPhraseCut_v0/images/'))
-
- def __len__(self):
- return len(self.sample_ids)
-
-
- def load_sample(self, sample_i, j):
-
- img_ref_data = self.refvg_loader.get_img_ref_data(sample_i)
-
- polys_phrase0 = img_ref_data['gt_Polygons'][j]
- phrase = img_ref_data['phrases'][j]
- phrase = self.phrase_form.format(phrase)
-
- masks = []
- for polys in polys_phrase0:
- for poly in polys:
- poly = [p[::-1] for p in poly] # swap x,y
- masks += [polygon2mask((img_ref_data['height'], img_ref_data['width']), poly)]
-
- seg = np.stack(masks).max(0)
- img = np.array(Image.open(join(self.base_path, str(img_ref_data['image_id']) + '.jpg')))
-
- min_shape = min(img.shape[:2])
-
- if self.aug_crop:
- sly, slx, exceed = find_crop(seg, (min_shape, min_shape), iterations=50, min_frac=0.05)
- else:
- sly, slx = slice(0, None), slice(0, None)
-
- seg = seg[sly, slx]
- img = img[sly, slx]
-
- seg = seg.astype('uint8')
- seg = torch.from_numpy(seg).view(1, 1, *seg.shape)
-
- if img.ndim == 2:
- img = np.dstack([img] * 3)
-
- img = torch.from_numpy(img).permute(2,0,1).unsqueeze(0).float()
-
- seg = nnf.interpolate(seg, (self.image_size, self.image_size), mode='nearest')[0,0]
- img = nnf.interpolate(img, (self.image_size, self.image_size), mode='bilinear', align_corners=True)[0]
-
- # img = img.permute([2,0, 1])
- img = img / 255.0
-
- if self.aug_color is not None:
- img = self.aug_color(img)
-
- img = self.normalize(img)
-
-
-
- return img, seg, phrase
-
- def __getitem__(self, i):
-
- sample_i, j = self.sample_ids[i]
-
- img, seg, phrase = self.load_sample(sample_i, j)
-
- if self.negative_prob > 0:
- if torch.rand((1,)).item() < self.negative_prob:
-
- new_phrase = None
- while new_phrase is None or new_phrase == phrase:
- idx = torch.randint(0, len(self.all_phrases), (1,)).item()
- new_phrase = self.all_phrases[idx]
- phrase = new_phrase
- seg = torch.zeros_like(seg)
-
- if self.with_visual:
- # find a corresponding visual image
- if phrase in self.samples_by_phrase and len(self.samples_by_phrase[phrase]) > 1:
- idx = torch.randint(0, len(self.samples_by_phrase[phrase]), (1,)).item()
- other_sample = self.samples_by_phrase[phrase][idx]
- #print(other_sample)
- img_s, seg_s, _ = self.load_sample(*other_sample)
-
- from datasets.utils import blend_image_segmentation
-
- if self.mask in {'separate', 'text_and_separate'}:
- # assert img.shape[1:] == img_s.shape[1:] == seg_s.shape == seg.shape[1:]
- add_phrase = [phrase] if self.mask == 'text_and_separate' else []
- vis_s = add_phrase + [img_s, seg_s, True]
- else:
- if self.mask.startswith('text_and_'):
- mask_mode = self.mask[9:]
- label_add = [phrase]
- else:
- mask_mode = self.mask
- label_add = []
-
- masked_img_s = torch.from_numpy(blend_image_segmentation(img_s, seg_s, mode=mask_mode, image_size=self.image_size)[0])
- vis_s = label_add + [masked_img_s, True]
-
- else:
- # phrase is unique
- vis_s = torch.zeros_like(img)
-
- if self.mask in {'separate', 'text_and_separate'}:
- add_phrase = [phrase] if self.mask == 'text_and_separate' else []
- vis_s = add_phrase + [vis_s, torch.zeros(*vis_s.shape[1:], dtype=torch.uint8), False]
- elif self.mask.startswith('text_and_'):
- vis_s = [phrase, vis_s, False]
- else:
- vis_s = [vis_s, False]
- else:
- assert self.mask == 'text'
- vis_s = [phrase]
-
- seg = seg.unsqueeze(0).float()
-
- data_x = (img,) + tuple(vis_s)
-
- return data_x, (seg, torch.zeros(0), i)
-
-
-class PhraseCutPlus(PhraseCut):
-
- def __init__(self, split, image_size=400, aug=None, aug_color=False, aug_crop=True, min_size=0, remove_classes=None, only_visual=False, mask=None):
- super().__init__(split, image_size=image_size, negative_prob=0.2, aug=aug, aug_color=aug_color, aug_crop=aug_crop, min_size=min_size,
- remove_classes=remove_classes, with_visual=True, only_visual=only_visual, mask=mask)
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Hash/TupleHash128.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Hash/TupleHash128.py
deleted file mode 100644
index 8fd32837a0641330d86eb613f838edd4b0dbea20..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Hash/TupleHash128.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# ===================================================================
-#
-# Copyright (c) 2021, Legrandin Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo
Corel Draw X4 Free Download Full Version For Windows 10
DOWNLOAD ⇒⇒⇒ https://urloso.com/2uyPLr
-
- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4fefd39f24
-
-
- diff --git a/spaces/bla/tranny/Dockerfile b/spaces/bla/tranny/Dockerfile deleted file mode 100644 index 9c64a4c8117bfa2b54b3afbc886239284ff31c01..0000000000000000000000000000000000000000 --- a/spaces/bla/tranny/Dockerfile +++ /dev/null @@ -1,28 +0,0 @@ -# Builder stage -FROM python:3.10.0 as builder - -RUN useradd -ms /bin/bash admin - -WORKDIR /srv -RUN chown -R admin:admin /srv -RUN chmod 755 /srv - - - -# Install dependencies and Python packages -RUN apt-get update && apt-get install -y git ffmpeg aria2 \ - && rm -rf /var/lib/apt/lists/* - -COPY requirements.txt . -RUN pip install --no-cache-dir -r requirements.txt - -# Copy the application code -USER admin - -COPY --chown=admin . /srv - -# Command to run the application -CMD uvicorn App.app:app --host 0.0.0.0 --port 7860 --workers 4 & celery -A App.Worker.celery worker -c 4 --loglevel=DEBUG - -# Expose the server port -EXPOSE 7860 \ No newline at end of file diff --git a/spaces/brainblow/MusiCreator/tests/modules/test_rope.py b/spaces/brainblow/MusiCreator/tests/modules/test_rope.py deleted file mode 100644 index 067c6f067acbf27fb0fef5c2b812c22474c4fcd0..0000000000000000000000000000000000000000 --- a/spaces/brainblow/MusiCreator/tests/modules/test_rope.py +++ /dev/null @@ -1,168 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -from audiocraft.modules.rope import RotaryEmbedding -from audiocraft.modules.transformer import StreamingTransformer, set_efficient_attention_backend - - -def test_rope(): - set_efficient_attention_backend('xformers') - B, T, H, C = 8, 75, 16, 128 - - rope = RotaryEmbedding(dim=C) - xq = torch.rand((B, T, H, C)) - xk = torch.rand((B, T, H, C)) - xq_out, xk_out = rope.rotate_qk(xq, xk, start=7) - - assert list(xq_out.shape) == [B, T, H, C] - assert list(xk_out.shape) == [B, T, H, C] - - -def test_rope_io_dtypes(): - set_efficient_attention_backend('xformers') - B, T, H, C = 8, 75, 16, 128 - - rope_32 = RotaryEmbedding(dim=C, dtype=torch.float32) - rope_64 = RotaryEmbedding(dim=C, dtype=torch.float64) - - # Test bfloat16 inputs w/ both 32 and 64 precision rope. - xq_16 = torch.rand((B, T, H, C)).to(torch.bfloat16) - xk_16 = torch.rand((B, T, H, C)).to(torch.bfloat16) - xq_out, xk_out = rope_32.rotate_qk(xq_16, xk_16) - assert xq_out.dtype == torch.bfloat16 - xq_out, xk_out = rope_64.rotate_qk(xq_16, xk_16) - assert xq_out.dtype == torch.bfloat16 - - # Test float32 inputs w/ both 32 and 64 precision rope. - xq_32 = torch.rand((B, T, H, C)).to(torch.float32) - xk_32 = torch.rand((B, T, H, C)).to(torch.float32) - xq_out, xk_out = rope_32.rotate_qk(xq_32, xk_32) - assert xq_out.dtype == torch.float32 - xq_out, xk_out = rope_64.rotate_qk(xq_32, xk_32) - assert xq_out.dtype == torch.float32 - - -def test_transformer_with_rope(): - set_efficient_attention_backend('xformers') - torch.manual_seed(1234) - for pos in ['rope', 'sin_rope']: - tr = StreamingTransformer( - 16, 4, 2, custom=True, dropout=0., layer_scale=0.1, - positional_embedding=pos) - tr.eval() - steps = 12 - x = torch.randn(3, steps, 16) - - out = tr(x) - assert list(out.shape) == list(x.shape) - - -@torch.no_grad() -def test_rope_streaming(): - set_efficient_attention_backend('xformers') - torch.manual_seed(1234) - tr = StreamingTransformer( - 16, 4, 2, causal=True, dropout=0., - custom=True, positional_embedding='rope') - tr.eval() - steps = 12 - x = torch.randn(3, steps, 16) - - ref = tr(x) - - with tr.streaming(): - outs = [] - frame_sizes = [1] * steps - - for frame_size in frame_sizes: - frame = x[:, :frame_size] - x = x[:, frame_size:] - outs.append(tr(frame)) - - out = torch.cat(outs, dim=1) - assert list(out.shape) == [3, steps, 16] - delta = torch.norm(out - ref) / torch.norm(out) - assert delta < 1e-6, delta - - -@torch.no_grad() -def test_rope_streaming_past_context(): - set_efficient_attention_backend('xformers') - torch.manual_seed(1234) - - for context in [None, 10]: - tr = StreamingTransformer( - 16, 4, 1 if context else 2, - causal=True, past_context=context, custom=True, - dropout=0., positional_embedding='rope') - tr.eval() - - steps = 20 - x = torch.randn(3, steps, 16) - ref = tr(x) - - with tr.streaming(): - outs = [] - frame_sizes = [1] * steps - - for frame_size in frame_sizes: - frame = x[:, :frame_size] - x = x[:, frame_size:] - outs.append(tr(frame)) - - out = torch.cat(outs, dim=1) - assert list(out.shape) == [3, steps, 16] - delta = torch.norm(out - ref) / torch.norm(out) - assert delta < 1e-6, delta - - -def test_rope_memory_efficient(): - set_efficient_attention_backend('xformers') - torch.manual_seed(1234) - tr = StreamingTransformer( - 16, 4, 2, custom=True, dropout=0., layer_scale=0.1, - positional_embedding='rope') - tr_mem_efficient = StreamingTransformer( - 16, 4, 2, dropout=0., memory_efficient=True, layer_scale=0.1, - positional_embedding='rope') - tr_mem_efficient.load_state_dict(tr.state_dict()) - tr.eval() - steps = 12 - x = torch.randn(3, steps, 16) - - with torch.no_grad(): - y = tr(x) - y2 = tr_mem_efficient(x) - # Check at float precision b/c this is the rope default. - assert torch.allclose(y, y2, atol=1e-7), (y - y2).norm() - - -def test_rope_with_xpos(): - set_efficient_attention_backend('xformers') - B, T, H, C = 8, 75, 16, 128 - - rope = RotaryEmbedding(dim=C, xpos=True) - xq = torch.rand((B, T, H, C)) - xk = torch.rand((B, T, H, C)) - xq_out, xk_out = rope.rotate_qk(xq, xk, start=7) - - assert list(xq_out.shape) == [B, T, H, C] - assert list(xk_out.shape) == [B, T, H, C] - - -def test_positional_scale(): - set_efficient_attention_backend('xformers') - B, T, H, C = 8, 75, 16, 128 - - rope = RotaryEmbedding(dim=C, xpos=True, scale=0.0) - xq = torch.rand((B, T, H, C)) - xk = torch.rand((B, T, H, C)) - xq_out, xk_out = rope.rotate_qk(xq, xk, start=7) - - assert torch.allclose(xq, xq_out) - assert torch.allclose(xk, xk_out) diff --git a/spaces/bruno16/massa_qa/README.md b/spaces/bruno16/massa_qa/README.md deleted file mode 100644 index 8d0d697875add8a5641230b5a0aa2a5495a687dc..0000000000000000000000000000000000000000 --- a/spaces/bruno16/massa_qa/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Massa Qa -emoji: 🏃 -colorFrom: yellow -colorTo: yellow -sdk: gradio -sdk_version: 4.0.2 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/cahya/websocket/Dockerfile b/spaces/cahya/websocket/Dockerfile deleted file mode 100644 index d41bad2d6891bf1c3aa9a8013b26820232e26d9e..0000000000000000000000000000000000000000 --- a/spaces/cahya/websocket/Dockerfile +++ /dev/null @@ -1,7 +0,0 @@ -FROM python:3.9 -WORKDIR /fastapi/app -COPY ./requirements.txt /fastapi/requirements.txt -RUN pip install --no-cache-dir --upgrade -r /fastapi/requirements.txt -COPY ./app /fastapi/app -EXPOSE 7860 -CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"] diff --git a/spaces/calmgoose/Talk2Book/README.md b/spaces/calmgoose/Talk2Book/README.md deleted file mode 100644 index 250835f59ba792c1859249efc89d9473a9d40a2b..0000000000000000000000000000000000000000 --- a/spaces/calmgoose/Talk2Book/README.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -title: 'Talk2Book' -emoji: 📚 -colorFrom: blue -colorTo: blue -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false -license: apache-2.0 -fullWidth: true -models: [hkunlp/instructor-large] -datasets: [calmgoose/book-embeddings] -tags: [Question Answering, LangChain, talk2book, Instructor Embeddings, faiss, LLM] ---- - -# Talk2Book 📖 - -Using large language models to talk to the book '1984'. Based on the notebooks in [Talk2Book](https://github.com/batmanscode/Talk2Book). - -*Update: added 'The Almanac of Naval Ravikant'* \ No newline at end of file diff --git a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/commons.py b/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/commons.py deleted file mode 100644 index 9ad0444b61cbadaa388619986c2889c707d873ce..0000000000000000000000000000000000000000 --- a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/commons.py +++ /dev/null @@ -1,161 +0,0 @@ -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size*dilation - dilation)/2) - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def intersperse(lst, item): - result = [item] * (len(lst) * 2 + 1) - result[1::2] = lst - return result - - -def kl_divergence(m_p, logs_p, m_q, logs_q): - """KL(P||Q)""" - kl = (logs_q - logs_p) - 0.5 - kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q) - return kl - - -def rand_gumbel(shape): - """Sample from the Gumbel distribution, protect from overflows.""" - uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 - return -torch.log(-torch.log(uniform_samples)) - - -def rand_gumbel_like(x): - g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) - return g - - -def slice_segments(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, :, idx_str:idx_end] - return ret - - -def rand_slice_segments(x, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = x_lengths - segment_size + 1 - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - return ret, ids_str - - -def get_timing_signal_1d( - length, channels, min_timescale=1.0, max_timescale=1.0e4): - position = torch.arange(length, dtype=torch.float) - num_timescales = channels // 2 - log_timescale_increment = ( - math.log(float(max_timescale) / float(min_timescale)) / - (num_timescales - 1)) - inv_timescales = min_timescale * torch.exp( - torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment) - scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) - signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) - signal = F.pad(signal, [0, 0, 0, channels % 2]) - signal = signal.view(1, channels, length) - return signal - - -def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return x + signal.to(dtype=x.dtype, device=x.device) - - -def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) - - -def subsequent_mask(length): - mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) - return mask - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def shift_1d(x): - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] - return x - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def generate_path(duration, mask): - """ - duration: [b, 1, t_x] - mask: [b, 1, t_y, t_x] - """ - device = duration.device - - b, _, t_y, t_x = mask.shape - cum_duration = torch.cumsum(duration, -1) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] - path = path.unsqueeze(1).transpose(2,3) * mask - return path - - -def clip_grad_value_(parameters, clip_value, norm_type=2): - if isinstance(parameters, torch.Tensor): - parameters = [parameters] - parameters = list(filter(lambda p: p.grad is not None, parameters)) - norm_type = float(norm_type) - if clip_value is not None: - clip_value = float(clip_value) - - total_norm = 0 - for p in parameters: - param_norm = p.grad.data.norm(norm_type) - total_norm += param_norm.item() ** norm_type - if clip_value is not None: - p.grad.data.clamp_(min=-clip_value, max=clip_value) - total_norm = total_norm ** (1. / norm_type) - return total_norm diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DeepLab/README.md b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DeepLab/README.md deleted file mode 100644 index bd03cf1c41f7b0358fb6988d6a387effbb328a50..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DeepLab/README.md +++ /dev/null @@ -1,100 +0,0 @@ -# DeepLab in Detectron2 - -In this repository, we implement DeepLabV3 and DeepLabV3+ in Detectron2. - -## Installation -Install Detectron2 following [the instructions](https://detectron2.readthedocs.io/tutorials/install.html). - -## Training - -To train a model with 8 GPUs run: -```bash -cd /path/to/detectron2/projects/DeepLab -python train_net.py --config-file configs/Cityscapes-SemanticSegmentation/deeplab_v3_plus_R_103_os16_mg124_poly_90k_bs16.yaml --num-gpus 8 -``` - -## Evaluation - -Model evaluation can be done similarly: -```bash -cd /path/to/detectron2/projects/DeepLab -python train_net.py --config-file configs/Cityscapes-SemanticSegmentation/deeplab_v3_plus_R_103_os16_mg124_poly_90k_bs16.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint -``` - -## Cityscapes Semantic Segmentation -Cityscapes models are trained with ImageNet pretraining. - -
| Method | -Backbone | -Output resolution |
-mIoU | -model id | -download | - -
|---|---|---|---|---|---|
| DeepLabV3 | -R101-DC5 | -1024×2048 | -76.7 | -- | -- | - | -
| DeepLabV3 | -R103-DC5 | -1024×2048 | -78.5 | -28041665 | -model | metrics | -
| DeepLabV3+ | -R101-DC5 | -1024×2048 | -78.1 | -- | -- | - | -
| DeepLabV3+ | -R103-DC5 | -1024×2048 | -80.0 | -28054032 | -model | metrics | -
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This is divided by 2 - for each consecutive upsampling layer. - upsample_factors (Tuple[int, ...]): upsampling factors (stride) for each upsampling layer. - inference_padding (int): constant padding applied to the input at inference time. - sample_rate (int): sample rate of the generated audio. - """ - super().__init__() - self.inference_padding = inference_padding - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_factors) - self.sample_rate = sample_rate - # initial upsampling layers - self.conv_pre = weight_norm( - nn.Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3) - ) - - # upsampling layers - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_factors, upsample_kernel_sizes)): - self.ups.append( - weight_norm( - nn.ConvTranspose1d( - upsample_initial_channel // (2 ** i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - # MRF blocks - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for _, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(ResBlock1(ch, k, d)) - # post convolution layer - self.conv_post = weight_norm(nn.Conv1d(ch, 1, 7, 1, padding=3)) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - o = self.conv_pre(x) - for i in range(self.num_upsamples): - o = F.leaky_relu(o, LRELU_SLOPE) - o = self.ups[i](o) - z_sum = None - for j in range(self.num_kernels): - if z_sum is None: - z_sum = self.resblocks[i * self.num_kernels + j](o) - else: - z_sum += self.resblocks[i * self.num_kernels + j](o) - o = z_sum / self.num_kernels - o = F.leaky_relu(o) - o = self.conv_post(o) - o = torch.tanh(o) - return o - - @torch.no_grad() - def generate(self, x: torch.Tensor) -> torch.Tensor: - x = F.pad(x, (self.inference_padding, self.inference_padding), "replicate") - return self(x), self.sample_rate - - def remove_weight_norm(self): - print("Removing weight norm...") - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - remove_weight_norm(self.conv_pre) - remove_weight_norm(self.conv_post) - - -class ResBlock1(torch.nn.Module): - def __init__( - self, channels: int, kernel_size: int = 3, dilation: Tuple[int, ...] = (1, 3, 5) - ) -> None: - super().__init__() - self.convs1 = nn.ModuleList( - [ - weight_norm( - nn.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - nn.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - nn.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - nn.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - nn.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - nn.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - xt = c2(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__( - self, channels: int, kernel_size: int = 3, dilation: Tuple[int, ...] = (1, 3) - ) -> None: - super().__init__() - self.convs = nn.ModuleList( - [ - weight_norm( - nn.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - nn.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - ] - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -def _hifigan( - name: str, - pretrained: bool = True, - progress: bool = True, - map_location=None, -) -> HifiganGenerator: - hifigan = HifiganGenerator() - if pretrained: - checkpoint = torch.hub.load_state_dict_from_url( - URLS[name], map_location=map_location, progress=progress - ) - consume_prefix_in_state_dict_if_present(checkpoint, "module.") - hifigan.load_state_dict(checkpoint) - hifigan.eval() - hifigan.remove_weight_norm() - return hifigan - - -def hifigan( - pretrained: bool = True, progress: bool = True, map_location=None -) -> HifiganGenerator: - return _hifigan("hifigan", pretrained, progress, map_location) - - -def hifigan_hubert_soft( - pretrained: bool = True, progress: bool = True, map_location=None -) -> HifiganGenerator: - return _hifigan("hifigan-hubert-soft", pretrained, progress, map_location=None) - - -def hifigan_hubert_discrete( - pretrained: bool = True, progress: bool = True, map_location=None -) -> HifiganGenerator: - return _hifigan("hifigan-hubert-discrete", pretrained, progress, map_location=None) diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/anyio/abc/_resources.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/anyio/abc/_resources.py deleted file mode 100644 index e0a283fc9873b524bbacb73624721353d82c34ab..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/anyio/abc/_resources.py +++ /dev/null @@ -1,31 +0,0 @@ -from __future__ import annotations - -from abc import ABCMeta, abstractmethod -from types import TracebackType -from typing import TypeVar - -T = TypeVar("T") - - -class AsyncResource(metaclass=ABCMeta): - """ - Abstract base class for all closeable asynchronous resources. - - Works as an asynchronous context manager which returns the instance itself on enter, and calls - :meth:`aclose` on exit. - """ - - async def __aenter__(self: T) -> T: - return self - - async def __aexit__( - self, - exc_type: type[BaseException] | None, - exc_val: BaseException | None, - exc_tb: TracebackType | None, - ) -> None: - await self.aclose() - - @abstractmethod - async def aclose(self) -> None: - """Close the resource.""" diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/attrs/exceptions.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/attrs/exceptions.py deleted file mode 100644 index bd9efed202ab1cdf57a9e99cb4e094ef6f38d0c0..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/attrs/exceptions.py +++ /dev/null @@ -1,3 +0,0 @@ -# SPDX-License-Identifier: MIT - -from attr.exceptions import * # noqa diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacdec_fixed.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacdec_fixed.c deleted file mode 100644 index 46f0ebf91bd6f866c6d38f274a043eb02e29e049..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacdec_fixed.c +++ /dev/null @@ -1,473 +0,0 @@ -/* - * Copyright (c) 2013 - * MIPS Technologies, Inc., California. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * 3. Neither the name of the MIPS Technologies, Inc., nor the names of its - * contributors may be used to endorse or promote products derived from - * this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE MIPS TECHNOLOGIES, INC. ``AS IS'' AND - * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - * ARE DISCLAIMED. IN NO EVENT SHALL THE MIPS TECHNOLOGIES, INC. BE LIABLE - * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL - * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS - * OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) - * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT - * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY - * OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF - * SUCH DAMAGE. - * - * AAC decoder fixed-point implementation - * - * Copyright (c) 2005-2006 Oded Shimon ( ods15 ods15 dyndns org ) - * Copyright (c) 2006-2007 Maxim Gavrilov ( maxim.gavrilov gmail com ) - * - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -/** - * @file - * AAC decoder - * @author Oded Shimon ( ods15 ods15 dyndns org ) - * @author Maxim Gavrilov ( maxim.gavrilov gmail com ) - * - * Fixed point implementation - * @author Stanislav Ocovaj ( stanislav.ocovaj imgtec com ) - */ - -#define USE_FIXED 1 -#define TX_TYPE AV_TX_INT32_MDCT - -#include "libavutil/fixed_dsp.h" -#include "libavutil/opt.h" -#include "avcodec.h" -#include "codec_internal.h" -#include "get_bits.h" -#include "lpc.h" -#include "kbdwin.h" -#include "sinewin_fixed_tablegen.h" - -#include "aac.h" -#include "aactab.h" -#include "aacdectab.h" -#include "adts_header.h" -#include "cbrt_data.h" -#include "sbr.h" -#include "aacsbr.h" -#include "mpeg4audio.h" -#include "profiles.h" -#include "libavutil/intfloat.h" - -#include
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-The third step is to install and launch the game on your PC. After clicking on the download button, you will see a pop-up window asking you to save the game file on your computer. Choose a location where you want to save the file and click on save. The file size is about 23 MB, so it should not take long to download. Once the download is complete, you will see another pop-up window asking you to run the game file. Click on run to start the installation process. Follow the instructions on the screen to complete the installation. After that, you will see a shortcut icon of Air Assault 2 on your desktop. Double-click on this icon to launch the game and enjoy!
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-Air Assault 2 is a fun and exciting game that will keep you entertained for hours. However, it is also a challenging game that will test your skills and reflexes. To help you get started and improve your performance in the game, here are some tips and tricks that you should know:
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-Air Assault 2 is a game that will appeal to anyone who loves arcade games, especially shoot 'em up games. It is a game that will provide you with hours of fun and entertainment, as well as challenge and satisfaction. Here are some of the reasons why you should play Air Assault 2:
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-Air Assault 2 is a game that you should not miss if you are looking for a thrilling and action-packed arcade game that will take you to the skies and challenge your piloting skills. It is a game that has many features that make it an exciting and enjoyable game to play, such as diverse aircraft, intense campaign, challenging missions, upgrade and customize, and realistic environments. It is also a game that you can download for free from a reliable and safe source: GameTop. All you need to do is follow the simple steps that we have explained in this article, and you will be able to enjoy this amazing game on your PC. So, what are you waiting for? Download Air Assault 2 today and experience the thrill of aerial combat!
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download and Install Xtreme Motorbikes Mod APK for Free on Android.md b/spaces/congsaPfin/Manga-OCR/logs/Download and Install Xtreme Motorbikes Mod APK for Free on Android.md deleted file mode 100644 index e46dd57809b29965876f86ba84efb95f098b56be..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Download and Install Xtreme Motorbikes Mod APK for Free on Android.md +++ /dev/null @@ -1,103 +0,0 @@ - -
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-If you are a fan of café racing and motorbike games, you will love APK Mod Xtreme Motorbikes. This is a thrilling racing game that allows you to experience the thrill of café racing on your mobile device. In this game, you can roam freely around the city, choosing from a variety of different motorbikes to ride. You can also perform stunts, drifts, wheelies, and burnouts to show off your skills. The game has realistic graphics and physics, giving you a realistic racing experience.
-But what if you want to enjoy the game without any limitations or restrictions? What if you want to have unlimited money and coins to buy and upgrade your bikes? What if you want to have all bikes unlocked to choose from different models and styles? What if you want to play the game without any ads to interrupt your gameplay?
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Well, there is a way to do that. You can download APK Mod Xtreme Motorbikes, which is a modified version of the original game that gives you all these benefits and more. In this article, we will show you how to download APK Mod Xtreme Motorbikes, what are the benefits and risks of using it, what are some alternatives to it, and some FAQs about it. Let's get started!
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There are many benefits of using APK Mod Xtreme Motorbikes over the original version of the game. Here are some of them:
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These are some of the benefits of using APK Mod Xtreme Motorbikes. However, there are also some risks that you should be aware of before using it.
-Risks of APK Mod Xtreme Motorbikes
-Using APK Mod Xtreme Motorbikes is not without risks. Here are some of them:
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These are some of the risks of using APK Mod Xtreme Motorbikes. However, there are also some alternatives that you can try if you don't want to use it.
-Alternatives to APK Mod Xtreme Motorbikes
-If you don't want to use APK Mod Xtreme Motorbikes, you can try these alternatives:
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These are some of the alternatives to APK Mod Xtreme Motorbikes that you can try. However, you should always compare the pros and cons of each option before choosing one.
-Conclusion
-In conclusion, APK Mod Xtreme Motorbikes is a thrilling racing game for Android that allows you to experience the thrill of café racing on your mobile device. You can download APK Mod Xtreme Motorbikes from APK Bounce, which is one of the best sources for downloading modded games and apps. By using APK Mod Xtreme Motorbikes, you can enjoy unlimited money and coins, all bikes unlocked, no ads, and realistic graphics and physics in the game. However, you should also be aware of the risks such as malware or virus infection, ban or suspension from the game server, or loss of data or progress. You can also try some alternatives to APK Mod Xtreme Motorbikes, such as the original version from Google Play Store or other modded versions from other websites. However, you should always compare the pros and cons of each option before choosing one. We hope that this article has helped you to learn more about APK Mod Xtreme Motorbikes and how to download and use it. If you have any questions or feedback, please feel free to leave a comment below. Happy racing!
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-Here are some frequently asked questions about APK Mod Xtreme Motorbikes:
-Q1: What is the difference between APK and mod APK?
-A: APK stands for Android Package Kit, which is the file format used by Android devices to install and distribute apps. Mod APK stands for modified APK, which is a modified version of an original app that has been altered to provide some extra features or benefits that are not available in the original app.
-Q2: Is APK Mod Xtreme Motorbikes safe to download and install?
-A: APK Mod Xtreme Motorbikes is safe to download and install if you download it from a trusted source such as APK Bounce. However, you should always scan the files before installing them and use a reliable antivirus app on your device to protect yourself from malware or virus infection.
-Q3: How can I update APK Mod Xtreme Motorbikes?
-A: You can update APK Mod Xtreme Motorbikes by visiting the official website of APK Bounce and downloading the latest version of the modded game. However, you should always backup your data and progress before updating the game and restore them if needed.
-Q4: What are some tips and tricks for playing APK Mod Xtreme Motorbikes?
-A: Here are some tips and tricks for playing APK Mod Xtreme Motorbikes:
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-A: You can find more information about APK Mod Xtreme Motorbikes by visiting the official website of APK Bounce, where you can also download the modded game. You can also visit the official website of Xtreme Motorbikes, where you can download the original version of the game from Google Play Store. You can also watch some videos of gameplay and reviews on YouTube or read some articles and blogs on the internet.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Pickle Pete Survivor - An Epic Adventure of Stacking Gearing and Fighting Evil Forces.md b/spaces/congsaPfin/Manga-OCR/logs/Pickle Pete Survivor - An Epic Adventure of Stacking Gearing and Fighting Evil Forces.md deleted file mode 100644 index 1a752e41ae4b967d32f05bf5530346a057a2ab51..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Pickle Pete Survivor - An Epic Adventure of Stacking Gearing and Fighting Evil Forces.md +++ /dev/null @@ -1,123 +0,0 @@ -
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-Introduction
-If you are looking for a fun and addictive action game for your Android device, you should check out Pickle Pete: Survivor APK. This game is a top-down arena shooter with autofire and a wide variety of guns to choose from. You play as Pete, a scientist who has been turned into a pickle by a mysterious substance. Your goal is to survive against the waves of evil forces that have taken over the world. You will need to stack up, gear up, and create countless combinations of unique builds to become stronger than your enemies. You will also need to find the antidote and save the world from the darkness.
-Pickle Pete: Survivor APK is a game that will keep you entertained for hours with its rich environments, epic boss fights, deep progression system, and various game-modes. You will love the super easy controls, the stylized graphics, and the humorous sound effects. You will also enjoy the challenge of finding the best strategy for each run and unlocking new items and abilities. Pickle Pete: Survivor APK is a game that will make you feel like a hero in a pickle.
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-How to play Pickle Pete: Survivor APK?
-Pickle Pete: Survivor APK is very easy to play. You just need to tap on the screen to move your character and aim at the enemies. Your character will automatically shoot at them with the weapon you have equipped. You can also use special abilities such as dodge roll, grenade, shield, or heal by tapping on their icons on the bottom of the screen. You can switch weapons by swiping left or right on the weapon icon.
-You will face different types of enemies in each biome, such as zombies, spiders, robots, aliens, and more. Each enemy has its own behavior and attack pattern, so you will need to adapt your strategy accordingly. You will also encounter boss fights at the end of each biome, which will test your skills and reflexes.
-You will earn coins and gems by killing enemies and completing objectives. You can use them to buy new weapons and items from the shop or upgrade your existing ones. You can also find chests and crates that contain loot such as weapons, items, or coins. You can equip up to four weapons and four items at a time.
-You can customize your character by choosing different skins, hats, glasses, or masks. You can also unlock new characters with different stats and abilities by completing achievements or buying them with gems.
-What are the features of Pickle Pete: Survivor APK?
-Pickle Pete: Survivor APK has many features that make it a fun and exciting game to play. Some of them are:
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Pickle Pete: Survivor APK is not available on the Google Play Store, so you will need to download it from a third-party source. One of the best and safest sources to download Pickle Pete: Survivor APK is APKPure. APKPure is a website that provides free and verified APK files for Android users. You can download Pickle Pete: Survivor APK from APKPure by following these steps:
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-Pickle Pete: Survivor APK is a juicy roguelike game for Android that will keep you hooked with its addictive gameplay, rich features, and humorous theme. You will have a blast playing as Pete, a pickle scientist who has to survive against the evil forces that have taken over the world. You will be able to customize your character, upgrade your weapons and items, unlock new game-modes, and compete with other players. Pickle Pete: Survivor APK is a game that you don't want to miss out on.
-If you are ready to join Pete in his quest to save the world, download Pickle Pete: Survivor APK from APKPure today and start playing. You will not regret it!
-FAQs
-What is the difference between Pickle Pete: Survivor APK and Pickle Pete: Survivor?
-Pickle Pete: Survivor APK is the modified version of Pickle Pete: Survivor, which is the original game developed by Pixel Perfect Studios. The APK version has some advantages over the original version, such as:
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Pickle Pete: Survivor APK is safe to download and install as long as you get it from a reliable source such as APKPure. APKPure is a website that verifies the authenticity and security of the APK files it provides. You can trust that Pickle Pete: Survivor APK from APKPure is free of viruses, malware, or any harmful code. However, you should always be careful when downloading and installing apps from unknown sources and check the permissions they require. You should also scan your device regularly with an antivirus app to ensure its safety.
-How can I play Pickle Pete: Survivor APK with my friends?
-Pickle Pete: Survivor APK has a multiplayer mode that allows you to play with your friends online. You can join or create a room and invite your friends to join you. You can also chat with them and see their stats and rankings. To play Pickle Pete: Survivor APK with your friends, you will need to have an internet connection and a compatible device. You will also need to have the same version of the game as your friends.
-How can I contact the developers of Pickle Pete: Survivor APK?
-If you have any questions, feedback, or suggestions for the developers of Pickle Pete: Survivor APK, you can contact them through their email address: pixelperfectstudios@gmail.com. You can also follow them on their social media accounts such as Facebook, Twitter, or Instagram. You can also visit their website for more information about their games and projects.
-What are some tips and tricks for playing Pickle Pete: Survivor APK?
-Here are some tips and tricks that can help you improve your gameplay and enjoy Pickle Pete: Survivor APK more:
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/aBox - Sandbox Game APK A Fun and Easy Way to Express Your Creativity.md b/spaces/congsaPfin/Manga-OCR/logs/aBox - Sandbox Game APK A Fun and Easy Way to Express Your Creativity.md deleted file mode 100644 index bbc9b20a8930964bdbd437b6f528f62773076851..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/aBox - Sandbox Game APK A Fun and Easy Way to Express Your Creativity.md +++ /dev/null @@ -1,98 +0,0 @@ -
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What is aBox - Sandbox Game APK and Why You Should Try It
-If you love sandbox games, you are in for a treat. Sandbox games are games that allow you to create, explore, and manipulate your own virtual world. They give you the freedom to experiment with different elements, scenarios, and possibilities. They are also great for stimulating your creativity, imagination, and problem-solving skills.
-One of the most fun and exciting sandbox games that you can play on your Android device is aBox. aBox is an early access sandbox game that lets you spawn items and mess with tools in various maps. You can also use weapons and fight enemies in different modes. You can customize your experience and share your creations with other players.
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In this article, we will show you how to download and install aBox - Sandbox Game APK on your Android device. We will also give you some tips on how to play and enjoy this amazing game. Let's get started!
-How to Download and Install aBox - Sandbox Game APK
-Before you can play aBox, you need to download and install its APK file on your device. An APK file is an Android application package that contains all the files and data needed to run an app. You can find the latest version of aBox - Sandbox Game APK on APKCombo, a reliable website that offers free downloads of Android apps and games.
-To download and install aBox - Sandbox Game APK, follow these steps:
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How to Play and Enjoy aBox - Sandbox Game APK
-Now that you have installed aBox on your device, you are ready to play and have fun. Here are some of the things that you can do in this game:
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-If you are looking for a sandbox game that will keep you entertained and engaged for hours, you should definitely try aBox - Sandbox Game APK. You will not regret it. Download it now and start playing!
-FAQs
-What are the minimum requirements for aBox?
-aBox requires Android 4.4 or higher, and at least 100 MB of free storage space on your device. It also requires an internet connection for some features, such as downloading and sharing creations.
-Is aBox safe and virus-free?
-Yes, aBox is safe and virus-free. It is developed by a reputable developer, called Abox Games, and it is verified by APKCombo. It does not contain any malware or harmful code that could harm your device or data.
-How often is aBox updated and what are the latest features?
-aBox is updated regularly to fix bugs, improve performance, and add new features. The latest version of aBox is 1.0.9, which was released on June 15, 2023. Some of the latest features include:
--
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Can I play aBox offline or online?
-You can play aBox both offline and online. Offline mode allows you to play without an internet connection, but you will not be able to access some features, such as downloading and sharing creations. Online mode allows you to access all the features of the game, but you will need an internet connection.
-Can I create my own maps and items in aBox?
-Yes, you can create your own maps and items in aBox. You can use the map editor to design your own maps using different terrains, props, lights, etc. You can also use the item editor to create your own items using different models, textures, sounds, etc. You can then save and load your creations, and share them with other players online.
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\ No newline at end of file diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/image/geometric.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/image/geometric.py deleted file mode 100644 index cf97c201cb4e43796c911919d03fb26a07ed817d..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/image/geometric.py +++ /dev/null @@ -1,728 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import numbers - -import cv2 -import numpy as np - -from ..utils import to_2tuple -from .io import imread_backend - -try: - from PIL import Image -except ImportError: - Image = None - - -def _scale_size(size, scale): - """Rescale a size by a ratio. - - Args: - size (tuple[int]): (w, h). - scale (float | tuple(float)): Scaling factor. - - Returns: - tuple[int]: scaled size. - """ - if isinstance(scale, (float, int)): - scale = (scale, scale) - w, h = size - return int(w * float(scale[0]) + 0.5), int(h * float(scale[1]) + 0.5) - - -cv2_interp_codes = { - 'nearest': cv2.INTER_NEAREST, - 'bilinear': cv2.INTER_LINEAR, - 'bicubic': cv2.INTER_CUBIC, - 'area': cv2.INTER_AREA, - 'lanczos': cv2.INTER_LANCZOS4 -} - -if Image is not None: - pillow_interp_codes = { - 'nearest': Image.NEAREST, - 'bilinear': Image.BILINEAR, - 'bicubic': Image.BICUBIC, - 'box': Image.BOX, - 'lanczos': Image.LANCZOS, - 'hamming': Image.HAMMING - } - - -def imresize(img, - size, - return_scale=False, - interpolation='bilinear', - out=None, - backend=None): - """Resize image to a given size. - - Args: - img (ndarray): The input image. - size (tuple[int]): Target size (w, h). - return_scale (bool): Whether to return `w_scale` and `h_scale`. - interpolation (str): Interpolation method, accepted values are - "nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2' - backend, "nearest", "bilinear" for 'pillow' backend. - out (ndarray): The output destination. - backend (str | None): The image resize backend type. Options are `cv2`, - `pillow`, `None`. If backend is None, the global imread_backend - specified by ``mmcv.use_backend()`` will be used. Default: None. - - Returns: - tuple | ndarray: (`resized_img`, `w_scale`, `h_scale`) or - `resized_img`. - """ - h, w = img.shape[:2] - if backend is None: - backend = imread_backend - if backend not in ['cv2', 'pillow']: - raise ValueError(f'backend: {backend} is not supported for resize.' - f"Supported backends are 'cv2', 'pillow'") - - if backend == 'pillow': - assert img.dtype == np.uint8, 'Pillow backend only support uint8 type' - pil_image = Image.fromarray(img) - pil_image = pil_image.resize(size, pillow_interp_codes[interpolation]) - resized_img = np.array(pil_image) - else: - resized_img = cv2.resize( - img, size, dst=out, interpolation=cv2_interp_codes[interpolation]) - if not return_scale: - return resized_img - else: - w_scale = size[0] / w - h_scale = size[1] / h - return resized_img, w_scale, h_scale - - -def imresize_to_multiple(img, - divisor, - size=None, - scale_factor=None, - keep_ratio=False, - return_scale=False, - interpolation='bilinear', - out=None, - backend=None): - """Resize image according to a given size or scale factor and then rounds - up the the resized or rescaled image size to the nearest value that can be - divided by the divisor. - - Args: - img (ndarray): The input image. - divisor (int | tuple): Resized image size will be a multiple of - divisor. If divisor is a tuple, divisor should be - (w_divisor, h_divisor). - size (None | int | tuple[int]): Target size (w, h). Default: None. - scale_factor (None | float | tuple[float]): Multiplier for spatial - size. Should match input size if it is a tuple and the 2D style is - (w_scale_factor, h_scale_factor). Default: None. - keep_ratio (bool): Whether to keep the aspect ratio when resizing the - image. Default: False. - return_scale (bool): Whether to return `w_scale` and `h_scale`. - interpolation (str): Interpolation method, accepted values are - "nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2' - backend, "nearest", "bilinear" for 'pillow' backend. - out (ndarray): The output destination. - backend (str | None): The image resize backend type. Options are `cv2`, - `pillow`, `None`. If backend is None, the global imread_backend - specified by ``mmcv.use_backend()`` will be used. Default: None. - - Returns: - tuple | ndarray: (`resized_img`, `w_scale`, `h_scale`) or - `resized_img`. - """ - h, w = img.shape[:2] - if size is not None and scale_factor is not None: - raise ValueError('only one of size or scale_factor should be defined') - elif size is None and scale_factor is None: - raise ValueError('one of size or scale_factor should be defined') - elif size is not None: - size = to_2tuple(size) - if keep_ratio: - size = rescale_size((w, h), size, return_scale=False) - else: - size = _scale_size((w, h), scale_factor) - - divisor = to_2tuple(divisor) - size = tuple([int(np.ceil(s / d)) * d for s, d in zip(size, divisor)]) - resized_img, w_scale, h_scale = imresize( - img, - size, - return_scale=True, - interpolation=interpolation, - out=out, - backend=backend) - if return_scale: - return resized_img, w_scale, h_scale - else: - return resized_img - - -def imresize_like(img, - dst_img, - return_scale=False, - interpolation='bilinear', - backend=None): - """Resize image to the same size of a given image. - - Args: - img (ndarray): The input image. - dst_img (ndarray): The target image. - return_scale (bool): Whether to return `w_scale` and `h_scale`. - interpolation (str): Same as :func:`resize`. - backend (str | None): Same as :func:`resize`. - - Returns: - tuple or ndarray: (`resized_img`, `w_scale`, `h_scale`) or - `resized_img`. - """ - h, w = dst_img.shape[:2] - return imresize(img, (w, h), return_scale, interpolation, backend=backend) - - -def rescale_size(old_size, scale, return_scale=False): - """Calculate the new size to be rescaled to. - - Args: - old_size (tuple[int]): The old size (w, h) of image. - scale (float | tuple[int]): The scaling factor or maximum size. - If it is a float number, then the image will be rescaled by this - factor, else if it is a tuple of 2 integers, then the image will - be rescaled as large as possible within the scale. - return_scale (bool): Whether to return the scaling factor besides the - rescaled image size. - - Returns: - tuple[int]: The new rescaled image size. - """ - w, h = old_size - if isinstance(scale, (float, int)): - if scale <= 0: - raise ValueError(f'Invalid scale {scale}, must be positive.') - scale_factor = scale - elif isinstance(scale, tuple): - max_long_edge = max(scale) - max_short_edge = min(scale) - scale_factor = min(max_long_edge / max(h, w), - max_short_edge / min(h, w)) - else: - raise TypeError( - f'Scale must be a number or tuple of int, but got {type(scale)}') - - new_size = _scale_size((w, h), scale_factor) - - if return_scale: - return new_size, scale_factor - else: - return new_size - - -def imrescale(img, - scale, - return_scale=False, - interpolation='bilinear', - backend=None): - """Resize image while keeping the aspect ratio. - - Args: - img (ndarray): The input image. - scale (float | tuple[int]): The scaling factor or maximum size. - If it is a float number, then the image will be rescaled by this - factor, else if it is a tuple of 2 integers, then the image will - be rescaled as large as possible within the scale. - return_scale (bool): Whether to return the scaling factor besides the - rescaled image. - interpolation (str): Same as :func:`resize`. - backend (str | None): Same as :func:`resize`. - - Returns: - ndarray: The rescaled image. - """ - h, w = img.shape[:2] - new_size, scale_factor = rescale_size((w, h), scale, return_scale=True) - rescaled_img = imresize( - img, new_size, interpolation=interpolation, backend=backend) - if return_scale: - return rescaled_img, scale_factor - else: - return rescaled_img - - -def imflip(img, direction='horizontal'): - """Flip an image horizontally or vertically. - - Args: - img (ndarray): Image to be flipped. - direction (str): The flip direction, either "horizontal" or - "vertical" or "diagonal". - - Returns: - ndarray: The flipped image. - """ - assert direction in ['horizontal', 'vertical', 'diagonal'] - if direction == 'horizontal': - return np.flip(img, axis=1) - elif direction == 'vertical': - return np.flip(img, axis=0) - else: - return np.flip(img, axis=(0, 1)) - - -def imflip_(img, direction='horizontal'): - """Inplace flip an image horizontally or vertically. - - Args: - img (ndarray): Image to be flipped. - direction (str): The flip direction, either "horizontal" or - "vertical" or "diagonal". - - Returns: - ndarray: The flipped image (inplace). - """ - assert direction in ['horizontal', 'vertical', 'diagonal'] - if direction == 'horizontal': - return cv2.flip(img, 1, img) - elif direction == 'vertical': - return cv2.flip(img, 0, img) - else: - return cv2.flip(img, -1, img) - - -def imrotate(img, - angle, - center=None, - scale=1.0, - border_value=0, - interpolation='bilinear', - auto_bound=False): - """Rotate an image. - - Args: - img (ndarray): Image to be rotated. - angle (float): Rotation angle in degrees, positive values mean - clockwise rotation. - center (tuple[float], optional): Center point (w, h) of the rotation in - the source image. If not specified, the center of the image will be - used. - scale (float): Isotropic scale factor. - border_value (int): Border value. - interpolation (str): Same as :func:`resize`. - auto_bound (bool): Whether to adjust the image size to cover the whole - rotated image. - - Returns: - ndarray: The rotated image. - """ - if center is not None and auto_bound: - raise ValueError('`auto_bound` conflicts with `center`') - h, w = img.shape[:2] - if center is None: - center = ((w - 1) * 0.5, (h - 1) * 0.5) - assert isinstance(center, tuple) - - matrix = cv2.getRotationMatrix2D(center, -angle, scale) - if auto_bound: - cos = np.abs(matrix[0, 0]) - sin = np.abs(matrix[0, 1]) - new_w = h * sin + w * cos - new_h = h * cos + w * sin - matrix[0, 2] += (new_w - w) * 0.5 - matrix[1, 2] += (new_h - h) * 0.5 - w = int(np.round(new_w)) - h = int(np.round(new_h)) - rotated = cv2.warpAffine( - img, - matrix, (w, h), - flags=cv2_interp_codes[interpolation], - borderValue=border_value) - return rotated - - -def bbox_clip(bboxes, img_shape): - """Clip bboxes to fit the image shape. - - Args: - bboxes (ndarray): Shape (..., 4*k) - img_shape (tuple[int]): (height, width) of the image. - - Returns: - ndarray: Clipped bboxes. - """ - assert bboxes.shape[-1] % 4 == 0 - cmin = np.empty(bboxes.shape[-1], dtype=bboxes.dtype) - cmin[0::2] = img_shape[1] - 1 - cmin[1::2] = img_shape[0] - 1 - clipped_bboxes = np.maximum(np.minimum(bboxes, cmin), 0) - return clipped_bboxes - - -def bbox_scaling(bboxes, scale, clip_shape=None): - """Scaling bboxes w.r.t the box center. - - Args: - bboxes (ndarray): Shape(..., 4). - scale (float): Scaling factor. - clip_shape (tuple[int], optional): If specified, bboxes that exceed the - boundary will be clipped according to the given shape (h, w). - - Returns: - ndarray: Scaled bboxes. - """ - if float(scale) == 1.0: - scaled_bboxes = bboxes.copy() - else: - w = bboxes[..., 2] - bboxes[..., 0] + 1 - h = bboxes[..., 3] - bboxes[..., 1] + 1 - dw = (w * (scale - 1)) * 0.5 - dh = (h * (scale - 1)) * 0.5 - scaled_bboxes = bboxes + np.stack((-dw, -dh, dw, dh), axis=-1) - if clip_shape is not None: - return bbox_clip(scaled_bboxes, clip_shape) - else: - return scaled_bboxes - - -def imcrop(img, bboxes, scale=1.0, pad_fill=None): - """Crop image patches. - - 3 steps: scale the bboxes -> clip bboxes -> crop and pad. - - Args: - img (ndarray): Image to be cropped. - bboxes (ndarray): Shape (k, 4) or (4, ), location of cropped bboxes. - scale (float, optional): Scale ratio of bboxes, the default value - 1.0 means no padding. - pad_fill (Number | list[Number]): Value to be filled for padding. - Default: None, which means no padding. - - Returns: - list[ndarray] | ndarray: The cropped image patches. - """ - chn = 1 if img.ndim == 2 else img.shape[2] - if pad_fill is not None: - if isinstance(pad_fill, (int, float)): - pad_fill = [pad_fill for _ in range(chn)] - assert len(pad_fill) == chn - - _bboxes = bboxes[None, ...] if bboxes.ndim == 1 else bboxes - scaled_bboxes = bbox_scaling(_bboxes, scale).astype(np.int32) - clipped_bbox = bbox_clip(scaled_bboxes, img.shape) - - patches = [] - for i in range(clipped_bbox.shape[0]): - x1, y1, x2, y2 = tuple(clipped_bbox[i, :]) - if pad_fill is None: - patch = img[y1:y2 + 1, x1:x2 + 1, ...] - else: - _x1, _y1, _x2, _y2 = tuple(scaled_bboxes[i, :]) - if chn == 1: - patch_shape = (_y2 - _y1 + 1, _x2 - _x1 + 1) - else: - patch_shape = (_y2 - _y1 + 1, _x2 - _x1 + 1, chn) - patch = np.array( - pad_fill, dtype=img.dtype) * np.ones( - patch_shape, dtype=img.dtype) - x_start = 0 if _x1 >= 0 else -_x1 - y_start = 0 if _y1 >= 0 else -_y1 - w = x2 - x1 + 1 - h = y2 - y1 + 1 - patch[y_start:y_start + h, x_start:x_start + w, - ...] = img[y1:y1 + h, x1:x1 + w, ...] - patches.append(patch) - - if bboxes.ndim == 1: - return patches[0] - else: - return patches - - -def impad(img, - *, - shape=None, - padding=None, - pad_val=0, - padding_mode='constant'): - """Pad the given image to a certain shape or pad on all sides with - specified padding mode and padding value. - - Args: - img (ndarray): Image to be padded. - shape (tuple[int]): Expected padding shape (h, w). Default: None. - padding (int or tuple[int]): Padding on each border. If a single int is - provided this is used to pad all borders. If tuple of length 2 is - provided this is the padding on left/right and top/bottom - respectively. If a tuple of length 4 is provided this is the - padding for the left, top, right and bottom borders respectively. - Default: None. Note that `shape` and `padding` can not be both - set. - pad_val (Number | Sequence[Number]): Values to be filled in padding - areas when padding_mode is 'constant'. Default: 0. - padding_mode (str): Type of padding. Should be: constant, edge, - reflect or symmetric. Default: constant. - - - constant: pads with a constant value, this value is specified - with pad_val. - - edge: pads with the last value at the edge of the image. - - reflect: pads with reflection of image without repeating the - last value on the edge. For example, padding [1, 2, 3, 4] - with 2 elements on both sides in reflect mode will result - in [3, 2, 1, 2, 3, 4, 3, 2]. - - symmetric: pads with reflection of image repeating the last - value on the edge. For example, padding [1, 2, 3, 4] with - 2 elements on both sides in symmetric mode will result in - [2, 1, 1, 2, 3, 4, 4, 3] - - Returns: - ndarray: The padded image. - """ - - assert (shape is not None) ^ (padding is not None) - if shape is not None: - padding = (0, 0, shape[1] - img.shape[1], shape[0] - img.shape[0]) - - # check pad_val - if isinstance(pad_val, tuple): - assert len(pad_val) == img.shape[-1] - elif not isinstance(pad_val, numbers.Number): - raise TypeError('pad_val must be a int or a tuple. ' - f'But received {type(pad_val)}') - - # check padding - if isinstance(padding, tuple) and len(padding) in [2, 4]: - if len(padding) == 2: - padding = (padding[0], padding[1], padding[0], padding[1]) - elif isinstance(padding, numbers.Number): - padding = (padding, padding, padding, padding) - else: - raise ValueError('Padding must be a int or a 2, or 4 element tuple.' - f'But received {padding}') - - # check padding mode - assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric'] - - border_type = { - 'constant': cv2.BORDER_CONSTANT, - 'edge': cv2.BORDER_REPLICATE, - 'reflect': cv2.BORDER_REFLECT_101, - 'symmetric': cv2.BORDER_REFLECT - } - img = cv2.copyMakeBorder( - img, - padding[1], - padding[3], - padding[0], - padding[2], - border_type[padding_mode], - value=pad_val) - - return img - - -def impad_to_multiple(img, divisor, pad_val=0): - """Pad an image to ensure each edge to be multiple to some number. - - Args: - img (ndarray): Image to be padded. - divisor (int): Padded image edges will be multiple to divisor. - pad_val (Number | Sequence[Number]): Same as :func:`impad`. - - Returns: - ndarray: The padded image. - """ - pad_h = int(np.ceil(img.shape[0] / divisor)) * divisor - pad_w = int(np.ceil(img.shape[1] / divisor)) * divisor - return impad(img, shape=(pad_h, pad_w), pad_val=pad_val) - - -def cutout(img, shape, pad_val=0): - """Randomly cut out a rectangle from the original img. - - Args: - img (ndarray): Image to be cutout. - shape (int | tuple[int]): Expected cutout shape (h, w). If given as a - int, the value will be used for both h and w. - pad_val (int | float | tuple[int | float]): Values to be filled in the - cut area. Defaults to 0. - - Returns: - ndarray: The cutout image. - """ - - channels = 1 if img.ndim == 2 else img.shape[2] - if isinstance(shape, int): - cut_h, cut_w = shape, shape - else: - assert isinstance(shape, tuple) and len(shape) == 2, \ - f'shape must be a int or a tuple with length 2, but got type ' \ - f'{type(shape)} instead.' - cut_h, cut_w = shape - if isinstance(pad_val, (int, float)): - pad_val = tuple([pad_val] * channels) - elif isinstance(pad_val, tuple): - assert len(pad_val) == channels, \ - 'Expected the num of elements in tuple equals the channels' \ - 'of input image. Found {} vs {}'.format( - len(pad_val), channels) - else: - raise TypeError(f'Invalid type {type(pad_val)} for `pad_val`') - - img_h, img_w = img.shape[:2] - y0 = np.random.uniform(img_h) - x0 = np.random.uniform(img_w) - - y1 = int(max(0, y0 - cut_h / 2.)) - x1 = int(max(0, x0 - cut_w / 2.)) - y2 = min(img_h, y1 + cut_h) - x2 = min(img_w, x1 + cut_w) - - if img.ndim == 2: - patch_shape = (y2 - y1, x2 - x1) - else: - patch_shape = (y2 - y1, x2 - x1, channels) - - img_cutout = img.copy() - patch = np.array( - pad_val, dtype=img.dtype) * np.ones( - patch_shape, dtype=img.dtype) - img_cutout[y1:y2, x1:x2, ...] = patch - - return img_cutout - - -def _get_shear_matrix(magnitude, direction='horizontal'): - """Generate the shear matrix for transformation. - - Args: - magnitude (int | float): The magnitude used for shear. - direction (str): The flip direction, either "horizontal" - or "vertical". - - Returns: - ndarray: The shear matrix with dtype float32. - """ - if direction == 'horizontal': - shear_matrix = np.float32([[1, magnitude, 0], [0, 1, 0]]) - elif direction == 'vertical': - shear_matrix = np.float32([[1, 0, 0], [magnitude, 1, 0]]) - return shear_matrix - - -def imshear(img, - magnitude, - direction='horizontal', - border_value=0, - interpolation='bilinear'): - """Shear an image. - - Args: - img (ndarray): Image to be sheared with format (h, w) - or (h, w, c). - magnitude (int | float): The magnitude used for shear. - direction (str): The flip direction, either "horizontal" - or "vertical". - border_value (int | tuple[int]): Value used in case of a - constant border. - interpolation (str): Same as :func:`resize`. - - Returns: - ndarray: The sheared image. - """ - assert direction in ['horizontal', - 'vertical'], f'Invalid direction: {direction}' - height, width = img.shape[:2] - if img.ndim == 2: - channels = 1 - elif img.ndim == 3: - channels = img.shape[-1] - if isinstance(border_value, int): - border_value = tuple([border_value] * channels) - elif isinstance(border_value, tuple): - assert len(border_value) == channels, \ - 'Expected the num of elements in tuple equals the channels' \ - 'of input image. Found {} vs {}'.format( - len(border_value), channels) - else: - raise ValueError( - f'Invalid type {type(border_value)} for `border_value`') - shear_matrix = _get_shear_matrix(magnitude, direction) - sheared = cv2.warpAffine( - img, - shear_matrix, - (width, height), - # Note case when the number elements in `border_value` - # greater than 3 (e.g. shearing masks whose channels large - # than 3) will raise TypeError in `cv2.warpAffine`. - # Here simply slice the first 3 values in `border_value`. - borderValue=border_value[:3], - flags=cv2_interp_codes[interpolation]) - return sheared - - -def _get_translate_matrix(offset, direction='horizontal'): - """Generate the translate matrix. - - Args: - offset (int | float): The offset used for translate. - direction (str): The translate direction, either - "horizontal" or "vertical". - - Returns: - ndarray: The translate matrix with dtype float32. - """ - if direction == 'horizontal': - translate_matrix = np.float32([[1, 0, offset], [0, 1, 0]]) - elif direction == 'vertical': - translate_matrix = np.float32([[1, 0, 0], [0, 1, offset]]) - return translate_matrix - - -def imtranslate(img, - offset, - direction='horizontal', - border_value=0, - interpolation='bilinear'): - """Translate an image. - - Args: - img (ndarray): Image to be translated with format - (h, w) or (h, w, c). - offset (int | float): The offset used for translate. - direction (str): The translate direction, either "horizontal" - or "vertical". - border_value (int | tuple[int]): Value used in case of a - constant border. - interpolation (str): Same as :func:`resize`. - - Returns: - ndarray: The translated image. - """ - assert direction in ['horizontal', - 'vertical'], f'Invalid direction: {direction}' - height, width = img.shape[:2] - if img.ndim == 2: - channels = 1 - elif img.ndim == 3: - channels = img.shape[-1] - if isinstance(border_value, int): - border_value = tuple([border_value] * channels) - elif isinstance(border_value, tuple): - assert len(border_value) == channels, \ - 'Expected the num of elements in tuple equals the channels' \ - 'of input image. Found {} vs {}'.format( - len(border_value), channels) - else: - raise ValueError( - f'Invalid type {type(border_value)} for `border_value`.') - translate_matrix = _get_translate_matrix(offset, direction) - translated = cv2.warpAffine( - img, - translate_matrix, - (width, height), - # Note case when the number elements in `border_value` - # greater than 3 (e.g. translating masks whose channels - # large than 3) will raise TypeError in `cv2.warpAffine`. - # Here simply slice the first 3 values in `border_value`. - borderValue=border_value[:3], - flags=cv2_interp_codes[interpolation]) - return translated diff --git a/spaces/coutant/yolov8-detection/app.py b/spaces/coutant/yolov8-detection/app.py deleted file mode 100644 index f311d8a838292337d0faa775518dcb7e24f31afa..0000000000000000000000000000000000000000 --- a/spaces/coutant/yolov8-detection/app.py +++ /dev/null @@ -1,41 +0,0 @@ -import cv2 -import gradio as gr -from ultralytics import YOLO - -def inference(path:str, threshold:float=0.6): - print("trying inference with path", path) - if path is None: - return None,0 - model = YOLO('yolov8m.pt') # Caution new API since Ultralytics 8.0.43 - # [0] # only considering class 'person' and not the 79 other classes... - outputs = model.predict(source=path, classes= [0], show=False, conf=threshold) # new API with Ultralytics 8.0.43. 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- diff --git a/spaces/doncamilom/ChemCrow/utils.py b/spaces/doncamilom/ChemCrow/utils.py deleted file mode 100644 index 6e475f759711debaabfe6af807c2a878c40eb313..0000000000000000000000000000000000000000 --- a/spaces/doncamilom/ChemCrow/utils.py +++ /dev/null @@ -1,11 +0,0 @@ -from langchain.llms import OpenAI - -def oai_key_isvalid(api_key): - """Check if a given OpenAI key is valid""" - try: - llm = OpenAI(openai_api_key = api_key) - out = llm("This is a test") - print(out) - return True - except: - return False diff --git a/spaces/dorkai/text-generation-webui-main/extensions/elevenlabs_tts/script.py b/spaces/dorkai/text-generation-webui-main/extensions/elevenlabs_tts/script.py deleted file mode 100644 index 327a9a1192d23847f8b214ca43b447251f928bec..0000000000000000000000000000000000000000 --- a/spaces/dorkai/text-generation-webui-main/extensions/elevenlabs_tts/script.py +++ /dev/null @@ -1,180 +0,0 @@ -import re -from pathlib import Path - -import elevenlabs -import gradio as gr -from modules import chat, shared - -params = { - 'activate': True, - 'api_key': None, - 'selected_voice': 'None', - 'autoplay': False, - 'show_text': True, -} - -voices = None -wav_idx = 0 - - -def update_api_key(key): - params['api_key'] = key - if key is not None: - elevenlabs.set_api_key(key) - - -def refresh_voices(): - global params - your_voices = elevenlabs.voices() - voice_names = [voice.name for voice in your_voices] - return voice_names - - -def refresh_voices_dd(): - all_voices = refresh_voices() - return gr.Dropdown.update(value=all_voices[0], choices=all_voices) - - -def remove_tts_from_history(): - for i, entry in enumerate(shared.history['internal']): - shared.history['visible'][i] = [shared.history['visible'][i][0], entry[1]] - - -def toggle_text_in_history(): - for i, entry in enumerate(shared.history['visible']): - visible_reply = entry[1] - if visible_reply.startswith('
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assert SpaceBuilder.check_space_name_availability(random_token, random_space_name) is False - - def test_load_and_check_spaces_1(self): - input = ( - f"spaces/nielsr/LayoutLMv2-FUNSD" - f"\nspaces/valhalla/glide-text2im" - ) - assert SpaceBuilder.load_and_check_spaces(names=input) is False - - def test_load_and_check_spaces_2(self): - input = ( - f"spaces/valhalla/glide-text2im" - f"\nspaces/valhalla/glide-text2im" - ) - assert SpaceBuilder.load_and_check_spaces(names=input) is True diff --git a/spaces/fashion-demo-organization/fashion_demo/app.py b/spaces/fashion-demo-organization/fashion_demo/app.py deleted file mode 100644 index 531a4fdd4e8bbea31f3f81611d2cc2a0a002a2d0..0000000000000000000000000000000000000000 --- a/spaces/fashion-demo-organization/fashion_demo/app.py +++ /dev/null @@ -1,250 +0,0 @@ -import os - -import clip -import faiss -import torch -import tarfile -import gradio as gr -import pandas as pd -from PIL import Image -from braceexpand import braceexpand -from torchvision import transforms - - -# Load model -checkpoint_path = "ViT-B/16" -device = "cpu" -model, preprocess = clip.load(checkpoint_path, device=device, jit=False) - - -def generate_caption(img): - # Load caption bank - df = pd.read_parquet("files/captions.parquet") - caption_list = df["caption"].tolist() - - # Load index - index = faiss.read_index("files/caption_bank.index") - - # Encode the image and query the caption bank index - query_features = model.encode_image(preprocess(img).unsqueeze(0).to(device)) - query_features = query_features / query_features.norm(dim=-1, keepdim=True) - query_features = query_features.cpu().detach().numpy().astype("float32") - - # Get nearest captions - d, i = index.search(query_features, 1) - d, i = d[0], i[0] - idx = i[0] - distance = d[0] - - # Start with a description of the image - caption = caption_list[idx] - - print(f"Index: {idx} - Distance: {distance:.2f}") - return caption - - -def predict_brand(img): - # Load brand bank - df = pd.read_parquet("files/brands.parquet") - brand_list = df["brands"].tolist() - - # Load index - index = faiss.read_index("files/brand_bank.index") - - # Encode the image and query the brand bank index - query_features = model.encode_image(preprocess(img).unsqueeze(0).to(device)) - query_features = query_features / query_features.norm(dim=-1, keepdim=True) - query_features = query_features.cpu().detach().numpy().astype("float32") - - # Get nearest brands - d, i = index.search(query_features, 1) - d, i = d[0], i[0] - idx = i[0] - distance = d[0] - - brand = brand_list[idx] - print(f"Index: {idx} - Distance: {distance:.2f}") - return brand - - -def estimate_price_and_usage(img): - query_features = model.encode_image(preprocess(img).unsqueeze(0).to(device)) - - # Estimate usage - num_classes = 2 - probe = torch.nn.Linear( - query_features.shape[-1], - num_classes, - dtype=torch.float16, - bias=False - ) - # Load weights for the linear layer as a tensor - linear_data = torch.load("files/reuse_linear.pth", map_location="cpu") - probe.weight.data = linear_data["weight"] - - # Do inference - with torch.autocast("cpu"): - probe.eval() - probe = probe.to(device) - output = probe(query_features) - output = torch.softmax(output, dim=-1) - #output = output.cpu().detach().numpy().astype("float32") - reuse = output.argmax(axis=-1)[0] - reuse_classes = ["Reuse", "Export"] - - # Estimate price - num_classes = 4 - probe = torch.nn.Linear( - query_features.shape[-1], - num_classes, - dtype=torch.float16, - bias=False - ) - # Print output shape for the linear layer - # Load weights for the linear layer as a tensor - linear_data = torch.load("files/price_linear.pth", map_location="cpu") - probe.weight.data = linear_data["weight"] - - # Do inference - with torch.autocast("cpu"): - probe.eval() - probe = probe.to(device) - output = probe(query_features) - output = torch.softmax(output, dim=-1) - #output = output.cpu().detach().numpy().astype("float32") - price = output.argmax(axis=-1)[0] - price_classes = ["<50", "50-100", "100-150", ">150"] - - return f"Estimated price: {price_classes[price]} SEK - Usage: {reuse_classes[reuse]}" - - -def retrieve(query): - index_folder = "files/index" - num_results = 3 - - # Read image metadata - metadata_df = pd.read_parquet(os.path.join(index_folder, "metadata.parquet")) - key_list = metadata_df["key"].tolist() - - # Load the index - index = faiss.read_index(os.path.join(index_folder, "image.index")) - - # Encode the query - if isinstance(query, str): - print("Query is a string") - text = clip.tokenize([query]).to(device) - query_features = model.encode_text(text) - else: - print("Query is an image") - query_features = model.encode_image(preprocess(query).unsqueeze(0).to(device)) - query_features = query_features / query_features.norm(dim=-1, keepdim=True) - query_features = query_features.cpu().detach().numpy().astype("float32") - - d, i = index.search(query_features, num_results) - print(f"Found {num_results} items with query '{query}'") - indices = i[0] - similarities = d[0] - - min_d = min(similarities) - max_d = max(similarities) - print(f"The minimum similarity is {min_d:.2f} and the maximum is {max_d:.2f}") - - # Uncomment to generate combined.tar, combine the image_tars into a single tarfile - """ - dataset_dir = "/fs/sefs1/circularfashion/wargon_webdataset/front_only" - image_tars = [os.path.join(dataset_dir, file) for file in sorted(braceexpand("{0000..0028}.tar"))] - with tarfile.open("files/combined.tar", "w") as combined_tar: - for tar in image_tars: - with tarfile.open(tar, "r") as tar_file: - for member in tar_file.getmembers(): - combined_tar.addfile(member, tar_file.extractfile(member)) - """ - - images = [] - for idx in indices: - image_name = key_list[idx] - with tarfile.open("files/combined.tar", "r") as tar_file: - image = tar_file.extractfile(f"{image_name}.jpg") - image = Image.open(image).copy() - # Center crop the image - width, height = image.size - new_size = min(width, height) - image = transforms.CenterCrop(new_size)(image) - # Resize the image - image = transforms.Resize((600, 600))(image) - images.append(image) - return images - - -theme = gr.Theme.from_hub("JohnSmith9982/small_and_pretty") - -with gr.Blocks( - theme=theme, - css="footer {visibility: hidden}", -) as demo: - with gr.Tab("Captioning and Prediction"): - with gr.Row(variant="compact"): - input_img = gr.Image(type="pil", show_label=False) - with gr.Column(min_width="80"): - btn_generate_caption = gr.Button("Create Description").style(size="sm") - generated_caption = gr.Textbox(label="Description", show_label=False) - gr.Examples( - examples=["files/examples/example_1.jpg", "files/examples/example_2.jpg"], - fn=generate_caption, - inputs=input_img, - outputs=generated_caption - ) - - with gr.Row(variant="compact"): - brand_img = gr.Image(type="pil", show_label=False) - with gr.Column(min_width="80"): - btn_predict_brand = gr.Button("Predict Brand").style(size="sm") - predicted_brand = gr.Textbox(label="Brand", show_label=False) - gr.Examples( - examples=["files/examples/example_brand_1.jpg", "files/examples/example_brand_2.jpg"], - fn=predict_brand, - inputs=brand_img, - outputs=predicted_brand - ) - - with gr.Column(variant="compact"): - btn_estimate = gr.Button("Estimate Price and Reuse").style(size="sm") - text_box = gr.Textbox(label="Estimates:", show_label=False) - - with gr.Tab("Image Retrieval"): - with gr.Row(variant="compact"): - with gr.Column(): - query_img = gr.Image(type="pil", label="Image Query") - btn_image_query = gr.Button("Retrieve Garments").style(size="sm") - img_query_gallery = gr.Gallery(show_label=False).style(rows=1, columns=3) - gr.Examples( - examples=["files/examples/example_retrieval_1.jpg", "files/examples/example_retrieval_2.jpg"], - fn=retrieve, - inputs=query_img, - outputs=img_query_gallery - ) - - with gr.Row(variant="compact"): - with gr.Column(): - query_text = gr.Textbox(label="Text Query", placeholder="Enter a description") - btn_text_query = gr.Button("Retrieve Garments").style(size="sm") - text_query_gallery = gr.Gallery(show_label=False).style(rows=1, columns=3) - gr.Examples( - examples=["A purple sweater", "A dress with a floral pattern"], - fn=retrieve, - inputs=query_text, - 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Blender 3D APK: How to Use Blender on Your Android Device
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How to Download and Install Blender 3D APK on Your Android Device
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Step 2: Enable unknown sources on your device settings
-The second step is to enable unknown sources on your device settings. This will allow you to install apps that are not from the Google Play Store. To do this, go to your device settings, then security, then toggle on the option for unknown sources. You may see a warning message that says installing apps from unknown sources may harm your device, but don't worry, as long as you trust the source of the APK file, you are safe.
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-The third step is to download and install the APK file. To do this, go to the website where you found the download link for Blender 3D APK, then tap on it. You may see a pop-up message that asks you to confirm the download, then tap on OK. Once the download is complete, go to your file manager app, then locate the APK file in your downloads folder. Tap on it, then tap on install. You may see another pop-up message that asks you to confirm the installation, then tap on install again. Wait for a few seconds until the installation is done.
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- Downloading and installing Clash of Clans Apkure is very easy and fast. Just follow these simple steps: 1. Go to [Apkure](^1^) on your browser. 2. Search for "Clash of Clans" in the search bar. 3. Click on the "Download APK" button next to the game icon. 4. Wait for the download to finish, then open the APK file. 5. Tap on "Install" when prompted by your device. 6. Wait for the installation to complete, then tap on "Open" to launch the game. That's it! You have successfully downloaded and installed Clash of Clans Apkure on your device. You can now enjoy playing the game without any restrictions or limitations.How to Play Clash of Clans Apkure?
- Playing Clash of Clans Apkure is similar to playing the original game from the Google Play Store. However, there are some tips and tricks that can help you get started and improve your gameplay: - When you first launch the game, you will be guided by a tutorial that will teach you the basics of building - your village, training your troops, and attacking other villages. You can skip the tutorial if you want, but we recommend that you complete it to learn the ropes. - After the tutorial, you will have a basic village with some buildings and resources. You can upgrade your buildings and resources by tapping on them and spending gold or elixir. You can also build new buildings and defenses by tapping on the "Shop" button at the bottom right corner of the screen. - To train your troops, you need to build and upgrade barracks and army camps. You can choose from different types of troops, such as barbarians, archers, giants, wizards, and more. Each troop has its own strengths and weaknesses, so you need to plan your army composition carefully. You can also train spells and heroes to support your troops in battle. - To attack other villages, you need to tap on the "Attack" button at the bottom left corner of the screen. You can choose from different modes, such as multiplayer, single player, clan wars, clan games, and clan leagues. In multiplayer mode, you can raid other players' villages and loot their resources. In single player mode, you can attack goblin villages and earn stars and loot. In clan wars, clan games, and clan leagues, you can cooperate with your clan members and compete against other clans for rewards and glory. - To defend your village, you need to build and upgrade walls, cannons, archer towers, mortars, traps, and more. You can also place your heroes and clan castle troops in strategic locations to fend off enemy attacks. You can also use shields and guard modes to protect your village from being raided when you are offline.Why Play Clash of Clans Apkure?
- There are many reasons why you should play Clash of Clans Apkure instead of the original game from the Google Play Store. Here are some of them: - You can download and install the game faster and easier with Apkure. You don't need to sign in with your Google account or update the game through the Play Store. You just need to download the APK file from Apkure and install it on your device. - You can access the game even if it is not available in your region or country. Some games may be restricted or banned in certain regions or countries due to various reasons. With Apkure, you can bypass these restrictions and enjoy the game without any problems. - You can get an older version of the game if you prefer it over the newer one. Sometimes, you may not like the changes or updates that the developers make to the game. With Apkure, you can choose which version of the game you want to download and install on your device. - You can play the game with more features and options with Apkure. Some APK files may have modded or hacked features that enhance your gameplay experience. For example, you may get unlimited resources, gems, coins, or elixir that you can use to upgrade your buildings and troops faster. However, be careful when using these features as they may violate the game's terms of service or cause errors or bugs.Conclusion
- Clash of Clans is a fun and exciting game that lets you build your own village, train your troops, and fight against other players in multiplayer battles. You can also join or create a clan with other players, and participate in clan wars, clan games, and clan leagues. If you want to play it on your Android device without any hassle, then you should download it from Apkure, a website that offers free and safe APK downloads for Android games and apps. With Apkure, you can download and install the game faster and easier, access it even if it is not available in your region or country, get an older version of the game if you prefer it over the newer one, and play it with more features and options. So what are you waiting for? Download Clash of Clans Apkure today and start your epic adventure!FAQs
- Here are some common questions and answers about Clash of Clans Apkure:Q: Is Clash of Clans Apkure safe to download and install?
-A: Yes, Clash of Clans Apkure is safe to download and install from Apkure website. Apkure scans all the APK files before uploading them on their website to ensure that they are free from malware or viruses. You can also read user reviews and ratings to see if an app is safe and reliable.Q: Do I need to root my device to play Clash of Clans Apkure?
-A: No, you don't need to root your device to play Clash of Clans Apkure. You just need to enable unknown sources on your device settings to allow installation of apps from sources other than the Google Play Store.Q: Will I lose my progress or - account if I play Clash of Clans Apkure?
-A: No, you won't lose your progress or account if you play Clash of Clans Apkure. You can link your game account to your Google Play, Facebook, or Supercell ID and sync your data across different devices. You can also switch between the original game and the Apkure version without any problem.Q: Can I play Clash of Clans Apkure with my friends who play the original game?
-A: Yes, you can play Clash of Clans Apkure with your friends who play the original game. You can join the same clan, chat with them, and fight alongside them in clan wars, clan games, and clan leagues. You can also challenge them to friendly battles and see who has the better strategy.Q: What are the differences between Clash of Clans Apkure and the original game?
-A: The main difference between Clash of Clans Apkure and the original game is the source of download and installation. Clash of Clans Apkure is downloaded and installed from Apkure website, while the original game is downloaded and installed from the Google Play Store. Other differences may include the version, features, and options of the game, depending on the APK file you choose to download from Apkure. -clash of clans apkure
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download Math Quizzes to Boost Your Skills and Confidence.md b/spaces/fatiXbelha/sd/Download Math Quizzes to Boost Your Skills and Confidence.md deleted file mode 100644 index 4ad25a90c2ffdba55f902e70e1b3219322cd5543..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Math Quizzes to Boost Your Skills and Confidence.md +++ /dev/null @@ -1,136 +0,0 @@ - -
Download Math Quizzes: A Fun and Effective Way to Learn Math
-Math is a subject that many people find challenging, boring, or intimidating. But it doesn't have to be that way. Math can be fun, interesting, and rewarding if you learn it in a way that suits your style and pace. One of the best ways to learn math is by taking math quizzes. Math quizzes are interactive and engaging activities that test your math skills in a variety of ways. They can help you reinforce mathematical concepts, improve problem-solving skills, build accuracy and speed, boost confidence, and make learning fun and enjoyable. In this article, we will explore what math quizzes are, why they are beneficial, what types of math quizzes there are, how to choose the best one for you, what are the best sources of math quizzes, how to download them, and how to study for a math test using them.
-download math quizzes
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What are math quizzes and why are they beneficial?
-Math quizzes are interactive and engaging activities that test your math skills
-A math quiz is a set of questions or problems that require you to apply your math knowledge and skills to find the correct answer. Math quizzes can be designed in various formats, such as multiple-choice, fill-in-the-blank, matching, or puzzle-solving. They can also cover different topics, such as arithmetic, algebra, geometry, trigonometry, calculus, statistics, etc. Math quizzes can be accessed online or offline, depending on your preference and availability. Online math quizzes offer more variety, interactivity, and feedback than offline ones. Offline math quizzes offer more convenience, flexibility, and privacy than online ones
Math quizzes have numerous benefits for learners of all ages and levels
-Math quizzes are not only fun and challenging, but also beneficial for learning math. Here are some of the benefits of math quizzes:
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What are the types of math quizzes and how to choose the best one for you?
-Math quizzes can be designed in various formats and cover different topics
-Math quizzes can vary in their format, content, difficulty, and length. Some of the common formats of math quizzes are:
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Math quizzes can also cover different topics, such as:
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Math quizzes can be accessed online or offline, depending on your preference and availability
-Math quizzes can be found online or offline. Online math quizzes are available on various websites that offer free or paid math quizzes for different grade levels and skills. Offline math quizzes are available on books, worksheets, flashcards, etc. that you can buy or print out. Here are some of the advantages and disadvantages of online and offline math quizzes:
-| Online Math Quizzes | Offline Math Quizzes | |
|---|---|---|
| Advantages | - Offer more variety, interactivity, and feedback - Can be accessed anytime and anywhere with an internet connection - Can be adjusted to your level and pace - Can be shared with others easily | - Offer more convenience, flexibility, and privacy - Can be accessed without an internet connection - Can be customized to your preference - Can be used for offline practice or testing |
| Disadvantages | - Require an internet connection - May have technical issues or errors - May have limited options or quality - May have security or privacy risks | - Require physical space and materials - May have limited availability or accessibility - May have outdated or inaccurate information - May have no feedback or correction |
To choose the best type of math quiz for you, you need to consider your learning goals, preferences, availability , and resources. For example, if you want to practice math skills that you have already learned, you may prefer offline math quizzes that you can do at your own pace and place. If you want to learn new math concepts or challenge yourself, you may prefer online math quizzes that offer more variety and feedback. You can also mix and match online and offline math quizzes to suit your needs and goals.
-What are the best sources of math quizzes and how to download them?
-There are many websites that offer free or paid math quizzes for different grade levels and skills
-If you are looking for online math quizzes, you can find many websites that offer them for free or for a fee. Some of these websites are:
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To download math quizzes from these websites, you need to follow their instructions and terms of use. Some of them may require you to sign up for an account or pay a subscription fee. Some of them may allow you to download the quizzes as PDF files or print them out. Some of them may not allow you to download the quizzes at all.
-There are also apps and software that allow you to create your own math quizzes or customize existing ones
-If you are looking for offline math quizzes or more personalized ones , you can use apps or software that allow you to create your own math quizzes or customize existing ones. Some of these apps or software are:
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To download math quizzes from these apps or software, you need to sign up for an account and export or share your quizzes. Some of them may allow you to download the quizzes as PDF files or print them out. Some of them may allow you to share the quizzes via email, link, QR code, etc.
-How to study for a math test using math quizzes?
-Math quizzes can help you prepare for a math test by reviewing the material and testing your knowledge
-Math quizzes are not only useful for learning math, but also for studying for a math test. Math quizzes can help you review the material that will be covered on the test and test your knowledge and skills on it. Math quizzes can also help you identify your strengths and weaknesses and focus on the areas that need more improvement.
-To study effectively using math quizzes, you need to follow some tips and strategies
-Math quizzes are not magic bullets that will guarantee you a perfect score on the test. You need to use them wisely and effectively to get the most out of them. Here are some tips and strategies for studying for a math test using math quizzes:
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Conclusion
-Math quizzes are a fun and effective way to learn math. They can help you reinforce mathematical concepts, improve problem-solving skills, build accuracy and speed, boost confidence, and make learning fun and enjoyable. They can also help you prepare for a math test by reviewing the material and testing your knowledge. You can find online or offline math quizzes that suit your needs and goals, or create your own math quizzes using apps or software. You can also download math quizzes from various sources and use them for studying or practice. To study effectively using math quizzes, you need to follow some tips and strategies, such as finding a quiet place, choosing the right type and level of math quizzes, practicing with different formats and topics, checking your answers, learning from your mistakes, tracking your progress, and rewarding yourself for your achievements. We hope this article has helped you understand what math quizzes are, why they are beneficial, what types of math quizzes there are, how to choose the best one for you, what are the best sources of math quizzes, how to download them, and how to study for a math test using them. We encourage you to download math quizzes and try them out for yourself. You will be amazed by how much fun and effective they are for learning math.
-FAQs
-What is a math quiz?
-A math quiz is a set of questions or problems that require you to apply your math knowledge and skills to find the correct answer.
-Why are math quizzes beneficial?
-Math quizzes are beneficial because they can help you reinforce mathematical concepts, improve problem-solving skills, build accuracy and speed, boost confidence, and make learning fun and enjoyable.
-What are the types of math quizzes?
-Math quizzes can be designed in various formats, such as multiple-choice, fill-in-the-blank, matching, or puzzle-solving. They can also cover different topics , such as arithmetic, algebra, geometry, trigonometry, calculus, statistics, etc.
-How to download math quizzes?
-You can download math quizzes from various sources, such as websites, apps, or software. You need to follow their instructions and terms of use. Some of them may require you to sign up for an account or pay a subscription fee. Some of them may allow you to download the quizzes as PDF files or print them out. Some of them may not allow you to download the quizzes at all.
-How to study for a math test using math quizzes?
-You can study for a math test using math quizzes by following some tips and strategies, such as finding a quiet place, choosing the right type and level of math quizzes, practicing with different formats and topics, checking your answers, learning from your mistakes, tracking your progress, and rewarding yourself for your achievements.
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\ No newline at end of file diff --git a/spaces/fclong/summary/fengshen/models/model_utils.py b/spaces/fclong/summary/fengshen/models/model_utils.py deleted file mode 100644 index 65699c45b660e17e05d116a04ae68911acea4b35..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/models/model_utils.py +++ /dev/null @@ -1,251 +0,0 @@ -from pytorch_lightning import LightningModule -from pytorch_lightning.strategies import DeepSpeedStrategy -from deepspeed.ops.adam import DeepSpeedCPUAdam, FusedAdam -from transformers.optimization import AdamW, TYPE_TO_SCHEDULER_FUNCTION -from torch.optim import Optimizer -from torch.optim.lr_scheduler import _LRScheduler -from transformers.trainer_utils import SchedulerType -from typing import Optional, Union -import warnings -import types - - -def add_module_args(parent_args): - parser = parent_args.add_argument_group('Basic Module') - parser.add_argument('--learning_rate', default=5e-5, type=float) - parser.add_argument('--min_learning_rate', default=1e-7, type=float) - parser.add_argument('--lr_decay_steps', default=0, type=int) - # lr decay的时候会依赖total_steps，这里设置的是total_steps的比例，比如我只需要前50%步做decay，ratio设置为0.5 - parser.add_argument('--lr_decay_ratio', default=1.0, type=float) - parser.add_argument('--warmup_steps', default=0, type=int) - parser.add_argument('--warmup_ratio', default=0.1, type=float) - parser.add_argument('--weight_decay', default=1e-1, type=float) - parser.add_argument('--adam_beta1', default=0.9, type=float) - parser.add_argument('--adam_beta2', default=0.999, type=float) - parser.add_argument('--adam_epsilon', default=1e-8, type=float) - parser.add_argument('--model_path', default=None, type=str) - parser.add_argument('--scheduler_type', default='polynomial', type=str) - return parent_args - - -def add_inverse_square_args(parent_args): - parser = parent_args.add_argument_group('Basic Module') - parser.add_argument('--warmup_min_lr', default=1e-9, type=float) - parser.add_argument('--warmup_max_lr', default=1e-4, type=float) - - return parent_args - - -def get_default_update_params(pl_model: LightningModule): - no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight', 'layer_norm.', 'layernorm.'] - optimizer_grouped_params = [ - {'params': [p for n, p in pl_model.named_parameters() if not any( - nd in n for nd in no_decay) and p.requires_grad], 'weight_decay': pl_model.hparams.weight_decay}, - {'params': [p for n, p in pl_model.named_parameters() if any( - nd in n for nd in no_decay) and p.requires_grad], 'weight_decay': 0.0} - ] - return optimizer_grouped_params - - -def configure_optimizers(pl_model: LightningModule, model_params=None): - ''' - Args: - pl_model： lightning module - model_params: 需要优化的模型参数 - ''' - # get params that optimizer need - if model_params is None: - optimizer_grouped_params = get_default_update_params(pl_model) - else: - optimizer_grouped_params = model_params - # Configure optimizer. - if isinstance(pl_model.trainer.strategy, DeepSpeedStrategy): - if 'offload_optimizer' in pl_model.trainer.strategy.config['zero_optimization']: - optimizer = DeepSpeedCPUAdam( - optimizer_grouped_params, adamw_mode=True, - lr=pl_model.hparams.learning_rate, - betas=(pl_model.hparams.adam_beta1, pl_model.hparams.adam_beta2), eps=pl_model.hparams.adam_epsilon) - else: - optimizer = FusedAdam( - optimizer_grouped_params, adam_w_mode=True, - lr=pl_model.hparams.learning_rate, - betas=(pl_model.hparams.adam_beta1, pl_model.hparams.adam_beta2), eps=pl_model.hparams.adam_epsilon) - # elif isinstance(pl_model.trainer.strategy, ColossalAIStrategy): - # from colossalai.nn.optimizer import HybridAdam - # optimizer = HybridAdam( - # optimizer_grouped_params, - # lr=pl_model.hparams.learning_rate, - # betas=(pl_model.hparams.adam_beta1, pl_model.hparams.adam_beta2), - # eps=pl_model.hparams.adam_epsilon) - else: - optimizer = AdamW(optimizer_grouped_params, lr=pl_model.hparams.learning_rate, - betas=(pl_model.hparams.adam_beta1, pl_model.hparams.adam_beta2), - eps=pl_model.hparams.adam_epsilon) - # Configure learning rate scheduler. - - warmup_steps = pl_model.hparams.warmup_ratio * \ - pl_model.total_steps if pl_model.hparams.warmup_steps == 0 else pl_model.hparams.warmup_steps - - if pl_model.hparams.scheduler_type == "inverse_sqrt": - scheduler = inverse_square_root_schedule(optimizer=optimizer, - num_warmup_steps=warmup_steps, lr_min=pl_model.hparams.warmup_min_lr, lr_max=pl_model.hparams.warmup_max_lr) - else: - total_steps = pl_model.hparams.lr_decay_ratio * \ - pl_model.total_steps if pl_model.hparams.lr_decay_steps == 0 else pl_model.hparams.lr_decay_steps - scheduler = get_scheduler(name=pl_model.hparams.scheduler_type, optimizer=optimizer, - num_warmup_steps=warmup_steps, num_training_steps=total_steps, - lr_end=pl_model.hparams.min_learning_rate) - scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1} - return [optimizer], [scheduler] - - -def inverse_square_root_schedule( - optimizer: Optimizer, - num_warmup_steps: int = 4000, - lr_min=1e-9, - lr_max=1e-4, - power=0.5, - last_epoch: int = -1): - - lr_init = optimizer.defaults["lr"] - if (lr_min > lr_max): - raise ValueError(f"lr_min ({lr_min}) must be be smaller than lr_max ({lr_max})") - - lr_step = (lr_max - lr_init) / num_warmup_steps - decay_factor = lr_max * num_warmup_steps**power - - def lr_lambda(current_step: int): - # 自定义函数 - if current_step < num_warmup_steps: - return lr_step * current_step - return decay_factor * current_step ** (-power) - - return Direct_LR(optimizer, lr_lambda, last_epoch, True) - - -class Direct_LR(_LRScheduler): - """ - Modified from LambdaLR - """ - - def __init__(self, optimizer, lr_lambda, last_epoch=-1, warmup_steps=4000, verbose=False): - self.optimizer = optimizer - self.warmup_steps = warmup_steps - if not isinstance(lr_lambda, list) and not isinstance(lr_lambda, tuple): - self.lr_lambdas = [lr_lambda] * len(optimizer.param_groups) - else: - if len(lr_lambda) != len(optimizer.param_groups): - raise ValueError("Expected {} lr_lambdas, but got {}".format( - len(optimizer.param_groups), len(lr_lambda))) - self.lr_lambdas = list(lr_lambda) - super(Direct_LR, self).__init__(optimizer, last_epoch, verbose) - - def state_dict(self): - """Returns the state of the scheduler as a :class:`dict`. - - It contains an entry for every variable in self.__dict__ which - is not the optimizer. - The learning rate lambda functions will only be saved if they are callable objects - and not if they are functions or lambdas. - - When saving or loading the scheduler, please make sure to also save or load the state of the optimizer. - """ - - state_dict = {key: value for key, value in self.__dict__.items() if key not in ('optimizer', 'lr_lambdas')} - state_dict['lr_lambdas'] = [None] * len(self.lr_lambdas) - - for idx, fn in enumerate(self.lr_lambdas): - if not isinstance(fn, types.FunctionType): - state_dict['lr_lambdas'][idx] = fn.__dict__.copy() - - return state_dict - - def load_state_dict(self, state_dict): - """Loads the schedulers state. - - When saving or loading the scheduler, please make sure to also save or load the state of the optimizer. - - Args: - state_dict (dict): scheduler state. Should be an object returned - from a call to :meth:`state_dict`. - """ - - lr_lambdas = state_dict.pop('lr_lambdas') - self.__dict__.update(state_dict) - # Restore state_dict keys in order to prevent side effects - # https://github.com/pytorch/pytorch/issues/32756 - state_dict['lr_lambdas'] = lr_lambdas - - for idx, fn in enumerate(lr_lambdas): - if fn is not None: - self.lr_lambdas[idx].__dict__.update(fn) - - def get_lr(self): - if not self._get_lr_called_within_step: - warnings.warn("To get the last learning rate computed by the scheduler, " - "please use `get_last_lr()`.") - - if self._step_count < self.warmup_steps: - return [base_lr + lmbda(self.last_epoch) - for lmbda, base_lr in zip(self.lr_lambdas, self.base_lrs)] - - return [lmbda(self.last_epoch) for lmbda in self.lr_lambdas] - - -def get_total_steps(trainer, hparams): - train_loader = trainer._data_connector._train_dataloader_source.dataloader() - # Calculate total steps - if trainer.max_epochs > 0: - world_size = trainer.world_size - tb_size = hparams.train_batchsize * max(1, world_size) - ab_size = trainer.accumulate_grad_batches - total_steps = (len(train_loader.dataset) * - trainer.max_epochs // tb_size) // ab_size - else: - total_steps = trainer.max_steps - return total_steps - - -def get_scheduler( - name: Union[str, SchedulerType], - optimizer: Optimizer, - num_warmup_steps: Optional[int] = None, - num_training_steps: Optional[int] = None, - lr_end: Optional[float] = None -): - """ - Unified API to get any scheduler from its name. - - Args: - name (`str` or `SchedulerType`): - The name of the scheduler to use. - optimizer (`torch.optim.Optimizer`): - The optimizer that will be used during training. - num_warmup_steps (`int`, *optional*): - The number of warmup steps to do. This is not required by all schedulers (hence the argument being - optional), the function will raise an error if it's unset and the scheduler type requires it. - num_training_steps (`int``, *optional*): - The number of training steps to do. This is not required by all schedulers (hence the argument being - optional), the function will raise an error if it's unset and the scheduler type requires it. - """ - name = SchedulerType(name) - schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name] - if name == SchedulerType.CONSTANT: - return schedule_func(optimizer) - - # All other schedulers require `num_warmup_steps` - if num_warmup_steps is None: - raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.") - - if name == SchedulerType.CONSTANT_WITH_WARMUP: - return schedule_func(optimizer, num_warmup_steps=num_warmup_steps) - - # All other schedulers require `num_training_steps` - if num_training_steps is None: - raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.") - - if name == SchedulerType.POLYNOMIAL: - return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, - num_training_steps=num_training_steps, lr_end=lr_end) - - return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps) diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Wii Sports Resort for Dolphin Emulator and Enjoy 12 Fun Games.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Wii Sports Resort for Dolphin Emulator and Enjoy 12 Fun Games.md deleted file mode 100644 index 85f117c8c4f885bb6a61aca3341a2a9dd6767a88..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Wii Sports Resort for Dolphin Emulator and Enjoy 12 Fun Games.md +++ /dev/null @@ -1,163 +0,0 @@ -
-
Introduction
-Wii Sports Resort is a popular sports game that was released for the Nintendo Wii in 2009. It is a sequel to Wii Sports, and features 12 different sports activities that can be played with the Wii Remote and Nunchuk, or with the Wii MotionPlus accessory for more accurate motion control. Wii Sports Resort takes place on a tropical island called Wuhu Island, where you can explore various locations and interact with other Mii characters.
-wii sports resort dolphin emulator download
DOWNLOAD ✺ https://gohhs.com/2uPnhH
-
But what if you don't have a Wii console, or you want to play Wii Sports Resort with better graphics and performance? That's where Dolphin Emulator comes in. Dolphin Emulator is a free and open-source program that can run GameCube and Wii games on your PC or Android device. It has high compatibility across the majority of titles for both platforms, and allows you to enjoy games in full HD (1080p) with several enhancements, such as compatibility with all PC controllers, turbo speed, networked multiplayer, and even more.
-In this article, I will guide you through the steps of downloading, installing, and configuring Dolphin Emulator, as well as how to obtain and play Wii Sports Resort on it. Let's get started!
-System Requirements
-Every game has different requirements, some titles may require a powerful computer or device while some other titles may not. Generally, these are the minimum and recommended requirements for running Dolphin Emulator on different platforms.
-How to play Wii Sports Resort on PC with Dolphin Emulator
-Wii Sports Resort HD Texture Pack for Dolphin Emulator
-Best settings for Wii Sports Resort on Dolphin Emulator
-Wii Sports Resort cheats and codes for Dolphin Emulator
-Wii Sports Resort save file download for Dolphin Emulator
-Wii Sports Resort motion plus fix for Dolphin Emulator
-Wii Sports Resort online multiplayer with Dolphin Emulator
-Wii Sports Resort 60 FPS hack for Dolphin Emulator
-Wii Sports Resort ROM download for Dolphin Emulator
-Wii Sports Resort ISO download for Dolphin Emulator
-Wii Sports Resort WBFS download for Dolphin Emulator
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-Wii Sports Resort NTSC version download for Dolphin Emulator
-Wii Sports Resort Japanese version download for Dolphin Emulator
-Wii Sports Resort Korean version download for Dolphin Emulator
-Wii Sports Resort Chinese version download for Dolphin Emulator
-Wii Sports Resort German version download for Dolphin Emulator
-Wii Sports Resort French version download for Dolphin Emulator
-Wii Sports Resort Spanish version download for Dolphin Emulator
-Wii Sports Resort Italian version download for Dolphin Emulator
-Wii Sports Resort Dutch version download for Dolphin Emulator
-Wii Sports Resort Portuguese version download for Dolphin Emulator
-Wii Sports Resort Russian version download for Dolphin Emulator
-Wii Sports Resort Swedish version download for Dolphin Emulator
-Wii Sports Resort Finnish version download for Dolphin Emulator
-Wii Sports Resort Norwegian version download for Dolphin Emulator
-Wii Sports Resort Danish version download for Dolphin Emulator
-Wii Sports Resort Polish version download for Dolphin Emulator
-Wii Sports Resort Turkish version download for Dolphin Emulator
-Wii Sports Resort Greek version download for Dolphin Emulator
-Wii Sports Resort Arabic version download for Dolphin Emulator
-Wii Sports Resort Hebrew version download for Dolphin Emulator
-How to install and configure Dolphin Emulator for Wii Sports Resort
-How to use a real Wiimote with Dolphin Emulator for Wii Sports Resort
-How to use a gamepad with Dolphin Emulator for Wii Sports Resort
-How to use a keyboard and mouse with Dolphin Emulator for Wii Sports Resort
-How to improve graphics quality of Wii Sports Resort on Dolphin Emulator
-How to fix sound issues of Wii Sports Resort on Dolphin Emulator
-How to fix performance issues of Wii Sports Resort on Dolphin Emulator
-How to fix compatibility issues of Wii Sports Resort on Dolphin Emulator
-How to update and patch Wii Sports Resort on Dolphin Emulator
-How to backup and restore Wii Sports Resort on Dolphin Emulator
-How to mod and customize Wii Sports Resort on Dolphin Emulator
Desktop Computers
--
-
Android/Mobile Devices
--
-
The first step to play Wii Sports Resort on your PC or Android device is to download Dolphin Emulator. You can get it from the official website, which is https://dolphin-emu.org. There are two versions of Dolphin Emulator available: the stable version and the development version. The stable version is more tested and reliable, but it may not have the latest features and improvements. The development version is updated frequently and has more features and fixes, but it may also have more bugs and issues. You can choose whichever version suits your preference, but I recommend the development version for better compatibility and performance.
-To download Dolphin Emulator, go to the official website and click on the Download button. You will see a list of platforms and versions to choose from. For desktop computers, you can download the Windows x64, Linux x64, or macOS x64 version. For Android devices, you can download the Android APK file. You can also scan the QR code on the website with your device's camera to download it directly. Once you have downloaded the file, you can proceed to install it.
-Installing Dolphin Emulator
-The installation process of Dolphin Emulator varies depending on your operating system. Here are the instructions for each platform:
-Windows
--
-
Linux
--
-
macOS
--
-
Android
--
-
Configuring Dolphin Emulator
-Now that you have installed Dolphin Emulator, you need to configure some settings and options to optimize its performance and compatibility. There are many settings and options available in Dolphin Emulator, but I will only cover the most important ones in this article. You can always explore and experiment with other settings and options later if you want.
-Main Configuration Window
-The main configuration window of Dolphin Emulator is where you can access and change various general settings, such as language, interface, paths, updates, etc. To open it, click on the Config button in the toolbar (or press Ctrl+S) for desktop computers, or tap on the Menu button in the top-right corner and select Settings for Android devices. Here are some of the settings that you should check or change:
--
-
Graphics Settings
-The graphics settings of Dolphin Emulator are where you can adjust and customize the visual aspects of the games, such as resolution, aspect ratio, anti-aliasing, texture filtering, etc. To open it, click on the Graphics button in the toolbar (or press Ctrl+G) for desktop computers, or tap on the Menu button in the top-right corner and select Graphics Settings for Android devices. Here are some of the settings that you should check or change:
--
-
Obtaining Wii Sports Resort
-Before you can play Wii Sports Resort on Dolphin Emulator, you need to have a copy of the game file on your computer or device. There are two ways to obtain Wii Sports Resort: legally and illegally. The legal way is to dump the game from your own Wii console and disc, using a homebrew application called CleanRip. The illegal way is to download the game from the internet, using a torrent or a direct link. I do not condone or support piracy, and I strongly advise you to follow the legal method if you own the game. However, for the sake of completeness, I will briefly explain both methods in this section.
-Dumping Wii Sports Resort from your own Wii console and disc
--
-
Downloading Wii Sports Resort from the internet
--
-
Playing Wii Sports Resort
-Now that you have Dolphin Emulator and Wii Sports Resort ready, you can finally start playing the game. Here are some tips and tricks for enjoying Wii Sports Resort on Dolphin Emulator:
--
-
Conclusion
-Wii Sports Resort is a fun and engaging game that you can play on your PC or Android device with Dolphin Emulator. Dolphin Emulator is a free and open-source program that can run GameCube and Wii games with high compatibility and various enhancements. In this article, I have shown you how to download, install, and configure Dolphin Emulator, as well as how to obtain and play Wii Sports Resort on it. I hope you have enjoyed this article and found it helpful. If you have any questions or feedback, please feel free to leave a comment below. Happy gaming!
-FAQs
--
-
-
-
\ No newline at end of file diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/http.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/http.d.ts deleted file mode 100644 index e14de6cf46b9b5da30da230697679d2a8499e4b2..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/http.d.ts +++ /dev/null @@ -1,1651 +0,0 @@ -/** - * To use the HTTP server and client one must `require('http')`. - * - * The HTTP interfaces in Node.js are designed to support many features - * of the protocol which have been traditionally difficult to use. - * In particular, large, possibly chunk-encoded, messages. The interface is - * careful to never buffer entire requests or responses, so the - * user is able to stream data. - * - * HTTP message headers are represented by an object like this: - * - * ```js - * { 'content-length': '123', - * 'content-type': 'text/plain', - * 'connection': 'keep-alive', - * 'host': 'example.com', - * 'accept': '*' } - * ``` - * - * Keys are lowercased. Values are not modified. - * - * In order to support the full spectrum of possible HTTP applications, the Node.js - * HTTP API is very low-level. It deals with stream handling and message - * parsing only. It parses a message into headers and body but it does not - * parse the actual headers or the body. - * - * See `message.headers` for details on how duplicate headers are handled. - * - * The raw headers as they were received are retained in the `rawHeaders`property, which is an array of `[key, value, key2, value2, ...]`. For - * example, the previous message header object might have a `rawHeaders`list like the following: - * - * ```js - * [ 'ConTent-Length', '123456', - * 'content-LENGTH', '123', - * 'content-type', 'text/plain', - * 'CONNECTION', 'keep-alive', - * 'Host', 'example.com', - * 'accepT', '*' ] - * ``` - * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/http.js) - */ -declare module 'http' { - import * as stream from 'node:stream'; - import { URL } from 'node:url'; - import { TcpSocketConnectOpts, Socket, Server as NetServer, LookupFunction } from 'node:net'; - import { LookupOptions } from 'node:dns'; - // incoming headers will never contain number - interface IncomingHttpHeaders extends NodeJS.Dict
"
-
-super_source_prefix = f""""""
-
-
-def t5_type(model_name):
- return 't5' == model_name.lower() or \
- 't5-' in model_name.lower() or \
- 'flan-' in model_name.lower() or \
- 'fastchat-t5' in model_name.lower()
-
-
-def get_langchain_prompts(pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary,
- model_name, inference_server, model_path_llama):
- if model_name and ('falcon' in model_name or
- 'Llama-2'.lower() in model_name.lower() or
- model_path_llama and 'llama-2' in model_path_llama.lower()) or \
- model_name in [None, '']:
- # use when no model, like no --base_model
- pre_prompt_query1 = "Pay attention and remember the information below, which will help to answer the question or imperative after the context ends.\n"
- prompt_query1 = "According to only the information in the document sources provided within the context above, "
- elif inference_server and inference_server.startswith('openai'):
- pre_prompt_query1 = "Pay attention and remember the information below, which will help to answer the question or imperative after the context ends. If the answer cannot be primarily obtained from information within the context, then respond that the answer does not appear in the context of the documents.\n"
- prompt_query1 = "According to (primarily) the information in the document sources provided within context above, "
- else:
- pre_prompt_query1 = ""
- prompt_query1 = ""
-
- pre_prompt_summary1 = """In order to write a concise single-paragraph or bulleted list summary, pay attention to the following text\n"""
- prompt_summary1 = "Using only the information in the document sources above, write a condensed and concise summary of key results (preferably as bullet points):\n"
-
- if pre_prompt_query is None:
- pre_prompt_query = pre_prompt_query1
- if prompt_query is None:
- prompt_query = prompt_query1
- if pre_prompt_summary is None:
- pre_prompt_summary = pre_prompt_summary1
- if prompt_summary is None:
- prompt_summary = prompt_summary1
-
- return pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary
-
-
-def gr_to_lg(image_loaders,
- pdf_loaders,
- url_loaders,
- **kwargs,
- ):
- if image_loaders is None:
- image_loaders = kwargs['image_loaders_options0']
- if pdf_loaders is None:
- pdf_loaders = kwargs['pdf_loaders_options0']
- if url_loaders is None:
- url_loaders = kwargs['url_loaders_options0']
- # translate:
- # 'auto' wouldn't be used here
- ret = dict(
- # urls
- use_unstructured='Unstructured' in url_loaders,
- use_playwright='PlayWright' in url_loaders,
- use_selenium='Selenium' in url_loaders,
-
- # pdfs
- use_pymupdf='on' if 'PyMuPDF' in pdf_loaders else 'off',
- use_unstructured_pdf='on' if 'Unstructured' in pdf_loaders else 'off',
- use_pypdf='on' if 'PyPDF' in pdf_loaders else 'off',
- enable_pdf_ocr='on' if 'OCR' in pdf_loaders else 'off',
- enable_pdf_doctr='on' if 'DocTR' in pdf_loaders else 'off',
- try_pdf_as_html='on' if 'TryHTML' in pdf_loaders else 'off',
-
- # images
- enable_ocr='OCR' in image_loaders,
- enable_doctr='DocTR' in image_loaders,
- enable_pix2struct='Pix2Struct' in image_loaders,
- enable_captions='Caption' in image_loaders or 'CaptionBlip2' in image_loaders,
- )
- if 'CaptionBlip2' in image_loaders:
- # just override, don't actually do both even if user chose both
- captions_model = "Salesforce/blip2-flan-t5-xl"
- else:
- captions_model = kwargs['captions_model']
- return ret, captions_model
-
-
-invalid_key_msg = 'Invalid Access Key, request access key from sales@h2o.ai or jon.mckinney@h2o.ai'
-
-docs_ordering_types = ['best_first', 'best_near_prompt', 'reverse_ucurve_sort']
diff --git a/spaces/hanxuan/XQSign/bin/unidbg-fetch-qsign.bat b/spaces/hanxuan/XQSign/bin/unidbg-fetch-qsign.bat
deleted file mode 100644
index 8b291e7303b0c07d14b714e5795473891363c85b..0000000000000000000000000000000000000000
--- a/spaces/hanxuan/XQSign/bin/unidbg-fetch-qsign.bat
+++ /dev/null
@@ -1,89 +0,0 @@
-@rem
-@rem Copyright 2015 the original author or authors.
-@rem
-@rem Licensed under the Apache License, Version 2.0 (the "License");
-@rem you may not use this file except in compliance with the License.
-@rem You may obtain a copy of the License at
-@rem
-@rem https://www.apache.org/licenses/LICENSE-2.0
-@rem
-@rem Unless required by applicable law or agreed to in writing, software
-@rem distributed under the License is distributed on an "AS IS" BASIS,
-@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-@rem See the License for the specific language governing permissions and
-@rem limitations under the License.
-@rem
-
-@if "%DEBUG%" == "" @echo off
-@rem ##########################################################################
-@rem
-@rem unidbg-fetch-qsign startup script for Windows
-@rem
-@rem ##########################################################################
-
-@rem Set local scope for the variables with windows NT shell
-if "%OS%"=="Windows_NT" setlocal
-
-set DIRNAME=%~dp0
-if "%DIRNAME%" == "" set DIRNAME=.
-set APP_BASE_NAME=%~n0
-set APP_HOME=%DIRNAME%..
-
-@rem Resolve any "." and ".." in APP_HOME to make it shorter.
-for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi
-
-@rem Add default JVM options here. You can also use JAVA_OPTS and UNIDBG_FETCH_QSIGN_OPTS to pass JVM options to this script.
-set DEFAULT_JVM_OPTS=
-
-@rem Find java.exe
-if defined JAVA_HOME goto findJavaFromJavaHome
-
-set JAVA_EXE=java.exe
-%JAVA_EXE% -version >NUL 2>&1
-if "%ERRORLEVEL%" == "0" goto execute
-
-echo.
-echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:findJavaFromJavaHome
-set JAVA_HOME=%JAVA_HOME:"=%
-set JAVA_EXE=%JAVA_HOME%/bin/java.exe
-
-if exist "%JAVA_EXE%" goto execute
-
-echo.
-echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:execute
-@rem Setup the command line
-
-set CLASSPATH=%APP_HOME%\lib\unidbg-fetch-qsign-1.1.9.jar;%APP_HOME%\lib\unidbg-android-105.jar;%APP_HOME%\lib\ktor-server-content-negotiation-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-kotlinx-json-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-status-pages-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-netty-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-host-common-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-core-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-kotlinx-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-events-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-websockets-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-http-cio-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-http-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-network-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-utils-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-io-jvm-2.3.1.jar;%APP_HOME%\lib\kotlin-stdlib-jdk8-1.8.22.jar;%APP_HOME%\lib\kotlinx-serialization-json-jvm-1.5.1.jar;%APP_HOME%\lib\kotlinx-serialization-protobuf-jvm-1.5.1.jar;%APP_HOME%\lib\kotlinx-serialization-core-jvm-1.5.1.jar;%APP_HOME%\lib\logback-classic-1.2.11.jar;%APP_HOME%\lib\kotlinx-coroutines-jdk8-1.7.1.jar;%APP_HOME%\lib\kotlinx-coroutines-core-jvm-1.7.1.jar;%APP_HOME%\lib\kotlin-stdlib-jdk7-1.8.22.jar;%APP_HOME%\lib\kotlin-reflect-1.8.10.jar;%APP_HOME%\lib\kotlin-stdlib-1.8.22.jar;%APP_HOME%\lib\slf4j-api-1.7.36.jar;%APP_HOME%\lib\kotlin-stdlib-common-1.8.22.jar;%APP_HOME%\lib\config-1.4.2.jar;%APP_HOME%\lib\jansi-2.4.0.jar;%APP_HOME%\lib\netty-codec-http2-4.1.92.Final.jar;%APP_HOME%\lib\alpn-api-1.1.3.v20160715.jar;%APP_HOME%\lib\netty-transport-native-kqueue-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-native-epoll-4.1.92.Final.jar;%APP_HOME%\lib\logback-core-1.2.11.jar;%APP_HOME%\lib\annotations-23.0.0.jar;%APP_HOME%\lib\netty-codec-http-4.1.92.Final.jar;%APP_HOME%\lib\netty-handler-4.1.92.Final.jar;%APP_HOME%\lib\netty-codec-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-classes-kqueue-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-classes-epoll-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-native-unix-common-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-4.1.92.Final.jar;%APP_HOME%\lib\netty-buffer-4.1.92.Final.jar;%APP_HOME%\lib\netty-resolver-4.1.92.Final.jar;%APP_HOME%\lib\netty-common-4.1.92.Final.jar
-
-
-@rem Execute unidbg-fetch-qsign
-"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %UNIDBG_FETCH_QSIGN_OPTS% -classpath "%CLASSPATH%" MainKt %*
-
-:end
-@rem End local scope for the variables with windows NT shell
-if "%ERRORLEVEL%"=="0" goto mainEnd
-
-:fail
-rem Set variable UNIDBG_FETCH_QSIGN_EXIT_CONSOLE if you need the _script_ return code instead of
-rem the _cmd.exe /c_ return code!
-if not "" == "%UNIDBG_FETCH_QSIGN_EXIT_CONSOLE%" exit 1
-exit /b 1
-
-:mainEnd
-if "%OS%"=="Windows_NT" endlocal
-
-:omega
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/modeling/meta_arch/build.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/modeling/meta_arch/build.py
deleted file mode 100644
index 630389dfca822f295447abd5e8424186d02e0465..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/modeling/meta_arch/build.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import torch
-
-from detectron2.utils.registry import Registry
-
-META_ARCH_REGISTRY = Registry("META_ARCH") # noqa F401 isort:skip
-META_ARCH_REGISTRY.__doc__ = """
-Registry for meta-architectures, i.e. the whole model.
-
-The registered object will be called with `obj(cfg)`
-and expected to return a `nn.Module` object.
-"""
-
-
-def build_model(cfg):
- """
- Build the whole model architecture, defined by ``cfg.MODEL.META_ARCHITECTURE``.
- Note that it does not load any weights from ``cfg``.
- """
- meta_arch = cfg.MODEL.META_ARCHITECTURE
- model = META_ARCH_REGISTRY.get(meta_arch)(cfg)
- model.to(torch.device(cfg.MODEL.DEVICE))
- return model
diff --git a/spaces/hasselhe2023/SoccerPosition2.0/app.py b/spaces/hasselhe2023/SoccerPosition2.0/app.py
deleted file mode 100644
index b8e324b9c29780cc194b84219d4782bd519931d7..0000000000000000000000000000000000000000
--- a/spaces/hasselhe2023/SoccerPosition2.0/app.py
+++ /dev/null
@@ -1,172 +0,0 @@
-### ----------------------------- ###
-### libraries ###
-### ----------------------------- ###
-
-import gradio as gr
-import pandas as pd
-import numpy as np
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from sklearn import metrics
-
-
-### ------------------------------ ###
-### data transformation ###
-### ------------------------------ ###
-
-# load dataset
-uncleaned_data = pd.read_csv('data.csv')
-
-# remove timestamp from dataset (always first column)
-uncleaned_data = uncleaned_data.iloc[: , 1:]
-data = pd.DataFrame()
-
-# keep track of which columns are categorical and what
-# those columns' value mappings are
-# structure: {colname1: {...}, colname2: {...} }
-cat_value_dicts = {}
-final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1]
-
-# for each column...
-for (colname, colval) in uncleaned_data.iteritems():
-
- # check if col is already a number; if so, add col directly
- # to new dataframe and skip to next column
- if isinstance(colval.values[0], (np.integer, float)):
- data[colname] = uncleaned_data[colname].copy()
- continue
-
- # structure: {0: "lilac", 1: "blue", ...}
- new_dict = {}
- val = 0 # first index per column
- transformed_col_vals = [] # new numeric datapoints
-
- # if not, for each item in that column...
- for (row, item) in enumerate(colval.values):
-
- # if item is not in this col's dict...
- if item not in new_dict:
- new_dict[item] = val
- val += 1
-
- # then add numerical value to transformed dataframe
- transformed_col_vals.append(new_dict[item])
-
- # reverse dictionary only for final col (0, 1) => (vals)
- if colname == final_colname:
- new_dict = {value : key for (key, value) in new_dict.items()}
-
- cat_value_dicts[colname] = new_dict
- data[colname] = transformed_col_vals
-
-
-### -------------------------------- ###
-### model training ###
-### -------------------------------- ###
-
-# select features and predicton; automatically selects last column as prediction
-cols = len(data.columns)
-num_features = cols - 1
-x = data.iloc[: , :num_features]
-y = data.iloc[: , num_features:]
-
-# split data into training and testing sets
-x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)
-
-# instantiate the model (using default parameters)
-model = LogisticRegression()
-model.fit(x_train, y_train.values.ravel())
-y_pred = model.predict(x_test)
-
-
-### -------------------------------- ###
-### article generation ###
-### -------------------------------- ###
-# borrow file reading function from reader.py
-
-def get_feat():
- feats = [abs(x) for x in model.coef_[0]]
- max_val = max(feats)
- idx = feats.index(max_val)
- return data.columns[idx]
-
-acc = str(round(metrics.accuracy_score(y_test, y_pred) * 100, 1)) + "%"
-most_imp_feat = get_feat()
-# info = get_article(acc, most_imp_feat)
-
-
-
-### ------------------------------- ###
-### interface creation ###
-### ------------------------------- ###
-
-
-# predictor for generic number of features
-def general_predictor(*args):
- features = []
-
- # transform categorical input
- for colname, arg in zip(data.columns, args):
- if (colname in cat_value_dicts):
- features.append(cat_value_dicts[colname][arg])
- else:
- features.append(arg)
-
- # predict single datapoint
- new_input = [features]
- result = model.predict(new_input)
- return cat_value_dicts[final_colname][result[0]]
-
-# add data labels to replace those lost via star-args
-
-
-block = gr.Blocks()
-
-with open('info.md') as f:
- with block:
- gr.Markdown(f.readline())
- gr.Markdown('Take the quiz to get a personalized recommendation using AI.')
-
- with gr.Row():
- with gr.Box():
- inputls = []
- for colname in data.columns:
- # skip last column
- if colname == final_colname:
- continue
-
- # access categories dict if data is categorical
- # otherwise, just use a number input
- if colname in cat_value_dicts:
- radio_options = list(cat_value_dicts[colname].keys())
- inputls.append(gr.inputs.Dropdown(choices=radio_options, type="value", label=colname))
- else:
- # add numerical input
- inputls.append(gr.inputs.Number(label=colname))
- gr.Markdown("Sources
Sources [Score | Link]:"""
-super_source_postfix = f"""End Sources
")
-
- submit = gr.Button("Click to see your personalized result!", variant="primary")
- gr.Markdown("
")
- output = gr.Textbox(label="Your recommendation:", placeholder="your recommendation will appear here")
-
- submit.click(fn=general_predictor, inputs=inputls, outputs=output)
- gr.Markdown("
")
-
- with gr.Row():
- with gr.Box():
- gr.Markdown(f"
Accuracy:
{acc}") - with gr.Box(): - gr.Markdown(f"Most important feature:
{most_imp_feat}") - - gr.Markdown("") - - with gr.Box(): - gr.Markdown('''⭐ Note that model accuracy is based on the uploaded data.csv and reflects how well the AI model can give correct recommendations for that dataset. Model accuracy and most important feature can be helpful for understanding how the model works, but should not be considered absolute facts about the real world.''') - - with gr.Box(): - with open('info.md') as f: - f.readline() - gr.Markdown(f.read()) - -# show the interface -block.launch() \ No newline at end of file diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/benchmarking/nnUNetTrainerV2_dummyLoad.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/benchmarking/nnUNetTrainerV2_dummyLoad.py deleted file mode 100644 index 355857a9eabd0f5f2201bf3923e343a3965956d4..0000000000000000000000000000000000000000 --- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/benchmarking/nnUNetTrainerV2_dummyLoad.py +++ /dev/null @@ -1,147 +0,0 @@ -# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Tuple - -import torch - -from nnunet.training.loss_functions.crossentropy import RobustCrossEntropyLoss -from nnunet.training.network_training.nnUNet_variants.architectural_variants.nnUNetTrainerV2_noDeepSupervision import \ - nnUNetTrainerV2_noDeepSupervision -from nnunet.training.network_training.nnUNet_variants.benchmarking.nnUNetTrainerV2_2epochs import nnUNetTrainerV2_5epochs -from torch.cuda.amp import autocast -import numpy as np - - -class nnUNetTrainerV2_5epochs_dummyLoad(nnUNetTrainerV2_5epochs): - def initialize(self, training=True, force_load_plans=False): - super().initialize(training, force_load_plans) - self.some_batch = torch.rand((self.batch_size, self.num_input_channels, *self.patch_size)).float().cuda() - - self.some_gt = [torch.round(torch.rand((self.batch_size, 1, *[int(i * j) for i, j in zip(self.patch_size, k)])) * (self.num_classes - 1)).float().cuda() for k in self.deep_supervision_scales] - - def run_iteration(self, data_generator, do_backprop=True, run_online_evaluation=False): - data = self.some_batch - target = self.some_gt - - self.optimizer.zero_grad() - - if self.fp16: - with autocast(): - output = self.network(data) - del data - l = self.loss(output, target) - - if do_backprop: - self.amp_grad_scaler.scale(l).backward() - self.amp_grad_scaler.unscale_(self.optimizer) - torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12) - self.amp_grad_scaler.step(self.optimizer) - self.amp_grad_scaler.update() - else: - output = self.network(data) - del data - l = self.loss(output, target) - - if do_backprop: - l.backward() - torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12) - self.optimizer.step() - - if run_online_evaluation: - self.run_online_evaluation(output, target) - - del target - - return l.detach().cpu().numpy() - - -class nnUNetTrainerV2_2epochs_dummyLoad(nnUNetTrainerV2_5epochs_dummyLoad): - def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, - unpack_data=True, deterministic=True, fp16=False): - super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, - deterministic, fp16) - self.max_num_epochs = 2 - - -class nnUNetTrainerV2_5epochs_dummyLoadCEnoDS(nnUNetTrainerV2_noDeepSupervision): - def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, - unpack_data=True, deterministic=True, fp16=False): - super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, - deterministic, fp16) - self.max_num_epochs = 5 - self.loss = RobustCrossEntropyLoss() - - def validate(self, do_mirroring: bool = True, use_sliding_window: bool = True, step_size: float = 0.5, - save_softmax: bool = True, use_gaussian: bool = True, overwrite: bool = True, - validation_folder_name: str = 'validation_raw', debug: bool = False, all_in_gpu: bool = False, - segmentation_export_kwargs=None, run_postprocessing_on_folds: bool = True): - pass - - def predict_preprocessed_data_return_seg_and_softmax(self, data: np.ndarray, do_mirroring: bool = True, - mirror_axes: Tuple[int] = None, - use_sliding_window: bool = True, step_size: float = 0.5, - use_gaussian: bool = True, pad_border_mode: str = 'constant', - pad_kwargs: dict = None, all_in_gpu: bool = False, - verbose: bool = True, mixed_precision=True) -> Tuple[np.ndarray, np.ndarray]: - pass - - def save_checkpoint(self, fname, save_optimizer=True): - pass - - def initialize(self, training=True, force_load_plans=False): - super().initialize(training, force_load_plans) - self.some_batch = torch.rand((self.batch_size, self.num_input_channels, *self.patch_size)).float().cuda() - - self.some_gt = torch.round(torch.rand((self.batch_size, *self.patch_size)) * (self.num_classes - 1)).long().cuda() - - def run_iteration(self, data_generator, do_backprop=True, run_online_evaluation=False): - data = self.some_batch - target = self.some_gt - - self.optimizer.zero_grad() - - if self.fp16: - with autocast(): - output = self.network(data) - del data - l = self.loss(output, target) - - if do_backprop: - self.amp_grad_scaler.scale(l).backward() - self.amp_grad_scaler.unscale_(self.optimizer) - torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12) - self.amp_grad_scaler.step(self.optimizer) - self.amp_grad_scaler.update() - else: - output = self.network(data) - del data - l = self.loss(output, target) - - if do_backprop: - l.backward() - torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12) - self.optimizer.step() - - if run_online_evaluation: - self.run_online_evaluation(output, target) - - del target - - return l.detach().cpu().numpy() - - def run_online_evaluation(self, output, target): - pass - - def finish_online_evaluation(self): - pass diff --git a/spaces/huang4414/Real-CUGAN/app.py b/spaces/huang4414/Real-CUGAN/app.py deleted file mode 100644 index 2439c5cec6b61e8a517f957daf710cbb6b5c3cf6..0000000000000000000000000000000000000000 --- a/spaces/huang4414/Real-CUGAN/app.py +++ /dev/null @@ -1,62 +0,0 @@ -from upcunet_v3 import RealWaifuUpScaler -import gradio as gr -import time -import logging -import os -from PIL import ImageOps -import numpy as np -import math - - -def greet(input_img, input_model_name, input_tile_mode): - # if input_img.size[0] * input_img.size[1] > 256 * 256: - # y = int(math.sqrt(256*256/input_img.size[0]*input_img.size[1])) - # x = int(input_img.size[0]/input_img.size[1]*y) - # input_img = ImageOps.fit(input_img, (x, y)) - input_img = np.array(input_img) - if input_model_name not in model_cache: - t1 = time.time() - upscaler = RealWaifuUpScaler(input_model_name[2], ModelPath + input_model_name, half=False, device="cpu") - t2 = time.time() - logger.info(f'load model time, {t2 - t1}') - model_cache[input_model_name] = upscaler - else: - upscaler = model_cache[input_model_name] - logger.info(f'load model from cache') - - start = time.time() - result = upscaler(input_img, tile_mode=input_tile_mode) - end = time.time() - logger.info(f'input_model_name, {input_model_name}') - logger.info(f'input_tile_mode, {input_tile_mode}') - logger.info(f'input shape, {input_img.shape}') - logger.info(f'output shape, {result.shape}') - logger.info(f'speed time, {end - start}') - return result - - -if __name__ == '__main__': - logging.basicConfig(level=logging.INFO, format="[%(asctime)s] [%(process)d] [%(levelname)s] %(message)s") - logger = logging.getLogger() - - ModelPath = "weights_v3/" - model_cache = {} - - input_model_name = gr.inputs.Dropdown(os.listdir(ModelPath), default="up2x-latest-denoise2x.pth", label='选择model') - input_tile_mode = gr.inputs.Dropdown([0, 1, 2, 3, 4], default=2, label='选择tile_mode') - input_img = gr.inputs.Image(label='image', type='pil') - - inputs = [input_img, input_model_name, input_tile_mode] - outputs = "image" - iface = gr.Interface(fn=greet, - inputs=inputs, - outputs=outputs, - allow_screenshot=False, - allow_flagging='never', - examples=[['test-img.jpg', "up2x-latest-denoise2x.pth", 2]], - article='[https://github.com/bilibili/ailab/tree/main/Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN)
' - '感谢b站开源的项目，图片过大会导致内存不足，所有我将图片裁剪小，想体验大图片的效果请自行前往上面的链接。
' - '修改bbb' - 'The large image will lead to memory limit exceeded. So I crop and resize image. ' - 'If you want to experience the large image, please go to the link above.') - iface.launch() diff --git a/spaces/huggan/BigGAN/README.md b/spaces/huggan/BigGAN/README.md deleted file mode 100644 index 7aaab06d08a5c7b85c0be4b81dfa6752dbe8212d..0000000000000000000000000000000000000000 --- a/spaces/huggan/BigGAN/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Huggan -emoji: 💩 -colorFrom: gray -colorTo: blue -sdk: gradio -sdk_version: 2.8.14 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/huggingface-projects/InstructPix2Pix-Chatbot-ui/frontend/src/app.css b/spaces/huggingface-projects/InstructPix2Pix-Chatbot-ui/frontend/src/app.css deleted file mode 100644 index b5c61c956711f981a41e95f7fcf0038436cfbb22..0000000000000000000000000000000000000000 --- a/spaces/huggingface-projects/InstructPix2Pix-Chatbot-ui/frontend/src/app.css +++ /dev/null @@ -1,3 +0,0 @@ -@tailwind base; -@tailwind components; -@tailwind utilities; diff --git a/spaces/hylee/u2net_portrait/U-2-Net/u2net_portrait_composite.py b/spaces/hylee/u2net_portrait/U-2-Net/u2net_portrait_composite.py deleted file mode 100644 index 74d5da5e2a6b7d2e0b859334972348770760d372..0000000000000000000000000000000000000000 --- a/spaces/hylee/u2net_portrait/U-2-Net/u2net_portrait_composite.py +++ /dev/null @@ -1,141 +0,0 @@ -import os -from skimage import io, transform -from skimage.filters import gaussian -import torch -import torchvision -from torch.autograd import Variable -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import Dataset, DataLoader -from torchvision import transforms#, utils -# import torch.optim as optim - -import numpy as np -from PIL import Image -import glob - -from data_loader import RescaleT -from data_loader import ToTensor -from data_loader import ToTensorLab -from data_loader import SalObjDataset - -from model import U2NET # full size version 173.6 MB -from model import U2NETP # small version u2net 4.7 MB - -import argparse - -# normalize the predicted SOD probability map -def normPRED(d): - ma = torch.max(d) - mi = torch.min(d) - - dn = (d-mi)/(ma-mi) - - return dn - -def save_output(image_name,pred,d_dir,sigma=2,alpha=0.5): - - predict = pred - predict = predict.squeeze() - predict_np = predict.cpu().data.numpy() - - image = io.imread(image_name) - pd = transform.resize(predict_np,image.shape[0:2],order=2) - pd = pd/(np.amax(pd)+1e-8)*255 - pd = pd[:,:,np.newaxis] - - print(image.shape) - print(pd.shape) - - ## fuse the orignal portrait image and the portraits into one composite image - ## 1. use gaussian filter to blur the orginal image - sigma=sigma - image = gaussian(image, sigma=sigma, preserve_range=True) - - ## 2. fuse these orignal image and the portrait with certain weight: alpha - alpha = alpha - im_comp = image*alpha+pd*(1-alpha) - - print(im_comp.shape) - - - img_name = image_name.split(os.sep)[-1] - aaa = img_name.split(".") - bbb = aaa[0:-1] - imidx = bbb[0] - for i in range(1,len(bbb)): - imidx = imidx + "." + bbb[i] - io.imsave(d_dir+'/'+imidx+'_sigma_' + str(sigma) + '_alpha_' + str(alpha) + '_composite.png',im_comp) - -def main(): - - parser = argparse.ArgumentParser(description="image and portrait composite") - parser.add_argument('-s',action='store',dest='sigma') - parser.add_argument('-a',action='store',dest='alpha') - args = parser.parse_args() - print(args.sigma) - print(args.alpha) - print("--------------------") - - # --------- 1. get image path and name --------- - model_name='u2net_portrait'#u2netp - - - image_dir = './test_data/test_portrait_images/your_portrait_im' - prediction_dir = './test_data/test_portrait_images/your_portrait_results' - if(not os.path.exists(prediction_dir)): - os.mkdir(prediction_dir) - - model_dir = './saved_models/u2net_portrait/u2net_portrait.pth' - - img_name_list = glob.glob(image_dir+'/*') - print("Number of images: ", len(img_name_list)) - - # --------- 2. dataloader --------- - #1. dataloader - test_salobj_dataset = SalObjDataset(img_name_list = img_name_list, - lbl_name_list = [], - transform=transforms.Compose([RescaleT(512), - ToTensorLab(flag=0)]) - ) - test_salobj_dataloader = DataLoader(test_salobj_dataset, - batch_size=1, - shuffle=False, - num_workers=1) - - # --------- 3. model define --------- - - print("...load U2NET---173.6 MB") - net = U2NET(3,1) - - net.load_state_dict(torch.load(model_dir)) - if torch.cuda.is_available(): - net.cuda() - net.eval() - - # --------- 4. inference for each image --------- - for i_test, data_test in enumerate(test_salobj_dataloader): - - print("inferencing:",img_name_list[i_test].split(os.sep)[-1]) - - inputs_test = data_test['image'] - inputs_test = inputs_test.type(torch.FloatTensor) - - if torch.cuda.is_available(): - inputs_test = Variable(inputs_test.cuda()) - else: - inputs_test = Variable(inputs_test) - - d1,d2,d3,d4,d5,d6,d7= net(inputs_test) - - # normalization - pred = 1.0 - d1[:,0,:,:] - pred = normPRED(pred) - - # save results to test_results folder - save_output(img_name_list[i_test],pred,prediction_dir,sigma=float(args.sigma),alpha=float(args.alpha)) - - del d1,d2,d3,d4,d5,d6,d7 - -if __name__ == "__main__": - main() diff --git a/spaces/iamkb/voc-demo/README.md b/spaces/iamkb/voc-demo/README.md deleted file mode 100644 index ccb5ddbd44981317e04b2093e8980b32caa6eb6c..0000000000000000000000000000000000000000 --- a/spaces/iamkb/voc-demo/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Mars Demo -emoji: 🐨 -colorFrom: green -colorTo: yellow -sdk: gradio -sdk_version: 2.9.4 -app_file: app.py -pinned: false -license: other ---- - - diff --git a/spaces/ichelp/AUTOMATIC1111-stable-diffusion-webui/command.py b/spaces/ichelp/AUTOMATIC1111-stable-diffusion-webui/command.py deleted file mode 100644 index 84f7952f75537ce11c3fb4520420ba5c7cadc5d4..0000000000000000000000000000000000000000 --- a/spaces/ichelp/AUTOMATIC1111-stable-diffusion-webui/command.py +++ /dev/null @@ -1,14 +0,0 @@ -import gradio as gr -from subprocess import getoutput - -def run(command): - out = getoutput(f"{command}") - return out - -with gr.Blocks() as app: - command = gr.Textbox(show_label=False, max_lines=1, placeholder="command") - out_text = gr.Textbox(show_label=False) - btn_run = gr.Button("run command") - btn_run.click(run, inputs=command, outputs=out_text) - -app.launch() \ No newline at end of file diff --git a/spaces/imseldrith/DeepFakeAI/DeepFakeAI/globals.py b/spaces/imseldrith/DeepFakeAI/DeepFakeAI/globals.py deleted file mode 100644 index aa63522665497a0301cd90b00e0ccc5a1b87ae2e..0000000000000000000000000000000000000000 --- a/spaces/imseldrith/DeepFakeAI/DeepFakeAI/globals.py +++ /dev/null @@ -1,30 +0,0 @@ -from typing import List, Optional - -from DeepFakeAI.typing import FaceRecognition, FaceAnalyserDirection, FaceAnalyserAge, FaceAnalyserGender, TempFrameFormat - -source_path : Optional[str] = None -target_path : Optional[str] = None -output_path : Optional[str] = None -headless : Optional[bool] = None -frame_processors : List[str] = [] -ui_layouts : List[str] = [] -keep_fps : Optional[bool] = None -keep_temp : Optional[bool] = None -skip_audio : Optional[bool] = None -face_recognition : Optional[FaceRecognition] = None -face_analyser_direction : Optional[FaceAnalyserDirection] = None -face_analyser_age : Optional[FaceAnalyserAge] = None -face_analyser_gender : Optional[FaceAnalyserGender] = None -reference_face_position : Optional[int] = None -reference_frame_number : Optional[int] = None -reference_face_distance : Optional[float] = None -trim_frame_start : Optional[int] = None -trim_frame_end : Optional[int] = None -temp_frame_format : Optional[TempFrameFormat] = None -temp_frame_quality : Optional[int] = None -output_video_encoder : Optional[str] = None -output_video_quality : Optional[int] = None -max_memory : Optional[int] = None -execution_providers : List[str] = [] -execution_thread_count : Optional[int] = None -execution_queue_count : Optional[int] = None diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Belotemultijoueurfacebookcheatengine VERIFIED.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Belotemultijoueurfacebookcheatengine VERIFIED.md deleted file mode 100644 index 5adbdc4a32514339bc121446c9ad118fa701d203..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Belotemultijoueurfacebookcheatengine VERIFIED.md +++ /dev/null @@ -1,18 +0,0 @@ - -
Belote Multijoueur Facebook Cheat Engine: How to Play and Win Belote Games Online
-Belote is a popular card game that originated in France and is played by four players in two teams. The game involves bidding, trick-taking, and scoring points based on the cards played. Belote can be played online on various platforms, such as Facebook, where you can challenge your friends or other players from around the world. But how can you improve your chances of winning Belote games online? One way is to use a cheat engine that can help you manipulate the game and gain an advantage over your opponents.
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A cheat engine is a software tool that can modify the memory and data of a computer program, such as a game. By using a cheat engine, you can alter various aspects of the game, such as the cards you have, the cards your opponents have, the score, the time limit, and so on. You can also use a cheat engine to access hidden features or unlock bonuses that are normally unavailable. A cheat engine can give you an edge over your competitors and make you win more games.
-However, using a cheat engine is not without risks. First of all, it is unethical and unfair to use a cheat engine to play online games, as it violates the rules and spoils the fun for other players. Secondly, it can expose your computer to viruses, malware, or other harmful programs that can damage your system or steal your personal information. Thirdly, it can get you banned from the game platform or the game itself, as most online games have anti-cheat mechanisms that can detect and prevent cheating. Therefore, using a cheat engine is not recommended and should be avoided.
-If you want to play and win Belote games online without cheating, you should follow some tips and strategies that can help you improve your skills and performance. Here are some of them:
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Belote Multijoueur Facebook Cheat Engine is a tool that can help you cheat in Belote games online by modifying the game data and memory. However, using this tool is not advisable, as it is unethical, risky, and punishable. Instead of cheating, you should play fair and square by following some tips and strategies that can help you improve your skills and performance in Belote games online.
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\ No newline at end of file diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Borang Nikah Perak Pdf 12 [EXCLUSIVE].md b/spaces/inplisQlawa/anything-midjourney-v4-1/Borang Nikah Perak Pdf 12 [EXCLUSIVE].md deleted file mode 100644 index 3f5b0033654d1058ca03fadc7986f2a32238505d..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Borang Nikah Perak Pdf 12 [EXCLUSIVE].md +++ /dev/null @@ -1,10 +0,0 @@ -
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-.557 foto untuk blogberita perayangan pernikahan kencan saat berkahbar dengan pasangan. Pada tahun 2017, Nabi Azhari dan Awang Afasa mengaku telah pemasangan kekalahan permainan kencan jalan-jalan yang dimaksud adalah aplikasi berkah baru yang dibentuk untuk kalangan pengunjung pemasangan. Mereka merasa hal ini membuat penasaran dengan jelas bahwa seluruh dunia sudah memiliki aplikasi kencan. Hal ini juga memberikan akses agar masyarakat bisa memasang diri dalam kehidupan sehari-hari. Pada akhirnya, media internet didirikan untuk memberikan akses masyarakat untuk mengawali kegiatan kencanan. Seiring dengan itu, media sosial itu berkah baru terbuka kemudian menjadi salah satu yang terbesar dalam segmen ini.In September, then-congressman Joe Crowley was given a primary challenge from Alexandria Ocasio-Cortez, and the Democratic establishment reacted like the establishment has reacted to such challenges for generations. Here’s a taste of that reaction, by the way: - -There are no constraints on how they respond to a primary challenge. Instead, the “establishment” will simply begin by declaring that they disagree with that challenger’s platform. They will do so loudly, and often by resorting to the favorite strategy of the so-called “mainstream” media — omitting the challenger’s name and merely referring to them by their initials: “AOC.” - -That’s what happened to Crowley, who represented the Queens and Bronx portions of the Bronx and Queens congressional districts in the House of Representatives. The first few months of the 2018 cycle proved him to be a weak general-election candidate and the biggest beneficiary of Ocasio-Cortez’s upset. He won reelection with a resounding 63% of the vote and, according to one analysis, had almost $1 million in cash on hand at the end of the 4fefd39f24
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- diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Cutmaster 2d Pro Full Cracked.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Cutmaster 2d Pro Full Cracked.md deleted file mode 100644 index 2a80d12c15bb155d9f29cddb4562018340135f9b..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Cutmaster 2d Pro Full Cracked.md +++ /dev/null @@ -1,10 +0,0 @@ -
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- diff --git a/spaces/introduck/introduck/introduck_tests/README.md b/spaces/introduck/introduck/introduck_tests/README.md deleted file mode 100644 index 1a6ad64adf4131b63ed47df71b053d29b5c0d090..0000000000000000000000000000000000000000 --- a/spaces/introduck/introduck/introduck_tests/README.md +++ /dev/null @@ -1 +0,0 @@ -# Introduck Tests diff --git a/spaces/ismot/8testi1/utils/google_utils.py b/spaces/ismot/8testi1/utils/google_utils.py deleted file mode 100644 index c2e7293de826d81fa05e01022dabdfbf74fa995e..0000000000000000000000000000000000000000 --- a/spaces/ismot/8testi1/utils/google_utils.py +++ /dev/null @@ -1,122 +0,0 @@ -# Google utils: https://cloud.google.com/storage/docs/reference/libraries - -import os -import platform -import subprocess -import time -from pathlib import Path - -import requests -import torch - - -def gsutil_getsize(url=''): - # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du - s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8') - return eval(s.split(' ')[0]) if len(s) else 0 # bytes - - -def attempt_download(file, repo='WongKinYiu/yolov6'): - # Attempt file download if does not exist - file = Path(str(file).strip().replace("'", '').lower()) - - if not file.exists(): - try: - response = requests.get(f'https://api.github.com/repos/{repo}/releases/weights').json() # github api - assets = [x['name'] for x in response['assets']] # release assets - tag = response['tag_name'] # i.e. 'v1.0' - except: # fallback plan - assets = ['yolov6.pt'] - tag = subprocess.check_output('git tag', shell=True).decode().split()[-1] - - name = file.name - if name in assets: - msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/' - redundant = False # second download option - try: # GitHub - url = f'https://github.com/{repo}/releases/download/{tag}/{name}' - print(f'Downloading {url} to {file}...') - torch.hub.download_url_to_file(url, file) - assert file.exists() and file.stat().st_size > 1E6 # check - except Exception as e: # GCP - print(f'Download error: {e}') - assert redundant, 'No secondary mirror' - url = f'https://storage.googleapis.com/{repo}/ckpt/{name}' - print(f'Downloading {url} to {file}...') - os.system(f'curl -L {url} -o {file}') # torch.hub.download_url_to_file(url, weights) - finally: - if not file.exists() or file.stat().st_size < 1E6: # check - file.unlink(missing_ok=True) # remove partial downloads - print(f'ERROR: Download failure: {msg}') - print('') - return - - -def gdrive_download(id='', file='tmp.zip'): - # Downloads a file from Google Drive. from yolov6.utils.google_utils import *; gdrive_download() - t = time.time() - file = Path(file) - cookie = Path('cookie') # gdrive cookie - print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='') - file.unlink(missing_ok=True) # remove existing file - cookie.unlink(missing_ok=True) # remove existing cookie - - # Attempt file download - out = "NUL" if platform.system() == "Windows" else "/dev/null" - os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}') - if os.path.exists('cookie'): # large file - s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}' - else: # small file - s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"' - r = os.system(s) # execute, capture return - cookie.unlink(missing_ok=True) # remove existing cookie - - # Error check - if r != 0: - file.unlink(missing_ok=True) # remove partial - print('Download error ') # raise Exception('Download error') - return r - - # Unzip if archive - if file.suffix == '.zip': - print('unzipping... ', end='') - os.system(f'unzip -q {file}') # unzip - file.unlink() # remove zip to free space - - print(f'Done ({time.time() - t:.1f}s)') - return r - - -def get_token(cookie="./cookie"): - with open(cookie) as f: - for line in f: - if "download" in line: - return line.split()[-1] - return "" - -# def upload_blob(bucket_name, source_file_name, destination_blob_name): -# # Uploads a file to a bucket -# # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python -# -# storage_client = storage.Client() -# bucket = storage_client.get_bucket(bucket_name) -# blob = bucket.blob(destination_blob_name) -# -# blob.upload_from_filename(source_file_name) -# -# print('File {} uploaded to {}.'.format( -# source_file_name, -# destination_blob_name)) -# -# -# def download_blob(bucket_name, source_blob_name, destination_file_name): -# # Uploads a blob from a bucket -# storage_client = storage.Client() -# bucket = storage_client.get_bucket(bucket_name) -# blob = bucket.blob(source_blob_name) -# -# blob.download_to_filename(destination_file_name) -# -# print('Blob {} downloaded to {}.'.format( -# source_blob_name, -# destination_file_name)) diff --git a/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h b/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h deleted file mode 100644 index c7408eba007b424194618baa63726657e36875e3..0000000000000000000000000000000000000000 --- a/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h +++ /dev/null @@ -1,64 +0,0 @@ -/*! -************************************************************************************************** -* Deformable DETR -* Copyright (c) 2020 SenseTime. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 [see LICENSE for details] -************************************************************************************************** -* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -************************************************************************************************** -*/ - -#pragma once - -#include "ms_deform_attn_cpu.h" - -#ifdef WITH_CUDA -#include "ms_deform_attn_cuda.h" -#endif - -namespace groundingdino { - -at::Tensor -ms_deform_attn_forward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const int im2col_step) -{ - if (value.type().is_cuda()) - { -#ifdef WITH_CUDA - return ms_deform_attn_cuda_forward( - value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step); -#else - AT_ERROR("Not compiled with GPU support"); -#endif - } - AT_ERROR("Not implemented on the CPU"); -} - -std::vector
'
- f'
' if cover else "")+
- '
'
- )
- with gr.Row():
- with gr.Column():
- vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
- # Input and Upload
- vc_input = gr.Textbox(label="Input audio path", visible=False)
- vc_upload = gr.Audio(label="Upload audio file", visible=True, interactive=True)
- # Youtube
- vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
- vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
- vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
- vc_split = gr.Button("Split Audio", variant="primary", visible=False)
- vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
- vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
- vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
- # TTS
- tts_text = gr.Textbox(visible=False, label="TTS text", info="Text to speech input")
- tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
- with gr.Column():
- vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
- f0method0 = gr.Radio(
- label="Pitch extraction algorithm",
- info=f0method_info,
- choices=f0method_mode,
- value="pm",
- interactive=True
- )
- index_rate1 = gr.Slider(
- minimum=0,
- maximum=1,
- label="Retrieval feature ratio",
- info="Accents controling. Too high prob gonna sounds too robotic (Default: 0.6)",
- value=0.6,
- interactive=True,
- )
- filter_radius0 = gr.Slider(
- minimum=0,
- maximum=7,
- label="Apply Median Filtering",
- info="The value represents the filter radius and can reduce breathiness.",
- value=3,
- step=1,
- interactive=True,
- )
- resample_sr0 = gr.Slider(
- minimum=0,
- maximum=48000,
- label="Resample the output audio",
- info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
- value=0,
- step=1,
- interactive=True,
- )
- rms_mix_rate0 = gr.Slider(
- minimum=0,
- maximum=1,
- label="Volume Envelope",
- info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
- value=1,
- interactive=True,
- )
- protect0 = gr.Slider(
- minimum=0,
- maximum=0.5,
- label="Voice Protection",
- info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
- value=0.4,
- step=0.01,
- interactive=True,
- )
- with gr.Column():
- vc_log = gr.Textbox(label="Output Information", interactive=False)
- vc_output = gr.Audio(label="Output Audio", interactive=False)
- vc_convert = gr.Button("Convert", variant="primary")
- vc_volume = gr.Slider(
- minimum=0,
- maximum=10,
- label="Vocal volume",
- value=4,
- interactive=True,
- step=1,
- info="Adjust vocal volume (Default: 4}",
- visible=False
- )
- vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
- vc_combine = gr.Button("Combine",variant="primary", visible=False)
- vc_convert.click(
- fn=vc_fn,
- inputs=[
- vc_audio_mode,
- vc_input,
- vc_upload,
- tts_text,
- tts_voice,
- vc_transform0,
- f0method0,
- index_rate1,
- filter_radius0,
- resample_sr0,
- rms_mix_rate0,
- protect0,
- ],
- outputs=[vc_log ,vc_output]
- )
- vc_split.click(
- fn=cut_vocal_and_inst,
- inputs=[vc_link, vc_download_audio, vc_split_model],
- outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
- )
- vc_combine.click(
- fn=combine_vocal_and_inst,
- inputs=[vc_output, vc_volume, vc_split_model],
- outputs=[vc_combined_output]
- )
- vc_audio_mode.change(
- fn=change_audio_mode,
- inputs=[vc_audio_mode],
- outputs=[
- vc_input,
- vc_upload,
- vc_download_audio,
- vc_link,
- vc_split_model,
- vc_split,
- vc_vocal_preview,
- vc_inst_preview,
- vc_audio_preview,
- vc_volume,
- vc_combined_output,
- vc_combine,
- tts_text,
- tts_voice
- ]
- )
- app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)
\ No newline at end of file
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/app.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/app.py
deleted file mode 100644
index 044f1adb29602d9332edc2d3368a5437918d26c0..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/app.py
+++ /dev/null
@@ -1,135 +0,0 @@
-
-import gradio as gr
-
-import re
-import random
-import string
-import librosa
-import numpy as np
-
-from pathlib import Path
-from scipy.io.wavfile import write
-
-from encoder import inference as encoder
-from vocoder.hifigan import inference as gan_vocoder
-from synthesizer.inference import Synthesizer
-
-class Mandarin:
- def __init__(self):
- self.encoder_path = "encoder/saved_models/pretrained.pt"
- self.vocoder_path = "vocoder/saved_models/pretrained/g_hifigan.pt"
- self.config_fpath = "vocoder/hifigan/config_16k_.json"
- self.accent = "synthesizer/saved_models/普通话.pt"
-
- synthesizers_cache = {}
- if synthesizers_cache.get(self.accent) is None:
- self.current_synt = Synthesizer(Path(self.accent))
- synthesizers_cache[self.accent] = self.current_synt
- else:
- self.current_synt = synthesizers_cache[self.accent]
-
- encoder.load_model(Path(self.encoder_path))
- gan_vocoder.load_model(Path(self.vocoder_path), self.config_fpath)
-
- def setVoice(self, timbre):
- self.timbre = timbre
- wav, sample_rate, = librosa.load(self.timbre)
-
- encoder_wav = encoder.preprocess_wav(wav, sample_rate)
- self.embed, _, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
-
- def say(self, text):
- texts = filter(None, text.split("\n"))
- punctuation = "！，。、？!,.?：:" # punctuate and split/clean text
- processed_texts = []
- for text in texts:
- for processed_text in re.sub(r'[{}]+'.format(punctuation), '\n', text).split('\n'):
- if processed_text:
- processed_texts.append(processed_text.strip())
- texts = processed_texts
- embeds = [self.embed] * len(texts)
-
- specs = self.current_synt.synthesize_spectrograms(texts, embeds)
- spec = np.concatenate(specs, axis=1)
- wav, sample_rate = gan_vocoder.infer_waveform(spec)
-
- return wav, sample_rate
-
-
-def greet(audio, text, voice=None):
- print(f"Log print: audio name=[{audio.name}], text=[{text}]")
-
- if voice is None:
- voice = Mandarin()
- voice.setVoice(audio.name)
- voice.say("加载成功")
- wav, sample_rate = voice.say(text)
-
- output_file = "".join( random.sample(string.ascii_lowercase + string.digits, 11) ) + ".wav"
-
- write(output_file, sample_rate, wav.astype(np.float32))
-
- return output_file, voice
-
-def new_greet(audio, text):
- audio_path = audio
- print(f"Log print: audio name=[{audio_path}], text=[{text}]")
- voice.setVoice(audio_path)
- wav, sample_rate = voice.say(text)
-
- output_file = "".join( random.sample(string.ascii_lowercase + string.digits, 11) ) + ".wav"
-
- write(output_file, sample_rate, wav.astype(np.float32))
-
- return output_file
-
-def main():
- demo = gr.Interface(
- fn=greet,
- inputs=[gr.inputs.Audio(type="file"),"text", "state"],
- outputs=[gr.outputs.Audio(type="file"), "state"],
- title="Tacotron Zero-short Voice Clone (Chinese Version)"
- )
-
- demo.launch()
-
-def new_main():
- with gr.Blocks() as demo:
- title = gr.Markdown("# {title}
\n'+
- f'RVC {model_version} Model
\n'+
- (f'Model author: {author}
' if author else "")+
- (f'- 1. 上传想要克隆的人的一段语音，长度3~8秒即可。
- 2. 输入想要合成的文本
- 3. 点击Submit按钮，稍等30秒便可合成语音 - """ - ) - with gr.Row(): - with gr.Column(): - # input_audio = gr.Audio(type="file", label="Source Audio", value="exp/lihao_01.wav") - input_audio = gr.Audio(type="filepath", label="Source Audio:") - input_text = gr.Textbox(value="大家好，我是正在搬砖的李昊，这是一段合成音频。", label="TTS Text:") - with gr.Row(): - # clear = gr.ClearButton() - submit = gr.Button(value="Submit", variant='primary') - - with gr.Column(): - output_audio = gr.Audio(type="filepath", label="Output Audio:") - gr.Markdown("Audio Examples:") - gr.Examples( - examples=[["exp/lihao_01.wav", "大家好，我是正在搬砖的李昊，这是一段合成音频。"]], - inputs=[input_audio, input_text], - outputs=[output_audio], - fn=new_greet, - cache_examples=True, - ) - - _ = submit.click(new_greet, inputs=[input_audio, input_text], outputs=[output_audio]) - - demo.queue(concurrency_count=20) - demo.launch() - -if __name__=="__main__": - voice = Mandarin() - # voice.setVoice(audio.name) - # voice.say("加载成功") - new_main() diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/utterance_mvn.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/utterance_mvn.py deleted file mode 100644 index 37fb0c1b918bff60d0c6b5fef883b2f735e7cd79..0000000000000000000000000000000000000000 --- a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/utterance_mvn.py +++ /dev/null @@ -1,82 +0,0 @@ -from typing import Tuple - -import torch - -from .nets_utils import make_pad_mask - - -class UtteranceMVN(torch.nn.Module): - def __init__( - self, norm_means: bool = True, norm_vars: bool = False, eps: float = 1.0e-20, - ): - super().__init__() - self.norm_means = norm_means - self.norm_vars = norm_vars - self.eps = eps - - def extra_repr(self): - return f"norm_means={self.norm_means}, norm_vars={self.norm_vars}" - - def forward( - self, x: torch.Tensor, ilens: torch.Tensor = None - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Forward function - - Args: - x: (B, L, ...) - ilens: (B,) - - """ - return utterance_mvn( - x, - ilens, - norm_means=self.norm_means, - norm_vars=self.norm_vars, - eps=self.eps, - ) - - -def utterance_mvn( - x: torch.Tensor, - ilens: torch.Tensor = None, - norm_means: bool = True, - norm_vars: bool = False, - eps: float = 1.0e-20, -) -> Tuple[torch.Tensor, torch.Tensor]: - """Apply utterance mean and variance normalization - - Args: - x: (B, T, D), assumed zero padded - ilens: (B,) - norm_means: - norm_vars: - eps: - - """ - if ilens is None: - ilens = x.new_full([x.size(0)], x.size(1)) - ilens_ = ilens.to(x.device, x.dtype).view(-1, *[1 for _ in range(x.dim() - 1)]) - # Zero padding - if x.requires_grad: - x = x.masked_fill(make_pad_mask(ilens, x, 1), 0.0) - else: - x.masked_fill_(make_pad_mask(ilens, x, 1), 0.0) - # mean: (B, 1, D) - mean = x.sum(dim=1, keepdim=True) / ilens_ - - if norm_means: - x -= mean - - if norm_vars: - var = x.pow(2).sum(dim=1, keepdim=True) / ilens_ - std = torch.clamp(var.sqrt(), min=eps) - x = x / std.sqrt() - return x, ilens - else: - if norm_vars: - y = x - mean - y.masked_fill_(make_pad_mask(ilens, y, 1), 0.0) - var = y.pow(2).sum(dim=1, keepdim=True) / ilens_ - std = torch.clamp(var.sqrt(), min=eps) - x /= std - return x, ilens diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/base_module.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/base_module.py deleted file mode 100644 index 617fad9bb89f10a9a0911d962dfb3bc8f3a3628c..0000000000000000000000000000000000000000 --- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/base_module.py +++ /dev/null @@ -1,195 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import copy -import warnings -from abc import ABCMeta -from collections import defaultdict -from logging import FileHandler - -import torch.nn as nn - -from annotator.uniformer.mmcv.runner.dist_utils import master_only -from annotator.uniformer.mmcv.utils.logging import get_logger, logger_initialized, print_log - - -class BaseModule(nn.Module, metaclass=ABCMeta): - """Base module for all modules in openmmlab. - - ``BaseModule`` is a wrapper of ``torch.nn.Module`` with additional - functionality of parameter initialization. Compared with - ``torch.nn.Module``, ``BaseModule`` mainly adds three attributes. - - - ``init_cfg``: the config to control the initialization. - - ``init_weights``: The function of parameter - initialization and recording initialization - information. - - ``_params_init_info``: Used to track the parameter - initialization information. This attribute only - exists during executing the ``init_weights``. - - Args: - init_cfg (dict, optional): Initialization config dict. - """ - - def __init__(self, init_cfg=None): - """Initialize BaseModule, inherited from `torch.nn.Module`""" - - # NOTE init_cfg can be defined in different levels, but init_cfg - # in low levels has a higher priority. - - super(BaseModule, self).__init__() - # define default value of init_cfg instead of hard code - # in init_weights() function - self._is_init = False - - self.init_cfg = copy.deepcopy(init_cfg) - - # Backward compatibility in derived classes - # if pretrained is not None: - # warnings.warn('DeprecationWarning: pretrained is a deprecated \ - # key, please consider using init_cfg') - # self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) - - @property - def is_init(self): - return self._is_init - - def init_weights(self): - """Initialize the weights.""" - - is_top_level_module = False - # check if it is top-level module - if not hasattr(self, '_params_init_info'): - # The `_params_init_info` is used to record the initialization - # information of the parameters - # the key should be the obj:`nn.Parameter` of model and the value - # should be a dict containing - # - init_info (str): The string that describes the initialization. - # - tmp_mean_value (FloatTensor): The mean of the parameter, - # which indicates whether the parameter has been modified. - # this attribute would be deleted after all parameters - # is initialized. - self._params_init_info = defaultdict(dict) - is_top_level_module = True - - # Initialize the `_params_init_info`, - # When detecting the `tmp_mean_value` of - # the corresponding parameter is changed, update related - # initialization information - for name, param in self.named_parameters(): - self._params_init_info[param][ - 'init_info'] = f'The value is the same before and ' \ - f'after calling `init_weights` ' \ - f'of {self.__class__.__name__} ' - self._params_init_info[param][ - 'tmp_mean_value'] = param.data.mean() - - # pass `params_init_info` to all submodules - # All submodules share the same `params_init_info`, - # so it will be updated when parameters are - # modified at any level of the model. - for sub_module in self.modules(): - sub_module._params_init_info = self._params_init_info - - # Get the initialized logger, if not exist, - # create a logger named `mmcv` - logger_names = list(logger_initialized.keys()) - logger_name = logger_names[0] if logger_names else 'mmcv' - - from ..cnn import initialize - from ..cnn.utils.weight_init import update_init_info - module_name = self.__class__.__name__ - if not self._is_init: - if self.init_cfg: - print_log( - f'initialize {module_name} with init_cfg {self.init_cfg}', - logger=logger_name) - initialize(self, self.init_cfg) - if isinstance(self.init_cfg, dict): - # prevent the parameters of - # the pre-trained model - # from being overwritten by - # the `init_weights` - if self.init_cfg['type'] == 'Pretrained': - return - - for m in self.children(): - if hasattr(m, 'init_weights'): - m.init_weights() - # users may overload the `init_weights` - update_init_info( - m, - init_info=f'Initialized by ' - f'user-defined `init_weights`' - f' in {m.__class__.__name__} ') - - self._is_init = True - else: - warnings.warn(f'init_weights of {self.__class__.__name__} has ' - f'been called more than once.') - - if is_top_level_module: - self._dump_init_info(logger_name) - - for sub_module in self.modules(): - del sub_module._params_init_info - - @master_only - def _dump_init_info(self, logger_name): - """Dump the initialization information to a file named - `initialization.log.json` in workdir. - - Args: - logger_name (str): The name of logger. - """ - - logger = get_logger(logger_name) - - with_file_handler = False - # dump the information to the logger file if there is a `FileHandler` - for handler in logger.handlers: - if isinstance(handler, FileHandler): - handler.stream.write( - 'Name of parameter - Initialization information\n') - for name, param in self.named_parameters(): - handler.stream.write( - f'\n{name} - {param.shape}: ' - f"\n{self._params_init_info[param]['init_info']} \n") - handler.stream.flush() - with_file_handler = True - if not with_file_handler: - for name, param in self.named_parameters(): - print_log( - f'\n{name} - {param.shape}: ' - f"\n{self._params_init_info[param]['init_info']} \n ", - logger=logger_name) - - def __repr__(self): - s = super().__repr__() - if self.init_cfg: - s += f'\ninit_cfg={self.init_cfg}' - return s - - -class Sequential(BaseModule, nn.Sequential): - """Sequential module in openmmlab. - - Args: - init_cfg (dict, optional): Initialization config dict. - """ - - def __init__(self, *args, init_cfg=None): - BaseModule.__init__(self, init_cfg) - nn.Sequential.__init__(self, *args) - - -class ModuleList(BaseModule, nn.ModuleList): - """ModuleList in openmmlab. - - Args: - modules (iterable, optional): an iterable of modules to add. - init_cfg (dict, optional): Initialization config dict. - """ - - def __init__(self, modules=None, init_cfg=None): - BaseModule.__init__(self, init_cfg) - nn.ModuleList.__init__(self, modules) diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/iter_based_runner.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/iter_based_runner.py deleted file mode 100644 index 1df4de8c0285669dec9b014dfd1f3dd1600f0831..0000000000000000000000000000000000000000 --- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/iter_based_runner.py +++ /dev/null @@ -1,273 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import os.path as osp -import platform -import shutil -import time -import warnings - -import torch -from torch.optim import Optimizer - -import annotator.uniformer.mmcv as mmcv -from .base_runner import BaseRunner -from .builder import RUNNERS -from .checkpoint import save_checkpoint -from .hooks import IterTimerHook -from .utils import get_host_info - - -class IterLoader: - - def __init__(self, dataloader): - self._dataloader = dataloader - self.iter_loader = iter(self._dataloader) - self._epoch = 0 - - @property - def epoch(self): - return self._epoch - - def __next__(self): - try: - data = next(self.iter_loader) - except StopIteration: - self._epoch += 1 - if hasattr(self._dataloader.sampler, 'set_epoch'): - self._dataloader.sampler.set_epoch(self._epoch) - time.sleep(2) # Prevent possible deadlock during epoch transition - self.iter_loader = iter(self._dataloader) - data = next(self.iter_loader) - - return data - - def __len__(self): - return len(self._dataloader) - - -@RUNNERS.register_module() -class IterBasedRunner(BaseRunner): - """Iteration-based Runner. - - This runner train models iteration by iteration. - """ - - def train(self, data_loader, **kwargs): - self.model.train() - self.mode = 'train' - self.data_loader = data_loader - self._epoch = data_loader.epoch - data_batch = next(data_loader) - self.call_hook('before_train_iter') - outputs = self.model.train_step(data_batch, self.optimizer, **kwargs) - if not isinstance(outputs, dict): - raise TypeError('model.train_step() must return a dict') - if 'log_vars' in outputs: - self.log_buffer.update(outputs['log_vars'], outputs['num_samples']) - self.outputs = outputs - self.call_hook('after_train_iter') - self._inner_iter += 1 - self._iter += 1 - - @torch.no_grad() - def val(self, data_loader, **kwargs): - self.model.eval() - self.mode = 'val' - self.data_loader = data_loader - data_batch = next(data_loader) - self.call_hook('before_val_iter') - outputs = self.model.val_step(data_batch, **kwargs) - if not isinstance(outputs, dict): - raise TypeError('model.val_step() must return a dict') - if 'log_vars' in outputs: - self.log_buffer.update(outputs['log_vars'], outputs['num_samples']) - self.outputs = outputs - self.call_hook('after_val_iter') - self._inner_iter += 1 - - def run(self, data_loaders, workflow, max_iters=None, **kwargs): - """Start running. - - Args: - data_loaders (list[:obj:`DataLoader`]): Dataloaders for training - and validation. - workflow (list[tuple]): A list of (phase, iters) to specify the - running order and iterations. E.g, [('train', 10000), - ('val', 1000)] means running 10000 iterations for training and - 1000 iterations for validation, iteratively. - """ - assert isinstance(data_loaders, list) - assert mmcv.is_list_of(workflow, tuple) - assert len(data_loaders) == len(workflow) - if max_iters is not None: - warnings.warn( - 'setting max_iters in run is deprecated, ' - 'please set max_iters in runner_config', DeprecationWarning) - self._max_iters = max_iters - assert self._max_iters is not None, ( - 'max_iters must be specified during instantiation') - - work_dir = self.work_dir if self.work_dir is not None else 'NONE' - self.logger.info('Start running, host: %s, work_dir: %s', - get_host_info(), work_dir) - self.logger.info('Hooks will be executed in the following order:\n%s', - self.get_hook_info()) - self.logger.info('workflow: %s, max: %d iters', workflow, - self._max_iters) - self.call_hook('before_run') - - iter_loaders = [IterLoader(x) for x in data_loaders] - - self.call_hook('before_epoch') - - while self.iter < self._max_iters: - for i, flow in enumerate(workflow): - self._inner_iter = 0 - mode, iters = flow - if not isinstance(mode, str) or not hasattr(self, mode): - raise ValueError( - 'runner has no method named "{}" to run a workflow'. - format(mode)) - iter_runner = getattr(self, mode) - for _ in range(iters): - if mode == 'train' and self.iter >= self._max_iters: - break - iter_runner(iter_loaders[i], **kwargs) - - time.sleep(1) # wait for some hooks like loggers to finish - self.call_hook('after_epoch') - self.call_hook('after_run') - - def resume(self, - checkpoint, - resume_optimizer=True, - map_location='default'): - """Resume model from checkpoint. - - Args: - checkpoint (str): Checkpoint to resume from. - resume_optimizer (bool, optional): Whether resume the optimizer(s) - if the checkpoint file includes optimizer(s). Default to True. - map_location (str, optional): Same as :func:`torch.load`. - Default to 'default'. - """ - if map_location == 'default': - device_id = torch.cuda.current_device() - checkpoint = self.load_checkpoint( - checkpoint, - map_location=lambda storage, loc: storage.cuda(device_id)) - else: - checkpoint = self.load_checkpoint( - checkpoint, map_location=map_location) - - self._epoch = checkpoint['meta']['epoch'] - self._iter = checkpoint['meta']['iter'] - self._inner_iter = checkpoint['meta']['iter'] - if 'optimizer' in checkpoint and resume_optimizer: - if isinstance(self.optimizer, Optimizer): - self.optimizer.load_state_dict(checkpoint['optimizer']) - elif isinstance(self.optimizer, dict): - for k in self.optimizer.keys(): - self.optimizer[k].load_state_dict( - checkpoint['optimizer'][k]) - else: - raise TypeError( - 'Optimizer should be dict or torch.optim.Optimizer ' - f'but got {type(self.optimizer)}') - - self.logger.info(f'resumed from epoch: {self.epoch}, iter {self.iter}') - - def save_checkpoint(self, - out_dir, - filename_tmpl='iter_{}.pth', - meta=None, - save_optimizer=True, - create_symlink=True): - """Save checkpoint to file. - - Args: - out_dir (str): Directory to save checkpoint files. - filename_tmpl (str, optional): Checkpoint file template. - Defaults to 'iter_{}.pth'. - meta (dict, optional): Metadata to be saved in checkpoint. - Defaults to None. - save_optimizer (bool, optional): Whether save optimizer. - Defaults to True. - create_symlink (bool, optional): Whether create symlink to the - latest checkpoint file. Defaults to True. - """ - if meta is None: - meta = {} - elif not isinstance(meta, dict): - raise TypeError( - f'meta should be a dict or None, but got {type(meta)}') - if self.meta is not None: - meta.update(self.meta) - # Note: meta.update(self.meta) should be done before - # meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise - # there will be problems with resumed checkpoints. - # More details in https://github.com/open-mmlab/mmcv/pull/1108 - meta.update(epoch=self.epoch + 1, iter=self.iter) - - filename = filename_tmpl.format(self.iter + 1) - filepath = osp.join(out_dir, filename) - optimizer = self.optimizer if save_optimizer else None - save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta) - # in some environments, `os.symlink` is not supported, you may need to - # set `create_symlink` to False - if create_symlink: - dst_file = osp.join(out_dir, 'latest.pth') - if platform.system() != 'Windows': - mmcv.symlink(filename, dst_file) - else: - shutil.copy(filepath, dst_file) - - def register_training_hooks(self, - lr_config, - optimizer_config=None, - checkpoint_config=None, - log_config=None, - momentum_config=None, - custom_hooks_config=None): - """Register default hooks for iter-based training. - - Checkpoint hook, optimizer stepper hook and logger hooks will be set to - `by_epoch=False` by default. - - Default hooks include: - - +----------------------+-------------------------+ - | Hooks | Priority | - +======================+=========================+ - | LrUpdaterHook | VERY_HIGH (10) | - +----------------------+-------------------------+ - | MomentumUpdaterHook | HIGH (30) | - +----------------------+-------------------------+ - | OptimizerStepperHook | ABOVE_NORMAL (40) | - +----------------------+-------------------------+ - | CheckpointSaverHook | NORMAL (50) | - +----------------------+-------------------------+ - | IterTimerHook | LOW (70) | - +----------------------+-------------------------+ - | LoggerHook(s) | VERY_LOW (90) | - +----------------------+-------------------------+ - | CustomHook(s) | defaults to NORMAL (50) | - +----------------------+-------------------------+ - - If custom hooks have same priority with default hooks, custom hooks - will be triggered after default hooks. - """ - if checkpoint_config is not None: - checkpoint_config.setdefault('by_epoch', False) - if lr_config is not None: - lr_config.setdefault('by_epoch', False) - if log_config is not None: - for info in log_config['hooks']: - info.setdefault('by_epoch', False) - super(IterBasedRunner, self).register_training_hooks( - lr_config=lr_config, - momentum_config=momentum_config, - optimizer_config=optimizer_config, - checkpoint_config=checkpoint_config, - log_config=log_config, - timer_config=IterTimerHook(), - custom_hooks_config=custom_hooks_config) diff --git a/spaces/koajoel/PolyFormer/data/refcoco_dataset.py b/spaces/koajoel/PolyFormer/data/refcoco_dataset.py deleted file mode 100644 index fb42f34ffe535863a7218adea36b184f73856602..0000000000000000000000000000000000000000 --- a/spaces/koajoel/PolyFormer/data/refcoco_dataset.py +++ /dev/null @@ -1,294 +0,0 @@ -# ------------------------------------------------------------------------ -# Modified from OFA (https://github.com/OFA-Sys/OFA) -# Copyright 2022 The OFA-Sys Team. -# All rights reserved. -# This source code is licensed under the Apache 2.0 license -# found in the LICENSE file in the root directory. -# ------------------------------------------------------------------------ -# Modifications Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. -# SPDX-License-Identifier: Apache-2.0 - -from io import BytesIO - -import logging -import warnings - -import numpy as np -import torch -import base64 -import utils.transforms as T -import math -from PIL import Image, ImageFile - -from data import data_utils -from data.base_dataset import BaseDataset -from bert.tokenization_bert import BertTokenizer -from data.poly_utils import string_to_polygons, downsample_polygons, polygons_to_string, points_to_token_string -import cv2 - -ImageFile.LOAD_TRUNCATED_IMAGES = True -ImageFile.MAX_IMAGE_PIXELS = None -Image.MAX_IMAGE_PIXELS = None - -logger = logging.getLogger(__name__) -warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning) - -IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) -IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) - - -class RefcocoDataset(BaseDataset): - def __init__( - self, - split, - dataset, - bpe, - src_dict, - tgt_dict=None, - max_src_length=80, - max_tgt_length=30, - patch_image_size=512, - imagenet_default_mean_and_std=False, - num_bins=1000, - max_image_size=512 - ): - super().__init__(split, dataset, bpe, src_dict, tgt_dict) - self.max_src_length = max_src_length - self.max_tgt_length = max_tgt_length - self.patch_image_size = patch_image_size - self.num_bins = num_bins - - if imagenet_default_mean_and_std: - mean = IMAGENET_DEFAULT_MEAN - std = IMAGENET_DEFAULT_STD - else: - mean = [0.5, 0.5, 0.5] - std = [0.5, 0.5, 0.5] - - # for positioning - self.positioning_transform = T.Compose([ - T.RandomResize([patch_image_size], max_size=patch_image_size), - T.ToTensor(), - T.Normalize(mean=mean, std=std, max_image_size=max_image_size) - ]) - self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') - - def __getitem__(self, index): - data = self.dataset[index] - if len(data) == 7: - uniq_id, base64_str, seg64_str, text, poly_original, region_coord, poly_interpolated = data - train = True - else: - uniq_id, base64_str, seg64_str, text, poly, region_coord = data - train = False - - # load image and segmentation labels - image = Image.open(BytesIO(base64.urlsafe_b64decode(base64_str))).convert("RGB") - label = Image.open(BytesIO(base64.urlsafe_b64decode(seg64_str))) - label = np.asarray(label) - label = cv2.resize(label, [self.patch_image_size, self.patch_image_size], interpolation=cv2.INTER_NEAREST) - - w, h = image.size - patch_image = self.positioning_transform(image, target=None) - resize_h = self.patch_image_size - resize_w = self.patch_image_size - patch_mask = torch.tensor([True]) - - if train: - prob = np.random.uniform() - if prob < 0.5: - polygons_interpolated = string_to_polygons(poly_interpolated) - ds_rate = np.random.randint(25, 41) - polygons_augmented = downsample_polygons(polygons_interpolated, ds_rate) - poly = polygons_to_string(polygons_augmented) - else: - poly = poly_original - - polygons = string_to_polygons(poly) - polygons_scaled = [] - for polygon in polygons: - n_point = len(polygon) // 2 - scale = np.concatenate([np.array([w, h]) for _ in range(n_point)], 0) - polygon = polygon / scale - polygon = polygon.reshape(n_point, 2) - polygons_scaled.append(polygon) - - x0, y0, x1, y1 = region_coord.strip().split(',') - region_points = [float(x0), float(y0), float(x1), float(y1)] - region = np.array(region_points) - - region_points = region_points / np.array([w, h, w, h]) # scaled to [0,1] - region_points = torch.tensor(region_points.reshape(2, 2)) - - quant_box = region_points * (self.num_bins - 1) - quant_box11 = [[math.floor(p[0]), math.floor(p[1])] for p in quant_box] - quant_box21 = [[math.ceil(p[0]), math.floor(p[1])] for p in quant_box] - quant_box12 = [[math.floor(p[0]), math.ceil(p[1])] for p in quant_box] - quant_box22 = [[math.ceil(p[0]), math.ceil(p[1])] for p in quant_box] - - quant_poly = [poly * (self.num_bins - 1) for poly in polygons_scaled] - quant_poly11 = [[[math.floor(p[0]), math.floor(p[1])] for p in poly] for poly in quant_poly] - quant_poly21 = [[[math.ceil(p[0]), math.floor(p[1])] for p in poly] for poly in quant_poly] - quant_poly12 = [[[math.floor(p[0]), math.ceil(p[1])] for p in poly] for poly in quant_poly] - quant_poly22 = [[[math.ceil(p[0]), math.ceil(p[1])] for p in poly] for poly in quant_poly] - - region_coord11, _ = points_to_token_string(quant_box11, quant_poly11) - region_coord21, _ = points_to_token_string(quant_box21, quant_poly21) - region_coord12, _ = points_to_token_string(quant_box12, quant_poly12) - region_coord22, token_type = points_to_token_string(quant_box22, quant_poly22) - - # compute bilinear interpolation coefficient - delta_x1 = [0] + [p[0] - math.floor(p[0]) for p in quant_box] # [0] for bos token - for polygon in quant_poly: - delta = [poly_point[0] - math.floor(poly_point[0]) for poly_point in polygon] - delta_x1.extend(delta) - delta_x1.extend([0]) # for separator token - delta_x1 = delta_x1[:-1] # there is no separator token in the end - delta_x1 = torch.tensor(delta_x1) - delta_x2 = 1 - delta_x1 - - delta_y1 = [0] + [p[1] - math.floor(p[1]) for p in quant_box] # [0] for bos token - for polygon in quant_poly: - delta = [poly_point[1] - math.floor(poly_point[1]) for poly_point in polygon] - delta_y1.extend(delta) - delta_y1.extend([0]) # for separator token - delta_y1 = delta_y1[:-1] # there is no separator token in the end - delta_y1 = torch.tensor(delta_y1) - delta_y2 = 1 - delta_y1 - - token_type.append(2) # 2 for eos token - - src_caption = self.pre_caption(text, self.max_src_length) - - prompt = ' which region does the text " {} " describe?'.format(src_caption) - - # tgt for input - tgt_item11 = self.encode_text(region_coord11, use_bpe=False) - tgt_item12 = self.encode_text(region_coord12, use_bpe=False) - tgt_item21 = self.encode_text(region_coord21, use_bpe=False) - tgt_item22 = self.encode_text(region_coord22, use_bpe=False) - - # tgt for output - target_item = region_points - for poly in polygons_scaled: - target_item = torch.cat([target_item, torch.tensor(poly), torch.tensor([[0, 0]])], dim=0) # [0, 0] is padding token for separator and eos - - #target_item = torch.cat([tgt_item, self.eos_item]) - prev_output_item11 = torch.cat([self.bos_item, tgt_item11]) - prev_output_item12 = torch.cat([self.bos_item, tgt_item12]) - prev_output_item21 = torch.cat([self.bos_item, tgt_item21]) - prev_output_item22 = torch.cat([self.bos_item, tgt_item22]) - example = { - "id": uniq_id, - "source": prompt, - "patch_image": patch_image, - "patch_mask": patch_mask, - "target": target_item, - "prev_output_tokens_11": prev_output_item11, - "prev_output_tokens_12": prev_output_item12, - "prev_output_tokens_21": prev_output_item21, - "prev_output_tokens_22": prev_output_item22, - "delta_x1": delta_x1, - "delta_y1": delta_y1, - "delta_x2": delta_x2, - "delta_y2": delta_y2, - "w_resize_ratio": torch.tensor(resize_w / w), - "h_resize_ratio": torch.tensor(resize_h / h), - "region_coord": torch.tensor(region), - "token_type": torch.tensor(token_type), - "w": torch.tensor(w), - "h": torch.tensor(h), - "label": label, - "n_poly": len(polygons), - "text": src_caption - } - return example - - def collate(self, samples, pad_idx, eos_idx): - if len(samples) == 0: - return {} - - def merge(key, padding_item): - return data_utils.collate_tokens( - [s[key] for s in samples], - padding_item, - eos_idx=eos_idx, - ) - - id = np.array([s["id"] for s in samples]) - captions = [s["source"] for s in samples] - tokenized = self.tokenizer.batch_encode_plus(captions, padding="longest", return_tensors="pt") - src_tokens = tokenized["input_ids"] - att_masks = tokenized["attention_mask"] - src_lengths = torch.LongTensor(att_masks.ne(0).long().sum()) - - patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0) - patch_masks = torch.cat([sample['patch_mask'] for sample in samples]) - - w_resize_ratios = torch.stack([s["w_resize_ratio"] for s in samples], dim=0) - h_resize_ratios = torch.stack([s["h_resize_ratio"] for s in samples], dim=0) - - delta_x1 = merge("delta_x1", 0) - delta_y1 = merge("delta_y1", 0) - delta_x2 = merge("delta_x2", 1) - delta_y2 = merge("delta_y2", 1) - - region_coords = torch.stack([s['region_coord'] for s in samples], dim=0) - - target = merge("target", pad_idx) - tgt_lengths = torch.LongTensor([s["target"].shape[0] for s in samples]) - ntokens = tgt_lengths.sum().item() - - prev_output_tokens_11 = merge("prev_output_tokens_11", pad_idx) - prev_output_tokens_12 = merge("prev_output_tokens_12", pad_idx) - prev_output_tokens_21 = merge("prev_output_tokens_21", pad_idx) - prev_output_tokens_22 = merge("prev_output_tokens_22", pad_idx) - - token_type = merge("token_type", -1) - w = torch.stack([s["w"] for s in samples], dim=0) - h = torch.stack([s["h"] for s in samples], dim=0) - n_poly = [s['n_poly'] for s in samples] - - labels = np.stack([sample['label'] for sample in samples], 0) - text = [s["text"] for s in samples] - batch = { - "id": id, - "nsentences": len(samples), - "ntokens": ntokens, - "net_input": { - "src_tokens": src_tokens, - "src_lengths": src_lengths, - "att_masks": att_masks, - "patch_images": patch_images, - "patch_masks": patch_masks, - "prev_output_tokens_11": prev_output_tokens_11, - "prev_output_tokens_12": prev_output_tokens_12, - "prev_output_tokens_21": prev_output_tokens_21, - "prev_output_tokens_22": prev_output_tokens_22, - "delta_x1": delta_x1, - "delta_y1": delta_y1, - "delta_x2": delta_x2, - "delta_y2": delta_y2 - }, - "target": target, - "w_resize_ratios": w_resize_ratios, - "h_resize_ratios": h_resize_ratios, - "region_coords": region_coords, - "label": labels, - "token_type": token_type, - "w": w, - "h": h, - "n_poly": n_poly, - "text": text - } - - return batch - - def collater(self, samples, pad_to_length=None): - """Merge a list of samples to form a mini-batch. - Args: - samples (List[dict]): samples to collate - Returns: - dict: a mini-batch containing the data of the task - """ - return self.collate(samples, pad_idx=self.pad, eos_idx=self.eos) \ No newline at end of file diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/nonautoregressive_translation/scripts.md b/spaces/koajoel/PolyFormer/fairseq/examples/nonautoregressive_translation/scripts.md deleted file mode 100644 index 9d3d7b67dc08440b5f4d1c5a7ffcd4bd6e76c14f..0000000000000000000000000000000000000000 --- a/spaces/koajoel/PolyFormer/fairseq/examples/nonautoregressive_translation/scripts.md +++ /dev/null @@ -1,179 +0,0 @@ -# Examples of Training scripts for Non-autoregressive Machine Translation models - -### Non-autoregressive Transformer (NAT, Gu et al., 2017) -Note that we need to have an additional module to perform "length prediction" (`--length-loss-factor`) before generating the whole sequence. -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch nonautoregressive_transformer \ - --noise full_mask \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --pred-length-offset \ - --length-loss-factor 0.1 \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - -### Fast Structured Decoding for Sequence Models (NAT-CRF, Sun et al., 2019) -Note that we implemented a low-rank appromixated CRF model by setting `--crf-lowrank-approx=32` and `--crf-beam-approx=64` as discribed in the original paper. All other settings are the same as the vanilla NAT model. -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch nacrf_transformer \ - --noise full_mask \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --pred-length-offset \ - --length-loss-factor 0.1 \ - --word-ins-loss-factor 0.5 \ - --crf-lowrank-approx 32 \ - --crf-beam-approx 64 \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - - -### Non-autoregressive Transformer with Iterative Refinement (iNAT, Lee et al., 2018) -Note that `--train-step` means how many iterations of refinement we used during training, and `--dae-ratio` controls the ratio of denoising auto-encoder training described in the original paper. -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch iterative_nonautoregressive_transformer \ - --noise full_mask \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --pred-length-offset \ - --length-loss-factor 0.1 \ - --train-step 4 \ - --dae-ratio 0.5 \ - --stochastic-approx \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - -### Insertion Transformer (InsT, Stern et al., 2019) -Note that we need to specify the "slot-loss" (uniform or balanced tree) described in the original paper. Here we use `--label-tau` to control the temperature. - -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch insertion_transformer \ - --noise random_delete \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - - -### Mask Predict (CMLM, Ghazvininejad et al., 2019) -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch cmlm_transformer \ - --noise random_mask \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - - - - -### Levenshtein Transformer (LevT, Gu et al., 2019) -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch levenshtein_transformer \ - --noise random_delete \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` diff --git a/spaces/kokofixcomputers/chat-ui/src/lib/utils/deepestChild.ts b/spaces/kokofixcomputers/chat-ui/src/lib/utils/deepestChild.ts deleted file mode 100644 index ac6ed1d1dd64e6f3a8bbd559887de77b3e49f8c3..0000000000000000000000000000000000000000 --- a/spaces/kokofixcomputers/chat-ui/src/lib/utils/deepestChild.ts +++ /dev/null @@ -1,6 +0,0 @@ -export function deepestChild(el: HTMLElement): HTMLElement { - if (el.lastElementChild && el.lastElementChild.nodeType !== Node.TEXT_NODE) { - return deepestChild(el.lastElementChild as HTMLElement); - } - return el; -} diff --git a/spaces/krazyxki/V-1488abed/README.md b/spaces/krazyxki/V-1488abed/README.md deleted file mode 100644 index d9d2baa9412971765f50f40f87bd2aae63d717bf..0000000000000000000000000000000000000000 --- a/spaces/krazyxki/V-1488abed/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: 2ch-reverse-proxy -emoji: ⚡ -colorFrom: yellow -colorTo: gray -sdk: docker -pinned: true -duplicated_from: neurokun/V-1488 ---- - \ No newline at end of file diff --git a/spaces/kumahiyo/line-bot-stable-diffusion/main.py b/spaces/kumahiyo/line-bot-stable-diffusion/main.py deleted file mode 100644 index f48496f13f8fb96cf485526aa101fc02dddba070..0000000000000000000000000000000000000000 --- a/spaces/kumahiyo/line-bot-stable-diffusion/main.py +++ /dev/null @@ -1,98 +0,0 @@ -import os -import time -import sys -import re -import random -import torch -from PIL import Image -from fastapi import FastAPI -from fastapi.staticfiles import StaticFiles -from pydantic import BaseModel -from pydantic import Field -from diffusers import StableDiffusionPipeline -from googletrans import Translator - -app = FastAPI() - -class Data(BaseModel): - string: str - member_secret: str - -class ItemOut(BaseModel): - status: str - file: str - -@app.get("/") -def index(): - return "SORRY! This file is member only." - -@app.post("/draw", response_model=ItemOut) -def draw(data: Data): - if data.member_secret != "" and data.member_secret == os.environ.get("MEMBER_SECRET"): - - device = "cuda" if torch.cuda.is_available() else "cpu" - device_dict = {"cuda": 0, "cpu": -1} - - seedno = 0 - if '_seed' in data.string: - seed = 1024 - stext = re.search(r'_seed[1-4]?', data.string) - stext = re.sub('_seed', '', stext.group()) - if stext.isnumeric() and 0 < int(stext) < 5: - seedno = stext - else: - seed = random.randrange(1024) - - text = re.sub('^#', '', data.string) - text = re.sub('_seed[1-4]?', '', text) - - translator = Translator() - translation = translator.translate(text, dest="en") - text = translation.text - - # prompt = '(('+text+')) (( photograph )), highly detailed, sharp focus, 8k, 4k, (( photorealism )), detailed, saturated, portrait, 50mm, F/2.8, 1m away, ( global illumination, studio light, volumetric light ), ((( multicolor lights )))' - prompt = '(('+text+')) (( photograph )), highly detailed, sharp focus, 8k, 4k, (( photorealism )), detailed, saturated, portrait, 50mm, F/2.8, 1m away, ((( multicolor lights )))' - n_prompt = 'text, blurry, art, painting, rendering, drawing, sketch, (( ugly )), (( duplicate )), ( morbid ), (( mutilated )), ( mutated ), ( deformed ), ( disfigured ), ( extra limbs ), ( malformed limbs ), ( missing arms ), ( missing legs ), ( extra arms ), ( extra legs ), ( fused fingers ), ( too many fingers ), long neck, low quality, worst quality' - - # https://huggingface.co/docs/hub/spaces-sdks-docker-first-demo - # how to validation: https://qiita.com/bee2/items/75d9c0d7ba20e7a4a0e9 - # https://github.com/huggingface/diffusers - - model_id = 'stabilityai/stable-diffusion-2' - - #pipe = StableDiffusionPipeline.from_pretrained(model_id) - pipe = StableDiffusionPipeline.from_pretrained(model_id, revision='fp16', torch_dtype=torch.float16) - pipe.enable_attention_slicing() # reduce gpu usage - pipe = pipe.to(device) - - generator = torch.Generator(device).manual_seed(seed) - images = pipe(prompt, negative_prompt=n_prompt, guidance_scale=7.5, generator=generator, num_images_per_prompt=1).images - - if 0 < int(seedno) < 5: - grid = images[(int(seedno) - 1)] - else: - # Limit of T4 small... - grid = image_grid(images, rows=1, cols=1) - - fileName = "sd_" + str(time.time()) + '.png' - grid.save("/code/tmpdir/" + fileName) - - print(fileName) - - return {"status": "OK", "file": fileName} - else: - return {"status": "SORRY! This file is member only.", "file": ""} - -app.mount("/static", StaticFiles(directory="/code/tmpdir"), name="/static") - -# helper function taken from: https://huggingface.co/blog/stable_diffusion -def image_grid(imgs, rows, cols): - assert len(imgs) == rows*cols - - w, h = imgs[0].size - grid = Image.new('RGB', size=(cols*w, rows*h)) - grid_w, grid_h = grid.size - - for i, img in enumerate(imgs): - grid.paste(img, box=(i%cols*w, i//cols*h)) - return grid diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/XpmImagePlugin.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/XpmImagePlugin.py deleted file mode 100644 index 5d5bdc3edfa7be8d235fd6ef4176cc6cebee541c..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/XpmImagePlugin.py +++ /dev/null @@ -1,128 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# XPM File handling -# -# History: -# 1996-12-29 fl Created -# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.7) -# -# Copyright (c) Secret Labs AB 1997-2001. -# Copyright (c) Fredrik Lundh 1996-2001. -# -# See the README file for information on usage and redistribution. -# - - -import re - -from . import Image, ImageFile, ImagePalette -from ._binary import o8 - -# XPM header -xpm_head = re.compile(b'"([0-9]*) ([0-9]*) ([0-9]*) ([0-9]*)') - - -def _accept(prefix): - return prefix[:9] == b"/* XPM */" - - -## -# Image plugin for X11 pixel maps. - - -class XpmImageFile(ImageFile.ImageFile): - format = "XPM" - format_description = "X11 Pixel Map" - - def _open(self): - if not _accept(self.fp.read(9)): - msg = "not an XPM file" - raise SyntaxError(msg) - - # skip forward to next string - while True: - s = self.fp.readline() - if not s: - msg = "broken XPM file" - raise SyntaxError(msg) - m = xpm_head.match(s) - if m: - break - - self._size = int(m.group(1)), int(m.group(2)) - - pal = int(m.group(3)) - bpp = int(m.group(4)) - - if pal > 256 or bpp != 1: - msg = "cannot read this XPM file" - raise ValueError(msg) - - # - # load palette description - - palette = [b"\0\0\0"] * 256 - - for _ in range(pal): - s = self.fp.readline() - if s[-2:] == b"\r\n": - s = s[:-2] - elif s[-1:] in b"\r\n": - s = s[:-1] - - c = s[1] - s = s[2:-2].split() - - for i in range(0, len(s), 2): - if s[i] == b"c": - # process colour key - rgb = s[i + 1] - if rgb == b"None": - self.info["transparency"] = c - elif rgb[:1] == b"#": - # FIXME: handle colour names (see ImagePalette.py) - rgb = int(rgb[1:], 16) - palette[c] = ( - o8((rgb >> 16) & 255) + o8((rgb >> 8) & 255) + o8(rgb & 255) - ) - else: - # unknown colour - msg = "cannot read this XPM file" - raise ValueError(msg) - break - - else: - # missing colour key - msg = "cannot read this XPM file" - raise ValueError(msg) - - self.mode = "P" - self.palette = ImagePalette.raw("RGB", b"".join(palette)) - - self.tile = [("raw", (0, 0) + self.size, self.fp.tell(), ("P", 0, 1))] - - def load_read(self, bytes): - # - # load all image data in one chunk - - xsize, ysize = self.size - - s = [None] * ysize - - for i in range(ysize): - s[i] = self.fp.readline()[1 : xsize + 1].ljust(xsize) - - return b"".join(s) - - -# -# Registry - - -Image.register_open(XpmImageFile.format, XpmImageFile, _accept) - -Image.register_extension(XpmImageFile.format, ".xpm") - -Image.register_mime(XpmImageFile.format, "image/xpm") diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/G__l_a_t.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/G__l_a_t.py deleted file mode 100644 index f1dfdaa031efa4cf733d31e7959cd906f2e4087c..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/G__l_a_t.py +++ /dev/null @@ -1,234 +0,0 @@ -from fontTools.misc import sstruct -from fontTools.misc.fixedTools import floatToFixedToStr -from fontTools.misc.textTools import safeEval - -# from itertools import * -from functools import partial -from . import DefaultTable -from . import grUtils -import struct - - -Glat_format_0 = """ - > # big endian - version: 16.16F -""" - -Glat_format_3 = """ - > - version: 16.16F - compression:L # compression scheme or reserved -""" - -Glat_format_1_entry = """ - > - attNum: B # Attribute number of first attribute - num: B # Number of attributes in this run -""" -Glat_format_23_entry = """ - > - attNum: H # Attribute number of first attribute - num: H # Number of attributes in this run -""" - -Glat_format_3_octabox_metrics = """ - > - subboxBitmap: H # Which subboxes exist on 4x4 grid - diagNegMin: B # Defines minimum negatively-sloped diagonal (si) - diagNegMax: B # Defines maximum negatively-sloped diagonal (sa) - diagPosMin: B # Defines minimum positively-sloped diagonal (di) - diagPosMax: B # Defines maximum positively-sloped diagonal (da) -""" - -Glat_format_3_subbox_entry = """ - > - left: B # xi - right: B # xa - bottom: B # yi - top: B # ya - diagNegMin: B # Defines minimum negatively-sloped diagonal (si) - diagNegMax: B # Defines maximum negatively-sloped diagonal (sa) - diagPosMin: B # Defines minimum positively-sloped diagonal (di) - diagPosMax: B # Defines maximum positively-sloped diagonal (da) -""" - - -class _Object: - pass - - -class _Dict(dict): - pass - - -class table_G__l_a_t(DefaultTable.DefaultTable): - """ - Support Graphite Glat tables - """ - - def __init__(self, tag=None): - DefaultTable.DefaultTable.__init__(self, tag) - self.scheme = 0 - - def decompile(self, data, ttFont): - sstruct.unpack2(Glat_format_0, data, self) - self.version = float(floatToFixedToStr(self.version, precisionBits=16)) - if self.version <= 1.9: - decoder = partial(self.decompileAttributes12, fmt=Glat_format_1_entry) - elif self.version <= 2.9: - decoder = partial(self.decompileAttributes12, fmt=Glat_format_23_entry) - elif self.version >= 3.0: - (data, self.scheme) = grUtils.decompress(data) - sstruct.unpack2(Glat_format_3, data, self) - self.hasOctaboxes = (self.compression & 1) == 1 - decoder = self.decompileAttributes3 - - gloc = ttFont["Gloc"] - self.attributes = {} - count = 0 - for s, e in zip(gloc, gloc[1:]): - self.attributes[ttFont.getGlyphName(count)] = decoder(data[s:e]) - count += 1 - - def decompileAttributes12(self, data, fmt): - attributes = _Dict() - while len(data) > 3: - e, data = sstruct.unpack2(fmt, data, _Object()) - keys = range(e.attNum, e.attNum + e.num) - if len(data) >= 2 * e.num: - vals = struct.unpack_from((">%dh" % e.num), data) - attributes.update(zip(keys, vals)) - data = data[2 * e.num :] - return attributes - - def decompileAttributes3(self, data): - if self.hasOctaboxes: - o, data = sstruct.unpack2(Glat_format_3_octabox_metrics, data, _Object()) - numsub = bin(o.subboxBitmap).count("1") - o.subboxes = [] - for b in range(numsub): - if len(data) >= 8: - subbox, data = sstruct.unpack2( - Glat_format_3_subbox_entry, data, _Object() - ) - o.subboxes.append(subbox) - attrs = self.decompileAttributes12(data, Glat_format_23_entry) - if self.hasOctaboxes: - attrs.octabox = o - return attrs - - def compile(self, ttFont): - data = sstruct.pack(Glat_format_0, self) - if self.version <= 1.9: - encoder = partial(self.compileAttributes12, fmt=Glat_format_1_entry) - elif self.version <= 2.9: - encoder = partial(self.compileAttributes12, fmt=Glat_format_1_entry) - elif self.version >= 3.0: - self.compression = (self.scheme << 27) + (1 if self.hasOctaboxes else 0) - data = sstruct.pack(Glat_format_3, self) - encoder = self.compileAttributes3 - - glocs = [] - for n in range(len(self.attributes)): - glocs.append(len(data)) - data += encoder(self.attributes[ttFont.getGlyphName(n)]) - glocs.append(len(data)) - ttFont["Gloc"].set(glocs) - - if self.version >= 3.0: - data = grUtils.compress(self.scheme, data) - return data - - def compileAttributes12(self, attrs, fmt): - data = b"" - for e in grUtils.entries(attrs): - data += sstruct.pack(fmt, {"attNum": e[0], "num": e[1]}) + struct.pack( - (">%dh" % len(e[2])), *e[2] - ) - return data - - def compileAttributes3(self, attrs): - if self.hasOctaboxes: - o = attrs.octabox - data = sstruct.pack(Glat_format_3_octabox_metrics, o) - numsub = bin(o.subboxBitmap).count("1") - for b in range(numsub): - data += sstruct.pack(Glat_format_3_subbox_entry, o.subboxes[b]) - else: - data = "" - return data + self.compileAttributes12(attrs, Glat_format_23_entry) - - def toXML(self, writer, ttFont): - writer.simpletag("version", version=self.version, compressionScheme=self.scheme) - writer.newline() - for n, a in sorted( - self.attributes.items(), key=lambda x: ttFont.getGlyphID(x[0]) - ): - writer.begintag("glyph", name=n) - writer.newline() - if hasattr(a, "octabox"): - o = a.octabox - formatstring, names, fixes = sstruct.getformat( - Glat_format_3_octabox_metrics - ) - vals = {} - for k in names: - if k == "subboxBitmap": - continue - vals[k] = "{:.3f}%".format(getattr(o, k) * 100.0 / 255) - vals["bitmap"] = "{:0X}".format(o.subboxBitmap) - writer.begintag("octaboxes", **vals) - writer.newline() - formatstring, names, fixes = sstruct.getformat( - Glat_format_3_subbox_entry - ) - for s in o.subboxes: - vals = {} - for k in names: - vals[k] = "{:.3f}%".format(getattr(s, k) * 100.0 / 255) - writer.simpletag("octabox", **vals) - writer.newline() - writer.endtag("octaboxes") - writer.newline() - for k, v in sorted(a.items()): - writer.simpletag("attribute", index=k, value=v) - writer.newline() - writer.endtag("glyph") - writer.newline() - - def fromXML(self, name, attrs, content, ttFont): - if name == "version": - self.version = float(safeEval(attrs["version"])) - self.scheme = int(safeEval(attrs["compressionScheme"])) - if name != "glyph": - return - if not hasattr(self, "attributes"): - self.attributes = {} - gname = attrs["name"] - attributes = _Dict() - for element in content: - if not isinstance(element, tuple): - continue - tag, attrs, subcontent = element - if tag == "attribute": - k = int(safeEval(attrs["index"])) - v = int(safeEval(attrs["value"])) - attributes[k] = v - elif tag == "octaboxes": - self.hasOctaboxes = True - o = _Object() - o.subboxBitmap = int(attrs["bitmap"], 16) - o.subboxes = [] - del attrs["bitmap"] - for k, v in attrs.items(): - setattr(o, k, int(float(v[:-1]) * 255.0 / 100.0 + 0.5)) - for element in subcontent: - if not isinstance(element, tuple): - continue - (tag, attrs, subcontent) = element - so = _Object() - for k, v in attrs.items(): - setattr(so, k, int(float(v[:-1]) * 255.0 / 100.0 + 0.5)) - o.subboxes.append(so) - attributes.octabox = o - self.attributes[gname] = attributes diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/functorch/_src/eager_transforms/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/functorch/_src/eager_transforms/__init__.py deleted file mode 100644 index e3e587c0978fad7d745e89ed4c5bd62f88de4685..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/functorch/_src/eager_transforms/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# This file has moved to under torch/_functorch. It is not public API. -# If you are not a PyTorch developer and you are relying on the following -# imports, please file an issue. -from torch._functorch.eager_transforms import ( - _unwrap_functional_tensor, - _assert_wrapped_functional, -) diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-93c91554.css b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-93c91554.css deleted file mode 100644 index beda351dfc765484ad744113e3d1734eb71cacd1..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-93c91554.css +++ /dev/null @@ -1 +0,0 @@ -div.svelte-15lo0d8{display:flex;flex-wrap:wrap;gap:var(--layout-gap);width:var(--size-full)}.hide.svelte-15lo0d8{display:none}.compact.svelte-15lo0d8>*,.compact.svelte-15lo0d8 .box{border-radius:0}.compact.svelte-15lo0d8,.panel.svelte-15lo0d8{border-radius:var(--container-radius);background:var(--background-fill-secondary);padding:var(--size-2)}.unequal-height.svelte-15lo0d8{align-items:flex-start}.stretch.svelte-15lo0d8{align-items:stretch}div.svelte-15lo0d8>*,div.svelte-15lo0d8>.form>*{flex:1 1 0%;flex-wrap:wrap;min-width:min(160px,100%)} diff --git a/spaces/lambdalabs/LambdaSuperRes/KAIR/models/network_usrnet.py b/spaces/lambdalabs/LambdaSuperRes/KAIR/models/network_usrnet.py deleted file mode 100644 index cf7c7177998f155422062bca4a30bbe6f75d77fa..0000000000000000000000000000000000000000 --- a/spaces/lambdalabs/LambdaSuperRes/KAIR/models/network_usrnet.py +++ /dev/null @@ -1,344 +0,0 @@ -import torch -import torch.nn as nn -import models.basicblock as B -import numpy as np -from utils import utils_image as util - - -""" -# -------------------------------------------- -# Kai Zhang (cskaizhang@gmail.com) -@inproceedings{zhang2020deep, - title={Deep unfolding network for image super-resolution}, - author={Zhang, Kai and Van Gool, Luc and Timofte, Radu}, - booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, - pages={0--0}, - year={2020} -} -# -------------------------------------------- -""" - - -""" -# -------------------------------------------- -# basic functions -# -------------------------------------------- -""" - - -def splits(a, sf): - '''split a into sfxsf distinct blocks - - Args: - a: NxCxWxHx2 - sf: split factor - - Returns: - b: NxCx(W/sf)x(H/sf)x2x(sf^2) - ''' - b = torch.stack(torch.chunk(a, sf, dim=2), dim=5) - b = torch.cat(torch.chunk(b, sf, dim=3), dim=5) - return b - - -def c2c(x): - return torch.from_numpy(np.stack([np.float32(x.real), np.float32(x.imag)], axis=-1)) - - -def r2c(x): - # convert real to complex - return torch.stack([x, torch.zeros_like(x)], -1) - - -def cdiv(x, y): - # complex division - a, b = x[..., 0], x[..., 1] - c, d = y[..., 0], y[..., 1] - cd2 = c**2 + d**2 - return torch.stack([(a*c+b*d)/cd2, (b*c-a*d)/cd2], -1) - - -def crdiv(x, y): - # complex/real division - a, b = x[..., 0], x[..., 1] - return torch.stack([a/y, b/y], -1) - - -def csum(x, y): - # complex + real - return torch.stack([x[..., 0] + y, x[..., 1]], -1) - - -def cabs(x): - # modulus of a complex number - return torch.pow(x[..., 0]**2+x[..., 1]**2, 0.5) - - -def cabs2(x): - return x[..., 0]**2+x[..., 1]**2 - - -def cmul(t1, t2): - '''complex multiplication - - Args: - t1: NxCxHxWx2, complex tensor - t2: NxCxHxWx2 - - Returns: - output: NxCxHxWx2 - ''' - real1, imag1 = t1[..., 0], t1[..., 1] - real2, imag2 = t2[..., 0], t2[..., 1] - return torch.stack([real1 * real2 - imag1 * imag2, real1 * imag2 + imag1 * real2], dim=-1) - - -def cconj(t, inplace=False): - '''complex's conjugation - - Args: - t: NxCxHxWx2 - - Returns: - output: NxCxHxWx2 - ''' - c = t.clone() if not inplace else t - c[..., 1] *= -1 - return c - - -def rfft(t): - # Real-to-complex Discrete Fourier Transform - return torch.rfft(t, 2, onesided=False) - - -def irfft(t): - # Complex-to-real Inverse Discrete Fourier Transform - return torch.irfft(t, 2, onesided=False) - - -def fft(t): - # Complex-to-complex Discrete Fourier Transform - return torch.fft(t, 2) - - -def ifft(t): - # Complex-to-complex Inverse Discrete Fourier Transform - return torch.ifft(t, 2) - - -def p2o(psf, shape): - ''' - Convert point-spread function to optical transfer function. - otf = p2o(psf) computes the Fast Fourier Transform (FFT) of the - point-spread function (PSF) array and creates the optical transfer - function (OTF) array that is not influenced by the PSF off-centering. - - Args: - psf: NxCxhxw - shape: [H, W] - - Returns: - otf: NxCxHxWx2 - ''' - otf = torch.zeros(psf.shape[:-2] + shape).type_as(psf) - otf[...,:psf.shape[2],:psf.shape[3]].copy_(psf) - for axis, axis_size in enumerate(psf.shape[2:]): - otf = torch.roll(otf, -int(axis_size / 2), dims=axis+2) - otf = torch.rfft(otf, 2, onesided=False) - n_ops = torch.sum(torch.tensor(psf.shape).type_as(psf) * torch.log2(torch.tensor(psf.shape).type_as(psf))) - otf[..., 1][torch.abs(otf[..., 1]) < n_ops*2.22e-16] = torch.tensor(0).type_as(psf) - return otf - - -def upsample(x, sf=3): - '''s-fold upsampler - - Upsampling the spatial size by filling the new entries with zeros - - x: tensor image, NxCxWxH - ''' - st = 0 - z = torch.zeros((x.shape[0], x.shape[1], x.shape[2]*sf, x.shape[3]*sf)).type_as(x) - z[..., st::sf, st::sf].copy_(x) - return z - - -def downsample(x, sf=3): - '''s-fold downsampler - - Keeping the upper-left pixel for each distinct sfxsf patch and discarding the others - - x: tensor image, NxCxWxH - ''' - st = 0 - return x[..., st::sf, st::sf] - - -def downsample_np(x, sf=3): - st = 0 - return x[st::sf, st::sf, ...] - - -""" -# -------------------------------------------- -# (1) Prior module; ResUNet: act as a non-blind denoiser -# x_k = P(z_k, beta_k) -# -------------------------------------------- -""" - - -class ResUNet(nn.Module): - def __init__(self, in_nc=4, out_nc=3, nc=[64, 128, 256, 512], nb=2, act_mode='R', downsample_mode='strideconv', upsample_mode='convtranspose'): - super(ResUNet, self).__init__() - - self.m_head = B.conv(in_nc, nc[0], bias=False, mode='C') - - # downsample - if downsample_mode == 'avgpool': - downsample_block = B.downsample_avgpool - elif downsample_mode == 'maxpool': - downsample_block = B.downsample_maxpool - elif downsample_mode == 'strideconv': - downsample_block = B.downsample_strideconv - else: - raise NotImplementedError('downsample mode [{:s}] is not found'.format(downsample_mode)) - - self.m_down1 = B.sequential(*[B.ResBlock(nc[0], nc[0], bias=False, mode='C'+act_mode+'C') for _ in range(nb)], downsample_block(nc[0], nc[1], bias=False, mode='2')) - self.m_down2 = B.sequential(*[B.ResBlock(nc[1], nc[1], bias=False, mode='C'+act_mode+'C') for _ in range(nb)], downsample_block(nc[1], nc[2], bias=False, mode='2')) - self.m_down3 = B.sequential(*[B.ResBlock(nc[2], nc[2], bias=False, mode='C'+act_mode+'C') for _ in range(nb)], downsample_block(nc[2], nc[3], bias=False, mode='2')) - - self.m_body = B.sequential(*[B.ResBlock(nc[3], nc[3], bias=False, mode='C'+act_mode+'C') for _ in range(nb)]) - - # upsample - if upsample_mode == 'upconv': - upsample_block = B.upsample_upconv - elif upsample_mode == 'pixelshuffle': - upsample_block = B.upsample_pixelshuffle - elif upsample_mode == 'convtranspose': - upsample_block = B.upsample_convtranspose - else: - raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode)) - - self.m_up3 = B.sequential(upsample_block(nc[3], nc[2], bias=False, mode='2'), *[B.ResBlock(nc[2], nc[2], bias=False, mode='C'+act_mode+'C') for _ in range(nb)]) - self.m_up2 = B.sequential(upsample_block(nc[2], nc[1], bias=False, mode='2'), *[B.ResBlock(nc[1], nc[1], bias=False, mode='C'+act_mode+'C') for _ in range(nb)]) - self.m_up1 = B.sequential(upsample_block(nc[1], nc[0], bias=False, mode='2'), *[B.ResBlock(nc[0], nc[0], bias=False, mode='C'+act_mode+'C') for _ in range(nb)]) - - self.m_tail = B.conv(nc[0], out_nc, bias=False, mode='C') - - def forward(self, x): - - h, w = x.size()[-2:] - paddingBottom = int(np.ceil(h/8)*8-h) - paddingRight = int(np.ceil(w/8)*8-w) - x = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x) - - x1 = self.m_head(x) - x2 = self.m_down1(x1) - x3 = self.m_down2(x2) - x4 = self.m_down3(x3) - x = self.m_body(x4) - x = self.m_up3(x+x4) - x = self.m_up2(x+x3) - x = self.m_up1(x+x2) - x = self.m_tail(x+x1) - - x = x[..., :h, :w] - - return x - - -""" -# -------------------------------------------- -# (2) Data module, closed-form solution -# It is a trainable-parameter-free module ^_^ -# z_k = D(x_{k-1}, s, k, y, alpha_k) -# some can be pre-calculated -# -------------------------------------------- -""" - - -class DataNet(nn.Module): - def __init__(self): - super(DataNet, self).__init__() - - def forward(self, x, FB, FBC, F2B, FBFy, alpha, sf): - FR = FBFy + torch.rfft(alpha*x, 2, onesided=False) - x1 = cmul(FB, FR) - FBR = torch.mean(splits(x1, sf), dim=-1, keepdim=False) - invW = torch.mean(splits(F2B, sf), dim=-1, keepdim=False) - invWBR = cdiv(FBR, csum(invW, alpha)) - FCBinvWBR = cmul(FBC, invWBR.repeat(1, 1, sf, sf, 1)) - FX = (FR-FCBinvWBR)/alpha.unsqueeze(-1) - Xest = torch.irfft(FX, 2, onesided=False) - - return Xest - - -""" -# -------------------------------------------- -# (3) Hyper-parameter module -# -------------------------------------------- -""" - - -class HyPaNet(nn.Module): - def __init__(self, in_nc=2, out_nc=8, channel=64): - super(HyPaNet, self).__init__() - self.mlp = nn.Sequential( - nn.Conv2d(in_nc, channel, 1, padding=0, bias=True), - nn.ReLU(inplace=True), - nn.Conv2d(channel, channel, 1, padding=0, bias=True), - nn.ReLU(inplace=True), - nn.Conv2d(channel, out_nc, 1, padding=0, bias=True), - nn.Softplus()) - - def forward(self, x): - x = self.mlp(x) + 1e-6 - return x - - -""" -# -------------------------------------------- -# main USRNet -# deep unfolding super-resolution network -# -------------------------------------------- -""" - - -class USRNet(nn.Module): - def __init__(self, n_iter=8, h_nc=64, in_nc=4, out_nc=3, nc=[64, 128, 256, 512], nb=2, act_mode='R', downsample_mode='strideconv', upsample_mode='convtranspose'): - super(USRNet, self).__init__() - - self.d = DataNet() - self.p = ResUNet(in_nc=in_nc, out_nc=out_nc, nc=nc, nb=nb, act_mode=act_mode, downsample_mode=downsample_mode, upsample_mode=upsample_mode) - self.h = HyPaNet(in_nc=2, out_nc=n_iter*2, channel=h_nc) - self.n = n_iter - - def forward(self, x, k, sf, sigma): - ''' - x: tensor, NxCxWxH - k: tensor, Nx(1,3)xwxh - sf: integer, 1 - sigma: tensor, Nx1x1x1 - ''' - - # initialization & pre-calculation - w, h = x.shape[-2:] - FB = p2o(k, (w*sf, h*sf)) - FBC = cconj(FB, inplace=False) - F2B = r2c(cabs2(FB)) - STy = upsample(x, sf=sf) - FBFy = cmul(FBC, torch.rfft(STy, 2, onesided=False)) - x = nn.functional.interpolate(x, scale_factor=sf, mode='nearest') - - # hyper-parameter, alpha & beta - ab = self.h(torch.cat((sigma, torch.tensor(sf).type_as(sigma).expand_as(sigma)), dim=1)) - - # unfolding - for i in range(self.n): - - x = self.d(x, FB, FBC, F2B, FBFy, ab[:, i:i+1, ...], sf) - x = self.p(torch.cat((x, ab[:, i+self.n:i+self.n+1, ...].repeat(1, 1, x.size(2), x.size(3))), dim=1)) - - return x diff --git a/spaces/leilevy/bingo/src/lib/bots/bing/utils.ts b/spaces/leilevy/bingo/src/lib/bots/bing/utils.ts deleted file mode 100644 index 6bbbc5e463ad55bc1219b63cf78013f5360fc908..0000000000000000000000000000000000000000 --- a/spaces/leilevy/bingo/src/lib/bots/bing/utils.ts +++ /dev/null @@ -1,87 +0,0 @@ -import { ChatResponseMessage, BingChatResponse } from './types' - -export function convertMessageToMarkdown(message: ChatResponseMessage): string { - if (message.messageType === 'InternalSearchQuery') { - return message.text - } - for (const card of message.adaptiveCards??[]) { - for (const block of card.body) { - if (block.type === 'TextBlock') { - return block.text - } - } - } - return '' -} - -const RecordSeparator = String.fromCharCode(30) - -export const websocketUtils = { - packMessage(data: any) { - return `${JSON.stringify(data)}${RecordSeparator}` - }, - unpackMessage(data: string | ArrayBuffer | Blob) { - if (!data) return {} - return data - .toString() - .split(RecordSeparator) - .filter(Boolean) - .map((s) => { - try { - return JSON.parse(s) - } catch (e) { - return {} - } - }) - }, -} - -export async function createImage(prompt: string, id: string, headers: HeadersInit): Promise
-
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\ No newline at end of file diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/DiskGetor Data Recovery 3.2 Key Crack Serial Keygen Cd 12.md b/spaces/lincquiQcaudo/Top-20-Diffusion/DiskGetor Data Recovery 3.2 Key Crack Serial Keygen Cd 12.md deleted file mode 100644 index b2916b4eb92ab42008360db066875f2b0c08b06b..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/DiskGetor Data Recovery 3.2 Key Crack Serial Keygen Cd 12.md +++ /dev/null @@ -1,130 +0,0 @@ -
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\ No newline at end of file diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Eagle Point 2011 Q1 11.1.0.zip.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Eagle Point 2011 Q1 11.1.0.zip.md deleted file mode 100644 index d59d5ddef841a1e531e154fb27ea1077a78a6991..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Eagle Point 2011 Q1 11.1.0.zip.md +++ /dev/null @@ -1,42 +0,0 @@ -
-
Eagle Point 2011 Q1 11.1.0.zip: A Comprehensive Review
-Eagle Point is a software company that provides e-learning solutions for the architecture, engineering, construction, and manufacturing industries. Eagle Point's flagship product is Pinnacle Series, an e-learning system that offers training content, productivity tools, and knowledge sharing features for dozens of software applications and topics. Eagle Point also offers Pinnacle Lite, a scaled-down version of Pinnacle Series for smaller companies.
-In this article, we will review one of Eagle Point's software products: Eagle Point 2011 Q1 11.1.0.zip. This is a zip file that contains Eagle Point software with its crack file[^5^]. Eagle Point software is designed to help engineers and architects with various tasks such as road design, contour drawing, volume calculation, mass haul diagramming, and more[^5^]. Eagle Point software can also interface with AutoCAD and other CAD software to enhance their functionality[^6^].
-Eagle Point 2011 Q1 11.1.0.zip
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We will evaluate Eagle Point 2011 Q1 11.1.0.zip based on the following criteria: installation, features, performance, compatibility, and support.
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-Eagle Point 2011 Q1 11.1.0.zip offers a variety of features for different engineering and architectural disciplines. Some of the main features are:
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Eagle Point 2011 Q1 11.1.0.zip also offers other features such as project management, -data management, -CAD standards, -and customization options. -You can access these features from the Eagle Point menu or toolbar in AutoCAD or other CAD software[^6^].
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-Eagle Point 2011 Q1 11.1.0.zip is a powerful software that can handle complex engineering and architectural projects with ease. -The software runs smoothly and efficiently on most computers with adequate specifications. -The software also has a user-friendly interface that makes it easy to navigate and use. -The software provides accurate calculations, -high-quality graphics, -and comprehensive reports for your projects[^5^] [^6^].
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\ No newline at end of file diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Libro Derecho De Familia Baqueiro Rojas.pdf.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Libro Derecho De Familia Baqueiro Rojas.pdf.md deleted file mode 100644 index c4fc113e57efc2b1ba767fa9ad4fc7d37afe4f83..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Libro Derecho De Familia Baqueiro Rojas.pdf.md +++ /dev/null @@ -1,6 +0,0 @@ -
Libro Derecho De Familia Baqueiro Rojas.pdf
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-
-Libro derecho de familia baqueiro rojas pdf files. This document was uploaded by user and they confirmed that they have the permission to ... 1fdad05405
-
-
- diff --git a/spaces/linfanluntan/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h b/spaces/linfanluntan/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h deleted file mode 100644 index ad1311a78f61303616504eb991aaa9c4a93d9948..0000000000000000000000000000000000000000 --- a/spaces/linfanluntan/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h +++ /dev/null @@ -1,33 +0,0 @@ -/*! -************************************************************************************************** -* Deformable DETR -* Copyright (c) 2020 SenseTime. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 [see LICENSE for details] -************************************************************************************************** -* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -************************************************************************************************** -*/ - -#pragma once -#include
-
- );
- }
- })
- );
- };
-
- return (
-
- {folders
- .filter((folder) => folder.type === 'chat')
- .sort((a, b) => a.name.localeCompare(b.name))
- .map((folder, index) => (
-
- );
-};
diff --git a/spaces/mattritchey/HRRR_animate/README.md b/spaces/mattritchey/HRRR_animate/README.md
deleted file mode 100644
index 750ad3b2edcd86bc2b4d4026f8e8e77748ab3704..0000000000000000000000000000000000000000
--- a/spaces/mattritchey/HRRR_animate/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: HRRR Animate
-emoji: 💩
-colorFrom: green
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/merve/Grounding_DINO_demo/groundingdino/models/GroundingDINO/__init__.py b/spaces/merve/Grounding_DINO_demo/groundingdino/models/GroundingDINO/__init__.py
deleted file mode 100644
index 2af819d61d589cfec2e0ca46612a7456f42b831a..0000000000000000000000000000000000000000
--- a/spaces/merve/Grounding_DINO_demo/groundingdino/models/GroundingDINO/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-from .groundingdino import build_groundingdino
diff --git a/spaces/merve/anonymization/style.css b/spaces/merve/anonymization/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
- font-size: 16px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- max-width: 620px;
- margin: 0 auto;
- padding: 16px;
- border: 1px solid lightgray;
- border-radius: 16px;
-}
-
-.card p:last-child {
- margin-bottom: 0;
-}
diff --git a/spaces/metricspace/OcTra/README.md b/spaces/metricspace/OcTra/README.md
deleted file mode 100644
index 1b6ba1dceec12c494699cc739b0df898f2682b95..0000000000000000000000000000000000000000
--- a/spaces/metricspace/OcTra/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: OcTra
-emoji: 🏆
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mfkeles/Track-Anything/inpainter/util/tensor_util.py b/spaces/mfkeles/Track-Anything/inpainter/util/tensor_util.py
deleted file mode 100644
index 71a4746a5ecde78dc582f6169d12db9ac58d209f..0000000000000000000000000000000000000000
--- a/spaces/mfkeles/Track-Anything/inpainter/util/tensor_util.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import cv2
-import numpy as np
-
-# resize frames
-def resize_frames(frames, size=None):
- """
- size: (w, h)
- """
- if size is not None:
- frames = [cv2.resize(f, size) for f in frames]
- frames = np.stack(frames, 0)
-
- return frames
-
-# resize frames
-def resize_masks(masks, size=None):
- """
- size: (w, h)
- """
- if size is not None:
- masks = [np.expand_dims(cv2.resize(m, size), 2) for m in masks]
- masks = np.stack(masks, 0)
-
- return masks
diff --git a/spaces/miku-hutao/vits-uma-genshin-honkai/text/symbols.py b/spaces/miku-hutao/vits-uma-genshin-honkai/text/symbols.py
deleted file mode 100644
index edfbd24247be8c757275ce80b9ec27a0ffa808f3..0000000000000000000000000000000000000000
--- a/spaces/miku-hutao/vits-uma-genshin-honkai/text/symbols.py
+++ /dev/null
@@ -1,39 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-'''# japanese_cleaners
-_pad = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-'''
-
-'''# japanese_cleaners2
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-'''# korean_cleaners
-_pad = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# zh_ja_mixture_cleaners
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
\ No newline at end of file
diff --git a/spaces/mixcard/Gustavosta-MagicPrompt-Dalle/README.md b/spaces/mixcard/Gustavosta-MagicPrompt-Dalle/README.md
deleted file mode 100644
index d596fae1c49231901a3eb3067aa2f7cf281136e7..0000000000000000000000000000000000000000
--- a/spaces/mixcard/Gustavosta-MagicPrompt-Dalle/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Gustavosta MagicPrompt Dalle
-emoji: 📚
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.50.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/miyaaa666/bingo/src/pages/api/healthz.ts b/spaces/miyaaa666/bingo/src/pages/api/healthz.ts
deleted file mode 100644
index f6ae44ff0fd66ccd3f7feaa550025fbf2a83bf77..0000000000000000000000000000000000000000
--- a/spaces/miyaaa666/bingo/src/pages/api/healthz.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- res.status(200).end('ok')
-}
diff --git a/spaces/ml6team/controlnet-interior-design/app.py b/spaces/ml6team/controlnet-interior-design/app.py
deleted file mode 100644
index dc683a86b5c04cbcd2a0e7776bdcccecd1696355..0000000000000000000000000000000000000000
--- a/spaces/ml6team/controlnet-interior-design/app.py
+++ /dev/null
@@ -1,396 +0,0 @@
-import streamlit as st
-# wide layout
-st.set_page_config(layout="wide")
-
-from streamlit_drawable_canvas import st_canvas
-from PIL import Image
-from typing import Union
-import random
-import numpy as np
-import os
-import time
-
-from models import make_image_controlnet, make_inpainting
-from segmentation import segment_image
-from config import HEIGHT, WIDTH, POS_PROMPT, NEG_PROMPT, COLOR_MAPPING, map_colors, map_colors_rgb
-from palette import COLOR_MAPPING_CATEGORY
-from preprocessing import preprocess_seg_mask, get_image, get_mask
-from explanation import make_inpainting_explanation, make_regeneration_explanation, make_segmentation_explanation
-
-
-def on_upload() -> None:
- """Upload image to the canvas."""
- if 'input_image' in st.session_state and st.session_state['input_image'] is not None:
- image = Image.open(st.session_state['input_image']).convert('RGB')
- st.session_state['initial_image'] = image
- if 'seg' in st.session_state:
- del st.session_state['seg']
- if 'unique_colors' in st.session_state:
- del st.session_state['unique_colors']
- if 'output_image' in st.session_state:
- del st.session_state['output_image']
-
-def make_image_row(image_0, image_1):
- col_0, col_1 = st.columns(2)
- with col_0:
- st.image(image_0, use_column_width=True)
- with col_1:
- st.image(image_1, use_column_width=True)
-
-
-def check_reset_state() -> bool:
- """Check whether the UI elements need to be reset
- Returns:
- bool: True if the UI elements need to be reset, False otherwise
- """
- if ('reset_canvas' in st.session_state and st.session_state['reset_canvas']):
- st.session_state['reset_canvas'] = False
- return True
- st.session_state['reset_canvas'] = False
- return False
-
-
-def move_image(source: Union[str, Image.Image],
- dest: str,
- rerun: bool = True,
- remove_state: bool = True) -> None:
- """Move image from source to destination.
- Args:
- source (Union[str, Image.Image]): source image
- dest (str): destination image location
- rerun (bool, optional): rerun streamlit. Defaults to True.
- remove_state (bool, optional): remove the canvas state. Defaults to True.
- """
- source_image = source if isinstance(source, Image.Image) else st.session_state[source]
-
- if remove_state:
- st.session_state['reset_canvas'] = True
- if 'seg' in st.session_state:
- del st.session_state['seg']
- if 'unique_colors' in st.session_state:
- del st.session_state['unique_colors']
-
- st.session_state[dest] = source_image
- st.session_state['dest'] = source_image
- if rerun:
- st.experimental_rerun()
-
-
-def on_change_radio() -> None:
- """Reset the UI elements when the radio button is changed."""
- st.session_state['reset_canvas'] = True
-
-
-def make_canvas_dict(canvas_color, brush, paint_mode, _reset_state):
- canvas_dict = dict(
- fill_color=canvas_color,
- stroke_color=canvas_color,
- background_color="#FFFFFF",
- background_image=st.session_state['initial_image'] if 'initial_image' in st.session_state else None,
- stroke_width=brush,
- initial_drawing={'version': '4.4.0', 'objects': []} if _reset_state else None,
- update_streamlit=True,
- height=512,
- width=512,
- drawing_mode=paint_mode,
- key="canvas",
- )
- return canvas_dict
-
-def make_prompt_row():
- col_0_0, col_0_1 = st.columns(2)
- with col_0_0:
- st.text_input(label="Positive prompt", value="a photograph of a room, interior design, 4k, high resolution", key='positive_prompt')
- with col_0_1:
- st.text_input(label="Negative prompt", value="lowres, watermark, banner, logo, watermark, contactinfo, text, deformed, blurry, blur, out of focus, out of frame, surreal, ugly", key='negative_prompt')
-
-def make_sidebar():
- with st.sidebar:
- input_image = st.file_uploader("", type=["png", "jpg"], key='input_image', on_change=on_upload)
- generation_mode = st.selectbox("Generation mode", ["Regenerate",
- "Segmentation",
- "Inpainting"], on_change=on_change_radio)
-
-
- if generation_mode == "Segmentation":
- paint_mode = st.sidebar.selectbox("Painting mode", ("freedraw", "polygon"))
- if paint_mode == "freedraw":
- brush = st.slider("Stroke width", 5, 140, 100, key='slider_seg')
- else:
- brush = 5
-
- category_chooser = st.sidebar.selectbox("Filter on category", list(
- COLOR_MAPPING_CATEGORY.keys()), index=0, key='category_chooser')
-
- chosen_colors = list(COLOR_MAPPING_CATEGORY[category_chooser].keys())
-
- color_chooser = st.sidebar.selectbox(
- "Choose a color", chosen_colors, index=0, format_func=map_colors, key='color_chooser'
- )
-
- elif generation_mode == "Regenerate":
- color_chooser = "rgba(0, 0, 0, 0.0)"
- paint_mode = 'freedraw'
- brush = 0
-
- else:
- paint_mode = st.sidebar.selectbox("Painting mode", ("freedraw", "polygon"))
- if paint_mode == "freedraw":
- brush = st.slider("Stroke width", 5, 140, 100, key='slider_seg')
- else:
- brush = 5
-
- color_chooser = "#000000"
- return input_image, generation_mode, brush, color_chooser, paint_mode
-
-
-def make_output_image():
- if 'output_image' in st.session_state:
- output_image = st.session_state['output_image']
- if isinstance(output_image, np.ndarray):
- output_image = Image.fromarray(output_image)
-
- if isinstance(output_image, Image.Image):
- output_image = output_image.resize((512, 512))
- else:
- output_image = Image.new('RGB', (512, 512), (255, 255, 255))
-
- st.write("#### Output image")
- st.image(output_image, width=512)
- if st.button("Move to input image"):
- move_image('output_image', 'initial_image', remove_state=True, rerun=True)
-
-def make_editing_canvas(canvas_color, brush, _reset_state, generation_mode, paint_mode):
- st.write("#### Input image")
- canvas_dict = make_canvas_dict(
- canvas_color=canvas_color,
- paint_mode=paint_mode,
- brush=brush,
- _reset_state=_reset_state
- )
- if generation_mode == "Segmentation":
- canvas = st_canvas(
- **canvas_dict,
- )
-
- if st.button("generate image", key='generate_button'):
- image = get_image()
- print("Preparing image segmentation")
- real_seg = segment_image(Image.fromarray(image))
- mask, seg = preprocess_seg_mask(canvas, real_seg)
-
- with st.spinner(text="Generating image"):
- print("Making image")
- result_image = make_image_controlnet(image=image,
- mask_image=mask,
- controlnet_conditioning_image=seg,
- positive_prompt=st.session_state['positive_prompt'],
- negative_prompt=st.session_state['negative_prompt'],
- seed=random.randint(0, 100000) # nosec
- )
- if isinstance(result_image, np.ndarray):
- result_image = Image.fromarray(result_image)
- st.session_state['output_image'] = result_image
-
-
- elif generation_mode == "Regenerate":
- canvas = st_canvas(
- **canvas_dict,
- )
- if 'seg' not in st.session_state:
- with st.spinner(text="Preparing image segmentation"):
- image = get_image()
- real_seg = np.array(segment_image(Image.fromarray(image)))
- st.session_state['seg'] = real_seg
-
- if 'unique_colors' not in st.session_state:
- real_seg = st.session_state['seg']
- unique_colors = np.unique(real_seg.reshape(-1, real_seg.shape[2]), axis=0)
- unique_colors = [tuple(color) for color in unique_colors]
- st.session_state['unique_colors'] = unique_colors
-
- with st.expander("Explanation", expanded=True):
- st.write("This mode allows you to choose which objects you want to re-generate in the image. "
- "Use the selection dropdown to add or remove objects. If you are ready, press the generate button"
- " to generate the image, which can take up to 30 seconds. If you want to improve the generated image, click"
- " the 'move image to input' button."
- )
-
- chosen_colors = st.multiselect(
- label="Choose which concepts you want to regenerate in the image",
- options=st.session_state['unique_colors'],
- key='chosen_colors',
- default=st.session_state['unique_colors'],
- format_func=map_colors_rgb,
- )
-
- if st.button("generate image", key='generate_button'):
- image = get_image()
- print(chosen_colors)
-
- segmentation = st.session_state['seg']
- mask = np.zeros_like(segmentation)
- for color in chosen_colors:
- # if the color is in the segmentation, set mask to 1
- mask[np.where((segmentation == color).all(axis=2))] = 1
-
- with st.spinner(text="Generating image"):
- result_image = make_image_controlnet(image=image,
- mask_image=mask,
- controlnet_conditioning_image=segmentation,
- positive_prompt=st.session_state['positive_prompt'],
- negative_prompt=st.session_state['negative_prompt'],
- seed=random.randint(0, 100000) # nosec
- )
- if isinstance(result_image, np.ndarray):
- result_image = Image.fromarray(result_image)
- st.session_state['output_image'] = result_image
-
- elif generation_mode == "Inpainting":
- image = get_image()
-
- canvas = st_canvas(
- **canvas_dict,
- )
-
- if st.button("generate images", key='generate_button'):
- canvas_mask = canvas.image_data
- if not isinstance(canvas_mask, np.ndarray):
- canvas_mask = np.array(canvas_mask)
- mask = get_mask(canvas_mask)
-
- with st.spinner(text="Generating new images"):
- print("Making image")
- result_image = make_inpainting(positive_prompt=st.session_state['positive_prompt'],
- image=Image.fromarray(image),
- mask_image=mask,
- negative_prompt=st.session_state['negative_prompt'],
- )
- if isinstance(result_image, np.ndarray):
- result_image = Image.fromarray(result_image)
- st.session_state['output_image'] = result_image
-
-def main():
- # center text
- st.write("## Controlnet sprint - interior design", unsafe_allow_html=True)
-
- input_image, generation_mode, brush, color_chooser, paint_mode = make_sidebar()
-
- # check if there is an input_image
- if not ('initial_image' in st.session_state and st.session_state['initial_image'] is not None):
- st.success("Upload an image to start")
- st.write("Welcome to the interior design controlnet demo! "
- "You can start by uploading a picture of your room, after which you will see "
- "a good variety of options to edit your current room to generate the room of your dreams! "
- "You can choose between inpainting, Segmentation and re-generating objects, which "
- "use our custom trained controlnet model. The main idea is that you can iterate over the "
- "generated images, because you will rarely get something perfect in one step (although it's possible). "
- "We added functionality to load in the generated image into the input, so you can keep "
- "changing the image until you are satisfied."
- )
- with st.expander("Useful information", expanded=True):
- st.write("### About the dataset")
- st.write("To make this demo as good as possible, our team spend a lot of time training a custom model. "
- "We used the LAION5B dataset to build our custom dataset, which contains 130k images of 15 types of rooms "
- "in almost 30 design styles. After fetching all these images, we started adding metadata such as "
- "captions (from the BLIP captioning model) and segmentation maps (from the HuggingFace UperNetForSemanticSegmentation model). "
- )
- st.write("For the gathering and inference of the metadata we used the Fondant framework (https://github.com/ml6team/fondant) provided by ML6 (https://www.ml6.eu/), which is an open source "
- "data centric framework for data preparation. The pipeline used for training this controlnet will soon be available as an "
- "example pipeline within Fondant and can be easily adapted for building your own dataset."
- )
- st.write("### About the model")
- st.write(
- "These were then used to train the controlnet model to generate quality interior design images by using "
- "the segmentation maps and prompts as conditioning information for the model. "
- "By training on segmentation maps, the enduser has a very finegrained control over which objects they "
- "want to place in their room. "
- "The resulting model is then used in a community pipeline that supports image2image and inpainting, "
- "so the user can keep elements of their room and change specific parts of the image."
- ""
- )
-
- st.write("### Trivia")
- st.write("The first time someone uses the demo after startup, the models still need to be loaded into memory. "
- "After this initial load, the model is cached as a resource and can be used for all the users. "
- "To avoid simultaneous requests, we have implemented a queueing mechanism that ensures that only one "
- "user accesses the model at a time (similar to the Gradio framework).\n"
- )
- st.write("To enable the features in the demo, we calculate the underlying segmentation maps and categories that "
- "are present in the image. This allows us to hide some of the manual work for the user, and "
- "by doing this, the users don't need to make a segmentation map in an external tool. Everything needed can be done within this demo."
- )
-
- # st.write("### News: Fondant - an open source data-centric framework for Foundation model finetuning")
- # st.write("The ML6 team is proud to announce that we are open sourcing our Fondant framework, which is a "
- # "data-centric framework that allows you to prepare large scale multimodal datasets with ease. We have implemented the components "
- # "that we used to train this controlnet model in Fondant as an example pipeline, and we are excited to see what you can do with it! In the future we will add a whole library of plug-and-play data preparation components, such as different ML models and filtering steps, in addition to dataset scraping components that connect to LAION5B."
- # )
- # st.write("The framework is built on top of kubeflow pipelines and abstracts all the complexity of efficient storing and moving of large datasets, so you can focus on implemented just that piece of code that you need without worrying about the rest. We also build it to run on each Cloud provider or VM. You can find the code on our github page: https://github.com/ml6team/fondant.")
-
- st.write("### Testing images")
- st.write("If you don't have any pictures close, you can use one of these images to test the model by clicking on the 'use example X' buttons")
-
- st.session_state['example_image_0'] = Image.open("content/example_0.png")
- st.session_state['example_image_1'] = Image.open("content/example_1.jpg")
- st.session_state['example_image_2'] = Image.open("content/example_2.jpg")
- st.session_state['example_image_3'] = Image.open("content/example_3.jpg")
-
- col_im_0, col_im_1 = st.columns(2)
-
- with col_im_0:
- st.image(st.session_state['example_image_0'], caption="Example image 1", use_column_width=True)
- if st.button("Use example 1"):
- move_image('example_image_0', 'initial_image', remove_state=True, rerun=True)
-
- st.image(st.session_state['example_image_2'], caption="Example image 3", use_column_width=True)
- if st.button("Use example 3"):
- move_image('example_image_2', 'initial_image', remove_state=True, rerun=True)
- with col_im_1:
- st.image(st.session_state['example_image_1'], caption="Example image 2", use_column_width=True)
- if st.button("Use example 2"):
- move_image('example_image_1', 'initial_image', remove_state=True, rerun=True)
-
- st.image(st.session_state['example_image_3'], caption="Example image 4", use_column_width=True)
- if st.button("Use example 4"):
- move_image('example_image_3', 'initial_image', remove_state=True, rerun=True)
-
- st.write("## Generated examples")
- make_image_row(Image.open("content/output_1.png"), Image.open("content/regen_example.png"))
- make_image_row(Image.open("content/keep background 2.png"), Image.open("content/output_0.png"))
- make_image_row(Image.open("content/segmentation window.png"), Image.open("content/output_3.png"))
-
- st.write("## Example video")
- st.write("### Video 1")
- st.video(open('content/controlnet_sprint_demo.mp4', 'rb').read())
- st.write("### Video 2")
- st.video(open('content/controlnet_demo_video_2.mp4', 'rb').read())
-
- else:
- make_prompt_row()
-
- _reset_state = check_reset_state()
-
- if generation_mode == "Inpainting":
- make_inpainting_explanation()
- elif generation_mode == "Segmentation":
- make_segmentation_explanation()
- elif generation_mode == "Regenerate":
- make_regeneration_explanation()
-
- col1, col2 = st.columns(2)
- with col1:
- make_editing_canvas(canvas_color=color_chooser,
- brush=brush,
- _reset_state=_reset_state,
- generation_mode=generation_mode,
- paint_mode=paint_mode
- )
-
- with col2:
- make_output_image()
-
-if __name__ == "__main__":
- main()
-
-
\ No newline at end of file
diff --git a/spaces/mohitmayank/law-finder-ipc/devganscrap/devganscrap/spiders/devgan.py b/spaces/mohitmayank/law-finder-ipc/devganscrap/devganscrap/spiders/devgan.py
deleted file mode 100644
index 670531650324e66e8aa3775ecbff89b0f820fa78..0000000000000000000000000000000000000000
--- a/spaces/mohitmayank/law-finder-ipc/devganscrap/devganscrap/spiders/devgan.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import scrapy
-
-class QuotesSpider(scrapy.Spider):
- name = "devgan"
- allowed_domains = ["devgan.in"]
-
- def start_requests(self):
- urls = [
- 'http://devgan.in/all_sections_ipc.php',
- ]
- for url in urls:
- yield scrapy.Request(url=url, callback=self.parse_mainpage)
-
- def parse_mainpage(self, response):
- # identify the links to the individual section pages
- sections = response.css('div#content').css('a')#.getall()
- # for each section
- for section in sections:
- # loc var
- loc = {
- 'title' : section.xpath('@title').extract(),
- 'link' : 'http://devgan.in' + section.xpath('@href').extract()[0],
- 'section': section.css('span.sectionlink::text').extract(),
- }
- # traverse again and extract the description
- yield scrapy.Request(loc['link'], callback=self.parse_section,
- cb_kwargs=dict(meta=loc))
-
- def parse_section(self, response, meta):
- # extract the description
- meta['description'] = " ".join(response.css('tr.mys-desc').css('::text').extract())
- # return
- return meta
-
-
\ No newline at end of file
diff --git a/spaces/monra/freegpt-webui/client/js/highlightjs-copy.min.js b/spaces/monra/freegpt-webui/client/js/highlightjs-copy.min.js
deleted file mode 100644
index ac11d33ec06e396c96b887494d9164a9b3996bef..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/client/js/highlightjs-copy.min.js
+++ /dev/null
@@ -1 +0,0 @@
-class CopyButtonPlugin{constructor(options={}){self.hook=options.hook;self.callback=options.callback}"after:highlightElement"({el,text}){let button=Object.assign(document.createElement("button"),{innerHTML:"Copy",className:"hljs-copy-button"});button.dataset.copied=false;el.parentElement.classList.add("hljs-copy-wrapper");el.parentElement.appendChild(button);el.parentElement.style.setProperty("--hljs-theme-background",window.getComputedStyle(el).backgroundColor);button.onclick=function(){if(!navigator.clipboard)return;let newText=text;if(hook&&typeof hook==="function"){newText=hook(text,el)||text}navigator.clipboard.writeText(newText).then(function(){button.innerHTML="Copied!";button.dataset.copied=true;let alert=Object.assign(document.createElement("div"),{role:"status",className:"hljs-copy-alert",innerHTML:"Copied to clipboard"});el.parentElement.appendChild(alert);setTimeout(()=>{button.innerHTML="Copy";button.dataset.copied=false;el.parentElement.removeChild(alert);alert=null},2e3)}).then(function(){if(typeof callback==="function")return callback(newText,el)})}}}
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/speech_to_text/docs/librispeech_example.md b/spaces/mshukor/UnIVAL/fairseq/examples/speech_to_text/docs/librispeech_example.md
deleted file mode 100644
index 4040fda9426027537036ba987d087a43e734bfd9..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/speech_to_text/docs/librispeech_example.md
+++ /dev/null
@@ -1,69 +0,0 @@
-[[Back]](..)
-
-# S2T Example: Speech Recognition (ASR) on LibriSpeech
-[LibriSpeech](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf) is a de-facto standard English ASR
-benchmark. We provide competitive
-vanilla [Transformer](https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) baselines.
-
-## Data preparation
-Download and preprocess LibriSpeech data with
-```bash
-# additional Python packages for S2T data processing/model training
-pip install pandas torchaudio sentencepiece
-
-python examples/speech_to_text/prep_librispeech_data.py \
- --output-root ${LS_ROOT} --vocab-type unigram --vocab-size 10000
-```
-where `LS_ROOT` is the root path for downloaded data as well as generated files (manifest, features, vocabulary and
-data configuration).
-
-[Download](https://dl.fbaipublicfiles.com/fairseq/s2t/librispeech_vocab_unigram10000.zip) our vocabulary files
-if you want to use our pre-trained models.
-
-## Training
-```bash
-fairseq-train ${LS_ROOT} --save-dir ${SAVE_DIR} \
- --config-yaml config.yaml --train-subset train-clean-100,train-clean-360,train-other-500 --valid-subset dev-clean,dev-other \
- --num-workers 4 --max-tokens 40000 --max-update 300000 \
- --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \
- --arch s2t_transformer_s --share-decoder-input-output-embed \
- --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt --warmup-updates 10000 \
- --clip-norm 10.0 --seed 1 --update-freq 8
-```
-where `SAVE_DIR` is the checkpoint root path. Here we use `--arch s2t_transformer_s` (31M parameters) as example.
-For better performance, you may switch to `s2t_transformer_m` (71M, with `--lr 1e-3`) or `s2t_transformer_l`
-(268M, with `--lr 5e-4`). We set `--update-freq 8` to simulate 8 GPUs with 1 GPU. You may want to update it accordingly
-when using more than 1 GPU.
-
-## Inference & Evaluation
-Average the last 10 checkpoints and evaluate on the 4 splits
-(`dev-clean`, `dev-other`, `test-clean` and `test-other`):
-```bash
-CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt
-python scripts/average_checkpoints.py --inputs ${SAVE_DIR} \
- --num-epoch-checkpoints 10 \
- --output "${SAVE_DIR}/${CHECKPOINT_FILENAME}"
-for SUBSET in dev-clean dev-other test-clean test-other; do
- fairseq-generate ${LS_ROOT} --config-yaml config.yaml --gen-subset ${SUBSET} \
- --task speech_to_text --path ${SAVE_DIR}/${CHECKPOINT_FILENAME} \
- --max-tokens 50000 --beam 5 --scoring wer
-done
-```
-
-## Interactive Decoding
-Launch the interactive console via
-```bash
-fairseq-interactive ${LS_ROOT} --config-yaml config.yaml --task speech_to_text \
- --path ${SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5
-```
-Type in WAV/FLAC/OGG audio paths (one per line) after the prompt.
-
-## Results
-
-| --arch | Params | dev-clean | dev-other | test-clean | test-other | Model |
-|---|---|---|---|---|---|---|
-| s2t_transformer_s | 30M | 3.8 | 8.9 | 4.4 | 9.0 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/librispeech_transformer_s.pt) |
-| s2t_transformer_m | 71M | 3.2 | 8.0 | 3.4 | 7.9 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/librispeech_transformer_m.pt) |
-| s2t_transformer_l | 268M | 3.0 | 7.5 | 3.2 | 7.5 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/librispeech_transformer_l.pt) |
-
-[[Back]](..)
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/data/extracted_features_dataset.py b/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/data/extracted_features_dataset.py
deleted file mode 100644
index d6ee9c4a3602be9db8ddfe67d41ce8a96a98ad1e..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/data/extracted_features_dataset.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import logging
-import os
-import contextlib
-
-import numpy as np
-import torch
-
-from fairseq.data import FairseqDataset, data_utils
-
-
-logger = logging.getLogger(__name__)
-
-
-class ExtractedFeaturesDataset(FairseqDataset):
- def __init__(
- self,
- path,
- split,
- min_length=3,
- max_length=None,
- labels=None,
- label_dict=None,
- shuffle=True,
- sort_by_length=True,
- ):
- super().__init__()
-
- self.min_length = min_length
- self.max_length = max_length
- self.shuffle = shuffle
- self.sort_by_length = sort_by_length
- self.label_dict = label_dict
-
- if labels is not None:
- assert label_dict is not None
-
- self.sizes = []
- self.offsets = []
- self.labels = []
-
- path = os.path.join(path, split)
- data_path = path
- self.data = np.load(data_path + ".npy", mmap_mode="r")
-
- offset = 0
- skipped = 0
-
- if not os.path.exists(path + f".{labels}"):
- labels = None
-
- with open(data_path + ".lengths", "r") as len_f, open(
- path + f".{labels}", "r"
- ) if labels is not None else contextlib.ExitStack() as lbl_f:
- for line in len_f:
- length = int(line.rstrip())
- lbl = None if labels is None else next(lbl_f).rstrip().split()
- if length >= min_length and (
- max_length is None or length <= max_length
- ):
- self.sizes.append(length)
- self.offsets.append(offset)
- if lbl is not None:
- self.labels.append(lbl)
- offset += length
-
- self.sizes = np.asarray(self.sizes)
- self.offsets = np.asarray(self.offsets)
-
- logger.info(f"loaded {len(self.offsets)}, skipped {skipped} samples")
-
- def __getitem__(self, index):
- offset = self.offsets[index]
- end = self.sizes[index] + offset
- feats = torch.from_numpy(self.data[offset:end].copy()).float()
-
- res = {"id": index, "features": feats}
- if len(self.labels) > 0:
- res["target"] = self.label_dict.encode_line(
- self.labels[index],
- line_tokenizer=lambda x: x,
- append_eos=False,
- )
-
- return res
-
- def __len__(self):
- return len(self.sizes)
-
- def collater(self, samples):
- if len(samples) == 0:
- return {}
-
- features = [s["features"] for s in samples]
- sizes = [len(s) for s in features]
-
- target_size = max(sizes)
-
- collated_features = features[0].new_zeros(
- len(features), target_size, features[0].size(-1)
- )
- padding_mask = torch.BoolTensor(collated_features.shape[:-1]).fill_(False)
- for i, (f, size) in enumerate(zip(features, sizes)):
- collated_features[i, :size] = f
- padding_mask[i, size:] = True
-
- res = {
- "id": torch.LongTensor([s["id"] for s in samples]),
- "net_input": {"features": collated_features, "padding_mask": padding_mask},
- }
-
- if len(self.labels) > 0:
- target = data_utils.collate_tokens(
- [s["target"] for s in samples],
- pad_idx=self.label_dict.pad(),
- left_pad=False,
- )
- res["target"] = target
- return res
-
- def num_tokens(self, index):
- return self.size(index)
-
- def size(self, index):
- return self.sizes[index]
-
- def ordered_indices(self):
- """Return an ordered list of indices. Batches will be constructed based
- on this order."""
- if self.shuffle:
- order = [np.random.permutation(len(self))]
- else:
- order = [np.arange(len(self))]
-
- if self.sort_by_length:
- order.append(self.sizes)
- return np.lexsort(order)[::-1]
- else:
- return order[0]
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/location_attention.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/location_attention.py
deleted file mode 100644
index a970876bba4369a93245fe73bd963566bfe4d63d..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/location_attention.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch.nn as nn
-import torch
-import torch.nn.functional as F
-
-
-class LocationAttention(nn.Module):
- """
- Attention-Based Models for Speech Recognition
- https://arxiv.org/pdf/1506.07503.pdf
-
- :param int encoder_dim: # projection-units of encoder
- :param int decoder_dim: # units of decoder
- :param int attn_dim: attention dimension
- :param int conv_dim: # channels of attention convolution
- :param int conv_kernel_size: filter size of attention convolution
- """
-
- def __init__(self, attn_dim, encoder_dim, decoder_dim,
- attn_state_kernel_size, conv_dim, conv_kernel_size,
- scaling=2.0):
- super(LocationAttention, self).__init__()
- self.attn_dim = attn_dim
- self.decoder_dim = decoder_dim
- self.scaling = scaling
- self.proj_enc = nn.Linear(encoder_dim, attn_dim)
- self.proj_dec = nn.Linear(decoder_dim, attn_dim, bias=False)
- self.proj_attn = nn.Linear(conv_dim, attn_dim, bias=False)
- self.conv = nn.Conv1d(attn_state_kernel_size, conv_dim,
- 2 * conv_kernel_size + 1,
- padding=conv_kernel_size, bias=False)
- self.proj_out = nn.Sequential(nn.Tanh(), nn.Linear(attn_dim, 1))
-
- self.proj_enc_out = None # cache
-
- def clear_cache(self):
- self.proj_enc_out = None
-
- def forward(self, encoder_out, encoder_padding_mask, decoder_h, attn_state):
- """
- :param torch.Tensor encoder_out: padded encoder hidden state B x T x D
- :param torch.Tensor encoder_padding_mask: encoder padding mask
- :param torch.Tensor decoder_h: decoder hidden state B x D
- :param torch.Tensor attn_prev: previous attention weight B x K x T
- :return: attention weighted encoder state (B, D)
- :rtype: torch.Tensor
- :return: previous attention weights (B x T)
- :rtype: torch.Tensor
- """
- bsz, seq_len, _ = encoder_out.size()
- if self.proj_enc_out is None:
- self.proj_enc_out = self.proj_enc(encoder_out)
-
- # B x K x T -> B x C x T
- attn = self.conv(attn_state)
- # B x C x T -> B x T x C -> B x T x D
- attn = self.proj_attn(attn.transpose(1, 2))
-
- if decoder_h is None:
- decoder_h = encoder_out.new_zeros(bsz, self.decoder_dim)
- dec_h = self.proj_dec(decoder_h).view(bsz, 1, self.attn_dim)
-
- out = self.proj_out(attn + self.proj_enc_out + dec_h).squeeze(2)
- out.masked_fill_(encoder_padding_mask, -float("inf"))
-
- w = F.softmax(self.scaling * out, dim=1)
- c = torch.sum(encoder_out * w.view(bsz, seq_len, 1), dim=1)
- return c, w
diff --git a/spaces/muhtasham/germanquad/README.md b/spaces/muhtasham/germanquad/README.md
deleted file mode 100644
index dd32872c9f004c31cfe5bb7d38a20976a0674955..0000000000000000000000000000000000000000
--- a/spaces/muhtasham/germanquad/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Germanquad
-emoji: 🌖
-colorFrom: gray
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/myrad01/Inpaint-Anything/third_party/lama/models/ade20k/segm_lib/utils/__init__.py b/spaces/myrad01/Inpaint-Anything/third_party/lama/models/ade20k/segm_lib/utils/__init__.py
deleted file mode 100644
index abe3cbe49477fe37d4fc16249de8a10f4fb4a013..0000000000000000000000000000000000000000
--- a/spaces/myrad01/Inpaint-Anything/third_party/lama/models/ade20k/segm_lib/utils/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .th import *
diff --git a/spaces/nakas/ChessGPT_Stockfish/model.py b/spaces/nakas/ChessGPT_Stockfish/model.py
deleted file mode 100644
index 73097be4500fd30db28896bc02cde9de1e8885db..0000000000000000000000000000000000000000
--- a/spaces/nakas/ChessGPT_Stockfish/model.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import openai
-
-poem = """
-{input}
-"""
-
-def set_openai_key(key):
- """Sets OpenAI key."""
- openai.api_key = key
-
-class GeneralModel:
- def __init__(self):
- print("Model Intilization--->")
- # set_openai_key(API_KEY)
-
- def query(self, prompt, myKwargs={}):
- """
- wrapper for the API to save the prompt and the result
- """
-
- # arguments to send the API
- kwargs = {
- "engine": "text-davinci-003",
- "temperature": 0.85,
- "max_tokens": 2400,
- "best_of": 1,
- "top_p": 1,
- "frequency_penalty": 0.5,
- "presence_penalty": 0.5,
- "stop": ["###"],
- }
-
-
- for kwarg in myKwargs:
- kwargs[kwarg] = myKwargs[kwarg]
-
-
- r = openai.Completion.create(prompt=prompt, **kwargs)["choices"][0][
- "text"
- ].strip()
- return r
-
- def model_prediction(self, input, api_key):
- """
- wrapper for the API to save the prompt and the result
- """
- # Setting the OpenAI API key got from the OpenAI dashboard
- set_openai_key(api_key)
- output = self.query(poem.format(input = input))
- return output
\ No newline at end of file
diff --git a/spaces/nateraw/dino-clips/dino/eval_video_segmentation.py b/spaces/nateraw/dino-clips/dino/eval_video_segmentation.py
deleted file mode 100644
index 08a18c475db9cbadb29d2e0f22113c0cc9efed49..0000000000000000000000000000000000000000
--- a/spaces/nateraw/dino-clips/dino/eval_video_segmentation.py
+++ /dev/null
@@ -1,292 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Some parts are taken from https://github.com/Liusifei/UVC
-"""
-import os
-import copy
-import glob
-import queue
-from urllib.request import urlopen
-import argparse
-import numpy as np
-from tqdm import tqdm
-
-import cv2
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from PIL import Image
-from torchvision import transforms
-
-import utils
-import vision_transformer as vits
-
-
-@torch.no_grad()
-def eval_video_tracking_davis(args, model, frame_list, video_dir, first_seg, seg_ori, color_palette):
- """
- Evaluate tracking on a video given first frame & segmentation
- """
- video_folder = os.path.join(args.output_dir, video_dir.split('/')[-1])
- os.makedirs(video_folder, exist_ok=True)
-
- # The queue stores the n preceeding frames
- que = queue.Queue(args.n_last_frames)
-
- # first frame
- frame1, ori_h, ori_w = read_frame(frame_list[0])
- # extract first frame feature
- frame1_feat = extract_feature(model, frame1).T # dim x h*w
-
- # saving first segmentation
- out_path = os.path.join(video_folder, "00000.png")
- imwrite_indexed(out_path, seg_ori, color_palette)
- mask_neighborhood = None
- for cnt in tqdm(range(1, len(frame_list))):
- frame_tar = read_frame(frame_list[cnt])[0]
-
- # we use the first segmentation and the n previous ones
- used_frame_feats = [frame1_feat] + [pair[0] for pair in list(que.queue)]
- used_segs = [first_seg] + [pair[1] for pair in list(que.queue)]
-
- frame_tar_avg, feat_tar, mask_neighborhood = label_propagation(args, model, frame_tar, used_frame_feats, used_segs, mask_neighborhood)
-
- # pop out oldest frame if neccessary
- if que.qsize() == args.n_last_frames:
- que.get()
- # push current results into queue
- seg = copy.deepcopy(frame_tar_avg)
- que.put([feat_tar, seg])
-
- # upsampling & argmax
- frame_tar_avg = F.interpolate(frame_tar_avg, scale_factor=args.patch_size, mode='bilinear', align_corners=False, recompute_scale_factor=False)[0]
- frame_tar_avg = norm_mask(frame_tar_avg)
- _, frame_tar_seg = torch.max(frame_tar_avg, dim=0)
-
- # saving to disk
- frame_tar_seg = np.array(frame_tar_seg.squeeze().cpu(), dtype=np.uint8)
- frame_tar_seg = np.array(Image.fromarray(frame_tar_seg).resize((ori_w, ori_h), 0))
- frame_nm = frame_list[cnt].split('/')[-1].replace(".jpg", ".png")
- imwrite_indexed(os.path.join(video_folder, frame_nm), frame_tar_seg, color_palette)
-
-
-def restrict_neighborhood(h, w):
- # We restrict the set of source nodes considered to a spatial neighborhood of the query node (i.e. ``local attention'')
- mask = torch.zeros(h, w, h, w)
- for i in range(h):
- for j in range(w):
- for p in range(2 * args.size_mask_neighborhood + 1):
- for q in range(2 * args.size_mask_neighborhood + 1):
- if i - args.size_mask_neighborhood + p < 0 or i - args.size_mask_neighborhood + p >= h:
- continue
- if j - args.size_mask_neighborhood + q < 0 or j - args.size_mask_neighborhood + q >= w:
- continue
- mask[i, j, i - args.size_mask_neighborhood + p, j - args.size_mask_neighborhood + q] = 1
-
- mask = mask.reshape(h * w, h * w)
- return mask.cuda(non_blocking=True)
-
-
-def norm_mask(mask):
- c, h, w = mask.size()
- for cnt in range(c):
- mask_cnt = mask[cnt,:,:]
- if(mask_cnt.max() > 0):
- mask_cnt = (mask_cnt - mask_cnt.min())
- mask_cnt = mask_cnt/mask_cnt.max()
- mask[cnt,:,:] = mask_cnt
- return mask
-
-
-def label_propagation(args, model, frame_tar, list_frame_feats, list_segs, mask_neighborhood=None):
- """
- propagate segs of frames in list_frames to frame_tar
- """
- ## we only need to extract feature of the target frame
- feat_tar, h, w = extract_feature(model, frame_tar, return_h_w=True)
-
- return_feat_tar = feat_tar.T # dim x h*w
-
- ncontext = len(list_frame_feats)
- feat_sources = torch.stack(list_frame_feats) # nmb_context x dim x h*w
-
- feat_tar = F.normalize(feat_tar, dim=1, p=2)
- feat_sources = F.normalize(feat_sources, dim=1, p=2)
-
- feat_tar = feat_tar.unsqueeze(0).repeat(ncontext, 1, 1)
- aff = torch.exp(torch.bmm(feat_tar, feat_sources) / 0.1) # nmb_context x h*w (tar: query) x h*w (source: keys)
-
- if args.size_mask_neighborhood > 0:
- if mask_neighborhood is None:
- mask_neighborhood = restrict_neighborhood(h, w)
- mask_neighborhood = mask_neighborhood.unsqueeze(0).repeat(ncontext, 1, 1)
- aff *= mask_neighborhood
-
- aff = aff.transpose(2, 1).reshape(-1, h * w) # nmb_context*h*w (source: keys) x h*w (tar: queries)
- tk_val, _ = torch.topk(aff, dim=0, k=args.topk)
- tk_val_min, _ = torch.min(tk_val, dim=0)
- aff[aff < tk_val_min] = 0
-
- aff = aff / torch.sum(aff, keepdim=True, axis=0)
-
- list_segs = [s.cuda() for s in list_segs]
- segs = torch.cat(list_segs)
- nmb_context, C, h, w = segs.shape
- segs = segs.reshape(nmb_context, C, -1).transpose(2, 1).reshape(-1, C).T # C x nmb_context*h*w
- seg_tar = torch.mm(segs, aff)
- seg_tar = seg_tar.reshape(1, C, h, w)
- return seg_tar, return_feat_tar, mask_neighborhood
-
-
-def extract_feature(model, frame, return_h_w=False):
- """Extract one frame feature everytime."""
- out = model.get_intermediate_layers(frame.unsqueeze(0).cuda(), n=1)[0]
- out = out[:, 1:, :] # we discard the [CLS] token
- h, w = int(frame.shape[1] / model.patch_embed.patch_size), int(frame.shape[2] / model.patch_embed.patch_size)
- dim = out.shape[-1]
- out = out[0].reshape(h, w, dim)
- out = out.reshape(-1, dim)
- if return_h_w:
- return out, h, w
- return out
-
-
-def imwrite_indexed(filename, array, color_palette):
- """ Save indexed png for DAVIS."""
- if np.atleast_3d(array).shape[2] != 1:
- raise Exception("Saving indexed PNGs requires 2D array.")
-
- im = Image.fromarray(array)
- im.putpalette(color_palette.ravel())
- im.save(filename, format='PNG')
-
-
-def to_one_hot(y_tensor, n_dims=None):
- """
- Take integer y (tensor or variable) with n dims &
- convert it to 1-hot representation with n+1 dims.
- """
- if(n_dims is None):
- n_dims = int(y_tensor.max()+ 1)
- _,h,w = y_tensor.size()
- y_tensor = y_tensor.type(torch.LongTensor).view(-1, 1)
- n_dims = n_dims if n_dims is not None else int(torch.max(y_tensor)) + 1
- y_one_hot = torch.zeros(y_tensor.size()[0], n_dims).scatter_(1, y_tensor, 1)
- y_one_hot = y_one_hot.view(h,w,n_dims)
- return y_one_hot.permute(2, 0, 1).unsqueeze(0)
-
-
-def read_frame_list(video_dir):
- frame_list = [img for img in glob.glob(os.path.join(video_dir,"*.jpg"))]
- frame_list = sorted(frame_list)
- return frame_list
-
-
-def read_frame(frame_dir, scale_size=[480]):
- """
- read a single frame & preprocess
- """
- img = cv2.imread(frame_dir)
- ori_h, ori_w, _ = img.shape
- if len(scale_size) == 1:
- if(ori_h > ori_w):
- tw = scale_size[0]
- th = (tw * ori_h) / ori_w
- th = int((th // 64) * 64)
- else:
- th = scale_size[0]
- tw = (th * ori_w) / ori_h
- tw = int((tw // 64) * 64)
- else:
- th, tw = scale_size
- img = cv2.resize(img, (tw, th))
- img = img.astype(np.float32)
- img = img / 255.0
- img = img[:, :, ::-1]
- img = np.transpose(img.copy(), (2, 0, 1))
- img = torch.from_numpy(img).float()
- img = color_normalize(img)
- return img, ori_h, ori_w
-
-
-def read_seg(seg_dir, factor, scale_size=[480]):
- seg = Image.open(seg_dir)
- _w, _h = seg.size # note PIL.Image.Image's size is (w, h)
- if len(scale_size) == 1:
- if(_w > _h):
- _th = scale_size[0]
- _tw = (_th * _w) / _h
- _tw = int((_tw // 64) * 64)
- else:
- _tw = scale_size[0]
- _th = (_tw * _h) / _w
- _th = int((_th // 64) * 64)
- else:
- _th = scale_size[1]
- _tw = scale_size[0]
- small_seg = np.array(seg.resize((_tw // factor, _th // factor), 0))
- small_seg = torch.from_numpy(small_seg.copy()).contiguous().float().unsqueeze(0)
- return to_one_hot(small_seg), np.asarray(seg)
-
-
-def color_normalize(x, mean=[0.485, 0.456, 0.406], std=[0.228, 0.224, 0.225]):
- for t, m, s in zip(x, mean, std):
- t.sub_(m)
- t.div_(s)
- return x
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser('Evaluation with video object segmentation on DAVIS 2017')
- parser.add_argument('--pretrained_weights', default='', type=str, help="Path to pretrained weights to evaluate.")
- parser.add_argument('--arch', default='vit_small', type=str,
- choices=['vit_tiny', 'vit_small', 'vit_base'], help='Architecture (support only ViT atm).')
- parser.add_argument('--patch_size', default=16, type=int, help='Patch resolution of the model.')
- parser.add_argument("--checkpoint_key", default="teacher", type=str, help='Key to use in the checkpoint (example: "teacher")')
- parser.add_argument('--output_dir', default=".", help='Path where to save segmentations')
- parser.add_argument('--data_path', default='/path/to/davis/', type=str)
- parser.add_argument("--n_last_frames", type=int, default=7, help="number of preceeding frames")
- parser.add_argument("--size_mask_neighborhood", default=12, type=int,
- help="We restrict the set of source nodes considered to a spatial neighborhood of the query node")
- parser.add_argument("--topk", type=int, default=5, help="accumulate label from top k neighbors")
- parser.add_argument("--bs", type=int, default=6, help="Batch size, try to reduce if OOM")
- args = parser.parse_args()
-
- print("git:\n {}\n".format(utils.get_sha()))
- print("\n".join("%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(args)).items())))
-
- # building network
- model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=0)
- print(f"Model {args.arch} {args.patch_size}x{args.patch_size} built.")
- model.cuda()
- utils.load_pretrained_weights(model, args.pretrained_weights, args.checkpoint_key, args.arch, args.patch_size)
- for param in model.parameters():
- param.requires_grad = False
- model.eval()
-
- color_palette = []
- for line in urlopen("https://raw.githubusercontent.com/Liusifei/UVC/master/libs/data/palette.txt"):
- color_palette.append([int(i) for i in line.decode("utf-8").split('\n')[0].split(" ")])
- color_palette = np.asarray(color_palette, dtype=np.uint8).reshape(-1,3)
-
- video_list = open(os.path.join(args.data_path, "ImageSets/2017/val.txt")).readlines()
- for i, video_name in enumerate(video_list):
- video_name = video_name.strip()
- print(f'[{i}/{len(video_list)}] Begin to segmentate video {video_name}.')
- video_dir = os.path.join(args.data_path, "JPEGImages/480p/", video_name)
- frame_list = read_frame_list(video_dir)
- seg_path = frame_list[0].replace("JPEGImages", "Annotations").replace("jpg", "png")
- first_seg, seg_ori = read_seg(seg_path, args.patch_size)
- eval_video_tracking_davis(args, model, frame_list, video_dir, first_seg, seg_ori, color_palette)
diff --git a/spaces/nateraw/lavila/lavila/models/coca.py b/spaces/nateraw/lavila/lavila/models/coca.py
deleted file mode 100644
index 9c27a9d5f5bbbbf9f109229d6768bfebee23ff24..0000000000000000000000000000000000000000
--- a/spaces/nateraw/lavila/lavila/models/coca.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Part of the code is from https://github.com/lucidrains/CoCa-pytorch/blob/main/coca_pytorch/coca_pytorch.py
-# Modified by Yue Zhao
-# The original code is under MIT License
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import einsum
-from einops import rearrange
-
-
-def exists(val):
- return val is not None
-
-
-def default(val, d):
- return val if exists(val) else d
-
-
-# normalization
-# they use layernorm without bias, something that pytorch does not offer
-class LayerNorm(nn.Module):
- def __init__(self, dim):
- super().__init__()
- self.gamma = nn.Parameter(torch.ones(dim))
- self.register_buffer("beta", torch.zeros(dim))
-
- def forward(self, x):
- return F.layer_norm(x, x.shape[-1:], self.gamma, self.beta)
-
-
-class Residual(nn.Module):
- def __init__(self, fn):
- super().__init__()
- self.fn = fn
-
- def forward(self, x, *args, **kwargs):
- return self.fn(x, *args, **kwargs) + x
-
-
-# classic Noam Shazeer paper, except here they use SwiGLU instead of the more popular GEGLU for gating the feedforward
-# https://arxiv.org/abs/2002.05202
-class SwiGLU(nn.Module):
- def forward(self, x):
- x, gate = x.chunk(2, dim=-1)
- return F.silu(gate) * x
-
-
-class CrossAttention(nn.Module):
- def __init__(
- self,
- dim,
- *,
- context_dim=None,
- dim_head=64,
- heads=8,
- parallel_ff=False,
- ff_mult=4,
- norm_context=False
- ):
- super().__init__()
- self.heads = heads
- self.scale = dim_head ** -0.5
- inner_dim = heads * dim_head
- context_dim = default(context_dim, dim)
-
- self.norm = LayerNorm(dim)
- self.context_norm = LayerNorm(context_dim) if norm_context else nn.Identity()
-
- self.to_q = nn.Linear(dim, inner_dim, bias=False)
- self.to_kv = nn.Linear(context_dim, dim_head * 2, bias=False)
- self.to_out = nn.Linear(inner_dim, dim, bias=False)
-
- # whether to have parallel feedforward
-
- ff_inner_dim = ff_mult * dim
-
- self.ff = nn.Sequential(
- nn.Linear(dim, ff_inner_dim * 2, bias=False),
- SwiGLU(),
- nn.Linear(ff_inner_dim, dim, bias=False)
- ) if parallel_ff else None
-
- def forward(self, x, context):
- """
- einstein notation
- b - batch
- h - heads
- n, i, j - sequence length (base sequence length, source, target)
- d - feature dimension
- """
-
- # pre-layernorm, for queries and context
- x = self.norm(x)
- context = self.context_norm(context)
-
- # get queries
- q = self.to_q(x)
- q = rearrange(q, 'b n (h d) -> b h n d', h=self.heads)
-
- # scale
- q = q * self.scale
-
- # get key / values
- k, v = self.to_kv(context).chunk(2, dim=-1)
-
- # query / key similarity
- sim = einsum('b h i d, b j d -> b h i j', q, k)
-
- # attention
- sim = sim - sim.amax(dim=-1, keepdim=True)
- attn = sim.softmax(dim=-1)
-
- # aggregate
- out = einsum('b h i j, b j d -> b h i d', attn, v)
-
- # merge and combine heads
- out = rearrange(out, 'b h n d -> b n (h d)')
- out = self.to_out(out)
-
- # add parallel feedforward (for multimodal layers)
- if exists(self.ff):
- out = out + self.ff(x)
-
- return out
diff --git a/spaces/nateraw/spotify-pedalboard-demo/app.py b/spaces/nateraw/spotify-pedalboard-demo/app.py
deleted file mode 100644
index f8d8b9c95dfe2ac21a2934738a1831987ccc3488..0000000000000000000000000000000000000000
--- a/spaces/nateraw/spotify-pedalboard-demo/app.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import streamlit as st
-import soundfile as sf
-from pedalboard import (
- Pedalboard,
- Chorus,
- Compressor,
- Convolution,
- Distortion,
- Gain,
- HighpassFilter,
- LadderFilter,
- Limiter,
- LowpassFilter,
- NoiseGate,
- Phaser,
- Reverb,
-)
-
-
-objects_and_init_kwargs = [
- (
- Chorus,
- {
- "rate_hz": 1.0,
- "depth": 0.25,
- "centre_delay_ms": 7.0,
- "feedback": 0.0,
- "mix": 0.5,
- },
- ),
- (Compressor, {"threshold_db": 0, "ratio": 1, "attack_ms": 1.0, "release_ms": 100}),
- # TODO - maybe add a few impulse responses to a folder + let folks cycle through them.
- # (Convolution, {'impulse_response_filename': None, 'mix': 1.0}),
- (Distortion, {"drive_db": 25}),
- (Gain, {"gain_db": 1.0}),
- (HighpassFilter, {"cutoff_frequency_hz": 50}),
- (LadderFilter, {"mode": LadderFilter.LPF12, "cutoff_hz": 200, "drive": 1.0}),
- (Limiter, {"threshold_db": -10.0, "release_ms": 100.0}),
- (LowpassFilter, {"cutoff_frequency_hz": 50}),
- (
- NoiseGate,
- {"threshold_db": -100.0, "ratio": 10, "attack_ms": 1.0, "release_ms": 100.0},
- ),
- (
- Phaser,
- {
- "rate_hz": 1.0,
- "depth": 0.5,
- "centre_frequency_hz": 1300.0,
- "feedback": 0.0,
- "mix": 0.5,
- },
- ),
- (
- Reverb,
- {
- "room_size": 0.5,
- "damping": 0.5,
- "wet_level": 0.33,
- "dry_level": 0.4,
- "width": 1.0,
- "freeze_mode": 0.0,
- },
- ),
-]
-
-name_to_object_init_kwargs = {
- obj.__name__: (obj, kwargs) for obj, kwargs in objects_and_init_kwargs
-}
-
-
-def get_transform_names(augmentations):
- transform_names = [st.sidebar.selectbox("Select transformation №1:", augmentations)]
- while transform_names[-1] != "None":
- transform_names.append(
- st.sidebar.selectbox(
- f"Select transformation №{len(transform_names) + 1}:",
- ["None"] + augmentations,
- )
- )
- transform_names = transform_names[:-1]
- return transform_names
-
-
-def get_transforms():
- transform_names = get_transform_names(sorted(name_to_object_init_kwargs.keys()))
-
- transforms = []
- for i, name in enumerate(transform_names):
- st.sidebar.markdown("---")
- st.sidebar.markdown(f"#### transformation №{i + 1}: {name}")
- obj, kwargs = name_to_object_init_kwargs[name]
-
- # 🚨 OMG pls no 🤮. It doesn't have to be this way. Do anything but this. 🚨
- inputs = {}
- for k, v in kwargs.items():
- if k in ["mix", "room_size", "damping", "wet_level", "dry_level", "width", "freeze_mode", 'feedback']:
- x = st.sidebar.slider(k, 0.0, 1.0, 0.1, key=f"{k}_{i}")
- elif k in ['threshold_db', 'gain_db']:
- x = st.sidebar.slider(k, -20., 20., 0., key=f"{k}_{i}")
- elif k in ['rate_hz', 'centre_delay_ms', 'depth']:
- x = st.sidebar.slider(k, 0., 10., 0.5, key=f"{k}_{i}")
- elif k in ['drive_db']:
- x = st.sidebar.slider(k, 0., 20., 0., key=f"{k}_{i}")
- elif k in ['release']:
- x = st.sidebar.slider(k, 0.01, 5000., 100., key=f"{k}_{i}")
- elif k in ['ratio', 'drive']:
- x = st.sidebar.slider(k, 1.0, 20., 4., key=f"{k}_{i}")
- elif k in ['attack_ms', 'release_ms']:
- x = st.sidebar.slider(k, 0.1, 2000., key=f"{k}_{i}")
- elif k in ['cutoff_frequency_hz', 'cutoff_hz', 'centre_frequency_hz']:
- x = st.sidebar.slider(k, 20, 20000, key=f"{k}_{i}")
- elif k in ['mode']:
- choices = [LadderFilter.LPF12, LadderFilter.HPF12, LadderFilter.BPF12, LadderFilter.LPF24, LadderFilter.HPF24, LadderFilter.BPF24]
- x = st.sidebar.selectbox(k, choices)
- else:
- x = type(v)(st.sidebar.text_input(k, value=v))
- inputs[k] = x
-
- # st.json(inputs)
- transforms.append(obj(**inputs))
- return transforms
-
-
-# 🚨 TODO - messy. clean me pls.
-
-audio_file = open('./example.wav', 'rb')
-audio_bytes = audio_file.read()
-
-st.markdown("## Input Audio")
-st.audio(audio_bytes, format='audio/ogg')
-
-# Run the audio through this pedalboard!
-audio, sample_rate = sf.read('./example.wav')
-# board = Pedalboard(get_transforms(), sample_rate=sample_rate)
-board = Pedalboard(get_transforms())
-effected = board(audio,sample_rate)
-
-# Write the audio back as a wav file:
-with sf.SoundFile('./outputs.wav', 'w', samplerate=sample_rate, channels=len(effected.shape)) as f:
- f.write(effected)
-
-
-audio_file = open('./outputs.wav', 'rb')
-audio_bytes = audio_file.read()
-
-st.markdown("## Effected Audio")
-st.audio(audio_bytes, format='audio/wav')
-
-
-from matplotlib import pyplot as plt
-import librosa
-import librosa.display
-
-fig = plt.figure(figsize=(14, 5))
-librosa.display.waveshow(effected, sr=sample_rate)
-st.pyplot(fig)
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Azamerica S810b Actualizacion Nagra 3 Cracked.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Azamerica S810b Actualizacion Nagra 3 Cracked.md
deleted file mode 100644
index c88967137b4d4affd54a73b1aa55c7b8af688a38..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Azamerica S810b Actualizacion Nagra 3 Cracked.md
+++ /dev/null
@@ -1,50 +0,0 @@
--Here is a possible title and article with HTML formatting for the keyword "Azamerica S810b Actualizacion Nagra 3 Cracked": - -
How to Update Azamerica S810b to Nagra 3 Cracked Version
-Azamerica S810b is a satellite receiver that can decode Nagra 2 and Nagra 3 encrypted channels. However, some users may encounter problems with the Nagra 3 encryption, which requires a periodic update of the firmware. In this article, we will show you how to update your Azamerica S810b to the latest Nagra 3 cracked version, which can bypass the encryption and allow you to watch all the channels without any interruption.
-Azamerica S810b Actualizacion Nagra 3 Cracked
DOWNLOAD https://urlcod.com/2uIaaP
-
Step 1: Download the Nagra 3 Cracked Firmware
-The first step is to download the Nagra 3 cracked firmware for your Azamerica S810b. You can find it on various websites, such as [^1^] or [^2^]. Make sure you download the correct file for your model and region. The file name should be something like "S810B_N3_2023_04_16.bin".
-Step 2: Transfer the Firmware to a USB Flash Drive
-The next step is to transfer the firmware file to a USB flash drive. You can use any USB flash drive that has enough space and is formatted in FAT32. Plug the USB flash drive into your computer and copy the firmware file to the root directory of the drive. Do not put it in any folder or subfolder. Safely eject the USB flash drive from your computer when done.
-Step 3: Update Your Azamerica S810b
-The final step is to update your Azamerica S810b with the new firmware. Follow these steps carefully:
--
-
Congratulations! You have successfully updated your Azamerica S810b to the Nagra 3 cracked version. If you have any questions or issues, please contact us at [email address]. We are happy to help you.
-Here are a few more paragraphs for the article: - -What is Nagra 3 and Why Do You Need to Crack It?
-Nagra 3 is a digital encryption system that is used by some satellite providers to protect their channels from unauthorized access. It is the successor of Nagra 2, which was cracked by hackers a few years ago. Nagra 3 uses a more advanced algorithm and a dynamic key exchange mechanism that makes it harder to crack.
-However, cracking Nagra 3 is not impossible. Some hackers have managed to find a way to bypass the encryption and create a modified firmware that can decode the Nagra 3 channels. This firmware is called Nagra 3 cracked version, and it can be installed on some satellite receivers, such as Azamerica S810b.
-By updating your Azamerica S810b to the Nagra 3 cracked version, you can enjoy watching all the channels that are encrypted by Nagra 3 without paying any subscription fee. This can save you a lot of money and give you access to more content and variety.
-What are the Risks and Benefits of Updating Your Azamerica S810b to the Nagra 3 Cracked Version?
-Updating your Azamerica S810b to the Nagra 3 cracked version has some risks and benefits that you should be aware of before doing it. Here are some of them:
--
-
The decision to update your Azamerica S810b to the Nagra 3 cracked version is yours. You should weigh the risks and benefits carefully and do it at your own risk. We are not responsible for any damage or loss that may result from updating your Azamerica S810b to the Nagra 3 cracked version.
7196e7f11a-
-
\ No newline at end of file diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/modeling/backbone/regnet.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/modeling/backbone/regnet.py deleted file mode 100644 index 3533d63385d1324cfc1559eae9576b3fa52585af..0000000000000000000000000000000000000000 --- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/modeling/backbone/regnet.py +++ /dev/null @@ -1,452 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Implementation of RegNet models from :paper:`dds` and :paper:`scaling`. - -This code is adapted from https://github.com/facebookresearch/pycls with minimal modifications. -Some code duplication exists between RegNet and ResNets (e.g., ResStem) in order to simplify -model loading. -""" - -import numpy as np -from torch import nn - -from detectron2.layers import CNNBlockBase, ShapeSpec, get_norm - -from .backbone import Backbone - -__all__ = [ - "AnyNet", - "RegNet", - "ResStem", - "SimpleStem", - "VanillaBlock", - "ResBasicBlock", - "ResBottleneckBlock", -] - - -def conv2d(w_in, w_out, k, *, stride=1, groups=1, bias=False): - """Helper for building a conv2d layer.""" - assert k % 2 == 1, "Only odd size kernels supported to avoid padding issues." - s, p, g, b = stride, (k - 1) // 2, groups, bias - return nn.Conv2d(w_in, w_out, k, stride=s, padding=p, groups=g, bias=b) - - -def gap2d(): - """Helper for building a global average pooling layer.""" - return nn.AdaptiveAvgPool2d((1, 1)) - - -def pool2d(k, *, stride=1): - """Helper for building a pool2d layer.""" - assert k % 2 == 1, "Only odd size kernels supported to avoid padding issues." - return nn.MaxPool2d(k, stride=stride, padding=(k - 1) // 2) - - -def init_weights(m): - """Performs ResNet-style weight initialization.""" - if isinstance(m, nn.Conv2d): - # Note that there is no bias due to BN - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - m.weight.data.normal_(mean=0.0, std=np.sqrt(2.0 / fan_out)) - elif isinstance(m, nn.BatchNorm2d): - m.weight.data.fill_(1.0) - m.bias.data.zero_() - elif isinstance(m, nn.Linear): - m.weight.data.normal_(mean=0.0, std=0.01) - m.bias.data.zero_() - - -class ResStem(CNNBlockBase): - """ResNet stem for ImageNet: 7x7, BN, AF, MaxPool.""" - - def __init__(self, w_in, w_out, norm, activation_class): - super().__init__(w_in, w_out, 4) - self.conv = conv2d(w_in, w_out, 7, stride=2) - self.bn = get_norm(norm, w_out) - self.af = activation_class() - self.pool = pool2d(3, stride=2) - - def forward(self, x): - for layer in self.children(): - x = layer(x) - return x - - -class SimpleStem(CNNBlockBase): - """Simple stem for ImageNet: 3x3, BN, AF.""" - - def __init__(self, w_in, w_out, norm, activation_class): - super().__init__(w_in, w_out, 2) - self.conv = conv2d(w_in, w_out, 3, stride=2) - self.bn = get_norm(norm, w_out) - self.af = activation_class() - - def forward(self, x): - for layer in self.children(): - x = layer(x) - return x - - -class SE(nn.Module): - """Squeeze-and-Excitation (SE) block: AvgPool, FC, Act, FC, Sigmoid.""" - - def __init__(self, w_in, w_se, activation_class): - super().__init__() - self.avg_pool = gap2d() - self.f_ex = nn.Sequential( - conv2d(w_in, w_se, 1, bias=True), - activation_class(), - conv2d(w_se, w_in, 1, bias=True), - nn.Sigmoid(), - ) - - def forward(self, x): - return x * self.f_ex(self.avg_pool(x)) - - -class VanillaBlock(CNNBlockBase): - """Vanilla block: [3x3 conv, BN, Relu] x2.""" - - def __init__(self, w_in, w_out, stride, norm, activation_class, _params): - super().__init__(w_in, w_out, stride) - self.a = conv2d(w_in, w_out, 3, stride=stride) - self.a_bn = get_norm(norm, w_out) - self.a_af = activation_class() - self.b = conv2d(w_out, w_out, 3) - self.b_bn = get_norm(norm, w_out) - self.b_af = activation_class() - - def forward(self, x): - for layer in self.children(): - x = layer(x) - return x - - -class BasicTransform(nn.Module): - """Basic transformation: [3x3 conv, BN, Relu] x2.""" - - def __init__(self, w_in, w_out, stride, norm, activation_class, _params): - super().__init__() - self.a = conv2d(w_in, w_out, 3, stride=stride) - self.a_bn = get_norm(norm, w_out) - self.a_af = activation_class() - self.b = conv2d(w_out, w_out, 3) - self.b_bn = get_norm(norm, w_out) - self.b_bn.final_bn = True - - def forward(self, x): - for layer in self.children(): - x = layer(x) - return x - - -class ResBasicBlock(CNNBlockBase): - """Residual basic block: x + f(x), f = basic transform.""" - - def __init__(self, w_in, w_out, stride, norm, activation_class, params): - super().__init__(w_in, w_out, stride) - self.proj, self.bn = None, None - if (w_in != w_out) or (stride != 1): - self.proj = conv2d(w_in, w_out, 1, stride=stride) - self.bn = get_norm(norm, w_out) - self.f = BasicTransform(w_in, w_out, stride, norm, activation_class, params) - self.af = activation_class() - - def forward(self, x): - x_p = self.bn(self.proj(x)) if self.proj else x - return self.af(x_p + self.f(x)) - - -class BottleneckTransform(nn.Module): - """Bottleneck transformation: 1x1, 3x3 [+SE], 1x1.""" - - def __init__(self, w_in, w_out, stride, norm, activation_class, params): - super().__init__() - w_b = int(round(w_out * params["bot_mul"])) - w_se = int(round(w_in * params["se_r"])) - groups = w_b // params["group_w"] - self.a = conv2d(w_in, w_b, 1) - self.a_bn = get_norm(norm, w_b) - self.a_af = activation_class() - self.b = conv2d(w_b, w_b, 3, stride=stride, groups=groups) - self.b_bn = get_norm(norm, w_b) - self.b_af = activation_class() - self.se = SE(w_b, w_se, activation_class) if w_se else None - self.c = conv2d(w_b, w_out, 1) - self.c_bn = get_norm(norm, w_out) - self.c_bn.final_bn = True - - def forward(self, x): - for layer in self.children(): - x = layer(x) - return x - - -class ResBottleneckBlock(CNNBlockBase): - """Residual bottleneck block: x + f(x), f = bottleneck transform.""" - - def __init__(self, w_in, w_out, stride, norm, activation_class, params): - super().__init__(w_in, w_out, stride) - self.proj, self.bn = None, None - if (w_in != w_out) or (stride != 1): - self.proj = conv2d(w_in, w_out, 1, stride=stride) - self.bn = get_norm(norm, w_out) - self.f = BottleneckTransform(w_in, w_out, stride, norm, activation_class, params) - self.af = activation_class() - - def forward(self, x): - x_p = self.bn(self.proj(x)) if self.proj else x - return self.af(x_p + self.f(x)) - - -class AnyStage(nn.Module): - """AnyNet stage (sequence of blocks w/ the same output shape).""" - - def __init__(self, w_in, w_out, stride, d, block_class, norm, activation_class, params): - super().__init__() - for i in range(d): - block = block_class(w_in, w_out, stride, norm, activation_class, params) - self.add_module("b{}".format(i + 1), block) - stride, w_in = 1, w_out - - def forward(self, x): - for block in self.children(): - x = block(x) - return x - - -class AnyNet(Backbone): - """AnyNet model. See :paper:`dds`.""" - - def __init__( - self, - *, - stem_class, - stem_width, - block_class, - depths, - widths, - group_widths, - strides, - bottleneck_ratios, - se_ratio, - activation_class, - freeze_at=0, - norm="BN", - out_features=None, - ): - """ - Args: - stem_class (callable): A callable taking 4 arguments (channels in, channels out, - normalization, callable returning an activation function) that returns another - callable implementing the stem module. - stem_width (int): The number of output channels that the stem produces. - block_class (callable): A callable taking 6 arguments (channels in, channels out, - stride, normalization, callable returning an activation function, a dict of - block-specific parameters) that returns another callable implementing the repeated - block module. - depths (list[int]): Number of blocks in each stage. - widths (list[int]): For each stage, the number of output channels of each block. - group_widths (list[int]): For each stage, the number of channels per group in group - convolution, if the block uses group convolution. - strides (list[int]): The stride that each network stage applies to its input. - bottleneck_ratios (list[float]): For each stage, the ratio of the number of bottleneck - channels to the number of block input channels (or, equivalently, output channels), - if the block uses a bottleneck. - se_ratio (float): The ratio of the number of channels used inside the squeeze-excitation - (SE) module to it number of input channels, if SE the block uses SE. - activation_class (callable): A callable taking no arguments that returns another - callable implementing an activation function. - freeze_at (int): The number of stages at the beginning to freeze. - see :meth:`freeze` for detailed explanation. - norm (str or callable): normalization for all conv layers. - See :func:`layers.get_norm` for supported format. - out_features (list[str]): name of the layers whose outputs should - be returned in forward. RegNet's use "stem" and "s1", "s2", etc for the stages after - the stem. If None, will return the output of the last layer. - """ - super().__init__() - self.stem = stem_class(3, stem_width, norm, activation_class) - - current_stride = self.stem.stride - self._out_feature_strides = {"stem": current_stride} - self._out_feature_channels = {"stem": self.stem.out_channels} - self.stages_and_names = [] - prev_w = stem_width - - for i, (d, w, s, b, g) in enumerate( - zip(depths, widths, strides, bottleneck_ratios, group_widths) - ): - params = {"bot_mul": b, "group_w": g, "se_r": se_ratio} - stage = AnyStage(prev_w, w, s, d, block_class, norm, activation_class, params) - name = "s{}".format(i + 1) - self.add_module(name, stage) - self.stages_and_names.append((stage, name)) - self._out_feature_strides[name] = current_stride = int( - current_stride * np.prod([k.stride for k in stage.children()]) - ) - self._out_feature_channels[name] = list(stage.children())[-1].out_channels - prev_w = w - - self.apply(init_weights) - - if out_features is None: - out_features = [name] - self._out_features = out_features - assert len(self._out_features) - children = [x[0] for x in self.named_children()] - for out_feature in self._out_features: - assert out_feature in children, "Available children: {} does not include {}".format( - ", ".join(children), out_feature - ) - self.freeze(freeze_at) - - def forward(self, x): - """ - Args: - x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. - - Returns: - dict[str->Tensor]: names and the corresponding features - """ - assert x.dim() == 4, f"Model takes an input of shape (N, C, H, W). Got {x.shape} instead!" - outputs = {} - x = self.stem(x) - if "stem" in self._out_features: - outputs["stem"] = x - for stage, name in self.stages_and_names: - x = stage(x) - if name in self._out_features: - outputs[name] = x - return outputs - - def output_shape(self): - return { - name: ShapeSpec( - channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] - ) - for name in self._out_features - } - - def freeze(self, freeze_at=0): - """ - Freeze the first several stages of the model. Commonly used in fine-tuning. - - Layers that produce the same feature map spatial size are defined as one - "stage" by :paper:`FPN`. - - Args: - freeze_at (int): number of stages to freeze. - `1` means freezing the stem. `2` means freezing the stem and - one residual stage, etc. - - Returns: - nn.Module: this model itself - """ - if freeze_at >= 1: - self.stem.freeze() - for idx, (stage, _) in enumerate(self.stages_and_names, start=2): - if freeze_at >= idx: - for block in stage.children(): - block.freeze() - return self - - -def adjust_block_compatibility(ws, bs, gs): - """Adjusts the compatibility of widths, bottlenecks, and groups.""" - assert len(ws) == len(bs) == len(gs) - assert all(w > 0 and b > 0 and g > 0 for w, b, g in zip(ws, bs, gs)) - vs = [int(max(1, w * b)) for w, b in zip(ws, bs)] - gs = [int(min(g, v)) for g, v in zip(gs, vs)] - ms = [np.lcm(g, b) if b > 1 else g for g, b in zip(gs, bs)] - vs = [max(m, int(round(v / m) * m)) for v, m in zip(vs, ms)] - ws = [int(v / b) for v, b in zip(vs, bs)] - assert all(w * b % g == 0 for w, b, g in zip(ws, bs, gs)) - return ws, bs, gs - - -def generate_regnet_parameters(w_a, w_0, w_m, d, q=8): - """Generates per stage widths and depths from RegNet parameters.""" - assert w_a >= 0 and w_0 > 0 and w_m > 1 and w_0 % q == 0 - # Generate continuous per-block ws - ws_cont = np.arange(d) * w_a + w_0 - # Generate quantized per-block ws - ks = np.round(np.log(ws_cont / w_0) / np.log(w_m)) - ws_all = w_0 * np.power(w_m, ks) - ws_all = np.round(np.divide(ws_all, q)).astype(int) * q - # Generate per stage ws and ds (assumes ws_all are sorted) - ws, ds = np.unique(ws_all, return_counts=True) - # Compute number of actual stages and total possible stages - num_stages, total_stages = len(ws), ks.max() + 1 - # Convert numpy arrays to lists and return - ws, ds, ws_all, ws_cont = (x.tolist() for x in (ws, ds, ws_all, ws_cont)) - return ws, ds, num_stages, total_stages, ws_all, ws_cont - - -class RegNet(AnyNet): - """RegNet model. See :paper:`dds`.""" - - def __init__( - self, - *, - stem_class, - stem_width, - block_class, - depth, - w_a, - w_0, - w_m, - group_width, - stride=2, - bottleneck_ratio=1.0, - se_ratio=0.0, - activation_class=None, - freeze_at=0, - norm="BN", - out_features=None, - ): - """ - Build a RegNet from the parameterization described in :paper:`dds` Section 3.3. - - Args: - See :class:`AnyNet` for arguments that are not listed here. - depth (int): Total number of blocks in the RegNet. - w_a (float): Factor by which block width would increase prior to quantizing block widths - by stage. See :paper:`dds` Section 3.3. - w_0 (int): Initial block width. See :paper:`dds` Section 3.3. - w_m (float): Parameter controlling block width quantization. - See :paper:`dds` Section 3.3. - group_width (int): Number of channels per group in group convolution, if the block uses - group convolution. - bottleneck_ratio (float): The ratio of the number of bottleneck channels to the number - of block input channels (or, equivalently, output channels), if the block uses a - bottleneck. - stride (int): The stride that each network stage applies to its input. - """ - ws, ds = generate_regnet_parameters(w_a, w_0, w_m, depth)[0:2] - ss = [stride for _ in ws] - bs = [bottleneck_ratio for _ in ws] - gs = [group_width for _ in ws] - ws, bs, gs = adjust_block_compatibility(ws, bs, gs) - - def default_activation_class(): - return nn.ReLU(inplace=True) - - super().__init__( - stem_class=stem_class, - stem_width=stem_width, - block_class=block_class, - depths=ds, - widths=ws, - strides=ss, - group_widths=gs, - bottleneck_ratios=bs, - se_ratio=se_ratio, - activation_class=default_activation_class - if activation_class is None - else activation_class, - freeze_at=freeze_at, - norm=norm, - out_features=out_features, - ) diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/hrnet.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/hrnet.py deleted file mode 100644 index ca2467107e8e5a50167de38ef6827fac646d1245..0000000000000000000000000000000000000000 --- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/hrnet.py +++ /dev/null @@ -1,474 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# ------------------------------------------------------------------------------ -# Copyright (c) Microsoft -# Licensed under the MIT License. -# Written by Bin Xiao (leoxiaobin@gmail.com) -# Modified by Bowen Cheng (bcheng9@illinois.edu) -# Adapted from https://github.com/HRNet/Higher-HRNet-Human-Pose-Estimation/blob/master/lib/models/pose_higher_hrnet.py # noqa -# ------------------------------------------------------------------------------ - -from __future__ import absolute_import, division, print_function -import logging -import torch.nn as nn - -from detectron2.layers import ShapeSpec -from detectron2.modeling.backbone import BACKBONE_REGISTRY -from detectron2.modeling.backbone.backbone import Backbone - -BN_MOMENTUM = 0.1 -logger = logging.getLogger(__name__) - -__all__ = ["build_pose_hrnet_backbone", "PoseHigherResolutionNet"] - - -def conv3x3(in_planes, out_planes, stride=1): - """3x3 convolution with padding""" - return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) - - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(BasicBlock, self).__init__() - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes) - self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class Bottleneck(nn.Module): - expansion = 4 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(Bottleneck, self).__init__() - self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) - self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) - self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) - self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM) - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class HighResolutionModule(nn.Module): - """HighResolutionModule - Building block of the PoseHigherResolutionNet (see lower) - arXiv: https://arxiv.org/abs/1908.10357 - Args: - num_branches (int): number of branches of the modyle - blocks (str): type of block of the module - num_blocks (int): number of blocks of the module - num_inchannels (int): number of input channels of the module - num_channels (list): number of channels of each branch - multi_scale_output (bool): only used by the last module of PoseHigherResolutionNet - """ - - def __init__( - self, - num_branches, - blocks, - num_blocks, - num_inchannels, - num_channels, - multi_scale_output=True, - ): - super(HighResolutionModule, self).__init__() - self._check_branches(num_branches, blocks, num_blocks, num_inchannels, num_channels) - - self.num_inchannels = num_inchannels - self.num_branches = num_branches - - self.multi_scale_output = multi_scale_output - - self.branches = self._make_branches(num_branches, blocks, num_blocks, num_channels) - self.fuse_layers = self._make_fuse_layers() - self.relu = nn.ReLU(True) - - def _check_branches(self, num_branches, blocks, num_blocks, num_inchannels, num_channels): - if num_branches != len(num_blocks): - error_msg = "NUM_BRANCHES({}) <> NUM_BLOCKS({})".format(num_branches, len(num_blocks)) - logger.error(error_msg) - raise ValueError(error_msg) - - if num_branches != len(num_channels): - error_msg = "NUM_BRANCHES({}) <> NUM_CHANNELS({})".format( - num_branches, len(num_channels) - ) - logger.error(error_msg) - raise ValueError(error_msg) - - if num_branches != len(num_inchannels): - error_msg = "NUM_BRANCHES({}) <> NUM_INCHANNELS({})".format( - num_branches, len(num_inchannels) - ) - logger.error(error_msg) - raise ValueError(error_msg) - - def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1): - downsample = None - if ( - stride != 1 - or self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion - ): - downsample = nn.Sequential( - nn.Conv2d( - self.num_inchannels[branch_index], - num_channels[branch_index] * block.expansion, - kernel_size=1, - stride=stride, - bias=False, - ), - nn.BatchNorm2d(num_channels[branch_index] * block.expansion, momentum=BN_MOMENTUM), - ) - - layers = [] - layers.append( - block(self.num_inchannels[branch_index], num_channels[branch_index], stride, downsample) - ) - self.num_inchannels[branch_index] = num_channels[branch_index] * block.expansion - for _ in range(1, num_blocks[branch_index]): - layers.append(block(self.num_inchannels[branch_index], num_channels[branch_index])) - - return nn.Sequential(*layers) - - def _make_branches(self, num_branches, block, num_blocks, num_channels): - branches = [] - - for i in range(num_branches): - branches.append(self._make_one_branch(i, block, num_blocks, num_channels)) - - return nn.ModuleList(branches) - - def _make_fuse_layers(self): - if self.num_branches == 1: - return None - - num_branches = self.num_branches - num_inchannels = self.num_inchannels - fuse_layers = [] - for i in range(num_branches if self.multi_scale_output else 1): - fuse_layer = [] - for j in range(num_branches): - if j > i: - fuse_layer.append( - nn.Sequential( - nn.Conv2d(num_inchannels[j], num_inchannels[i], 1, 1, 0, bias=False), - nn.BatchNorm2d(num_inchannels[i]), - nn.Upsample(scale_factor=2 ** (j - i), mode="nearest"), - ) - ) - elif j == i: - fuse_layer.append(None) - else: - conv3x3s = [] - for k in range(i - j): - if k == i - j - 1: - num_outchannels_conv3x3 = num_inchannels[i] - conv3x3s.append( - nn.Sequential( - nn.Conv2d( - num_inchannels[j], - num_outchannels_conv3x3, - 3, - 2, - 1, - bias=False, - ), - nn.BatchNorm2d(num_outchannels_conv3x3), - ) - ) - else: - num_outchannels_conv3x3 = num_inchannels[j] - conv3x3s.append( - nn.Sequential( - nn.Conv2d( - num_inchannels[j], - num_outchannels_conv3x3, - 3, - 2, - 1, - bias=False, - ), - nn.BatchNorm2d(num_outchannels_conv3x3), - nn.ReLU(True), - ) - ) - fuse_layer.append(nn.Sequential(*conv3x3s)) - fuse_layers.append(nn.ModuleList(fuse_layer)) - - return nn.ModuleList(fuse_layers) - - def get_num_inchannels(self): - return self.num_inchannels - - def forward(self, x): - if self.num_branches == 1: - return [self.branches[0](x[0])] - - for i in range(self.num_branches): - x[i] = self.branches[i](x[i]) - - x_fuse = [] - - for i in range(len(self.fuse_layers)): - y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) - for j in range(1, self.num_branches): - if i == j: - y = y + x[j] - else: - z = self.fuse_layers[i][j](x[j])[:, :, : y.shape[2], : y.shape[3]] - y = y + z - x_fuse.append(self.relu(y)) - - return x_fuse - - -blocks_dict = {"BASIC": BasicBlock, "BOTTLENECK": Bottleneck} - - -class PoseHigherResolutionNet(Backbone): - """PoseHigherResolutionNet - Composed of several HighResolutionModule tied together with ConvNets - Adapted from the GitHub version to fit with HRFPN and the Detectron2 infrastructure - arXiv: https://arxiv.org/abs/1908.10357 - """ - - def __init__(self, cfg, **kwargs): - self.inplanes = cfg.MODEL.HRNET.STEM_INPLANES - super(PoseHigherResolutionNet, self).__init__() - - # stem net - self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) - self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) - self.relu = nn.ReLU(inplace=True) - self.layer1 = self._make_layer(Bottleneck, 64, 4) - - self.stage2_cfg = cfg.MODEL.HRNET.STAGE2 - num_channels = self.stage2_cfg.NUM_CHANNELS - block = blocks_dict[self.stage2_cfg.BLOCK] - num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))] - self.transition1 = self._make_transition_layer([256], num_channels) - self.stage2, pre_stage_channels = self._make_stage(self.stage2_cfg, num_channels) - - self.stage3_cfg = cfg.MODEL.HRNET.STAGE3 - num_channels = self.stage3_cfg.NUM_CHANNELS - block = blocks_dict[self.stage3_cfg.BLOCK] - num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))] - self.transition2 = self._make_transition_layer(pre_stage_channels, num_channels) - self.stage3, pre_stage_channels = self._make_stage(self.stage3_cfg, num_channels) - - self.stage4_cfg = cfg.MODEL.HRNET.STAGE4 - num_channels = self.stage4_cfg.NUM_CHANNELS - block = blocks_dict[self.stage4_cfg.BLOCK] - num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))] - self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels) - self.stage4, pre_stage_channels = self._make_stage( - self.stage4_cfg, num_channels, multi_scale_output=True - ) - - self._out_features = [] - self._out_feature_channels = {} - self._out_feature_strides = {} - - for i in range(cfg.MODEL.HRNET.STAGE4.NUM_BRANCHES): - self._out_features.append("p%d" % (i + 1)) - self._out_feature_channels.update( - {self._out_features[-1]: cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS[i]} - ) - self._out_feature_strides.update({self._out_features[-1]: 1}) - - def _get_deconv_cfg(self, deconv_kernel): - if deconv_kernel == 4: - padding = 1 - output_padding = 0 - elif deconv_kernel == 3: - padding = 1 - output_padding = 1 - elif deconv_kernel == 2: - padding = 0 - output_padding = 0 - - return deconv_kernel, padding, output_padding - - def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer): - num_branches_cur = len(num_channels_cur_layer) - num_branches_pre = len(num_channels_pre_layer) - - transition_layers = [] - for i in range(num_branches_cur): - if i < num_branches_pre: - if num_channels_cur_layer[i] != num_channels_pre_layer[i]: - transition_layers.append( - nn.Sequential( - nn.Conv2d( - num_channels_pre_layer[i], - num_channels_cur_layer[i], - 3, - 1, - 1, - bias=False, - ), - nn.BatchNorm2d(num_channels_cur_layer[i]), - nn.ReLU(inplace=True), - ) - ) - else: - transition_layers.append(None) - else: - conv3x3s = [] - for j in range(i + 1 - num_branches_pre): - inchannels = num_channels_pre_layer[-1] - outchannels = ( - num_channels_cur_layer[i] if j == i - num_branches_pre else inchannels - ) - conv3x3s.append( - nn.Sequential( - nn.Conv2d(inchannels, outchannels, 3, 2, 1, bias=False), - nn.BatchNorm2d(outchannels), - nn.ReLU(inplace=True), - ) - ) - transition_layers.append(nn.Sequential(*conv3x3s)) - - return nn.ModuleList(transition_layers) - - def _make_layer(self, block, planes, blocks, stride=1): - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.Conv2d( - self.inplanes, - planes * block.expansion, - kernel_size=1, - stride=stride, - bias=False, - ), - nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), - ) - - layers = [] - layers.append(block(self.inplanes, planes, stride, downsample)) - self.inplanes = planes * block.expansion - for _ in range(1, blocks): - layers.append(block(self.inplanes, planes)) - - return nn.Sequential(*layers) - - def _make_stage(self, layer_config, num_inchannels, multi_scale_output=True): - num_modules = layer_config["NUM_MODULES"] - num_branches = layer_config["NUM_BRANCHES"] - num_blocks = layer_config["NUM_BLOCKS"] - num_channels = layer_config["NUM_CHANNELS"] - block = blocks_dict[layer_config["BLOCK"]] - - modules = [] - for i in range(num_modules): - # multi_scale_output is only used last module - if not multi_scale_output and i == num_modules - 1: - reset_multi_scale_output = False - else: - reset_multi_scale_output = True - - modules.append( - HighResolutionModule( - num_branches, - block, - num_blocks, - num_inchannels, - num_channels, - reset_multi_scale_output, - ) - ) - num_inchannels = modules[-1].get_num_inchannels() - - return nn.Sequential(*modules), num_inchannels - - def forward(self, x): - x = self.conv1(x) - x = self.bn1(x) - x = self.relu(x) - x = self.conv2(x) - x = self.bn2(x) - x = self.relu(x) - x = self.layer1(x) - - x_list = [] - for i in range(self.stage2_cfg.NUM_BRANCHES): - if self.transition1[i] is not None: - x_list.append(self.transition1[i](x)) - else: - x_list.append(x) - y_list = self.stage2(x_list) - - x_list = [] - for i in range(self.stage3_cfg.NUM_BRANCHES): - if self.transition2[i] is not None: - x_list.append(self.transition2[i](y_list[-1])) - else: - x_list.append(y_list[i]) - y_list = self.stage3(x_list) - - x_list = [] - for i in range(self.stage4_cfg.NUM_BRANCHES): - if self.transition3[i] is not None: - x_list.append(self.transition3[i](y_list[-1])) - else: - x_list.append(y_list[i]) - y_list = self.stage4(x_list) - - assert len(self._out_features) == len(y_list) - return dict(zip(self._out_features, y_list)) # final_outputs - - -@BACKBONE_REGISTRY.register() -def build_pose_hrnet_backbone(cfg, input_shape: ShapeSpec): - model = PoseHigherResolutionNet(cfg) - return model diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/TensorMask/setup.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/TensorMask/setup.py deleted file mode 100644 index f6980e0dd2d2d239faed11e1474e1a8394c9b843..0000000000000000000000000000000000000000 --- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/TensorMask/setup.py +++ /dev/null @@ -1,69 +0,0 @@ -#!/usr/bin/env python -# Copyright (c) Facebook, Inc. and its affiliates. - -import glob -import os -from setuptools import find_packages, setup -import torch -from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension - - -def get_extensions(): - this_dir = os.path.dirname(os.path.abspath(__file__)) - extensions_dir = os.path.join(this_dir, "tensormask", "layers", "csrc") - - main_source = os.path.join(extensions_dir, "vision.cpp") - sources = glob.glob(os.path.join(extensions_dir, "**", "*.cpp")) - source_cuda = glob.glob(os.path.join(extensions_dir, "**", "*.cu")) + glob.glob( - os.path.join(extensions_dir, "*.cu") - ) - - sources = [main_source] + sources - - extension = CppExtension - - extra_compile_args = {"cxx": []} - define_macros = [] - - if (torch.cuda.is_available() and CUDA_HOME is not None) or os.getenv("FORCE_CUDA", "0") == "1": - extension = CUDAExtension - sources += source_cuda - define_macros += [("WITH_CUDA", None)] - extra_compile_args["nvcc"] = [ - "-DCUDA_HAS_FP16=1", - "-D__CUDA_NO_HALF_OPERATORS__", - "-D__CUDA_NO_HALF_CONVERSIONS__", - "-D__CUDA_NO_HALF2_OPERATORS__", - ] - - # It's better if pytorch can do this by default .. - CC = os.environ.get("CC", None) - if CC is not None: - extra_compile_args["nvcc"].append("-ccbin={}".format(CC)) - - sources = [os.path.join(extensions_dir, s) for s in sources] - - include_dirs = [extensions_dir] - - ext_modules = [ - extension( - "tensormask._C", - sources, - include_dirs=include_dirs, - define_macros=define_macros, - extra_compile_args=extra_compile_args, - ) - ] - - return ext_modules - - -setup( - name="tensormask", - version="0.1", - author="FAIR", - packages=find_packages(exclude=("configs", "tests")), - python_requires=">=3.7", - ext_modules=get_extensions(), - cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, -) diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/tests/data/__init__.py b/spaces/nikitaPDL2023/assignment4/detectron2/tests/data/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/nomic-ai/dair-ai_emotion/README.md b/spaces/nomic-ai/dair-ai_emotion/README.md deleted file mode 100644 index 411f623d0f64c83b2dfea233cca941e895900123..0000000000000000000000000000000000000000 --- a/spaces/nomic-ai/dair-ai_emotion/README.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -title: dair-ai/emotion -emoji: 🗺️ -colorFrom: purple -colorTo: red -sdk: static -pinned: false ---- diff --git a/spaces/osanseviero/test_gradio/app.py b/spaces/osanseviero/test_gradio/app.py deleted file mode 100644 index 70857a9ce51d2230747274b7c25e5cdb4be26547..0000000000000000000000000000000000000000 --- a/spaces/osanseviero/test_gradio/app.py +++ /dev/null @@ -1,20 +0,0 @@ -import gradio as gr - -from transformers import pipeline - -pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es") - -def predict(text): - return pipe(text)[0]["translation_text"] - -title = "Interactive demo: Helsinki-NLP English to Spanish Translation" - -iface = gr.Interface( - fn=predict, - inputs=[gr.inputs.Textbox(label="text", lines=3)], - outputs='text', - title=title, - examples=[["Hello! My name is Omar"], ["I like this workshop"]] -) - -iface.launch(debug=True) \ No newline at end of file diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/convert_vae_pt_to_diffusers.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/convert_vae_pt_to_diffusers.py deleted file mode 100644 index a8ba48bc001c86186d3aad9ee3bcd208ceca5ea3..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/convert_vae_pt_to_diffusers.py +++ /dev/null @@ -1,159 +0,0 @@ -import argparse -import io - -import requests -import torch -from omegaconf import OmegaConf - -from diffusers import AutoencoderKL -from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( - assign_to_checkpoint, - conv_attn_to_linear, - create_vae_diffusers_config, - renew_vae_attention_paths, - renew_vae_resnet_paths, -) - - -def custom_convert_ldm_vae_checkpoint(checkpoint, config): - vae_state_dict = checkpoint - - new_checkpoint = {} - - new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] - - new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] - new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] - new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] - new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_down_blocks): - resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] - - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.bias" - ) - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - resnets = [ - key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key - ] - - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - return new_checkpoint - - -def vae_pt_to_vae_diffuser( - checkpoint_path: str, - output_path: str, -): - # Only support V1 - r = requests.get( - " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" - ) - io_obj = io.BytesIO(r.content) - - original_config = OmegaConf.load(io_obj) - image_size = 512 - device = "cuda" if torch.cuda.is_available() else "cpu" - if checkpoint_path.endswith("safetensors"): - from safetensors import safe_open - - checkpoint = {} - with safe_open(checkpoint_path, framework="pt", device="cpu") as f: - for key in f.keys(): - checkpoint[key] = f.get_tensor(key) - else: - checkpoint = torch.load(checkpoint_path, map_location=device)["state_dict"] - - # Convert the VAE model. - vae_config = create_vae_diffusers_config(original_config, image_size=image_size) - converted_vae_checkpoint = custom_convert_ldm_vae_checkpoint(checkpoint, vae_config) - - vae = AutoencoderKL(**vae_config) - vae.load_state_dict(converted_vae_checkpoint) - vae.save_pretrained(output_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.") - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.") - - args = parser.parse_args() - - vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path) diff --git a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/train_hybrid_inv.py b/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/train_hybrid_inv.py deleted file mode 100644 index d8cc673804ea1267d09f98cc995854244fd30ab1..0000000000000000000000000000000000000000 --- a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/train_hybrid_inv.py +++ /dev/null @@ -1,317 +0,0 @@ -import torch, multiprocessing, itertools, os, shutil, PIL, argparse, numpy -from torch.nn.functional import mse_loss -from collections import defaultdict, OrderedDict -from . import encoder_net, setting -from .encoder_loss import cor_square_error -from . import zdataset, pbar, nethook -from . import proggan, customnet, parallelfolder -from torchvision import transforms, models -from .pidfile import exit_if_job_done, mark_job_done - -torch.backends.cudnn.benchmark = True - -parser = argparse.ArgumentParser() -parser.add_argument('--lr', type=float, help='Learning rate', default=1e-5) -parser.add_argument('--model', type=str, help='Dataset being modeled', - default='church') -parser.add_argument('--estimate', nargs='+', default=None, - help='estimation losses: subset of z, layer1, layer2, layer3, layer4') -parser.add_argument('--recover', nargs='+', default=None, - help='recovey losses: subset of layer1, layer2, layer3, layer4, x') -args = parser.parse_args() - -global_seed = 1 -variant = None -if args.estimate == args.recover == None: - variant = ['cse'] - args.estimate = ['layer4'] - args.recover = ['z', 'layer4', 'x'] -if args.estimate is None: - args.estimate = [] -if args.recover is None: - args.recover = [] -if variant is None: - variant = sum([ - [prefix + k.replace('layer', '') for k in lst] - for prefix, lst in [ - ('e', args.estimate), - ('r', args.recover)] - ], []) -# make a directory name like 'invert_hybrid_s1_rz_r2_r4_rx' -# default '_cse' corresponds to '_i4_rz_r4_rx' -expname = 'invert_hybrid_' + '_'.join(variant) -expdir = os.path.join('results', args.model, expname) -os.makedirs(expdir, exist_ok=True) - -num_epochs = 1000 -lr_milestones = [50, 150, 350] # Reduce learning rate after these epochs - -def main(): - torch.manual_seed(global_seed) - pbar.print('Training %s' % expname) - - # Load a progressive GAN - generator = setting.load_proggan(args.model) - # Make a stacked encoder, and load each of its pretrained layers - encoder = encoder_net.HybridLayerNormEncoder() - for layernum in range(1, 4+1): - layer = getattr(encoder, 'inv%d' % layernum) - layer_filename = (os.path.join('results', args.model, - 'invert_layer_%d_cse/snapshots/epoch_100.pth.tar' % layernum)) - pbar.print('Loading %s' % layer_filename) - layer.load_state_dict(torch.load(layer_filename)['state_dict']) - resnet_filename = (os.path.join('results', args.model, - 'invert_over5_resnet/snapshots/epoch_100.pth.tar')) - pbar.print('Loading %s' % resnet_filename) - encoder.resnet.load_state_dict(torch.load(resnet_filename)['state_dict']) - - # Instrument both the encoder and generator model so that we can add - # extra loss terms based on reconstruction of intermediate layers. - generator = nethook.InstrumentedModel(generator) - generator_layer_list = ['layer1', 'layer2', 'layer3', 'layer4'] - for n in args.recover: - if n not in generator_layer_list and n not in ['x', 'z']: - generator_layer_list.append(n) - generator.retain_layers(generator_layer_list, detach=False) - encoder = nethook.InstrumentedModel(encoder) - encoder.retain_layers([ - ('resnet', 'layer4'), - ('inv4', 'layer3'), - ('inv3', 'layer2'), - ('inv2', 'layer1')], detach=False) - - # Move models to GPU - for m in [generator, encoder]: - m.cuda() - - # Set up a training data loader: unending batches of random z. - batch_size = 32 - train_loader = training_loader(generator, batch_size) - - # Test data loader is finite, fixed set of z. - test_loader = testing_loader(generator, batch_size) - - # Set up optimizer - set_requires_grad(False, generator) - optimize_conv3 = False - if optimize_conv3: - target_params = encoder.parameters() - else: - target_params = [] - # The conv3 filters of each layer are redundant with - # the conv1 in the following layer, so freeze the conv3 layers. - for n, p in encoder.named_parameters(): - if (n.startswith('inv') and not n.startswith('inv1.') - and 'conv3' in n): - p.requires_grad = False - else: - target_params.append(p) - learning_rate = args.lr - optimizer = torch.optim.Adam(target_params, lr=learning_rate) - scheduler = torch.optim.lr_scheduler.MultiStepLR( - optimizer, milestones=lr_milestones, gamma=0.5) - - epoch_batches = 100 - for epoch, epoch_loader in enumerate(pbar( - epoch_grouper(train_loader, epoch_batches), total=(1+num_epochs))): - # Training loop (for 0th epoch, do no training, just testing) - if epoch > 0: - for (z_batch,) in pbar(epoch_loader, total=epoch_batches): - (z_batch,) = [d.cuda() for d in [z_batch]] - loss = encoder_loss(z_batch, generator, encoder) - loss.backward() - pbar.post(l=loss.item()) - optimizer.step() - scheduler.step() - # Testing loop - with torch.no_grad(): - losses = defaultdict(float) - count = 0 - for i, (z_batch,) in enumerate(pbar(test_loader)): - (z_batch,) = [d.cuda() for d in [z_batch]] - nb = len(z_batch) - # Some other debugging losses - count += nb - losses['loss'] += nb * ( - encoder_loss(z_batch, generator, encoder).item()) - for name, mloss in monitor_losses( - z_batch, generator, encoder).items(): - losses[name] += nb * mloss.item() - if epoch % 10 == 0 and i == 0: - visualize_results(epoch, z_batch, generator, encoder) - losses = { name: loss / count for name, loss in losses.items() } - logline = '%d ' % epoch + ' '.join("%s=%4g" % (name, losses[name]) - for name in sorted(losses.keys())) - pbar.print(logline) - with open(os.path.join(expdir, 'log.txt'), 'a') as f: - f.write(logline + '\n') - if epoch % 10 == 0: - save_checkpoint( - epoch=epoch, - state_dict=encoder.state_dict(), - lr=learning_rate, - optimizer=optimizer.state_dict(), - **losses) - if epoch == num_epochs: - break - -def save_checkpoint(**kwargs): - dirname = os.path.join(expdir, 'snapshots') - os.makedirs(dirname, exist_ok=True) - filename = 'epoch_%d.pth.tar' % kwargs['epoch'] - torch.save(kwargs, os.path.join(dirname, filename)) - -def visualize_results(epoch, true_z, generator, encoder): - dirname = os.path.join(expdir, 'images') - os.makedirs(dirname, exist_ok=True) - true_r, estimated_r, recovered_r = ( - generate_and_recover_features(true_z, generator, encoder)) - num_images = 6 - for i in range(min(len(true_z), num_images)): - for name, im in [ - ('epoch_%d_%d_g.png', true_r['x']), - ('epoch_%d_%d_r.png', recovered_r['x']), - ]: - save_tensor_image(im[i], os.path.join(dirname, name % (epoch, i))) - rawdat = OrderedDict(sum([ - [(template % k.replace('layer', ''), v[i].cpu().numpy()) - for k, v in feats.items()] - for template, feats in [ - ('t_%s', true_r), - ('e_%s', estimated_r), - ('r_%s', recovered_r)]], [])) - numpy.savez(os.path.join(dirname, 'epoch_%d_%d.npz' % (epoch, i)), - **rawdat) - shutil.copy(os.path.join(os.dirname(__file__), 'lightbox.html'), - os.path.join(dirname, '+lightbox.html')) - -def save_tensor_image(img, filename): - np_data = ((img.permute(1, 2, 0) / 2 + 0.5) * 255).byte().cpu().numpy() - PIL.Image.fromarray(np_data).save(filename) - -def generate_recovered(generator, estimated_r): - return generator(estimated_r['z']) - -def generate_and_recover_features(true_z, generator, encoder): - global args - true_x = generator(true_z) - true_r = generator.retained_features(clear=True) - true_r['z'] = true_z - true_r['x'] = true_x - recovered_z = encoder(true_x) - estimated_r = encoder.retained_features(clear=True) - estimated_r['z'] = recovered_z - recovered_x = generate_recovered(generator, estimated_r) - recovered_r = generator.retained_features(clear=True) - recovered_r['z'] = recovered_z - recovered_r['x'] = recovered_x - return true_r, estimated_r, recovered_r - -def monitor_losses(true_z, generator, encoder, all_losses=True): - global args - true_r, estimated_r, recovered_r = ( - generate_and_recover_features(true_z, generator, encoder)) - losses = {} - for layer in estimated_r.keys() if all_losses else args.estimate: - losses['i' + layer.replace('layer', '')] = ( - cor_square_error(true_r[layer], estimated_r[layer])) - for layer in recovered_r.keys() if all_losses else args.recover: - if layer == 'x': - losses['rx'] = ( - mse_loss(true_r[layer], recovered_r[layer])) - else: - losses['r' + layer.replace('layer', '')] = ( - cor_square_error(true_r[layer], recovered_r[layer])) - return losses - -def encoder_loss(true_z, generator, encoder): - return sum(monitor_losses(true_z, generator, encoder, - all_losses=False).values()) - -def old_encoder_loss(true_z, generator, encoder): - true_p = generator(true_z) - true_r = generator.retained_features(clear=True)['layer4'] - recovered_z = encoder(true_p) - estimated_r = encoder.retained_features(clear=True)['layer4'] - recovered_p = generator(recovered_z) - recovered_r = generator.retained_features(clear=True)['layer4'] - loss_z = cor_square_error(true_z, recovered_z) - loss_ir = cor_square_error(true_r, estimated_r) - loss_r = cor_square_error(true_r, recovered_r) - loss_p = mse_loss(true_p, recovered_p) - return loss_z + loss_ir + loss_r + loss_p - -def old_monitor_losses(true_z, generator, encoder): - true_p = generator(true_z) - true_r = generator.retained_features(clear=True)['layer4'] - recovered_z = encoder(true_p) - estimated_r = encoder.retained_features(clear=True)['layer4'] - recovered_p = generator(recovered_z) - recovered_r = generator.retained_features(clear=True)['layer4'] - return dict( - loss_z=cor_square_error(true_z, recovered_z), - loss_ir=cor_square_error(true_r, estimated_r), - loss_r=cor_square_error(true_r, recovered_r), - loss_p=mse_loss(true_p, recovered_p)) - -def training_loader(z_generator, batch_size): - ''' - Returns an infinite generator that runs through randomized z - batches, forever. - ''' - g_epoch = 1 - while True: - z_data = zdataset.z_dataset_for_model( - z_generator, size=10000, seed=g_epoch + global_seed) - dataloader = torch.utils.data.DataLoader( - z_data, - shuffle=False, - batch_size=batch_size, - num_workers=10, - pin_memory=True) - for batch in dataloader: - yield batch - g_epoch += 1 - -def testing_loader(z_generator, batch_size): - ''' - Returns an a short iterator that returns a small set of test data. - ''' - z_data = zdataset.z_dataset_for_model( - z_generator, size=1000, seed=global_seed) - dataloader = torch.utils.data.DataLoader( - z_data, - shuffle=False, - batch_size=batch_size, - num_workers=10, - pin_memory=True) - return dataloader - -def epoch_grouper(loader, epoch_size): - ''' - To use with the infinite training loader: groups the training data - batches into epochs of the given size. - ''' - it = iter(loader) - while True: - chunk_it = itertools.islice(it, epoch_size) - try: - first_el = next(chunk_it) - except StopIteration: - return - yield itertools.chain((first_el,), chunk_it) - -def set_requires_grad(requires_grad, *models): - for model in models: - if model is not None: - for param in model.parameters(): - param.requires_grad = requires_grad - -class IdentityLayer(torch.nn.Module): - def forward(self, x): - return x - -if __name__ == '__main__': - exit_if_job_done(expdir) - main() - mark_job_done(expdir) diff --git a/spaces/phucpd53/DocVQA_LayoutLMV2/README.md b/spaces/phucpd53/DocVQA_LayoutLMV2/README.md deleted file mode 100644 index ea1fcee8b61fb9a05b45f3c84124c7e9d1daf66d..0000000000000000000000000000000000000000 --- a/spaces/phucpd53/DocVQA_LayoutLMV2/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: DocVQA_LayoutLMV2 -emoji: 🐨 -colorFrom: red -colorTo: pink -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/charsetgroupprober.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/charsetgroupprober.py deleted file mode 100644 index 6def56b4a75f67000ed8181ae2d2c40eefb645fb..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/charsetgroupprober.py +++ /dev/null @@ -1,106 +0,0 @@ -######################## BEGIN LICENSE BLOCK ######################## -# The Original Code is Mozilla Communicator client code. -# -# The Initial Developer of the Original Code is -# Netscape Communications Corporation. -# Portions created by the Initial Developer are Copyright (C) 1998 -# the Initial Developer. All Rights Reserved. -# -# Contributor(s): -# Mark Pilgrim - port to Python -# -# This library is free software; you can redistribute it and/or -# modify it under the terms of the GNU Lesser General Public -# License as published by the Free Software Foundation; either -# version 2.1 of the License, or (at your option) any later version. -# -# This library is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU -# Lesser General Public License for more details. -# -# You should have received a copy of the GNU Lesser General Public -# License along with this library; if not, write to the Free Software -# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA -# 02110-1301 USA -######################### END LICENSE BLOCK ######################### - -from typing import List, Optional, Union - -from .charsetprober import CharSetProber -from .enums import LanguageFilter, ProbingState - - -class CharSetGroupProber(CharSetProber): - def __init__(self, lang_filter: LanguageFilter = LanguageFilter.NONE) -> None: - super().__init__(lang_filter=lang_filter) - self._active_num = 0 - self.probers: List[CharSetProber] = [] - self._best_guess_prober: Optional[CharSetProber] = None - - def reset(self) -> None: - super().reset() - self._active_num = 0 - for prober in self.probers: - prober.reset() - prober.active = True - self._active_num += 1 - self._best_guess_prober = None - - @property - def charset_name(self) -> Optional[str]: - if not self._best_guess_prober: - self.get_confidence() - if not self._best_guess_prober: - return None - return self._best_guess_prober.charset_name - - @property - def language(self) -> Optional[str]: - if not self._best_guess_prober: - self.get_confidence() - if not self._best_guess_prober: - return None - return self._best_guess_prober.language - - def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: - for prober in self.probers: - if not prober.active: - continue - state = prober.feed(byte_str) - if not state: - continue - if state == ProbingState.FOUND_IT: - self._best_guess_prober = prober - self._state = ProbingState.FOUND_IT - return self.state - if state == ProbingState.NOT_ME: - prober.active = False - self._active_num -= 1 - if self._active_num <= 0: - self._state = ProbingState.NOT_ME - return self.state - return self.state - - def get_confidence(self) -> float: - state = self.state - if state == ProbingState.FOUND_IT: - return 0.99 - if state == ProbingState.NOT_ME: - return 0.01 - best_conf = 0.0 - self._best_guess_prober = None - for prober in self.probers: - if not prober.active: - self.logger.debug("%s not active", prober.charset_name) - continue - conf = prober.get_confidence() - self.logger.debug( - "%s %s confidence = %s", prober.charset_name, prober.language, conf - ) - if best_conf < conf: - best_conf = conf - self._best_guess_prober = prober - if not self._best_guess_prober: - return 0.0 - return best_conf diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/jaraco/functools.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/jaraco/functools.py deleted file mode 100644 index 67aeadc353ce372565a86da03913c80f14a2fddf..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/jaraco/functools.py +++ /dev/null @@ -1,556 +0,0 @@ -import functools -import time -import inspect -import collections -import types -import itertools -import warnings - -import pkg_resources.extern.more_itertools - -from typing import Callable, TypeVar - - -CallableT = TypeVar("CallableT", bound=Callable[..., object]) - - -def compose(*funcs): - """ - Compose any number of unary functions into a single unary function. - - >>> import textwrap - >>> expected = str.strip(textwrap.dedent(compose.__doc__)) - >>> strip_and_dedent = compose(str.strip, textwrap.dedent) - >>> strip_and_dedent(compose.__doc__) == expected - True - - Compose also allows the innermost function to take arbitrary arguments. - - >>> round_three = lambda x: round(x, ndigits=3) - >>> f = compose(round_three, int.__truediv__) - >>> [f(3*x, x+1) for x in range(1,10)] - [1.5, 2.0, 2.25, 2.4, 2.5, 2.571, 2.625, 2.667, 2.7] - """ - - def compose_two(f1, f2): - return lambda *args, **kwargs: f1(f2(*args, **kwargs)) - - return functools.reduce(compose_two, funcs) - - -def method_caller(method_name, *args, **kwargs): - """ - Return a function that will call a named method on the - target object with optional positional and keyword - arguments. - - >>> lower = method_caller('lower') - >>> lower('MyString') - 'mystring' - """ - - def call_method(target): - func = getattr(target, method_name) - return func(*args, **kwargs) - - return call_method - - -def once(func): - """ - Decorate func so it's only ever called the first time. - - This decorator can ensure that an expensive or non-idempotent function - will not be expensive on subsequent calls and is idempotent. - - >>> add_three = once(lambda a: a+3) - >>> add_three(3) - 6 - >>> add_three(9) - 6 - >>> add_three('12') - 6 - - To reset the stored value, simply clear the property ``saved_result``. - - >>> del add_three.saved_result - >>> add_three(9) - 12 - >>> add_three(8) - 12 - - Or invoke 'reset()' on it. - - >>> add_three.reset() - >>> add_three(-3) - 0 - >>> add_three(0) - 0 - """ - - @functools.wraps(func) - def wrapper(*args, **kwargs): - if not hasattr(wrapper, 'saved_result'): - wrapper.saved_result = func(*args, **kwargs) - return wrapper.saved_result - - wrapper.reset = lambda: vars(wrapper).__delitem__('saved_result') - return wrapper - - -def method_cache( - method: CallableT, - cache_wrapper: Callable[ - [CallableT], CallableT - ] = functools.lru_cache(), # type: ignore[assignment] -) -> CallableT: - """ - Wrap lru_cache to support storing the cache data in the object instances. - - Abstracts the common paradigm where the method explicitly saves an - underscore-prefixed protected property on first call and returns that - subsequently. - - >>> class MyClass: - ... calls = 0 - ... - ... @method_cache - ... def method(self, value): - ... self.calls += 1 - ... return value - - >>> a = MyClass() - >>> a.method(3) - 3 - >>> for x in range(75): - ... res = a.method(x) - >>> a.calls - 75 - - Note that the apparent behavior will be exactly like that of lru_cache - except that the cache is stored on each instance, so values in one - instance will not flush values from another, and when an instance is - deleted, so are the cached values for that instance. - - >>> b = MyClass() - >>> for x in range(35): - ... res = b.method(x) - >>> b.calls - 35 - >>> a.method(0) - 0 - >>> a.calls - 75 - - Note that if method had been decorated with ``functools.lru_cache()``, - a.calls would have been 76 (due to the cached value of 0 having been - flushed by the 'b' instance). - - Clear the cache with ``.cache_clear()`` - - >>> a.method.cache_clear() - - Same for a method that hasn't yet been called. - - >>> c = MyClass() - >>> c.method.cache_clear() - - Another cache wrapper may be supplied: - - >>> cache = functools.lru_cache(maxsize=2) - >>> MyClass.method2 = method_cache(lambda self: 3, cache_wrapper=cache) - >>> a = MyClass() - >>> a.method2() - 3 - - Caution - do not subsequently wrap the method with another decorator, such - as ``@property``, which changes the semantics of the function. - - See also - http://code.activestate.com/recipes/577452-a-memoize-decorator-for-instance-methods/ - for another implementation and additional justification. - """ - - def wrapper(self: object, *args: object, **kwargs: object) -> object: - # it's the first call, replace the method with a cached, bound method - bound_method: CallableT = types.MethodType( # type: ignore[assignment] - method, self - ) - cached_method = cache_wrapper(bound_method) - setattr(self, method.__name__, cached_method) - return cached_method(*args, **kwargs) - - # Support cache clear even before cache has been created. - wrapper.cache_clear = lambda: None # type: ignore[attr-defined] - - return ( # type: ignore[return-value] - _special_method_cache(method, cache_wrapper) or wrapper - ) - - -def _special_method_cache(method, cache_wrapper): - """ - Because Python treats special methods differently, it's not - possible to use instance attributes to implement the cached - methods. - - Instead, install the wrapper method under a different name - and return a simple proxy to that wrapper. - - https://github.com/jaraco/jaraco.functools/issues/5 - """ - name = method.__name__ - special_names = '__getattr__', '__getitem__' - if name not in special_names: - return - - wrapper_name = '__cached' + name - - def proxy(self, *args, **kwargs): - if wrapper_name not in vars(self): - bound = types.MethodType(method, self) - cache = cache_wrapper(bound) - setattr(self, wrapper_name, cache) - else: - cache = getattr(self, wrapper_name) - return cache(*args, **kwargs) - - return proxy - - -def apply(transform): - """ - Decorate a function with a transform function that is - invoked on results returned from the decorated function. - - >>> @apply(reversed) - ... def get_numbers(start): - ... "doc for get_numbers" - ... return range(start, start+3) - >>> list(get_numbers(4)) - [6, 5, 4] - >>> get_numbers.__doc__ - 'doc for get_numbers' - """ - - def wrap(func): - return functools.wraps(func)(compose(transform, func)) - - return wrap - - -def result_invoke(action): - r""" - Decorate a function with an action function that is - invoked on the results returned from the decorated - function (for its side-effect), then return the original - result. - - >>> @result_invoke(print) - ... def add_two(a, b): - ... return a + b - >>> x = add_two(2, 3) - 5 - >>> x - 5 - """ - - def wrap(func): - @functools.wraps(func) - def wrapper(*args, **kwargs): - result = func(*args, **kwargs) - action(result) - return result - - return wrapper - - return wrap - - -def invoke(f, *args, **kwargs): - """ - Call a function for its side effect after initialization. - - The benefit of using the decorator instead of simply invoking a function - after defining it is that it makes explicit the author's intent for the - function to be called immediately. Whereas if one simply calls the - function immediately, it's less obvious if that was intentional or - incidental. It also avoids repeating the name - the two actions, defining - the function and calling it immediately are modeled separately, but linked - by the decorator construct. - - The benefit of having a function construct (opposed to just invoking some - behavior inline) is to serve as a scope in which the behavior occurs. It - avoids polluting the global namespace with local variables, provides an - anchor on which to attach documentation (docstring), keeps the behavior - logically separated (instead of conceptually separated or not separated at - all), and provides potential to re-use the behavior for testing or other - purposes. - - This function is named as a pithy way to communicate, "call this function - primarily for its side effect", or "while defining this function, also - take it aside and call it". It exists because there's no Python construct - for "define and call" (nor should there be, as decorators serve this need - just fine). The behavior happens immediately and synchronously. - - >>> @invoke - ... def func(): print("called") - called - >>> func() - called - - Use functools.partial to pass parameters to the initial call - - >>> @functools.partial(invoke, name='bingo') - ... def func(name): print("called with", name) - called with bingo - """ - f(*args, **kwargs) - return f - - -def call_aside(*args, **kwargs): - """ - Deprecated name for invoke. - """ - warnings.warn("call_aside is deprecated, use invoke", DeprecationWarning) - return invoke(*args, **kwargs) - - -class Throttler: - """ - Rate-limit a function (or other callable) - """ - - def __init__(self, func, max_rate=float('Inf')): - if isinstance(func, Throttler): - func = func.func - self.func = func - self.max_rate = max_rate - self.reset() - - def reset(self): - self.last_called = 0 - - def __call__(self, *args, **kwargs): - self._wait() - return self.func(*args, **kwargs) - - def _wait(self): - "ensure at least 1/max_rate seconds from last call" - elapsed = time.time() - self.last_called - must_wait = 1 / self.max_rate - elapsed - time.sleep(max(0, must_wait)) - self.last_called = time.time() - - def __get__(self, obj, type=None): - return first_invoke(self._wait, functools.partial(self.func, obj)) - - -def first_invoke(func1, func2): - """ - Return a function that when invoked will invoke func1 without - any parameters (for its side-effect) and then invoke func2 - with whatever parameters were passed, returning its result. - """ - - def wrapper(*args, **kwargs): - func1() - return func2(*args, **kwargs) - - return wrapper - - -def retry_call(func, cleanup=lambda: None, retries=0, trap=()): - """ - Given a callable func, trap the indicated exceptions - for up to 'retries' times, invoking cleanup on the - exception. On the final attempt, allow any exceptions - to propagate. - """ - attempts = itertools.count() if retries == float('inf') else range(retries) - for attempt in attempts: - try: - return func() - except trap: - cleanup() - - return func() - - -def retry(*r_args, **r_kwargs): - """ - Decorator wrapper for retry_call. Accepts arguments to retry_call - except func and then returns a decorator for the decorated function. - - Ex: - - >>> @retry(retries=3) - ... def my_func(a, b): - ... "this is my funk" - ... print(a, b) - >>> my_func.__doc__ - 'this is my funk' - """ - - def decorate(func): - @functools.wraps(func) - def wrapper(*f_args, **f_kwargs): - bound = functools.partial(func, *f_args, **f_kwargs) - return retry_call(bound, *r_args, **r_kwargs) - - return wrapper - - return decorate - - -def print_yielded(func): - """ - Convert a generator into a function that prints all yielded elements - - >>> @print_yielded - ... def x(): - ... yield 3; yield None - >>> x() - 3 - None - """ - print_all = functools.partial(map, print) - print_results = compose(more_itertools.consume, print_all, func) - return functools.wraps(func)(print_results) - - -def pass_none(func): - """ - Wrap func so it's not called if its first param is None - - >>> print_text = pass_none(print) - >>> print_text('text') - text - >>> print_text(None) - """ - - @functools.wraps(func) - def wrapper(param, *args, **kwargs): - if param is not None: - return func(param, *args, **kwargs) - - return wrapper - - -def assign_params(func, namespace): - """ - Assign parameters from namespace where func solicits. - - >>> def func(x, y=3): - ... print(x, y) - >>> assigned = assign_params(func, dict(x=2, z=4)) - >>> assigned() - 2 3 - - The usual errors are raised if a function doesn't receive - its required parameters: - - >>> assigned = assign_params(func, dict(y=3, z=4)) - >>> assigned() - Traceback (most recent call last): - TypeError: func() ...argument... - - It even works on methods: - - >>> class Handler: - ... def meth(self, arg): - ... print(arg) - >>> assign_params(Handler().meth, dict(arg='crystal', foo='clear'))() - crystal - """ - sig = inspect.signature(func) - params = sig.parameters.keys() - call_ns = {k: namespace[k] for k in params if k in namespace} - return functools.partial(func, **call_ns) - - -def save_method_args(method): - """ - Wrap a method such that when it is called, the args and kwargs are - saved on the method. - - >>> class MyClass: - ... @save_method_args - ... def method(self, a, b): - ... print(a, b) - >>> my_ob = MyClass() - >>> my_ob.method(1, 2) - 1 2 - >>> my_ob._saved_method.args - (1, 2) - >>> my_ob._saved_method.kwargs - {} - >>> my_ob.method(a=3, b='foo') - 3 foo - >>> my_ob._saved_method.args - () - >>> my_ob._saved_method.kwargs == dict(a=3, b='foo') - True - - The arguments are stored on the instance, allowing for - different instance to save different args. - - >>> your_ob = MyClass() - >>> your_ob.method({str('x'): 3}, b=[4]) - {'x': 3} [4] - >>> your_ob._saved_method.args - ({'x': 3},) - >>> my_ob._saved_method.args - () - """ - args_and_kwargs = collections.namedtuple('args_and_kwargs', 'args kwargs') - - @functools.wraps(method) - def wrapper(self, *args, **kwargs): - attr_name = '_saved_' + method.__name__ - attr = args_and_kwargs(args, kwargs) - setattr(self, attr_name, attr) - return method(self, *args, **kwargs) - - return wrapper - - -def except_(*exceptions, replace=None, use=None): - """ - Replace the indicated exceptions, if raised, with the indicated - literal replacement or evaluated expression (if present). - - >>> safe_int = except_(ValueError)(int) - >>> safe_int('five') - >>> safe_int('5') - 5 - - Specify a literal replacement with ``replace``. - - >>> safe_int_r = except_(ValueError, replace=0)(int) - >>> safe_int_r('five') - 0 - - Provide an expression to ``use`` to pass through particular parameters. - - >>> safe_int_pt = except_(ValueError, use='args[0]')(int) - >>> safe_int_pt('five') - 'five' - - """ - - def decorate(func): - @functools.wraps(func) - def wrapper(*args, **kwargs): - try: - return func(*args, **kwargs) - except exceptions: - try: - return eval(use) - except TypeError: - return replace - - return wrapper - - return decorate diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/discovery.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/discovery.py deleted file mode 100644 index 3110b72794f1e4fd75254bdb3dbf81a89918596e..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/discovery.py +++ /dev/null @@ -1,611 +0,0 @@ -"""Automatic discovery of Python modules and packages (for inclusion in the -distribution) and other config values. - -For the purposes of this module, the following nomenclature is used: - -- "src-layout": a directory representing a Python project that contains a "src" - folder. Everything under the "src" folder is meant to be included in the - distribution when packaging the project. Example:: - - . - ├── tox.ini - ├── pyproject.toml - └── src/ - └── mypkg/ - ├── __init__.py - ├── mymodule.py - └── my_data_file.txt - -- "flat-layout": a Python project that does not use "src-layout" but instead - have a directory under the project root for each package:: - - . - ├── tox.ini - ├── pyproject.toml - └── mypkg/ - ├── __init__.py - ├── mymodule.py - └── my_data_file.txt - -- "single-module": a project that contains a single Python script direct under - the project root (no directory used):: - - . - ├── tox.ini - ├── pyproject.toml - └── mymodule.py - -""" - -import itertools -import os -from fnmatch import fnmatchcase -from glob import glob -from pathlib import Path -from typing import ( - TYPE_CHECKING, - Dict, - Iterable, - Iterator, - List, - Mapping, - Optional, - Tuple, - Union -) - -import _distutils_hack.override # noqa: F401 - -from distutils import log -from distutils.util import convert_path - -_Path = Union[str, os.PathLike] -StrIter = Iterator[str] - -chain_iter = itertools.chain.from_iterable - -if TYPE_CHECKING: - from setuptools import Distribution # noqa - - -def _valid_name(path: _Path) -> bool: - # Ignore invalid names that cannot be imported directly - return os.path.basename(path).isidentifier() - - -class _Filter: - """ - Given a list of patterns, create a callable that will be true only if - the input matches at least one of the patterns. - """ - - def __init__(self, *patterns: str): - self._patterns = dict.fromkeys(patterns) - - def __call__(self, item: str) -> bool: - return any(fnmatchcase(item, pat) for pat in self._patterns) - - def __contains__(self, item: str) -> bool: - return item in self._patterns - - -class _Finder: - """Base class that exposes functionality for module/package finders""" - - ALWAYS_EXCLUDE: Tuple[str, ...] = () - DEFAULT_EXCLUDE: Tuple[str, ...] = () - - @classmethod - def find( - cls, - where: _Path = '.', - exclude: Iterable[str] = (), - include: Iterable[str] = ('*',) - ) -> List[str]: - """Return a list of all Python items (packages or modules, depending on - the finder implementation) found within directory 'where'. - - 'where' is the root directory which will be searched. - It should be supplied as a "cross-platform" (i.e. URL-style) path; - it will be converted to the appropriate local path syntax. - - 'exclude' is a sequence of names to exclude; '*' can be used - as a wildcard in the names. - When finding packages, 'foo.*' will exclude all subpackages of 'foo' - (but not 'foo' itself). - - 'include' is a sequence of names to include. - If it's specified, only the named items will be included. - If it's not specified, all found items will be included. - 'include' can contain shell style wildcard patterns just like - 'exclude'. - """ - - exclude = exclude or cls.DEFAULT_EXCLUDE - return list( - cls._find_iter( - convert_path(str(where)), - _Filter(*cls.ALWAYS_EXCLUDE, *exclude), - _Filter(*include), - ) - ) - - @classmethod - def _find_iter(cls, where: _Path, exclude: _Filter, include: _Filter) -> StrIter: - raise NotImplementedError - - -class PackageFinder(_Finder): - """ - Generate a list of all Python packages found within a directory - """ - - ALWAYS_EXCLUDE = ("ez_setup", "*__pycache__") - - @classmethod - def _find_iter(cls, where: _Path, exclude: _Filter, include: _Filter) -> StrIter: - """ - All the packages found in 'where' that pass the 'include' filter, but - not the 'exclude' filter. - """ - for root, dirs, files in os.walk(str(where), followlinks=True): - # Copy dirs to iterate over it, then empty dirs. - all_dirs = dirs[:] - dirs[:] = [] - - for dir in all_dirs: - full_path = os.path.join(root, dir) - rel_path = os.path.relpath(full_path, where) - package = rel_path.replace(os.path.sep, '.') - - # Skip directory trees that are not valid packages - if '.' in dir or not cls._looks_like_package(full_path, package): - continue - - # Should this package be included? - if include(package) and not exclude(package): - yield package - - # Early pruning if there is nothing else to be scanned - if f"{package}*" in exclude or f"{package}.*" in exclude: - continue - - # Keep searching subdirectories, as there may be more packages - # down there, even if the parent was excluded. - dirs.append(dir) - - @staticmethod - def _looks_like_package(path: _Path, _package_name: str) -> bool: - """Does a directory look like a package?""" - return os.path.isfile(os.path.join(path, '__init__.py')) - - -class PEP420PackageFinder(PackageFinder): - @staticmethod - def _looks_like_package(_path: _Path, _package_name: str) -> bool: - return True - - -class ModuleFinder(_Finder): - """Find isolated Python modules. - This function will **not** recurse subdirectories. - """ - - @classmethod - def _find_iter(cls, where: _Path, exclude: _Filter, include: _Filter) -> StrIter: - for file in glob(os.path.join(where, "*.py")): - module, _ext = os.path.splitext(os.path.basename(file)) - - if not cls._looks_like_module(module): - continue - - if include(module) and not exclude(module): - yield module - - _looks_like_module = staticmethod(_valid_name) - - -# We have to be extra careful in the case of flat layout to not include files -# and directories not meant for distribution (e.g. tool-related) - - -class FlatLayoutPackageFinder(PEP420PackageFinder): - _EXCLUDE = ( - "ci", - "bin", - "doc", - "docs", - "documentation", - "manpages", - "news", - "changelog", - "test", - "tests", - "unit_test", - "unit_tests", - "example", - "examples", - "scripts", - "tools", - "util", - "utils", - "python", - "build", - "dist", - "venv", - "env", - "requirements", - # ---- Task runners / Build tools ---- - "tasks", # invoke - "fabfile", # fabric - "site_scons", # SCons - # ---- Other tools ---- - "benchmark", - "benchmarks", - "exercise", - "exercises", - "htmlcov", # Coverage.py - # ---- Hidden directories/Private packages ---- - "[._]*", - ) - - DEFAULT_EXCLUDE = tuple(chain_iter((p, f"{p}.*") for p in _EXCLUDE)) - """Reserved package names""" - - @staticmethod - def _looks_like_package(_path: _Path, package_name: str) -> bool: - names = package_name.split('.') - # Consider PEP 561 - root_pkg_is_valid = names[0].isidentifier() or names[0].endswith("-stubs") - return root_pkg_is_valid and all(name.isidentifier() for name in names[1:]) - - -class FlatLayoutModuleFinder(ModuleFinder): - DEFAULT_EXCLUDE = ( - "setup", - "conftest", - "test", - "tests", - "example", - "examples", - "build", - # ---- Task runners ---- - "toxfile", - "noxfile", - "pavement", - "dodo", - "tasks", - "fabfile", - # ---- Other tools ---- - "[Ss][Cc]onstruct", # SCons - "conanfile", # Connan: C/C++ build tool - "manage", # Django - "benchmark", - "benchmarks", - "exercise", - "exercises", - # ---- Hidden files/Private modules ---- - "[._]*", - ) - """Reserved top-level module names""" - - -def _find_packages_within(root_pkg: str, pkg_dir: _Path) -> List[str]: - nested = PEP420PackageFinder.find(pkg_dir) - return [root_pkg] + [".".join((root_pkg, n)) for n in nested] - - -class ConfigDiscovery: - """Fill-in metadata and options that can be automatically derived - (from other metadata/options, the file system or conventions) - """ - - def __init__(self, distribution: "Distribution"): - self.dist = distribution - self._called = False - self._disabled = False - self._skip_ext_modules = False - - def _disable(self): - """Internal API to disable automatic discovery""" - self._disabled = True - - def _ignore_ext_modules(self): - """Internal API to disregard ext_modules. - - Normally auto-discovery would not be triggered if ``ext_modules`` are set - (this is done for backward compatibility with existing packages relying on - ``setup.py`` or ``setup.cfg``). However, ``setuptools`` can call this function - to ignore given ``ext_modules`` and proceed with the auto-discovery if - ``packages`` and ``py_modules`` are not given (e.g. when using pyproject.toml - metadata). - """ - self._skip_ext_modules = True - - @property - def _root_dir(self) -> _Path: - # The best is to wait until `src_root` is set in dist, before using _root_dir. - return self.dist.src_root or os.curdir - - @property - def _package_dir(self) -> Dict[str, str]: - if self.dist.package_dir is None: - return {} - return self.dist.package_dir - - def __call__(self, force=False, name=True, ignore_ext_modules=False): - """Automatically discover missing configuration fields - and modifies the given ``distribution`` object in-place. - - Note that by default this will only have an effect the first time the - ``ConfigDiscovery`` object is called. - - To repeatedly invoke automatic discovery (e.g. when the project - directory changes), please use ``force=True`` (or create a new - ``ConfigDiscovery`` instance). - """ - if force is False and (self._called or self._disabled): - # Avoid overhead of multiple calls - return - - self._analyse_package_layout(ignore_ext_modules) - if name: - self.analyse_name() # depends on ``packages`` and ``py_modules`` - - self._called = True - - def _explicitly_specified(self, ignore_ext_modules: bool) -> bool: - """``True`` if the user has specified some form of package/module listing""" - ignore_ext_modules = ignore_ext_modules or self._skip_ext_modules - ext_modules = not (self.dist.ext_modules is None or ignore_ext_modules) - return ( - self.dist.packages is not None - or self.dist.py_modules is not None - or ext_modules - or hasattr(self.dist, "configuration") and self.dist.configuration - # ^ Some projects use numpy.distutils.misc_util.Configuration - ) - - def _analyse_package_layout(self, ignore_ext_modules: bool) -> bool: - if self._explicitly_specified(ignore_ext_modules): - # For backward compatibility, just try to find modules/packages - # when nothing is given - return True - - log.debug( - "No `packages` or `py_modules` configuration, performing " - "automatic discovery." - ) - - return ( - self._analyse_explicit_layout() - or self._analyse_src_layout() - # flat-layout is the trickiest for discovery so it should be last - or self._analyse_flat_layout() - ) - - def _analyse_explicit_layout(self) -> bool: - """The user can explicitly give a package layout via ``package_dir``""" - package_dir = self._package_dir.copy() # don't modify directly - package_dir.pop("", None) # This falls under the "src-layout" umbrella - root_dir = self._root_dir - - if not package_dir: - return False - - log.debug(f"`explicit-layout` detected -- analysing {package_dir}") - pkgs = chain_iter( - _find_packages_within(pkg, os.path.join(root_dir, parent_dir)) - for pkg, parent_dir in package_dir.items() - ) - self.dist.packages = list(pkgs) - log.debug(f"discovered packages -- {self.dist.packages}") - return True - - def _analyse_src_layout(self) -> bool: - """Try to find all packages or modules under the ``src`` directory - (or anything pointed by ``package_dir[""]``). - - The "src-layout" is relatively safe for automatic discovery. - We assume that everything within is meant to be included in the - distribution. - - If ``package_dir[""]`` is not given, but the ``src`` directory exists, - this function will set ``package_dir[""] = "src"``. - """ - package_dir = self._package_dir - src_dir = os.path.join(self._root_dir, package_dir.get("", "src")) - if not os.path.isdir(src_dir): - return False - - log.debug(f"`src-layout` detected -- analysing {src_dir}") - package_dir.setdefault("", os.path.basename(src_dir)) - self.dist.package_dir = package_dir # persist eventual modifications - self.dist.packages = PEP420PackageFinder.find(src_dir) - self.dist.py_modules = ModuleFinder.find(src_dir) - log.debug(f"discovered packages -- {self.dist.packages}") - log.debug(f"discovered py_modules -- {self.dist.py_modules}") - return True - - def _analyse_flat_layout(self) -> bool: - """Try to find all packages and modules under the project root. - - Since the ``flat-layout`` is more dangerous in terms of accidentally including - extra files/directories, this function is more conservative and will raise an - error if multiple packages or modules are found. - - This assumes that multi-package dists are uncommon and refuse to support that - use case in order to be able to prevent unintended errors. - """ - log.debug(f"`flat-layout` detected -- analysing {self._root_dir}") - return self._analyse_flat_packages() or self._analyse_flat_modules() - - def _analyse_flat_packages(self) -> bool: - self.dist.packages = FlatLayoutPackageFinder.find(self._root_dir) - top_level = remove_nested_packages(remove_stubs(self.dist.packages)) - log.debug(f"discovered packages -- {self.dist.packages}") - self._ensure_no_accidental_inclusion(top_level, "packages") - return bool(top_level) - - def _analyse_flat_modules(self) -> bool: - self.dist.py_modules = FlatLayoutModuleFinder.find(self._root_dir) - log.debug(f"discovered py_modules -- {self.dist.py_modules}") - self._ensure_no_accidental_inclusion(self.dist.py_modules, "modules") - return bool(self.dist.py_modules) - - def _ensure_no_accidental_inclusion(self, detected: List[str], kind: str): - if len(detected) > 1: - from inspect import cleandoc - - from setuptools.errors import PackageDiscoveryError - - msg = f"""Multiple top-level {kind} discovered in a flat-layout: {detected}. - - To avoid accidental inclusion of unwanted files or directories, - setuptools will not proceed with this build. - - If you are trying to create a single distribution with multiple {kind} - on purpose, you should not rely on automatic discovery. - Instead, consider the following options: - - 1. set up custom discovery (`find` directive with `include` or `exclude`) - 2. use a `src-layout` - 3. explicitly set `py_modules` or `packages` with a list of names - - To find more information, look for "package discovery" on setuptools docs. - """ - raise PackageDiscoveryError(cleandoc(msg)) - - def analyse_name(self): - """The packages/modules are the essential contribution of the author. - Therefore the name of the distribution can be derived from them. - """ - if self.dist.metadata.name or self.dist.name: - # get_name() is not reliable (can return "UNKNOWN") - return None - - log.debug("No `name` configuration, performing automatic discovery") - - name = ( - self._find_name_single_package_or_module() - or self._find_name_from_packages() - ) - if name: - self.dist.metadata.name = name - - def _find_name_single_package_or_module(self) -> Optional[str]: - """Exactly one module or package""" - for field in ('packages', 'py_modules'): - items = getattr(self.dist, field, None) or [] - if items and len(items) == 1: - log.debug(f"Single module/package detected, name: {items[0]}") - return items[0] - - return None - - def _find_name_from_packages(self) -> Optional[str]: - """Try to find the root package that is not a PEP 420 namespace""" - if not self.dist.packages: - return None - - packages = remove_stubs(sorted(self.dist.packages, key=len)) - package_dir = self.dist.package_dir or {} - - parent_pkg = find_parent_package(packages, package_dir, self._root_dir) - if parent_pkg: - log.debug(f"Common parent package detected, name: {parent_pkg}") - return parent_pkg - - log.warn("No parent package detected, impossible to derive `name`") - return None - - -def remove_nested_packages(packages: List[str]) -> List[str]: - """Remove nested packages from a list of packages. - - >>> remove_nested_packages(["a", "a.b1", "a.b2", "a.b1.c1"]) - ['a'] - >>> remove_nested_packages(["a", "b", "c.d", "c.d.e.f", "g.h", "a.a1"]) - ['a', 'b', 'c.d', 'g.h'] - """ - pkgs = sorted(packages, key=len) - top_level = pkgs[:] - size = len(pkgs) - for i, name in enumerate(reversed(pkgs)): - if any(name.startswith(f"{other}.") for other in top_level): - top_level.pop(size - i - 1) - - return top_level - - -def remove_stubs(packages: List[str]) -> List[str]: - """Remove type stubs (:pep:`561`) from a list of packages. - - >>> remove_stubs(["a", "a.b", "a-stubs", "a-stubs.b.c", "b", "c-stubs"]) - ['a', 'a.b', 'b'] - """ - return [pkg for pkg in packages if not pkg.split(".")[0].endswith("-stubs")] - - -def find_parent_package( - packages: List[str], package_dir: Mapping[str, str], root_dir: _Path -) -> Optional[str]: - """Find the parent package that is not a namespace.""" - packages = sorted(packages, key=len) - common_ancestors = [] - for i, name in enumerate(packages): - if not all(n.startswith(f"{name}.") for n in packages[i+1:]): - # Since packages are sorted by length, this condition is able - # to find a list of all common ancestors. - # When there is divergence (e.g. multiple root packages) - # the list will be empty - break - common_ancestors.append(name) - - for name in common_ancestors: - pkg_path = find_package_path(name, package_dir, root_dir) - init = os.path.join(pkg_path, "__init__.py") - if os.path.isfile(init): - return name - - return None - - -def find_package_path( - name: str, package_dir: Mapping[str, str], root_dir: _Path -) -> str: - """Given a package name, return the path where it should be found on - disk, considering the ``package_dir`` option. - - >>> path = find_package_path("my.pkg", {"": "root/is/nested"}, ".") - >>> path.replace(os.sep, "/") - './root/is/nested/my/pkg' - - >>> path = find_package_path("my.pkg", {"my": "root/is/nested"}, ".") - >>> path.replace(os.sep, "/") - './root/is/nested/pkg' - - >>> path = find_package_path("my.pkg", {"my.pkg": "root/is/nested"}, ".") - >>> path.replace(os.sep, "/") - './root/is/nested' - - >>> path = find_package_path("other.pkg", {"my.pkg": "root/is/nested"}, ".") - >>> path.replace(os.sep, "/") - './other/pkg' - """ - parts = name.split(".") - for i in range(len(parts), 0, -1): - # Look backwards, the most specific package_dir first - partial_name = ".".join(parts[:i]) - if partial_name in package_dir: - parent = package_dir[partial_name] - return os.path.join(root_dir, parent, *parts[i:]) - - parent = package_dir.get("") or "" - return os.path.join(root_dir, *parent.split("/"), *parts) - - -def construct_package_dir(packages: List[str], package_path: _Path) -> Dict[str, str]: - parent_pkgs = remove_nested_packages(packages) - prefix = Path(package_path).parts - return {pkg: "/".join([*prefix, *pkg.split(".")]) for pkg in parent_pkgs} diff --git a/spaces/prithvihehe/TheBotFather/README.md b/spaces/prithvihehe/TheBotFather/README.md deleted file mode 100644 index 6f2e259bae49bf327d623992c1f6a951578a8117..0000000000000000000000000000000000000000 --- a/spaces/prithvihehe/TheBotFather/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: TheBotFather -emoji: 👁 -colorFrom: red -colorTo: pink -sdk: gradio -sdk_version: 3.24.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/node/dev/files/placeholder-6e1f6a96.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/node/dev/files/placeholder-6e1f6a96.js deleted file mode 100644 index 119b1595759c1509f64eb112e45309f4ec3dc61c..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/node/dev/files/placeholder-6e1f6a96.js +++ /dev/null @@ -1,3 +0,0 @@ -var placeholder = {}; - 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\ No newline at end of file diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Krishnayan By Kajal Oza In Gujarati Free Download NEW!.md b/spaces/quidiaMuxgu/Expedit-SAM/Krishnayan By Kajal Oza In Gujarati Free Download NEW!.md deleted file mode 100644 index 65134bb50e8fceb79831b5801ddf39a34796f344..0000000000000000000000000000000000000000 --- a/spaces/quidiaMuxgu/Expedit-SAM/Krishnayan By Kajal Oza In Gujarati Free Download NEW!.md +++ /dev/null @@ -1,86 +0,0 @@ -
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\ No newline at end of file diff --git a/spaces/rajeshradhakrishnan/malayalam-tamil/README.md b/spaces/rajeshradhakrishnan/malayalam-tamil/README.md deleted file mode 100644 index ffb276faf70a2b7871d6a2365de4b151161daaa1..0000000000000000000000000000000000000000 --- a/spaces/rajeshradhakrishnan/malayalam-tamil/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Malayalam Tamil -emoji: 🐢 -colorFrom: pink -colorTo: blue -sdk: gradio -sdk_version: 3.0.24 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/rajistics/interpet_transformers/app.py b/spaces/rajistics/interpet_transformers/app.py deleted file mode 100644 index 7cba489119ac03cd9b6505605654ae2b9c96d6a8..0000000000000000000000000000000000000000 --- a/spaces/rajistics/interpet_transformers/app.py +++ /dev/null @@ -1,101 +0,0 @@ -import os -import pandas as pd -import streamlit as st -from PIL import Image -from streamlit import components -from transformers import AutoModelForSequenceClassification, AutoTokenizer -from transformers_interpret import SequenceClassificationExplainer - -@st.cache(allow_output_mutation=True, suppress_st_warning=True, max_entries=1) - -def load_model(model_name): - return ( - AutoModelForSequenceClassification.from_pretrained(model_name), - AutoTokenizer.from_pretrained(model_name), - ) - - -st.title("Transformers Interpet Demo App") - -image = Image.open("./images/tight@1920x_transparent.png") -st.sidebar.image(image, use_column_width=True) -st.sidebar.markdown( -"Check out the package on [Github](https://github.com/cdpierse/transformers-interpret)" -) - -# uncomment the options below to test out the app with a variety of classification models. -models = { - "mrm8488/bert-mini-finetuned-age_news-classification": "BERT-Mini finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.", - "nateraw/bert-base-uncased-ag-news": "BERT finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.", - "distilbert-base-uncased-finetuned-sst-2-english": "DistilBERT model finetuned on SST-2 sentiment analysis task. Predicts positive/negative sentiment.", - "ProsusAI/finbert": "BERT model finetuned to predict sentiment of financial text. Finetuned on Financial PhraseBank data. Predicts positive/negative/neutral.", - "sampathkethineedi/industry-classification": "DistilBERT Model to classify a business description into one of 62 industry tags.", - "MoritzLaurer/policy-distilbert-7d": "DistilBERT model finetuned to classify text into one of seven political categories.", - # # "MoritzLaurer/covid-policy-roberta-21": "(Under active development ) RoBERTA model finetuned to identify COVID policy measure classes ", - "mrm8488/bert-tiny-finetuned-sms-spam-detection": "Tiny bert model finetuned for spam detection. 0 == not spam, 1 == spam", -} -model_name = st.sidebar.selectbox( - "Choose a classification model", list(models.keys()) -) -model, tokenizer = load_model(model_name) - -model.eval() - -cls_explainer = SequenceClassificationExplainer(model=model, tokenizer=tokenizer) - -if cls_explainer.accepts_position_ids: - emb_type_name = st.sidebar.selectbox( - "Choose embedding type for attribution.", ["word", "position"] - ) - if emb_type_name == "word": - emb_type_num = 0 - if emb_type_name == "position": - emb_type_num = 1 -else: - emb_type_num = 0 - -explanation_classes = ["predicted"] + list(model.config.label2id.keys()) -explanation_class_choice = st.sidebar.selectbox( - "Explanation class: The class you would like to explain output with respect to.", - explanation_classes, -) -my_expander = st.expander( - "Click here for a description of models and their tasks" -) -with my_expander: - st.json(models) - -# st.info("Max char limit of 350 (memory management)") -text = st.text_area( - "Enter text to be interpreted", - "I like you, I love you", - height=400, - max_chars=850, -) - -if st.button("Interpret Text"): - st.text("Output") - with st.spinner("Interpreting your text (This may take some time)"): - print ("Interpreting text") - if explanation_class_choice != "predicted": - word_attributions = cls_explainer( - text, - class_name=explanation_class_choice, - embedding_type=emb_type_num, - internal_batch_size=2, - ) - else: - word_attributions = cls_explainer( - text, embedding_type=emb_type_num, internal_batch_size=2 - ) - - if word_attributions: - print ("Word Attributions") - word_attributions_expander = st.expander( - "Click here for raw word attributions" - ) - with word_attributions_expander: - st.json(word_attributions) - components.v1.html( - cls_explainer.visualize()._repr_html_(), scrolling=True, height=350 - ) \ No newline at end of file diff --git a/spaces/randstad/ResumeSummarizer/style.css b/spaces/randstad/ResumeSummarizer/style.css deleted file mode 100644 index 7fb3962fbf1c3d0bba1701ff53c2fbbb8f3475e7..0000000000000000000000000000000000000000 --- a/spaces/randstad/ResumeSummarizer/style.css +++ /dev/null @@ -1,26 +0,0 @@ -#col-container { - max-width: 600px; - margin-left: auto; - margin-right: auto; -} - -#row-flex { - display: flex; - align-items: center; - justify-content: center; -} -.leftimage .rightimage{ - float:left; - filter: drop-shadow(20px 20px 10px white); -} -.leftimage{ - padding-top:0px; - margin-left:260px; -} -.rightimage{ - padding-top:35px; - margin-right:320px; -} -.heightfit{ - height:85px; -} \ No newline at end of file diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Alldata Repair Download And Crack UPDATED.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Alldata Repair Download And Crack UPDATED.md deleted file mode 100644 index 7c7cb6090e83ba51109e9118734f5869ca336dc7..0000000000000000000000000000000000000000 --- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Alldata Repair Download And Crack UPDATED.md +++ /dev/null @@ -1,8 +0,0 @@ -
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- diff --git a/spaces/rewoo/ReWOO-Demo/utils/DataLoader.py b/spaces/rewoo/ReWOO-Demo/utils/DataLoader.py deleted file mode 100644 index 084a4f7ea9511c7700787893e9a93317b064ad57..0000000000000000000000000000000000000000 --- a/spaces/rewoo/ReWOO-Demo/utils/DataLoader.py +++ /dev/null @@ -1,93 +0,0 @@ -import datasets -import pandas as pd - - -class DataLoader: - def __init__(self, data="hotpot_qa", seed=2023): - self.data = data - self.seed = seed - - def load(self, sample_size=None, type="train"): - if self.data == "hotpot_qa": - return self.load_hotpot_qa(sample_size=sample_size, type=type) - elif self.data == "fever": - return self.load_fever(sample_size=sample_size, type=type) - elif self.data == "trivia_qa": - return self.load_trivia_qa(sample_size=sample_size, type=type) - elif self.data == "gsm8k": - return self.load_gsm8k(sample_size=sample_size, type=type) - elif self.data == "physics_question": - return self.load_physics_question(sample_size=sample_size) - elif self.data == "disfl_qa": - return self.load_disfl_qa(sample_size=sample_size) - elif self.data == "sports_understanding": - return self.load_sports_understanding(sample_size=sample_size) - elif self.data == "strategy_qa": - return self.load_strategy_qa(sample_size=sample_size) - elif self.data == "sotu_qa": - return self.load_sotu_qa(sample_size=sample_size) - else: - raise ValueError("Data not supported.") - - def load_hotpot_qa(self, cache_dir="data/hotpot_qa", sample_size=100, type="test"): - assert type in ["train", "validation", "test"] - data = datasets.load_dataset('hotpot_qa', 'fullwiki', cache_dir=cache_dir) - df = data[type].to_pandas() - sampled_df = df.sample(sample_size, random_state=self.seed)[["question", "answer"]].reset_index(drop=True) - return sampled_df - - def load_fever(self, cache_dir="data/fever", sample_size=100, type="test"): - assert type in ["train", "validation", "test"] - data = datasets.load_dataset('copenlu/fever_gold_evidence', cache_dir=cache_dir) - df = data[type].to_pandas() - sampled_df = df.sample(sample_size, random_state=self.seed)[["claim", "label"]].reset_index(drop=True) - return sampled_df - - def load_trivia_qa(self, cache_dir="data/trivia_qa", sample_size=100, type="test"): - assert type in ["train", "validation", "test"] - data = datasets.load_dataset('trivia_qa', 'rc.nocontext', cache_dir=cache_dir) - df = data[type].to_pandas() - sampled_df = df.sample(sample_size, random_state=self.seed)[["question", "answer"]].reset_index(drop=True) - return sampled_df - - def load_gsm8k(self, cache_dir="data/gsm8k", sample_size=100, type="test"): - assert type in ["train", "validation", "test"] - data = datasets.load_dataset('gsm8k', name="main", cache_dir=cache_dir) - df = data[type].to_pandas() - sampled_df = df.sample(sample_size, random_state=self.seed)[["question", "answer"]].reset_index(drop=True) - return sampled_df - - def load_physics_question(self, cache_dir="data/bigbench/physics_question.csv", sample_size=None): - df = pd.read_csv(cache_dir) - if sample_size is not None: - sampled_df = df.sample(sample_size, random_state=self.seed)[["input", "target"]].reset_index(drop=True) - return sampled_df - return df - - def load_sports_understanding(self, cache_dir="data/bigbench/sports_understanding.csv", sample_size=None): - df = pd.read_csv(cache_dir) - if sample_size is not None: - sampled_df = df.sample(sample_size, random_state=self.seed)[["input", "target"]].reset_index(drop=True) - return sampled_df - return df - - def load_disfl_qa(self, cache_dir="data/bigbench/disfl_qa.csv", sample_size=None): - df = pd.read_csv(cache_dir) - if sample_size is not None: - sampled_df = df.sample(sample_size, random_state=self.seed)[["input", "target"]].reset_index(drop=True) - return sampled_df - return df - - def load_strategy_qa(self, cache_dir="data/bigbench/strategy_qa.csv", sample_size=None): - df = pd.read_csv(cache_dir) - if sample_size is not None: - sampled_df = df.sample(sample_size, random_state=self.seed)[["input", "target"]].reset_index(drop=True) - return sampled_df - return df - - def load_sotu_qa(self, cache_dir="data/SOTU/SOTU_QA.csv", sample_size=None): - df = pd.read_csv(cache_dir) - if sample_size is not None: - sampled_df = df.sample(sample_size, random_state=self.seed)[["question", "answer"]].reset_index(drop=True) - return sampled_df - return df \ No newline at end of file diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/necks/yolox_pafpn.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/necks/yolox_pafpn.py deleted file mode 100644 index b0f6f7068645b6ce722556fa29c7e3d349934e74..0000000000000000000000000000000000000000 --- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/necks/yolox_pafpn.py +++ /dev/null @@ -1,156 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import math - -import torch -import torch.nn as nn -from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule -from mmcv.runner import BaseModule - -from ..builder import NECKS -from ..utils import CSPLayer - - -@NECKS.register_module() -class YOLOXPAFPN(BaseModule): - """Path Aggregation Network used in YOLOX. - - Args: - in_channels (List[int]): Number of input channels per scale. - out_channels (int): Number of output channels (used at each scale) - num_csp_blocks (int): Number of bottlenecks in CSPLayer. Default: 3 - use_depthwise (bool): Whether to depthwise separable convolution in - blocks. Default: False - upsample_cfg (dict): Config dict for interpolate layer. - Default: `dict(scale_factor=2, mode='nearest')` - conv_cfg (dict, optional): Config dict for convolution layer. - Default: None, which means using conv2d. - norm_cfg (dict): Config dict for normalization layer. - Default: dict(type='BN') - act_cfg (dict): Config dict for activation layer. - Default: dict(type='Swish') - init_cfg (dict or list[dict], optional): Initialization config dict. - Default: None. - """ - - def __init__(self, - in_channels, - out_channels, - num_csp_blocks=3, - use_depthwise=False, - upsample_cfg=dict(scale_factor=2, mode='nearest'), - conv_cfg=None, - norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), - act_cfg=dict(type='Swish'), - init_cfg=dict( - type='Kaiming', - layer='Conv2d', - a=math.sqrt(5), - distribution='uniform', - mode='fan_in', - nonlinearity='leaky_relu')): - super(YOLOXPAFPN, self).__init__(init_cfg) - self.in_channels = in_channels - self.out_channels = out_channels - - conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule - - # build top-down blocks - self.upsample = nn.Upsample(**upsample_cfg) - self.reduce_layers = nn.ModuleList() - self.top_down_blocks = nn.ModuleList() - for idx in range(len(in_channels) - 1, 0, -1): - self.reduce_layers.append( - ConvModule( - in_channels[idx], - in_channels[idx - 1], - 1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg)) - self.top_down_blocks.append( - CSPLayer( - in_channels[idx - 1] * 2, - in_channels[idx - 1], - num_blocks=num_csp_blocks, - add_identity=False, - use_depthwise=use_depthwise, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg)) - - # build bottom-up blocks - self.downsamples = nn.ModuleList() - self.bottom_up_blocks = nn.ModuleList() - for idx in range(len(in_channels) - 1): - self.downsamples.append( - conv( - in_channels[idx], - in_channels[idx], - 3, - stride=2, - padding=1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg)) - self.bottom_up_blocks.append( - CSPLayer( - in_channels[idx] * 2, - in_channels[idx + 1], - num_blocks=num_csp_blocks, - add_identity=False, - use_depthwise=use_depthwise, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg)) - - self.out_convs = nn.ModuleList() - for i in range(len(in_channels)): - self.out_convs.append( - ConvModule( - in_channels[i], - out_channels, - 1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg)) - - def forward(self, inputs): - """ - Args: - inputs (tuple[Tensor]): input features. - - Returns: - tuple[Tensor]: YOLOXPAFPN features. - """ - assert len(inputs) == len(self.in_channels) - - # top-down path - inner_outs = [inputs[-1]] - for idx in range(len(self.in_channels) - 1, 0, -1): - feat_heigh = inner_outs[0] - feat_low = inputs[idx - 1] - feat_heigh = self.reduce_layers[len(self.in_channels) - 1 - idx]( - feat_heigh) - inner_outs[0] = feat_heigh - - upsample_feat = self.upsample(feat_heigh) - - inner_out = self.top_down_blocks[len(self.in_channels) - 1 - idx]( - torch.cat([upsample_feat, feat_low], 1)) - inner_outs.insert(0, inner_out) - - # bottom-up path - outs = [inner_outs[0]] - for idx in range(len(self.in_channels) - 1): - feat_low = outs[-1] - feat_height = inner_outs[idx + 1] - downsample_feat = self.downsamples[idx](feat_low) - out = self.bottom_up_blocks[idx]( - torch.cat([downsample_feat, feat_height], 1)) - outs.append(out) - - # out convs - for idx, conv in enumerate(self.out_convs): - outs[idx] = conv(outs[idx]) - - return tuple(outs) diff --git a/spaces/romero61/hendata/Dockerfile b/spaces/romero61/hendata/Dockerfile deleted file mode 100644 index a17bad8f9b9f7649eb1c65ed3ee695af80a6d84f..0000000000000000000000000000000000000000 --- a/spaces/romero61/hendata/Dockerfile +++ /dev/null @@ -1,24 +0,0 @@ -FROM jupyter/base-notebook:latest - -RUN mamba install -c conda-forge leafmap geopandas localtileserver -y && \ - fix-permissions "${CONDA_DIR}" && \ - fix-permissions "/home/${NB_USER}" - -COPY requirements.txt . -RUN pip install -r requirements.txt - -RUN mkdir ./pages -COPY /pages ./pages - -RUN mkdir ./public -COPY /pages ./public - -ENV PROJ_LIB='/opt/conda/share/proj' - -USER root -RUN chown -R ${NB_UID} ${HOME} -USER ${NB_USER} - -EXPOSE 8765 - -CMD ["solara", "run", "./pages", "--host=0.0.0.0"] diff --git a/spaces/rorallitri/biomedical-language-models/logs/Arturia Minimoog V VSTi RTAS V251 Incl Keygen AiR Deepstatus UPD.md b/spaces/rorallitri/biomedical-language-models/logs/Arturia Minimoog V VSTi RTAS V251 Incl Keygen AiR Deepstatus UPD.md deleted file mode 100644 index 1c16561011cdcaa0fc1aa00279a6da7933eb6456..0000000000000000000000000000000000000000 --- a/spaces/rorallitri/biomedical-language-models/logs/Arturia Minimoog V VSTi RTAS V251 Incl Keygen AiR Deepstatus UPD.md +++ /dev/null @@ -1,8 +0,0 @@ - -
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First, let's credit "Boomerang" for what it is: an all-Black cast movie not centered on urban crime, drugs, racism, hood etc.
This is not the first of its genre: Spike Lee built his reputation by portraying African-Americans in narrative realms outside the usual dictated tropes, but director Reginald Hudlin and writer Barry Blaustein went even further by exploring the world of glam, cosmetics agency, marketing, female power and reversed the roles with white people playing comic reliefs and women dominating men; it's "The Cosby Show" meeting "Working Girl". And from that starting point, it creates a whole new outlet for romantic comedies whose tropes were codified by Meg Ryan or Julia Roberts movies.
And in this seemingly implausible world, Eddie Murphy plays Marcus, the smooth-talking womanizer who can get any girl. A lesser movie would've made him arrogant and detestable but Marcus plays in a whole other league, taking women as seriously as any part of his professional endeavor. His character-establishing moment occurs when he improvises a lost-dog scenario by buying a leash on the spot, Lela Ronchon falls in his trap. The trick could work by earning him a number but it works so well she gives hers. One ellipse takes us to him decorating his house with the cautiousness of a caterer and ignoring the insult of his neighbor (Tisha Campbell) who keeps warning new girlfriends about the predator.
Yes, Marcus is always on the prowl but his perfectionism is rather impressive: he could have the girl in his bed but he plays it so smooth again he ends up in hers. Then a quick stare on her feet while she's sleeping reveals ugly soles calling for immediate dumping. This is neither a gag, nor a hint at a foot fetish but a revealer of the unconscious overlapping of his trade with his relationships. Indeed, advertisement is all about attentiveness to image or packaging, and so the man regards his preys as 'objects'. But take it for someone who worked in that racket, this is a woman's world, as image-awareness is largely considered a female trait, so for all his masculine act, Marcus got entrapped in the cult-of-image. It's an interesting comment on how image is a double-edged sword for both sexes, while more of a burden for women.
The 'feet' aftermath is discussed with his two buddies Gerald (David Alan Grier) and Tyler (Martin Lawrence). They're outsiders who don't understand his reaction but then again he's the Alpha-male while Gerald pushes the platonic button so hard it always propels him into friend-zone and Tyler didn't have sex in the 90s (the film is from 1992). They're too admirative of Marcus to see the problem: being as much a sexual object as the women he objectifies. Later, he spends a night with the president of the agency Lady Eloise (Eartha Kitt) counting on a casting couch promotion. Kitt, 65, gives herself totally to the role and clap to turn off the lights before the rodeo starts, Marcus asks if it can be darker, the line isn't serious but reveals how seriously willing he is.
But the promotion is given to Jacqueline, played by the breathtakingly sexy Robin Givens. What Marcus realizes, besides having been used as a sex toy, is that she's his boss, and she's out of the fooling-around zone. It's interesting to see that man whose reputation as a sex-collector makes mail guys bet on his next performances, becomes the subject of his own shenanigans. But Marcus smartly dodges the woman/man issue by inviting Jacqueline for dinner (after all, male colleagues would do that), the result is literally the sprinkler sprinkled. After all his efforts to cook a sumptuous dinner, all she wants is watching the Knicks. The scene is intercut with a cute dinner between Gerald and the new art-director Barbara (Halle Berry) and their interactions and clumsy, cute, genuine but somewhat authentic. The parallel between the two scenes highlights the position of Marcus, awkward only by the standards of usual rom-coms.
That's how inventive and innovative the film is, showing a confident men getting a taste of his own medicine, which is the antidote to his toxic relationship with women. He's hit by the boomerang that puts his idea in the right place. And there's something about Murphy's performance: he doesn't overplay his laugh, when he's upset, he asks for the kind of respect women usually demand. The film is so effective in its comment on intersex relationships that the scene with the racist store owner feels too forced and could have been cut without hurting the rest.
But there's more in "Boomerang". This is an adult movie that doesn't hide behind its comedic premise, there are soft-core elements making the relationships feel real. In your average rom-com, it's a passionate kiss and before you know it, the L-shaped bed, in "Boomerang" even the sex position or a climax become a grammar that verbalizes the statuses. And sexiness is also the source of hilarity, besides Eartha Kitt, there's Geoffrey Holder as Nelson the goofy video-maker, and there's Grace Jones as Strangé the French mascot for a new perfume, her scenes are so outrageous and over-the-top that I burst out laughing, from the 'stink so good' clip to the infamous restaurant scene, she takes movies to places you wouldn't suspect.
Halle Berry brings such a sweet and lovable presence that it's a foregone conclusion she and Murphy will end together, though it's a little unfair for poor Gerald, but otherwise "Boomerang" hits everything right, it uses Murphy's usual persona for a story arc that ends up displaying more respect toward women and accepting that they too can have sexual appetites, in many aspects, the film is avant-guardist, bold and straightforward, and should have a higher reputation, because of its uniqueness.
Indeed, I don't recall a movie like "Boomerang" before and after it, that's the mark of great films.
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Womanizing executive Marcus Graham (Eddie Murphy) meets his match when his workplace is bought out by Lady Eloise (Eartha Kitt). In reality, Jacqueline (Robin Givens) is the true queen on the throne. He finds the table turned on him as the power dynamic is switched. Kind-hearted Angela (Halle Berry) is her eager assistant. The outrageous Strangé (Grace Jones) is the new brand ambassador. Gerard (David Alan Grier) and Tyler (Martin Lawrence) are his best friends. Bony T (Chris Rock) is the mailroom boy.
The premise is interesting although the all black main cast is probably the most compelling. The boomerang doesn't get going until the midpoint moment. The power dynamic switch needs to get going a lot sooner. When they first meet at the lobby, she needs to pursue him right away. Instead she resisted him like most women thereby setting off his hunting instincts. For it to be a true switcharoo, she needs to be the hunter and he be the hunted. Once the switch happens, it is a fun turnaround. I do like the womanizing Eddie Murphy getting his comeuppance. The heart of this is actually pretty good. Khanya may be the funny one in the movie.
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- diff --git a/spaces/sarahyoung/taltech/test3_translator.py b/spaces/sarahyoung/taltech/test3_translator.py deleted file mode 100644 index 2473fa27ee51fcfd10ac3adf0d7243b65946a251..0000000000000000000000000000000000000000 --- a/spaces/sarahyoung/taltech/test3_translator.py +++ /dev/null @@ -1,35 +0,0 @@ -"translator working, multiple inputs" - -import gradio as gr -from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline -import torch - -# this model was loaded from https://hf.co/models -model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") -tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M") -device = 0 if torch.cuda.is_available() else -1 -LANGS = ["ace_Arab", "eng_Latn", "fra_Latn", "spa_Latn"] - -def translate(text, src_lang, tgt_lang): - """ - Translate the text from source lang to target lang - """ - translation_pipeline = pipeline("translation", model=model, tokenizer=tokenizer, src_lang=src_lang, tgt_lang=tgt_lang, max_length=400, device=device) - result = translation_pipeline(text) - return result[0]['translation_text'] - -demo = gr.Interface( - fn=translate, - inputs=[ - gr.components.Textbox(label="Text"), - gr.components.Dropdown(label="Source Language", choices=LANGS), - gr.components.Dropdown(label="Target Language", choices=LANGS), - ], - outputs=["text"], - examples=[["I have edited this!", "eng_Latn", "spa_Latn"]], - cache_examples=False, - title="I have edited this HI ", - description="I have adapted this sy This demo is a simplified version of the original [NLLB-Translator](https://huggingface.co/spaces/Narrativaai/NLLB-Translator) space" -) - -# demo.launch() \ No newline at end of file diff --git a/spaces/sbroy10/02-NLP-Seq2SeqQAGenerator/README.md b/spaces/sbroy10/02-NLP-Seq2SeqQAGenerator/README.md deleted file mode 100644 index b09fac886316642b67246213165c5a71ecbc40fa..0000000000000000000000000000000000000000 --- a/spaces/sbroy10/02-NLP-Seq2SeqQAGenerator/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: 02 NLP Seq2SeqQAGenerator -emoji: 🌖 -colorFrom: purple -colorTo: red -sdk: gradio -sdk_version: 3.1.7 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/scedlatioru/img-to-music/example/Golden Eye 4.50.rar Download REPACK.md b/spaces/scedlatioru/img-to-music/example/Golden Eye 4.50.rar Download REPACK.md deleted file mode 100644 index ce3b67d5af4a572c6f4df4cec1a6ea9064f7bb86..0000000000000000000000000000000000000000 --- a/spaces/scedlatioru/img-to-music/example/Golden Eye 4.50.rar Download REPACK.md +++ /dev/null @@ -1,98 +0,0 @@ - -
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\ No newline at end of file diff --git a/spaces/scedlatioru/img-to-music/example/Ping Manager Crack Serial Download [TOP].md b/spaces/scedlatioru/img-to-music/example/Ping Manager Crack Serial Download [TOP].md deleted file mode 100644 index 803542a65c48cd86c9156023faec6c1eb123fdd8..0000000000000000000000000000000000000000 --- a/spaces/scedlatioru/img-to-music/example/Ping Manager Crack Serial Download [TOP].md +++ /dev/null @@ -1,6 +0,0 @@ -
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- diff --git a/spaces/sdeeas/ChuanhuChatGPT/assets/custom.js b/spaces/sdeeas/ChuanhuChatGPT/assets/custom.js deleted file mode 100644 index ae5a76b5e791be8b107126889519e37d89fc80f0..0000000000000000000000000000000000000000 --- a/spaces/sdeeas/ChuanhuChatGPT/assets/custom.js +++ /dev/null @@ -1,607 +0,0 @@ - -// custom javascript here - -const MAX_HISTORY_LENGTH = 32; - -var key_down_history = []; -var currentIndex = -1; -var user_input_ta; - -var gradioContainer = null; -var user_input_ta = null; -var user_input_tb = null; -var userInfoDiv = null; -var appTitleDiv = null; -var chatbot = null; -var chatbotWrap = null; -var apSwitch = null; -var empty_botton = null; -var messageBotDivs = null; -// var renderLatex = null; -var loginUserForm = null; -var logginUser = null; - -var userLogged = false; -var usernameGotten = false; -var shouldRenderLatex = false; -var historyLoaded = false; - -var ga = document.getElementsByTagName("gradio-app"); -var targetNode = ga[0]; -var isInIframe = (window.self !== window.top); -var language = navigator.language.slice(0,2); - -var forView_i18n = { - 'zh': "仅供查看", - 'en': "For viewing only", - 'ja': "閲覧専用", - 'fr': "Pour consultation seulement", - 'es': "Solo para visualización", -}; - -// gradio 页面加载好了么??? 我能动你的元素了么?? -function gradioLoaded(mutations) { - for (var i = 0; i < mutations.length; i++) { - if (mutations[i].addedNodes.length) { - loginUserForm = document.querySelector(".gradio-container > .main > .wrap > .panel > .form") - gradioContainer = document.querySelector(".gradio-container"); - user_input_tb = document.getElementById('user_input_tb'); - userInfoDiv = document.getElementById("user_info"); - appTitleDiv = document.getElementById("app_title"); - chatbot = document.querySelector('#chuanhu_chatbot'); - chatbotWrap = document.querySelector('#chuanhu_chatbot > .wrap'); - apSwitch = document.querySelector('.apSwitch input[type="checkbox"]'); - // renderLatex = document.querySelector("#render_latex_checkbox > label > input"); - empty_botton = document.getElementById("empty_btn") - - if (loginUserForm) { - localStorage.setItem("userLogged", true); - userLogged = true; - } - - if (gradioContainer && apSwitch) { // gradioCainter 加载出来了没? - adjustDarkMode(); - } - if (user_input_tb) { // user_input_tb 加载出来了没? - selectHistory(); - } - if (userInfoDiv && appTitleDiv) { // userInfoDiv 和 appTitleDiv 加载出来了没? - if (!usernameGotten) { - getUserInfo(); - } - setTimeout(showOrHideUserInfo(), 2000); - } - if (chatbot) { // chatbot 加载出来了没? - setChatbotHeight(); - } - if (chatbotWrap) { - if (!historyLoaded) { - loadHistoryHtml(); - } - setChatbotScroll(); - } - // if (renderLatex) { // renderLatex 加载出来了没? - // shouldRenderLatex = renderLatex.checked; - // updateMathJax(); - // } - if (empty_botton) { - emptyHistory(); - } - } - } -} - -function webLocale() { - console.log("webLocale", language); - if (forView_i18n.hasOwnProperty(language)) { - var forView = forView_i18n[language]; - var forViewStyle = document.createElement('style'); - forViewStyle.innerHTML = '.wrap>.history-message>:last-child::after { content: "' + forView + '"!important; }'; - document.head.appendChild(forViewStyle); - // console.log("added forViewStyle", forView); - } -} - -function selectHistory() { - user_input_ta = user_input_tb.querySelector("textarea"); - if (user_input_ta) { - observer.disconnect(); // 停止监听 - // 在 textarea 上监听 keydown 事件 - user_input_ta.addEventListener("keydown", function (event) { - var value = user_input_ta.value.trim(); - // 判断按下的是否为方向键 - if (event.code === 'ArrowUp' || event.code === 'ArrowDown') { - // 如果按下的是方向键，且输入框中有内容，且历史记录中没有该内容，则不执行操作 - if (value && key_down_history.indexOf(value) === -1) - return; - // 对于需要响应的动作，阻止默认行为。 - event.preventDefault(); - var length = key_down_history.length; - if (length === 0) { - currentIndex = -1; // 如果历史记录为空，直接将当前选中的记录重置 - return; - } - if (currentIndex === -1) { - currentIndex = length; - } - if (event.code === 'ArrowUp' && currentIndex > 0) { - currentIndex--; - user_input_ta.value = key_down_history[currentIndex]; - } else if (event.code === 'ArrowDown' && currentIndex < length - 1) { - currentIndex++; - user_input_ta.value = key_down_history[currentIndex]; - } - user_input_ta.selectionStart = user_input_ta.value.length; - user_input_ta.selectionEnd = user_input_ta.value.length; - const input_event = new InputEvent("input", { bubbles: true, cancelable: true }); - user_input_ta.dispatchEvent(input_event); - } else if (event.code === "Enter") { - if (value) { - currentIndex = -1; - if (key_down_history.indexOf(value) === -1) { - key_down_history.push(value); - if (key_down_history.length > MAX_HISTORY_LENGTH) { - key_down_history.shift(); - } - } - } - } - }); - } -} - -var username = null; -function getUserInfo() { - if (usernameGotten) { - return; - } - userLogged = localStorage.getItem('userLogged'); - if (userLogged) { - username = userInfoDiv.innerText; - if (username) { - if (username.includes("getting user info…")) { - setTimeout(getUserInfo, 500); - return; - } else if (username === " ") { - localStorage.removeItem("username"); - localStorage.removeItem("userLogged") - userLogged = false; - usernameGotten = true; - return; - } else { - username = username.match(/User:\s*(.*)/)[1] || username; - localStorage.setItem("username", username); - usernameGotten = true; - clearHistoryHtml(); - } - } - } -} - -function toggleUserInfoVisibility(shouldHide) { - if (userInfoDiv) { - if (shouldHide) { - userInfoDiv.classList.add("hideK"); - } else { - userInfoDiv.classList.remove("hideK"); - } - } -} -function showOrHideUserInfo() { - var sendBtn = document.getElementById("submit_btn"); - - // Bind mouse/touch events to show/hide user info - appTitleDiv.addEventListener("mouseenter", function () { - toggleUserInfoVisibility(false); - }); - userInfoDiv.addEventListener("mouseenter", function () { - toggleUserInfoVisibility(false); - }); - sendBtn.addEventListener("mouseenter", function () { - toggleUserInfoVisibility(false); - }); - - appTitleDiv.addEventListener("mouseleave", function () { - toggleUserInfoVisibility(true); - }); - userInfoDiv.addEventListener("mouseleave", function () { - toggleUserInfoVisibility(true); - }); - sendBtn.addEventListener("mouseleave", function () { - toggleUserInfoVisibility(true); - }); - - appTitleDiv.ontouchstart = function () { - toggleUserInfoVisibility(false); - }; - userInfoDiv.ontouchstart = function () { - toggleUserInfoVisibility(false); - }; - sendBtn.ontouchstart = function () { - toggleUserInfoVisibility(false); - }; - - appTitleDiv.ontouchend = function () { - setTimeout(function () { - toggleUserInfoVisibility(true); - }, 3000); - }; - userInfoDiv.ontouchend = function () { - setTimeout(function () { - toggleUserInfoVisibility(true); - }, 3000); - }; - sendBtn.ontouchend = function () { - setTimeout(function () { - toggleUserInfoVisibility(true); - }, 3000); // Delay 1 second to hide user info - }; - - // Hide user info after 2 second - setTimeout(function () { - toggleUserInfoVisibility(true); - }, 2000); -} - -function toggleDarkMode(isEnabled) { - if (isEnabled) { - gradioContainer.classList.add("dark"); - document.body.style.setProperty("background-color", "var(--neutral-950)", "important"); - } else { - gradioContainer.classList.remove("dark"); - document.body.style.backgroundColor = ""; - } -} -function adjustDarkMode() { - const darkModeQuery = window.matchMedia("(prefers-color-scheme: dark)"); - - // 根据当前颜色模式设置初始状态 - apSwitch.checked = darkModeQuery.matches; - toggleDarkMode(darkModeQuery.matches); - // 监听颜色模式变化 - darkModeQuery.addEventListener("change", (e) => { - apSwitch.checked = e.matches; - toggleDarkMode(e.matches); - }); - // apSwitch = document.querySelector('.apSwitch input[type="checkbox"]'); - apSwitch.addEventListener("change", (e) => { - toggleDarkMode(e.target.checked); - }); -} - -function setChatbotHeight() { - const screenWidth = window.innerWidth; - const statusDisplay = document.querySelector('#status_display'); - const statusDisplayHeight = statusDisplay ? statusDisplay.offsetHeight : 0; - const wrap = chatbot.querySelector('.wrap'); - const vh = window.innerHeight * 0.01; - document.documentElement.style.setProperty('--vh', `${vh}px`); - if (isInIframe) { - chatbot.style.height = `700px`; - wrap.style.maxHeight = `calc(700px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))` - } else { - if (screenWidth <= 320) { - chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px)`; - wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`; - } else if (screenWidth <= 499) { - chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px)`; - wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`; - } else { - chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px)`; - wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`; - } - } -} -function setChatbotScroll() { - var scrollHeight = chatbotWrap.scrollHeight; - chatbotWrap.scrollTo(0,scrollHeight) -} -var rangeInputs = null; -var numberInputs = null; -function setSlider() { - rangeInputs = document.querySelectorAll('input[type="range"]'); - numberInputs = document.querySelectorAll('input[type="number"]') - setSliderRange(); - rangeInputs.forEach(rangeInput => { - rangeInput.addEventListener('input', setSliderRange); - }); - numberInputs.forEach(numberInput => { - numberInput.addEventListener('input', setSliderRange); - }) -} -function setSliderRange() { - var range = document.querySelectorAll('input[type="range"]'); - range.forEach(range => { - range.style.backgroundSize = (range.value - range.min) / (range.max - range.min) * 100 + '% 100%'; - }); -} - -function addChuanhuButton(botElement) { - var rawMessage = null; - var mdMessage = null; - rawMessage = botElement.querySelector('.raw-message'); - mdMessage = botElement.querySelector('.md-message'); - if (!rawMessage) { - var buttons = botElement.querySelectorAll('button.chuanhu-btn'); - for (var i = 0; i < buttons.length; i++) { - buttons[i].parentNode.removeChild(buttons[i]); - } - return; - } - var copyButton = null; - var toggleButton = null; - copyButton = botElement.querySelector('button.copy-bot-btn'); - toggleButton = botElement.querySelector('button.toggle-md-btn'); - if (copyButton) copyButton.remove(); - if (toggleButton) toggleButton.remove(); - - // Copy bot button - var copyButton = document.createElement('button'); - copyButton.classList.add('chuanhu-btn'); - copyButton.classList.add('copy-bot-btn'); - copyButton.setAttribute('aria-label', 'Copy'); - copyButton.innerHTML = copyIcon; - copyButton.addEventListener('click', () => { - const textToCopy = rawMessage.innerText; - navigator.clipboard - .writeText(textToCopy) - .then(() => { - copyButton.innerHTML = copiedIcon; - setTimeout(() => { - copyButton.innerHTML = copyIcon; - }, 1500); - }) - .catch(() => { - console.error("copy failed"); - }); - }); - botElement.appendChild(copyButton); - - // Toggle button - var toggleButton = document.createElement('button'); - toggleButton.classList.add('chuanhu-btn'); - toggleButton.classList.add('toggle-md-btn'); - toggleButton.setAttribute('aria-label', 'Toggle'); - var renderMarkdown = mdMessage.classList.contains('hideM'); - toggleButton.innerHTML = renderMarkdown ? mdIcon : rawIcon; - toggleButton.addEventListener('click', () => { - renderMarkdown = mdMessage.classList.contains('hideM'); - if (renderMarkdown){ - renderMarkdownText(botElement); - toggleButton.innerHTML=rawIcon; - } else { - removeMarkdownText(botElement); - toggleButton.innerHTML=mdIcon; - } - }); - botElement.insertBefore(toggleButton, copyButton); -} - -function addCopyCodeButton(pre) { - var code = null; - var firstChild = null; - code = pre.querySelector('code'); - if (!code) return; - firstChild = code.querySelector('div'); - if (!firstChild) return; - var oldCopyButton = null; - oldCopyButton = code.querySelector('button.copy-code-btn'); - // if (oldCopyButton) oldCopyButton.remove(); - if (oldCopyButton) return; // 没太有用，新生成的对话中始终会被pre覆盖，导致按钮消失，这段代码不启用…… - var codeButton = document.createElement('button'); - codeButton.classList.add('copy-code-btn'); - codeButton.textContent = '\uD83D\uDCCE'; - - code.insertBefore(codeButton, firstChild); - codeButton.addEventListener('click', function () { - var range = document.createRange(); - range.selectNodeContents(code); - range.setStartBefore(firstChild); - navigator.clipboard - .writeText(range.toString()) - .then(() => { - codeButton.textContent = '\u2714'; - setTimeout(function () { - codeButton.textContent = '\uD83D\uDCCE'; - }, 2000); - }) - .catch(e => { - console.error(e); - codeButton.textContent = '\u2716'; - }); - }); -} - -function renderMarkdownText(message) { - var mdDiv = message.querySelector('.md-message'); - if (mdDiv) mdDiv.classList.remove('hideM'); - var rawDiv = message.querySelector('.raw-message'); - if (rawDiv) rawDiv.classList.add('hideM'); -} -function removeMarkdownText(message) { - var rawDiv = message.querySelector('.raw-message'); - if (rawDiv) rawDiv.classList.remove('hideM'); - var mdDiv = message.querySelector('.md-message'); - if (mdDiv) mdDiv.classList.add('hideM'); -} - -var rendertime = 0; // for debugging -var mathjaxUpdated = false; - -function renderMathJax() { - messageBotDivs = document.querySelectorAll('.message.bot .md-message'); - for (var i = 0; i < messageBotDivs.length; i++) { - var mathJaxSpan = messageBotDivs[i].querySelector('.MathJax_Preview'); - if (!mathJaxSpan && shouldRenderLatex && !mathjaxUpdated) { - MathJax.Hub.Queue(["Typeset", MathJax.Hub, messageBotDivs[i]]); - rendertime +=1; // for debugging - // console.log("renderingMathJax", i) - } - } - mathjaxUpdated = true; - // console.log("MathJax Rendered") -} - -function removeMathjax() { - // var jax = MathJax.Hub.getAllJax(); - // for (var i = 0; i < jax.length; i++) { - // // MathJax.typesetClear(jax[i]); - // jax[i].Text(newmath) - // jax[i].Reprocess() - // } - // 我真的不会了啊啊啊，mathjax并没有提供转换为原先文本的办法。 - mathjaxUpdated = true; - // console.log("MathJax removed!"); -} - -function updateMathJax() { - // renderLatex.addEventListener("change", function() { - // shouldRenderLatex = renderLatex.checked; - // if (!mathjaxUpdated) { - // if (shouldRenderLatex) { - // renderMathJax(); - // } else { - // console.log("MathJax Disabled") - // removeMathjax(); - // } - // } else { - // if (!shouldRenderLatex) { - // mathjaxUpdated = false; // reset - // } - // } - // }); - if (shouldRenderLatex && !mathjaxUpdated) { - renderMathJax(); - } - mathjaxUpdated = false; -} - -let timeoutId; -let isThrottled = false; -var mmutation -// 监听所有元素中 bot message 的变化，用来查找需要渲染的mathjax, 并为 bot 消息添加复制按钮。 -var mObserver = new MutationObserver(function (mutationsList) { - for (mmutation of mutationsList) { - if (mmutation.type === 'childList') { - for (var node of mmutation.addedNodes) { - if (node.nodeType === 1 && node.classList.contains('message') && node.getAttribute('data-testid') === 'bot') { - if (shouldRenderLatex) { - renderMathJax(); - mathjaxUpdated = false; - } - saveHistoryHtml(); - document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot').forEach(addChuanhuButton); - document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot pre').forEach(addCopyCodeButton); - } - if (node.tagName === 'INPUT' && node.getAttribute('type') === 'range') { - setSlider(); - } - } - for (var node of mmutation.removedNodes) { - if (node.nodeType === 1 && node.classList.contains('message') && node.getAttribute('data-testid') === 'bot') { - if (shouldRenderLatex) { - renderMathJax(); - mathjaxUpdated = false; - } - saveHistoryHtml(); - document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot').forEach(addChuanhuButton); - document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot pre').forEach(addCopyCodeButton); - } - } - } else if (mmutation.type === 'attributes') { - if (mmutation.target.nodeType === 1 && mmutation.target.classList.contains('message') && mmutation.target.getAttribute('data-testid') === 'bot') { - document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot pre').forEach(addCopyCodeButton); // 目前写的是有点问题的，会导致加button次数过多，但是bot对话内容生成时又是不断覆盖pre的…… - if (isThrottled) break; // 为了防止重复不断疯狂渲染，加上等待_(:з」∠)_ - isThrottled = true; - clearTimeout(timeoutId); - timeoutId = setTimeout(() => { - isThrottled = false; - if (shouldRenderLatex) { - renderMathJax(); - mathjaxUpdated = false; - } - document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot').forEach(addChuanhuButton); - saveHistoryHtml(); - }, 500); - } - } - } -}); -mObserver.observe(document.documentElement, { attributes: true, childList: true, subtree: true }); - -var loadhistorytime = 0; // for debugging -function saveHistoryHtml() { - var historyHtml = document.querySelector('#chuanhu_chatbot > .wrap'); - localStorage.setItem('chatHistory', historyHtml.innerHTML); - // console.log("History Saved") - historyLoaded = false; -} -function loadHistoryHtml() { - var historyHtml = localStorage.getItem('chatHistory'); - if (!historyHtml) { - historyLoaded = true; - return; // no history, do nothing - } - userLogged = localStorage.getItem('userLogged'); - if (userLogged){ - historyLoaded = true; - return; // logged in, do nothing - } - if (!historyLoaded) { - var tempDiv = document.createElement('div'); - tempDiv.innerHTML = historyHtml; - var buttons = tempDiv.querySelectorAll('button.chuanhu-btn'); - for (var i = 0; i < buttons.length; i++) { - buttons[i].parentNode.removeChild(buttons[i]); - } - var fakeHistory = document.createElement('div'); - fakeHistory.classList.add('history-message'); - fakeHistory.innerHTML = tempDiv.innerHTML; - webLocale(); - chatbotWrap.insertBefore(fakeHistory, chatbotWrap.firstChild); - // var fakeHistory = document.createElement('div'); - // fakeHistory.classList.add('history-message'); - // fakeHistory.innerHTML = historyHtml; - // chatbotWrap.insertBefore(fakeHistory, chatbotWrap.firstChild); - historyLoaded = true; - console.log("History Loaded"); - loadhistorytime += 1; // for debugging - } else { - historyLoaded = false; - } -} -function clearHistoryHtml() { - localStorage.removeItem("chatHistory"); - historyMessages = chatbotWrap.querySelector('.history-message'); - if (historyMessages) { - chatbotWrap.removeChild(historyMessages); - console.log("History Cleared"); - } -} -function emptyHistory() { - empty_botton.addEventListener("click", function () { - clearHistoryHtml(); - }); -} - -// 监视页面内部 DOM 变动 -var observer = new MutationObserver(function (mutations) { - gradioLoaded(mutations); -}); -observer.observe(targetNode, { childList: true, subtree: true }); - -// 监视页面变化 -window.addEventListener("DOMContentLoaded", function () { - isInIframe = (window.self !== window.top); - historyLoaded = false; - shouldRenderLatex = !!document.querySelector('script[src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-MML-AM_CHTML"]'); -}); -window.addEventListener('resize', setChatbotHeight); -window.addEventListener('scroll', setChatbotHeight); -window.matchMedia("(prefers-color-scheme: dark)").addEventListener("change", adjustDarkMode); - -// button svg code -const copyIcon = ''; -const copiedIcon = ''; -const mdIcon = ''; -const rawIcon = ''; diff --git a/spaces/sdhsdhk/bingosjj/src/components/chat-suggestions.tsx b/spaces/sdhsdhk/bingosjj/src/components/chat-suggestions.tsx deleted file mode 100644 index 00c2fee295c9e010946046eb71705a5e131f7a5a..0000000000000000000000000000000000000000 --- a/spaces/sdhsdhk/bingosjj/src/components/chat-suggestions.tsx +++ /dev/null @@ -1,45 +0,0 @@ -import React, { useMemo } from 'react' -import Image from 'next/image' -import HelpIcon from '@/assets/images/help.svg' -import { SuggestedResponse } from '@/lib/bots/bing/types' -import { useBing } from '@/lib/hooks/use-bing' -import { atom, useAtom } from 'jotai' - -type Suggestions = SuggestedResponse[] -const helpSuggestions = ['为什么不回应某些主题', '告诉我更多关于必应的资迅', '必应如何使用 AI?'].map((text) => ({ text })) -const suggestionsAtom = atom
-
- ) : null
-}
diff --git a/spaces/shencc/gpt/crazy_functions/test_project/cpp/cppipc/prod_cons.h b/spaces/shencc/gpt/crazy_functions/test_project/cpp/cppipc/prod_cons.h
deleted file mode 100644
index c9004bb8043a12e32814436baa6262a00c8ef68e..0000000000000000000000000000000000000000
--- a/spaces/shencc/gpt/crazy_functions/test_project/cpp/cppipc/prod_cons.h
+++ /dev/null
@@ -1,433 +0,0 @@
-#pragma once
-
-#include
-
- {
- currentSuggestions.map(suggestion => (
-
- ))
- }
-
-
-
-
-
-
-
-
-
diff --git a/spaces/sino72/Passenger_Reconization/deep_sort/utils/draw.py b/spaces/sino72/Passenger_Reconization/deep_sort/utils/draw.py
deleted file mode 100644
index bc7cb537978e86805d5d9789785a8afe67df9030..0000000000000000000000000000000000000000
--- a/spaces/sino72/Passenger_Reconization/deep_sort/utils/draw.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import numpy as np
-import cv2
-
-palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
-
-
-def compute_color_for_labels(label):
- """
- Simple function that adds fixed color depending on the class
- """
- color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
- return tuple(color)
-
-
-def draw_boxes(img, bbox, identities=None, offset=(0,0)):
- for i,box in enumerate(bbox):
- x1,y1,x2,y2 = [int(i) for i in box]
- x1 += offset[0]
- x2 += offset[0]
- y1 += offset[1]
- y2 += offset[1]
- # box text and bar
- id = int(identities[i]) if identities is not None else 0
- color = compute_color_for_labels(id)
- label = '{}{:d}'.format("", id)
- t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]
- cv2.rectangle(img,(x1, y1),(x2,y2),color,3)
- cv2.rectangle(img,(x1, y1),(x1+t_size[0]+3,y1+t_size[1]+4), color,-1)
- cv2.putText(img,label,(x1,y1+t_size[1]+4), cv2.FONT_HERSHEY_PLAIN, 2, [255,255,255], 2)
- return img
-
-
-
-if __name__ == '__main__':
- for i in range(82):
- print(compute_color_for_labels(i))
diff --git a/spaces/skf15963/summary/fengshen/examples/clue1.1/data_preprocessing/cmrc2018_preprocessing.py b/spaces/skf15963/summary/fengshen/examples/clue1.1/data_preprocessing/cmrc2018_preprocessing.py
deleted file mode 100644
index 7a8a1de0f6a99062512246f1761ba47324a38998..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/clue1.1/data_preprocessing/cmrc2018_preprocessing.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import json
-from tqdm import tqdm
-import os
-from sklearn.utils import shuffle
-import re
-import argparse
-
-
-def cut_sent(para):
- para = re.sub('([。，,！？\?])([^”’])', r"\1\n\2", para) # 单字符断句符
- para = re.sub('(\.{6})([^”’])', r"\1\n\2", para) # 英文省略号
- para = re.sub('(\…{2})([^”’])', r"\1\n\2", para) # 中文省略号
- para = re.sub('([。！？\?][”’])([^，。！？\?])', r'\1\n\2', para)
- # 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\n放到双引号后，注意前面的几句都小心保留了双引号
- para = para.rstrip() # 段尾如果有多余的\n就去掉它
- # 很多规则中会考虑分号;，但是这里
- return para.split("\n")
-
-
-def search(pattern, sequence):
- n = len(pattern)
- res=[]
- for i in range(len(sequence)):
- if sequence[i:i + n] == pattern:
- res.append([i,i + n-1])
- return res
-
-max_length=512
-stride=128
-def stride_split(question, context, answer, start):
- end = start + len(answer) -1
- results, n = [], 0
- max_c_len = max_length - len(question) - 3
- while True:
- left, right = n * stride, n * stride + max_c_len
- if left <= start < end <= right:
- results.append((question, context[left:right], answer, start - left, end - left))
- elif right < start or end < right:
- results.append((question, context[left:right], '', -1, -1))
- if right >= len(context):
- return results
- n += 1
-
-
-def load_data(file_path,is_training=False):
- task_type='抽取任务'
- subtask_type='抽取式阅读理解'
- with open(file_path, 'r', encoding='utf8') as f:
- lines = json.loads(''.join(f.readlines()))
- result=[]
- lines = lines['data']
- for line in tqdm(lines):
- if line['paragraphs']==[]:
- continue
- data = line['paragraphs'][0]
- context=data['context'].strip()
- for qa in data['qas']:
- question=qa['question'].strip()
- rcv=[]
- for a in qa['answers']:
- if a not in rcv:
- rcv.append(a)
- split=stride_split(question, context, a['text'], a['answer_start'])
- for sp in split:
- choices = []
-
- choice = {}
- choice['id']=qa['id']
- choice['entity_type'] = qa['question']
- choice['label']=0
- entity_list=[]
- if sp[3]>=0 and sp[4]>=0:
- entity_list.append({'entity_name':sp[2],'entity_type':'','entity_idx':[[sp[3],sp[4]]]})
-
- choice['entity_list']=entity_list
- choices.append(choice)
-
- if choices==[]:
- print(data)
- continue
- result.append({ 'task_type':task_type,
- 'subtask_type':subtask_type,
- 'text':sp[1],
- 'choices':choices,
- 'id':0})
-
- return result
-
-
-def save_data(data,file_path):
- with open(file_path, 'w', encoding='utf8') as f:
- for line in data:
- json_data=json.dumps(line,ensure_ascii=False)
- f.write(json_data+'\n')
-
-
-
-if __name__=="__main__":
- parser = argparse.ArgumentParser(description="train")
- parser.add_argument("--data_path", type=str,default="")
- parser.add_argument("--save_path", type=str,default="")
-
- args = parser.parse_args()
-
-
- data_path = args.data_path
- save_path = args.save_path
-
- if not os.path.exists(save_path):
- os.makedirs(save_path)
-
- file_list=['dev','train','trial','test']
- train_data = []
- dev_data = []
- for file in file_list:
- file_path = os.path.join(data_path,file+'.json')
- data=load_data(file_path=file_path)
- if 'train' in file or 'trial' in file:
- train_data.extend(data)
- else:
- output_path = os.path.join(save_path,file+'.json')
- save_data(data,output_path)
-
- output_path = os.path.join(save_path,'train.json')
- save_data(train_data,output_path)
-
\ No newline at end of file
diff --git a/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/seg2art/options/__init__.py b/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/seg2art/options/__init__.py
deleted file mode 100644
index ba4ddb2d78be7bbf79bb0671eefd9296d3a168b7..0000000000000000000000000000000000000000
--- a/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/seg2art/options/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-"""
-Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
-Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
-"""
\ No newline at end of file
diff --git a/spaces/skyler36237/vits-uma-genshin-honkai/utils.py b/spaces/skyler36237/vits-uma-genshin-honkai/utils.py
deleted file mode 100644
index ee4b01ddfbe8173965371b29f770f3e87615fe71..0000000000000000000000000000000000000000
--- a/spaces/skyler36237/vits-uma-genshin-honkai/utils.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-import librosa
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict= {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})" .format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
- audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
- return torch.FloatTensor(audio.astype(np.float32))
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/songwy/VITS-Umamusume-voice-synthesizer/text/thai.py b/spaces/songwy/VITS-Umamusume-voice-synthesizer/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/songwy/VITS-Umamusume-voice-synthesizer/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'เอ'),
- ('b','บี'),
- ('c','ซี'),
- ('d','ดี'),
- ('e','อี'),
- ('f','เอฟ'),
- ('g','จี'),
- ('h','เอช'),
- ('i','ไอ'),
- ('j','เจ'),
- ('k','เค'),
- ('l','แอล'),
- ('m','เอ็ม'),
- ('n','เอ็น'),
- ('o','โอ'),
- ('p','พี'),
- ('q','คิว'),
- ('r','แอร์'),
- ('s','เอส'),
- ('t','ที'),
- ('u','ยู'),
- ('v','วี'),
- ('w','ดับเบิลยู'),
- ('x','เอ็กซ์'),
- ('y','วาย'),
- ('z','ซี')
-]]
-
-
-def num_to_thai(text):
- return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
- for regex, replacement in _latin_to_thai:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/spacerini/miracl-french/app.py b/spaces/spacerini/miracl-french/app.py
deleted file mode 100644
index 82eb5b343dbcbf40675a2bc44275163bdf4d9285..0000000000000000000000000000000000000000
--- a/spaces/spacerini/miracl-french/app.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import http.client as http_client
-import json
-import logging
-import os
-import pprint
-import re
-import time
-import string
-
-import streamlit as st
-
-import streamlit.components.v1 as components
-from typing import Callable, Optional, Tuple, Union
-from pyserini import util
-from pyserini.search import LuceneSearcher, FaissSearcher, AutoQueryEncoder
-
-
-VERSION = '1.0'
-st.set_page_config(page_title="Miracl Search - French", layout="wide")
-
-Searcher = Union[FaissSearcher, LuceneSearcher]
-LANG_MAPPING = {'French':'fr'}
-
-
-st.sidebar.markdown(
-"""
-
-| - | ||
| 题目 | -- | |
| 答案 | -- - | |
| 正误 | -- | |
| 得分 | -- | |
| - | ||
MIRACL French Demo
-🌍🙌🌏
-MIRACL is a multilingual dataset for ad hoc retrieval that consists of 18 different languages, collectively encompassing over three billion native speakers around the world.
-""", -unsafe_allow_html=True, -) - -st.sidebar.markdown( -""" - - -""", -unsafe_allow_html=True, -) - -query = st.sidebar.text_input(label='Search query', value='') -language = 'French' - -max_results = st.sidebar.slider( - "Maximum Number of Results", - min_value=1, - max_value=1000, - step=1, - value=10, - help="Maximum Number of Documents to return", -) - - -def _load_sparse_searcher(language: str, k1: Optional[float]=None, b: Optional[float]=None) -> (Searcher): - searcher = LuceneSearcher(f'lucene-index.miracl-v1.0-{language}.20221004.2b2856') - searcher.set_language(language) - if k1 is not None and b is not None: - searcher.set_bm25(k1, b) - retriever_name = f'BM25 (k1={k1}, b={b})' - else: - retriever_name = 'BM25' - - return searcher - -def search(query, language, num_results=10): - searcher = _load_sparse_searcher(language=LANG_MAPPING[language]) - - t_0 = time.time() - search_results = searcher.search(query, k=num_results) - search_time = time.time() - t_0 - - results_dict ={"docs": [], "doc_ids": [], "score":[], "lang": language} - for i, result in enumerate(search_results): - result = json.loads(result.raw) - results_dict["docs"].append(result["text"]) - results_dict["doc_ids"].append(result["docid"]) - results_dict["score"].append(search_results[i].score) - - return results_dict, search_time - - - -def highlight_string(paragraph: str, highlight_terms: list) -> str: - for term in highlight_terms: - paragraph = re.sub(f"\\b{term}\\b", f"{term}", paragraph, flags=re.I) - return paragraph - -def process_results(hits: dict, highlight_terms: list) -> str: - hit_list = [] - for i in range(len(hits['doc_ids'])): - res_head = f""" -
-
- {i+1}. Document ID: {hits['doc_ids'][i]}
-Language:
{highlight_string(hits['docs'][i], highlight_terms)}
-- """ - hit_list.append(res_head) - return " ".join(hit_list) - - - -if st.sidebar.button("Search"): - hits, search_time = search(query, language, max_results) - html_results = process_results(hits, []) - rendered_results = f""" -
-
-
- """
- st.markdown("""
-
- """,
- unsafe_allow_html=True)
- st.markdown(
- """
-
- """,
- unsafe_allow_html=True)
- st.markdown(
- f"""
- -
About {max_results} results
- {html_results} -
-
- """,
- unsafe_allow_html=True)
- components.html(
- """
-
-
-
- """ + rendered_results, height=800, scrolling=True
- )
diff --git a/spaces/sparkyrider/OpenAI-SHAP-E/README.md b/spaces/sparkyrider/OpenAI-SHAP-E/README.md
deleted file mode 100644
index 9382d854fc2f219c355b2a65ab80d1d3b02edf6b..0000000000000000000000000000000000000000
--- a/spaces/sparkyrider/OpenAI-SHAP-E/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: OpenAI SHAP E
-emoji: 🌍
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.28.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/spritlesoftware/Spritle-Bot/index.py b/spaces/spritlesoftware/Spritle-Bot/index.py
deleted file mode 100644
index f1cd4e4b0c7aa6ee4c4cda9f6847c0b3c01fc62b..0000000000000000000000000000000000000000
--- a/spaces/spritlesoftware/Spritle-Bot/index.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from langchain.document_loaders import PyPDFLoader
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS
-from langchain.chains import RetrievalQA
-from langchain.chat_models import ChatOpenAI
-import os
-os.environ["OPENAI_API_KEY"]
-
-# loader = PyPDFLoader("spritle.pdf")
-# pages = loader.load_and_split()
-# print(len(pages), pages)
-
-embeddings = OpenAIEmbeddings()
-# Create DB
-# db = FAISS.from_documents(pages, embeddings)
-
-# Save the DB in your local
-# db.save_local("faiss_index")
-
-# load the DB
-new_db = FAISS.load_local("faiss_index", embeddings)
-
-# Init LLM
-llm = ChatOpenAI()
-qa_chain = RetrievalQA.from_chain_type(llm, retriever=new_db.as_retriever())
-
-def ask(user_query):
- res=qa_chain({"query": user_query})
- return res["result"]
\ No newline at end of file
diff --git a/spaces/sqc1729/bingi/src/app/page.tsx b/spaces/sqc1729/bingi/src/app/page.tsx
deleted file mode 100644
index 0dff3431b098ce4fe282cc83fc87a93a28a43090..0000000000000000000000000000000000000000
--- a/spaces/sqc1729/bingi/src/app/page.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-import dynamic from 'next/dynamic'
-
-const DynamicComponentWithNoSSR = dynamic(
- () => import('../components/chat'),
- { ssr: false }
-)
-
-export default function IndexPage() {
- return (
- <>
-
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\ No newline at end of file diff --git a/spaces/stomexserde/gpt4-ui/Examples/Delhi Belly Full Movie In English VERIFIED Free Download Mp4.md b/spaces/stomexserde/gpt4-ui/Examples/Delhi Belly Full Movie In English VERIFIED Free Download Mp4.md deleted file mode 100644 index e9409c5029ece2241b0be744fddde29983915024..0000000000000000000000000000000000000000 --- a/spaces/stomexserde/gpt4-ui/Examples/Delhi Belly Full Movie In English VERIFIED Free Download Mp4.md +++ /dev/null @@ -1,20 +0,0 @@ - -
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\ No newline at end of file diff --git a/spaces/stomexserde/gpt4-ui/Examples/Download Prtg Network Monitor Full Cracked WORK.md b/spaces/stomexserde/gpt4-ui/Examples/Download Prtg Network Monitor Full Cracked WORK.md deleted file mode 100644 index c1f6b86cdc6a8093fd3fda39570efe3e02518a2a..0000000000000000000000000000000000000000 --- a/spaces/stomexserde/gpt4-ui/Examples/Download Prtg Network Monitor Full Cracked WORK.md +++ /dev/null @@ -1,84 +0,0 @@ - -
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-Conclusion
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-Instead of downloading PRTG Network Monitor full cracked, you should use the official version of PRTG Network Monitor for a reliable and secure network monitoring experience. You can enjoy a free trial and a transparent licensing model, access all the features and customization options, and get regular updates and professional support from Paessler AG. You can also benefit from the high-quality and user-friendly network monitoring software that can help you monitor your entire IT infrastructure with ease and confidence.
-FAQs
-How much does PRTG Network Monitor cost?
-The cost of PRTG Network Monitor depends on the number of sensors that you want to monitor. The more sensors you need, the higher the license tier you need to purchase. The license tiers range from 500 sensors to unlimited sensors, with different prices and features. You can check the pricing details on the Paessler AG website. You can also use the price calculator to estimate your license cost based on your needs.
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-The number of sensors that you can monitor with PRTG Network Monitor depends on the license tier that you purchase. The license tiers range from 500 sensors to unlimited sensors, with different prices and features. You can check the license details on the Paessler AG website. You can also use the sensor calculator to estimate your sensor needs based on your devices.
-What are the system requirements for PRTG Network Monitor?
-The system requirements for PRTG Network Monitor vary depending on the size and complexity of your network. However, the minimum system requirements are as follows:
-| Component | Requirement |
|---|---|
| Operating System | Windows Server 2019/2016/2012 R2/2012/2008 R2 SP1 (64-bit) |
| CPU | Dual core processor (at least 2 GHz) |
| RAM | At least 4 GB (more recommended) |
| Disk Space | At least 250 GB (more recommended) |
| Network Card | At least 100 Mbit/s (1 Gbit/s recommended) |
| Browser | Chrome 74+, Firefox 66+, Edge 44+, Safari 12+ |
How can I contact Paessler AG for support or feedback?
-You can contact Paessler AG for support or feedback via various channels, such as email, phone, chat, forum, knowledge base, or ticket system. You can find the contact details on the Paessler AG website. You can also access various resources and documentation that are available on the Paessler AG website or blog.
-Where can I find more information about PRTG Network Monitor?
-You can find more information about PRTG Network Monitor on the Paessler AG website or blog. You can also watch some videos on the Paessler AG YouTube channel or read some testimonials on the Paessler AG customer stories page. You can also follow Paessler AG on social media platforms such as Facebook, Twitter, LinkedIn, or Instagram to get the latest news and updates about PRTG Network Monitor.
-I hope this article has helped you understand the pros and cons of downloading PRTG Network Monitor full cracked. If you have any questions or comments, please feel free to leave them below. Thank you for reading and happy network monitoring!
-References:
--
-
-
-
\ No newline at end of file diff --git a/spaces/sub314xxl/MetaGPT/metagpt/learn/skill_loader.py b/spaces/sub314xxl/MetaGPT/metagpt/learn/skill_loader.py deleted file mode 100644 index 83200bca6fefe528c7e93c18ffb6d5a8da64ac61..0000000000000000000000000000000000000000 --- a/spaces/sub314xxl/MetaGPT/metagpt/learn/skill_loader.py +++ /dev/null @@ -1,96 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/8/18 -@Author : mashenquan -@File : skill_loader.py -@Desc : Skill YAML Configuration Loader. -""" -from pathlib import Path -from typing import Dict, List, Optional - -import yaml -from pydantic import BaseModel, Field - -from metagpt.config import CONFIG - - -class Example(BaseModel): - ask: str - answer: str - - -class Returns(BaseModel): - type: str - format: Optional[str] = None - - -class Prerequisite(BaseModel): - name: str - type: Optional[str] = None - description: Optional[str] = None - default: Optional[str] = None - - -class Skill(BaseModel): - name: str - description: str - id: str - x_prerequisite: Optional[List[Prerequisite]] = Field(default=None, alias="x-prerequisite") - arguments: Dict - examples: List[Example] - returns: Returns - - -class EntitySkills(BaseModel): - skills: List[Skill] - - -class SkillsDeclaration(BaseModel): - entities: Dict[str, EntitySkills] - - -class SkillLoader: - def __init__(self, skill_yaml_file_name: Path = None): - if not skill_yaml_file_name: - skill_yaml_file_name = Path(__file__).parent.parent.parent / ".well-known/skills.yaml" - with open(str(skill_yaml_file_name), "r") as file: - skills = yaml.safe_load(file) - self._skills = SkillsDeclaration(**skills) - - def get_skill_list(self, entity_name: str = "Assistant") -> Dict: - """Return the skill name based on the skill description.""" - entity_skills = self.get_entity(entity_name) - if not entity_skills: - return {} - - agent_skills = CONFIG.agent_skills - if not agent_skills: - return {} - - class AgentSkill(BaseModel): - name: str - - names = [AgentSkill(**i).name for i in agent_skills] - description_to_name_mappings = {} - for s in entity_skills.skills: - if s.name not in names: - continue - description_to_name_mappings[s.description] = s.name - - return description_to_name_mappings - - def get_skill(self, name, entity_name: str = "Assistant") -> Skill: - """Return a skill by name.""" - entity = self.get_entity(entity_name) - if not entity: - return None - for sk in entity.skills: - if sk.name == name: - return sk - - def get_entity(self, name) -> EntitySkills: - """Return a list of skills for the entity.""" - if not self._skills: - return None - return self._skills.entities.get(name) diff --git a/spaces/subwayman/btc-chat-bot/pinecone_vector_store.py b/spaces/subwayman/btc-chat-bot/pinecone_vector_store.py deleted file mode 100644 index 6f54b5f454a03664a91b58f2aa1eab0e89ede3e6..0000000000000000000000000000000000000000 --- a/spaces/subwayman/btc-chat-bot/pinecone_vector_store.py +++ /dev/null @@ -1,95 +0,0 @@ -from dotenv import load_dotenv -import tiktoken - -# langchain libraries -from langchain.document_loaders import DirectoryLoader, TextLoader -from langchain.embeddings.openai import OpenAIEmbeddings -from langchain.text_splitter import RecursiveCharacterTextSplitter -from langchain.vectorstores import FAISS, Pinecone - -import pinecone -import openai -import os - -load_dotenv() - -tiktoken.encoding_for_model('gpt-3.5-turbo') - -openai.api_key = os.getenv("OPENAI_API_KEY") -OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") -PINECONE_API_KEY = os.getenv("PINECONE_API_KEY") -PINECONE_ENVIRONMENT = os.getenv("PINECONE_ENVIRONMENT") - - -def load_local_documents(test=True): - if test: - doc_dir = os.path.join(os.getcwd() + '/docs', 'test') - else: - doc_dir = os.path.join(os.getcwd() + '/docs', 'processed') - loader = DirectoryLoader(doc_dir) - documents = loader.load() - assert len(documents) > 0 - return documents - - -def split_documents(documents): - text_splitter = RecursiveCharacterTextSplitter( - chunk_size=1000, - chunk_overlap=20, - length_function=len, - separators=["\n\n", "\n", " ", ""] - ) - chunks = text_splitter.split_documents(documents) - return chunks - - -def tiktoken_len(text): - tokenizer = tiktoken.get_encoding('cl100k_base') - tokens = tokenizer.encode( - text, - disallowed_special=() - ) - return len(tokens) - - -def create_embedding(): - model_name = 'text-embedding-ada-002' - - embed = OpenAIEmbeddings( - document_model_name=model_name, - query_model_name=model_name, - openai_api_key=OPENAI_API_KEY, - ) - return embed - - -def create_pinecone_store(index_name='btc-chat-bot'): - pinecone.init(api_key=PINECONE_API_KEY, - environment=PINECONE_ENVIRONMENT) - if index_name not in pinecone.list_indexes(): - pinecone.create_index(index_name, dimension=1536, metric='cosine') - print(pinecone.list_indexes()) - index = pinecone.GRPCIndex(index_name) - print(index.describe_index_stats()) - documents = load_local_documents(test=False) - documents = split_documents(documents) - embeddings = OpenAIEmbeddings() - result = Pinecone.from_documents( - documents, embeddings, index_name=index_name) - return result - - -def get_pinecone_store(index_name='btc-chat-bot'): - pinecone.init(api_key=PINECONE_API_KEY, - environment=PINECONE_ENVIRONMENT) - vector_store = pinecone.Index(index_name) - return Pinecone.from_existing_index(index_name, OpenAIEmbeddings()) - # return vector_store - - -if __name__ == "__main__": - # chunks = split_documents(load_local_documents()) - # for c in chunks: - # print(tiktoken_len(c.page_content)) - # create_pinecone_store() - vector_store = get_pinecone_store() diff --git a/spaces/sudthakur/yt_summary/README.md b/spaces/sudthakur/yt_summary/README.md deleted file mode 100644 index af06e476f785c56224dd037620998952af569030..0000000000000000000000000000000000000000 --- a/spaces/sudthakur/yt_summary/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Yt Summary -emoji: 🦀 -colorFrom: pink -colorTo: purple -sdk: gradio -sdk_version: 3.24.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Badoo Premium Ipa Cracked Ipad.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Badoo Premium Ipa Cracked Ipad.md deleted file mode 100644 index 54c33e7818785973eeea0cab151db30a00c9ad49..0000000000000000000000000000000000000000 --- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Badoo Premium Ipa Cracked Ipad.md +++ /dev/null @@ -1,6 +0,0 @@ -
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- diff --git a/spaces/syam417/rvc/infer_pack/models.py b/spaces/syam417/rvc/infer_pack/models.py deleted file mode 100644 index 96165f73644e6fb92d0ffedb4a3c9e1a457cb989..0000000000000000000000000000000000000000 --- a/spaces/syam417/rvc/infer_pack/models.py +++ /dev/null @@ -1,982 +0,0 @@ -import math, pdb, os -from time import time as ttime -import torch -from torch import nn -from torch.nn import functional as F -from infer_pack import modules -from infer_pack import attentions -from infer_pack import commons -from infer_pack.commons import init_weights, get_padding -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from infer_pack.commons import init_weights -import numpy as np -from infer_pack import commons - - -class TextEncoder256(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=True, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emb_phone = nn.Linear(256, hidden_channels) - self.lrelu = nn.LeakyReLU(0.1, inplace=True) - if f0 == True: - self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, phone, pitch, lengths): - if pitch == None: - x = self.emb_phone(phone) - else: - x = self.emb_phone(phone) + self.emb_pitch(pitch) - x = x * math.sqrt(self.hidden_channels) # [b, t, h] - x = self.lrelu(x) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return m, logs, x_mask - - -class TextEncoder256Sim(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=True, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emb_phone = nn.Linear(256, hidden_channels) - self.lrelu = nn.LeakyReLU(0.1, inplace=True) - if f0 == True: - self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - - def forward(self, phone, pitch, lengths): - if pitch == None: - x = self.emb_phone(phone) - else: - x = self.emb_phone(phone) + self.emb_pitch(pitch) - x = x * math.sqrt(self.hidden_channels) # [b, t, h] - x = self.lrelu(x) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.encoder(x * x_mask, x_mask) - x = self.proj(x) * x_mask - return x, x_mask - - -class ResidualCouplingBlock(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0, - ): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append( - modules.ResidualCouplingLayer( - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - mean_only=True, - ) - ) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - def remove_weight_norm(self): - for i in range(self.n_flows): - self.flows[i * 2].remove_weight_norm() - - -class PosteriorEncoder(nn.Module): - def __init__( - self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - def remove_weight_norm(self): - self.enc.remove_weight_norm() - - -class Generator(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=0, - ): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class SineGen(torch.nn.Module): - """Definition of sine generator - SineGen(samp_rate, harmonic_num = 0, - sine_amp = 0.1, noise_std = 0.003, - voiced_threshold = 0, - flag_for_pulse=False) - samp_rate: sampling rate in Hz - harmonic_num: number of harmonic overtones (default 0) - sine_amp: amplitude of sine-wavefrom (default 0.1) - noise_std: std of Gaussian noise (default 0.003) - voiced_thoreshold: F0 threshold for U/V classification (default 0) - flag_for_pulse: this SinGen is used inside PulseGen (default False) - Note: when flag_for_pulse is True, the first time step of a voiced - segment is always sin(np.pi) or cos(0) - """ - - def __init__( - self, - samp_rate, - harmonic_num=0, - sine_amp=0.1, - noise_std=0.003, - voiced_threshold=0, - flag_for_pulse=False, - ): - super(SineGen, self).__init__() - self.sine_amp = sine_amp - self.noise_std = noise_std - self.harmonic_num = harmonic_num - self.dim = self.harmonic_num + 1 - self.sampling_rate = samp_rate - self.voiced_threshold = voiced_threshold - - def _f02uv(self, f0): - # generate uv signal - uv = torch.ones_like(f0) - uv = uv * (f0 > self.voiced_threshold) - return uv - - def forward(self, f0, upp): - """sine_tensor, uv = forward(f0) - input F0: tensor(batchsize=1, length, dim=1) - f0 for unvoiced steps should be 0 - output sine_tensor: tensor(batchsize=1, length, dim) - output uv: tensor(batchsize=1, length, 1) - """ - with torch.no_grad(): - f0 = f0[:, None].transpose(1, 2) - f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device) - # fundamental component - f0_buf[:, :, 0] = f0[:, :, 0] - for idx in np.arange(self.harmonic_num): - f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * ( - idx + 2 - ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic - rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化 - rand_ini = torch.rand( - f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device - ) - rand_ini[:, 0] = 0 - rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini - tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化 - tmp_over_one *= upp - tmp_over_one = F.interpolate( - tmp_over_one.transpose(2, 1), - scale_factor=upp, - mode="linear", - align_corners=True, - ).transpose(2, 1) - rad_values = F.interpolate( - rad_values.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose( - 2, 1 - ) ####### - tmp_over_one %= 1 - tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 - cumsum_shift = torch.zeros_like(rad_values) - cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 - sine_waves = torch.sin( - torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi - ) - sine_waves = sine_waves * self.sine_amp - uv = self._f02uv(f0) - uv = F.interpolate( - uv.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose(2, 1) - noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 - noise = noise_amp * torch.randn_like(sine_waves) - sine_waves = sine_waves * uv + noise - return sine_waves, uv, noise - - -class SourceModuleHnNSF(torch.nn.Module): - """SourceModule for hn-nsf - SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, - add_noise_std=0.003, voiced_threshod=0) - sampling_rate: sampling_rate in Hz - harmonic_num: number of harmonic above F0 (default: 0) - sine_amp: amplitude of sine source signal (default: 0.1) - add_noise_std: std of additive Gaussian noise (default: 0.003) - note that amplitude of noise in unvoiced is decided - by sine_amp - voiced_threshold: threhold to set U/V given F0 (default: 0) - Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) - F0_sampled (batchsize, length, 1) - Sine_source (batchsize, length, 1) - noise_source (batchsize, length 1) - uv (batchsize, length, 1) - """ - - def __init__( - self, - sampling_rate, - harmonic_num=0, - sine_amp=0.1, - add_noise_std=0.003, - voiced_threshod=0, - is_half=True, - ): - super(SourceModuleHnNSF, self).__init__() - - self.sine_amp = sine_amp - self.noise_std = add_noise_std - self.is_half = is_half - # to produce sine waveforms - self.l_sin_gen = SineGen( - sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod - ) - - # to merge source harmonics into a single excitation - self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) - self.l_tanh = torch.nn.Tanh() - - def forward(self, x, upp=None): - sine_wavs, uv, _ = self.l_sin_gen(x, upp) - if self.is_half: - sine_wavs = sine_wavs.half() - sine_merge = self.l_tanh(self.l_linear(sine_wavs)) - return sine_merge, None, None # noise, uv - - -class GeneratorNSF(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels, - sr, - is_half=False, - ): - super(GeneratorNSF, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - - self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates)) - self.m_source = SourceModuleHnNSF( - sampling_rate=sr, harmonic_num=0, is_half=is_half - ) - self.noise_convs = nn.ModuleList() - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - c_cur = upsample_initial_channel // (2 ** (i + 1)) - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - if i + 1 < len(upsample_rates): - stride_f0 = np.prod(upsample_rates[i + 1 :]) - self.noise_convs.append( - Conv1d( - 1, - c_cur, - kernel_size=stride_f0 * 2, - stride=stride_f0, - padding=stride_f0 // 2, - ) - ) - else: - self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - self.upp = np.prod(upsample_rates) - - def forward(self, x, f0, g=None): - har_source, noi_source, uv = self.m_source(f0, self.upp) - har_source = har_source.transpose(1, 2) - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - x_source = self.noise_convs[i](har_source) - x = x + x_source - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -sr2sr = { - "32k": 32000, - "40k": 40000, - "48k": 48000, -} - - -class SynthesizerTrnMs256NSFsid(nn.Module): - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - gin_channels, - sr, - **kwargs - ): - super().__init__() - if type(sr) == type("strr"): - sr = sr2sr[sr] - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder256( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - self.dec = GeneratorNSF( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - sr=sr, - is_half=kwargs["is_half"], - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward( - self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds - ): # 这里ds是id，[bs,1] - # print(1,pitch.shape)#[bs,t] - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length) - pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) - # print(-2,pitchf.shape,z_slice.shape) - o = self.dec(z_slice, pitchf, g=g) - return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None): - g = self.emb_g(sid).unsqueeze(-1) - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g) - return o, x_mask, (z, z_p, m_p, logs_p) - - -class SynthesizerTrnMs256NSFsid_nono(nn.Module): - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - gin_channels, - sr=None, - **kwargs - ): - super().__init__() - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder256( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=False, - ) - self.dec = Generator( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1] - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - o = self.dec(z_slice, g=g) - return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, phone, phone_lengths, sid, max_len=None): - g = self.emb_g(sid).unsqueeze(-1) - m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec((z * x_mask)[:, :, :max_len], g=g) - return o, x_mask, (z, z_p, m_p, logs_p) - - -class SynthesizerTrnMs256NSFsid_sim(nn.Module): - """ - Synthesizer for Training - """ - - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - # hop_length, - gin_channels=0, - use_sdp=True, - **kwargs - ): - super().__init__() - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder256Sim( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - self.dec = GeneratorNSF( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - is_half=kwargs["is_half"], - ) - - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward( - self, phone, phone_lengths, pitch, pitchf, y_lengths, ds - ): # y是spec不需要了现在 - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - x, x_mask = self.enc_p(phone, pitch, phone_lengths) - x = self.flow(x, x_mask, g=g, reverse=True) - z_slice, ids_slice = commons.rand_slice_segments( - x, y_lengths, self.segment_size - ) - - pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) - o = self.dec(z_slice, pitchf, g=g) - return o, ids_slice - - def infer( - self, phone, phone_lengths, pitch, pitchf, ds, max_len=None - ): # y是spec不需要了现在 - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - x, x_mask = self.enc_p(phone, pitch, phone_lengths) - x = self.flow(x, x_mask, g=g, reverse=True) - o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g) - return o, o - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2, 3, 5, 7, 11, 17] - # periods = [3, 5, 7, 11, 17, 23, 37] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [ - DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods - ] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] # - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - # for j in range(len(fmap_r)): - # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ] - ) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f( - Conv2d( - 1, - 32, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 32, - 128, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 128, - 512, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 512, - 1024, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 1024, - 1024, - (kernel_size, 1), - 1, - padding=(get_padding(kernel_size, 1), 0), - ) - ), - ] - ) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap diff --git a/spaces/szukevin/VISOR-GPT/train/scripts/convert_gpt2_from_huggingface_to_tencentpretrain.py b/spaces/szukevin/VISOR-GPT/train/scripts/convert_gpt2_from_huggingface_to_tencentpretrain.py deleted file mode 100644 index b0d6b8c68227695024be0214f8d5de3168712fbc..0000000000000000000000000000000000000000 --- a/spaces/szukevin/VISOR-GPT/train/scripts/convert_gpt2_from_huggingface_to_tencentpretrain.py +++ /dev/null @@ -1,59 +0,0 @@ -import argparse -import collections -import torch - - -parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) -parser.add_argument("--input_model_path", type=str, default="models/input_model.bin", - help=".") -parser.add_argument("--output_model_path", type=str, default="models/output_model.bin", - help=".") -parser.add_argument("--layers_num", type=int, default=12) - -args = parser.parse_args() - -input_model = torch.load(args.input_model_path, map_location="cpu") - -output_model = collections.OrderedDict() -emb_size = input_model["transformer.h." + str(0) + ".attn.c_attn.weight"].shape[0] - -output_model["embedding.word.embedding.weight"] = input_model["transformer.wte.weight"] -output_model["embedding.pos.embedding.weight"] = input_model["transformer.wpe.weight"] - -for i in range(args.layers_num): - for j in range(3): - output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers." + str(j) + ".weight"] = \ - input_model["transformer.h." + str(i) + ".attn.c_attn.weight"].t()[j*emb_size:(j+1)*emb_size, :] - output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers." + str(j) + ".bias"] = \ - input_model["transformer.h." + str(i) + ".attn.c_attn.bias"][j*emb_size:(j+1)*emb_size] - - output_model["encoder.transformer." + str(i) + ".self_attn.final_linear.weight"] = \ - input_model["transformer.h." + str(i) + ".attn.c_proj.weight"].t() - output_model["encoder.transformer." + str(i) + ".self_attn.final_linear.bias"] = \ - input_model["transformer.h." + str(i) + ".attn.c_proj.bias"] - - output_model["encoder.transformer." + str(i) + ".layer_norm_1.gamma"] = \ - input_model["transformer.h." + str(i) + ".ln_1.weight"] - output_model["encoder.transformer." + str(i) + ".layer_norm_1.beta"] = \ - input_model["transformer.h." + str(i) + ".ln_1.bias"] - - output_model["encoder.transformer." + str(i) + ".feed_forward.linear_1.weight"] = \ - input_model["transformer.h." + str(i) + ".mlp.c_fc.weight"].t() - output_model["encoder.transformer." + str(i) + ".feed_forward.linear_1.bias"] = \ - input_model["transformer.h." + str(i) + ".mlp.c_fc.bias"] - output_model["encoder.transformer." + str(i) + ".feed_forward.linear_2.weight"] = \ - input_model["transformer.h." + str(i) + ".mlp.c_proj.weight"].t() - output_model["encoder.transformer." + str(i) + ".feed_forward.linear_2.bias"] = \ - input_model["transformer.h." + str(i) + ".mlp.c_proj.bias"] - - output_model["encoder.transformer." + str(i) + ".layer_norm_2.gamma"] = \ - 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This method - also maintains the moving average on the master device.""" - assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' - mean = sum_ / size - sumvar = ssum - sum_ * mean - unbias_var = sumvar / (size - 1) - bias_var = sumvar / size - - self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data - self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data - - return mean, bias_var.clamp(self.eps) ** -0.5 - - -class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): - r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a - mini-batch. - - .. math:: - - y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta - - This module differs from the built-in PyTorch BatchNorm1d as the mean and - standard-deviation are reduced across all devices during training. - - For example, when one uses `nn.DataParallel` to wrap the network during - training, PyTorch's implementation normalize the tensor on each device using - the statistics only on that device, which accelerated the computation and - is also easy to implement, but the statistics might be inaccurate. - Instead, in this synchronized version, the statistics will be computed - over all training samples distributed on multiple devices. - - Note that, for one-GPU or CPU-only case, this module behaves exactly same - as the built-in PyTorch implementation. - - The mean and standard-deviation are calculated per-dimension over - the mini-batches and gamma and beta are learnable parameter vectors - of size C (where C is the input size). - - During training, this layer keeps a running estimate of its computed mean - and variance. 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Lightroom Classic CC 2020 also supports various file formats, such as JPEG, TIFF, PSD, PNG, DNG, RAW, and more. It also supports various languages, such as English, German, French, Spanish, Italian, Portuguese, Dutch, Swedish, Danish, Norwegian, Finnish, Russian, Polish, Czech, Hungarian, Turkish, Arabic, Chinese (Simplified), Chinese (Traditional), Japanese, Korean , and more.
-How to download and install Lightroom Classic CC 2020
-If you want to download and install Lightroom Classic CC 2020 on your desktop computer, you will need to follow these steps:
-System requirements
-Before you download and install Lightroom Classic CC 2020, you will need to make sure that your computer meets the minimum system requirements for running the software. Here are the system requirements for Windows and Mac OS:
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Once you have checked that your computer meets the system requirements, you can download Lightroom Classic CC 2020 from the official Adobe website. You will need to sign in with your Adobe ID or create one if you don't have one already. You will also need to choose a subscription plan that suits your needs and budget. You can choose from the following plans:
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You can also try Lightroom Classic CC 2020 for free for 7 days before you decide to buy a subscription plan. You can download the trial version from here.
-Installation steps
-After you have downloaded the Lightroom Classic CC 2020 installer file from the Adobe website, you can follow these steps to install it on your computer:
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Congratulations! You have successfully downloaded and installed Lightroom Classic CC 2020 on your computer. Now you can start editing your photos with this powerful desktop photo editor.
-How to use Lightroom Classic CC 2020
-Now that you have installed Lightroom Classic CC 2020 on your computer, you might be wondering how to use it. In this section, we will give you a brief overview of how to use Lightroom Classic CC 2020 to import, organize, edit, export, and share your photos.
-The layout of Lightroom Classic
-When you open Lightroom Classic CC 2020 for the first time, you will see a window that looks something like this:
-This is the main interface of Lightroom Classic CC 2020. It consists of several parts:
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Now that you are familiar with the layout of Lightroom Classic CC 2020, let's see how to use it to import, organize, edit, export, and share your photos.
-How to import and organize photos
-The first step to using Lightroom Classic CC 2020 is to import your photos into the software. You can import photos from various sources, such as your camera, your computer, an external drive, a memory card, or a CD/DVD. You can also import photos from other applications, such as Photoshop, Bridge, or Camera Raw.
-To import photos into Lightroom Classic CC 2020, you will need to follow these steps:
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Congratulations! You have successfully imported your photos into Lightroom Classic CC 2020. Now you can organize them in various ways in the Library module.
-To organize your photos in Lightroom Classic CC 2020, you can use various features and tools in the Library module. Here are some of them:
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By using these features and tools, you can organize your photos in Lightroom Classic CC 2020 in a way that makes sense to you and helps you work more efficiently.
-How to edit photos in Lightroom Classic CC 2020
-The next step to using Lightroom Classic CC 2020 is to edit your photos in the software. You can edit your photos in various ways in Lightroom Classic CC 2020, from basic adjustments to advanced transformations. You can also use presets and profiles to quickly change the look of your photos.
-To edit your photos in Lightroom Classic CC 2020, you will need to follow these steps:
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Congratulations! You have successfully edited your photo in Lightroom Classic CC 2020. Now you can export and share it with others.
-How to export and share photos from Lightroom Classic CC 2020
-The final step to using Lightroom Classic CC 2020 is to export and share your photos from the software. You can export your photos in various formats, sizes, qualities, and settings. You can also share your photos directly to various online platforms, such as Facebook, Flickr, Adobe Portfolio, and more.
-To export and share your photos from Lightroom Classic CC 2020, you will need to follow these steps:
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Congratulations! You have successfully exported and shared your photos from Lightroom Classic CC 2020. Now you can enjoy your photos and show them to others.
-Tips and tricks for using Lightroom Classic CC 2020
-Lightroom Classic CC 2020 is a powerful desktop photo editor that has many features and tools that can help you improve your photos and workflow. However, there are also some tips and tricks that can help you use Lightroom Classic CC 2020 more effectively and efficiently. Here are some of them:
-How to use presets and profiles
-Presets and profiles are one of the easiest ways to change the look of your photos in Lightroom Classic CC 2020. Presets are sets of adjustments that you can apply to your photos with one click. Profiles are sets of color and tonal adjustments that you can apply to your photos without affecting other settings.
-You can find presets and profiles in the left panel of the Develop module. You can choose from various presets and profiles that are built-in or downloaded from other sources. You can also create your own presets and profiles and save them for future use.
-To apply a preset or a profile to your photo, simply click on its name or thumbnail in the left panel. You can also preview how it will look by hovering over it with your mouse cursor. You can also apply multiple presets or profiles to your photo by holding down Shift while clicking on them.
-To create a preset or a profile from your photo, simply make the adjustments that you want in the right panel and then click on the plus icon next to the Presets or Profiles panel in the left panel. You can then name your preset or profile and choose which settings you want to include or exclude. You can also choose which folder or group you want to save your preset or profile in.
-To edit or delete a preset or a profile that you have created, simply right-click on its name or thumbnail in the left panel and choose Edit or Delete from the menu. You can also rename, move, or export your presets or profiles by right-clicking on them.
-Presets and profiles are a great way to save time and create consistent looks for your photos. You can also use them to experiment with different styles and effects for your photos.
-How to use range masking tools
-Range masking tools are one of the most advanced and useful features of Lightroom Classic CC 2020. Range masking tools allow you to apply adjustments to specific areas of your photo based on their color, luminance, or depth values. This can help you create more precise and realistic edits for your photos.
-You can find range masking tools in the adjustment brush, radial filter, and gradient filter tools in the toolbar below the loupe view. To use range masking tools, you will need to follow these steps:
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Congratulations! You have successfully used range masking tools to apply adjustments to specific areas of your photo. Now you can create more refined and realistic edits for your photos.
-How to create HDR panoramas
-HDR panoramas are one of the most impressive and creative features of Lightroom Classic CC 2020. HDR panoramas allow you to merge multiple photos that have different exposures and angles into one photo that has a high dynamic range and a wide field of view. This can help you capture scenes that have a lot of contrast and detail.
-To create HDR panoramas in Lightroom Classic CC 2020, you will need to follow these steps:
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Congratulations! You have successfully created an HDR panorama in Lightroom Classic CC 2020. Now you can edit and share it as you wish.
-How to fill uneven edges of your panorama
-One of the common problems that you might encounter when creating panoramas in Lightroom Classic CC 2020 is that the edges of your panorama might be uneven or have gaps. This can happen because of the distortion or alignment of your photos. To fix this problem, you can use the Fill Edges feature in Lightroom Classic CC 2020. This feature uses content-aware technology to fill in the gaps and create a seamless panorama.
-To use the Fill Edges feature in Lightroom Classic CC 2020, you will need to follow these steps:
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Congratulations! You have successfully filled in the uneven edges of your panorama in Lightroom Classic CC 2020. Now you can enjoy your panorama without any distractions.
-How to use multi-batch export
-Multi-batch export is one of the most convenient and time-saving features of Lightroom Classic CC 2020. Multi-batch export allows you to export multiple photos with different settings and destinations at once. This can help you save time and effort when you need to export your photos for different purposes or platforms.
-To use multi-batch export in Lightroom Classic CC 2020, you will need to follow these steps:
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Congratulations! You have successfully used multi-batch export in Lightroom Classic CC 2020. Now you can export your photos with ease and efficiency.
-Conclusion and FAQs
-In this article, we have given you an overview of what Adobe Photoshop Lightroom Classic CC 2020 is, how to download and install it, how to use it, and some tips and tricks for getting the most out of it. We hope that this article has helped you understand how to use this powerful desktop photo editor to enhance your photos and share them with the world.
-If you have any questions or doubts about Lightroom Classic CC 2020, you might find the answers in these FAQs:
-Q: How can I update Lightroom Classic CC 2020 to the latest version?
-A: You can update Lightroom Classic CC 2020 to the latest version by using the Creative Cloud desktop app. Simply open the app and go to the Apps tab. You will see a list of your installed apps and their update status. If there is an update available for Lightroom Classic CC 2020, you will see an Update button next to it. Click on it and wait for the update process to complete. You can also check for updates manually by clicking on the Help menu in Lightroom Classic CC 2020 and choosing Check for Updates.
-Q: How can I backup my Lightroom Classic CC 2020 catalog?
-A: You can backup your Lightroom Classic CC 2020 catalog by using the Catalog Settings dialog box. Simply open Lightroom Classic CC 2020 and go to the Edit menu (Windows) or the Lightroom Classic menu (Mac OS) and choose Catalog Settings. In the Catalog Settings dialog box, go to the General tab and find the Backup section. You can choose when you want Lightroom Classic CC 2020 to prompt you to backup your catalog, such as every time Lightroom exits, once a day, once a week, or once a month. You can also choose where you want to save your backup files by clicking on the Choose button. You can also backup your catalog manually by clicking on the Backup Now button.
-Q: How can I sync my photos and edits across devices?
-A: You can sync your photos and edits across devices by using Lightroom (the cloud-based app) or Adobe Creative Cloud. To use Lightroom, you will need to sign in with your Adobe ID and password in both Lightroom Classic CC 2020 and Lightroom. Then, you will need to enable sync in Lightroom Classic CC 2020 by clicking on the cloud icon on the top right corner of the window and choosing Start Syncing. Then, you will need to select which collections or albums you want to sync by clicking on the sync icon next to them in the left panel. You can also create new collections or albums and sync them by clicking on the plus icon next to the Collections or Albums panel in the left panel. Once you have synced your photos and edits, you can access them from any device that has Lightroom installed and signed in with your Adobe ID and password. You can also access them from a web browser by going to https://lightroom.adobe.com.
-To use Adobe Creative Cloud, you will need to sign in with your Adobe ID and password in both Lightroom Classic CC 2020 and the Creative Cloud desktop app. Then, you will need to enable sync in Lightroom Classic CC 2020 by clicking on the cloud icon on the top right corner of the window and choosing Start Syncing. Then, you will need to select which photos or folders you want to sync by right-clicking on them in the left panel and choosing Sync with Lightroom. You can also create new folders and sync them by clicking on the plus icon next to the Folders panel in the left panel. Once you have synced your photos and edits, you can access them from any device that has the Creative Cloud desktop app installed and signed in with your Adobe ID and password. You can also access them from a web browser by going to https://assets.adobe.com.
-Q: How can I get help or support for Lightroom Classic CC 2020?
-A: You can get help or support for Lightroom Classic CC 2020 by using various resources that are available online or offline. Here are some of them:
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\ No newline at end of file diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Fae Tactics Download For Pc Key Serial Numberl.md b/spaces/tioseFevbu/cartoon-converter/scripts/Fae Tactics Download For Pc Key Serial Numberl.md deleted file mode 100644 index 426b9d86e89ac3e4f2740e5fb396c01cc51c0e28..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Fae Tactics Download For Pc Key Serial Numberl.md +++ /dev/null @@ -1,16 +0,0 @@ - -
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\ No newline at end of file diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/utf1632prober.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/utf1632prober.py deleted file mode 100644 index 9fd1580b8378723fce2bc5b8c7460b72d7c778c6..0000000000000000000000000000000000000000 --- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/utf1632prober.py +++ /dev/null @@ -1,223 +0,0 @@ -######################## BEGIN LICENSE BLOCK ######################## -# -# Contributor(s): -# Jason Zavaglia -# -# This library is free software; you can redistribute it and/or -# modify it under the terms of the GNU Lesser General Public -# License as published by the Free Software Foundation; either -# version 2.1 of the License, or (at your option) any later version. -# -# This library is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU -# Lesser General Public License for more details. -# -# You should have received a copy of the GNU Lesser General Public -# License along with this library; if not, write to the Free Software -# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA -# 02110-1301 USA -######################### END LICENSE BLOCK ######################### -from .charsetprober import CharSetProber -from .enums import ProbingState - - -class UTF1632Prober(CharSetProber): - """ - This class simply looks for occurrences of zero bytes, and infers - whether the file is UTF16 or UTF32 (low-endian or big-endian) - For instance, files looking like ( \0 \0 \0 [nonzero] )+ - have a good probability to be UTF32BE. Files looking like ( \0 [nonzero] )+ - may be guessed to be UTF16BE, and inversely for little-endian varieties. - """ - - # how many logical characters to scan before feeling confident of prediction - MIN_CHARS_FOR_DETECTION = 20 - # a fixed constant ratio of expected zeros or non-zeros in modulo-position. - EXPECTED_RATIO = 0.94 - - def __init__(self): - super().__init__() - self.position = 0 - self.zeros_at_mod = [0] * 4 - self.nonzeros_at_mod = [0] * 4 - self._state = ProbingState.DETECTING - self.quad = [0, 0, 0, 0] - self.invalid_utf16be = False - self.invalid_utf16le = False - self.invalid_utf32be = False - self.invalid_utf32le = False - self.first_half_surrogate_pair_detected_16be = False - self.first_half_surrogate_pair_detected_16le = False - self.reset() - - def reset(self): - super().reset() - self.position = 0 - self.zeros_at_mod = [0] * 4 - self.nonzeros_at_mod = [0] * 4 - self._state = ProbingState.DETECTING - self.invalid_utf16be = False - self.invalid_utf16le = False - self.invalid_utf32be = False - self.invalid_utf32le = False - self.first_half_surrogate_pair_detected_16be = False - self.first_half_surrogate_pair_detected_16le = False - self.quad = [0, 0, 0, 0] - - @property - def charset_name(self): - if self.is_likely_utf32be(): - return "utf-32be" - if self.is_likely_utf32le(): - return "utf-32le" - if self.is_likely_utf16be(): - return "utf-16be" - if self.is_likely_utf16le(): - return "utf-16le" - # default to something valid - return "utf-16" - - @property - def language(self): - return "" - - def approx_32bit_chars(self): - return max(1.0, self.position / 4.0) - - def approx_16bit_chars(self): - return max(1.0, self.position / 2.0) - - def is_likely_utf32be(self): - approx_chars = self.approx_32bit_chars() - return approx_chars >= self.MIN_CHARS_FOR_DETECTION and ( - self.zeros_at_mod[0] / approx_chars > self.EXPECTED_RATIO - and self.zeros_at_mod[1] / approx_chars > self.EXPECTED_RATIO - and self.zeros_at_mod[2] / approx_chars > self.EXPECTED_RATIO - and self.nonzeros_at_mod[3] / approx_chars > self.EXPECTED_RATIO - and not self.invalid_utf32be - ) - - def is_likely_utf32le(self): - approx_chars = self.approx_32bit_chars() - return approx_chars >= self.MIN_CHARS_FOR_DETECTION and ( - self.nonzeros_at_mod[0] / approx_chars > self.EXPECTED_RATIO - and self.zeros_at_mod[1] / approx_chars > self.EXPECTED_RATIO - and self.zeros_at_mod[2] / approx_chars > self.EXPECTED_RATIO - and self.zeros_at_mod[3] / approx_chars > self.EXPECTED_RATIO - and not self.invalid_utf32le - ) - - def is_likely_utf16be(self): - approx_chars = self.approx_16bit_chars() - return approx_chars >= self.MIN_CHARS_FOR_DETECTION and ( - (self.nonzeros_at_mod[1] + self.nonzeros_at_mod[3]) / approx_chars - > self.EXPECTED_RATIO - and (self.zeros_at_mod[0] + self.zeros_at_mod[2]) / approx_chars - > self.EXPECTED_RATIO - and not self.invalid_utf16be - ) - - def is_likely_utf16le(self): - approx_chars = self.approx_16bit_chars() - return approx_chars >= self.MIN_CHARS_FOR_DETECTION and ( - (self.nonzeros_at_mod[0] + self.nonzeros_at_mod[2]) / approx_chars - > self.EXPECTED_RATIO - and (self.zeros_at_mod[1] + self.zeros_at_mod[3]) / approx_chars - > self.EXPECTED_RATIO - and not self.invalid_utf16le - ) - - def validate_utf32_characters(self, quad): - """ - Validate if the quad of bytes is valid UTF-32. - - UTF-32 is valid in the range 0x00000000 - 0x0010FFFF - excluding 0x0000D800 - 0x0000DFFF - - https://en.wikipedia.org/wiki/UTF-32 - """ - if ( - quad[0] != 0 - or quad[1] > 0x10 - or (quad[0] == 0 and quad[1] == 0 and 0xD8 <= quad[2] <= 0xDF) - ): - self.invalid_utf32be = True - if ( - quad[3] != 0 - or quad[2] > 0x10 - or (quad[3] == 0 and quad[2] == 0 and 0xD8 <= quad[1] <= 0xDF) - ): - self.invalid_utf32le = True - - def validate_utf16_characters(self, pair): - """ - Validate if the pair of bytes is valid UTF-16. - - UTF-16 is valid in the range 0x0000 - 0xFFFF excluding 0xD800 - 0xFFFF - with an exception for surrogate pairs, which must be in the range - 0xD800-0xDBFF followed by 0xDC00-0xDFFF - - https://en.wikipedia.org/wiki/UTF-16 - """ - if not self.first_half_surrogate_pair_detected_16be: - if 0xD8 <= pair[0] <= 0xDB: - self.first_half_surrogate_pair_detected_16be = True - elif 0xDC <= pair[0] <= 0xDF: - self.invalid_utf16be = True - else: - if 0xDC <= pair[0] <= 0xDF: - self.first_half_surrogate_pair_detected_16be = False - else: - self.invalid_utf16be = True - - if not self.first_half_surrogate_pair_detected_16le: - if 0xD8 <= pair[1] <= 0xDB: - self.first_half_surrogate_pair_detected_16le = True - elif 0xDC <= pair[1] <= 0xDF: - self.invalid_utf16le = True - else: - if 0xDC <= pair[1] <= 0xDF: - self.first_half_surrogate_pair_detected_16le = False - else: - self.invalid_utf16le = True - - def feed(self, byte_str): - for c in byte_str: - mod4 = self.position % 4 - self.quad[mod4] = c - if mod4 == 3: - self.validate_utf32_characters(self.quad) - self.validate_utf16_characters(self.quad[0:2]) - self.validate_utf16_characters(self.quad[2:4]) - if c == 0: - self.zeros_at_mod[mod4] += 1 - else: - self.nonzeros_at_mod[mod4] += 1 - self.position += 1 - return self.state - - @property - def state(self): - if self._state in {ProbingState.NOT_ME, ProbingState.FOUND_IT}: - # terminal, decided states - return self._state - if self.get_confidence() > 0.80: - self._state = ProbingState.FOUND_IT - elif self.position > 4 * 1024: - # if we get to 4kb into the file, and we can't conclude it's UTF, - # let's give up - self._state = ProbingState.NOT_ME - return self._state - - def get_confidence(self): - return ( - 0.85 - if ( - self.is_likely_utf16le() - or self.is_likely_utf16be() - or self.is_likely_utf32le() - or self.is_likely_utf32be() - ) - else 0.00 - ) diff --git a/spaces/tmaham/DS-Fusion-Express/notebook_helpers.py b/spaces/tmaham/DS-Fusion-Express/notebook_helpers.py deleted file mode 100644 index 5d0ebd7e1f8095053f34b1d7652b55d165097f0e..0000000000000000000000000000000000000000 --- a/spaces/tmaham/DS-Fusion-Express/notebook_helpers.py +++ /dev/null @@ -1,270 +0,0 @@ -from torchvision.datasets.utils import download_url -from ldm.util import instantiate_from_config -import torch -import os -# todo ? -from google.colab import files -from IPython.display import Image as ipyimg -import ipywidgets as widgets -from PIL import Image -from numpy import asarray -from einops import rearrange, repeat -import torch, torchvision -from ldm.models.diffusion.ddim import DDIMSampler -from ldm.util import ismap -import time -from omegaconf import OmegaConf - - -def download_models(mode): - - if mode == "superresolution": - # this is the small bsr light model - url_conf = 'https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1' - url_ckpt = 'https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1' - - path_conf = 'logs/diffusion/superresolution_bsr/configs/project.yaml' - path_ckpt = 'logs/diffusion/superresolution_bsr/checkpoints/last.ckpt' - - download_url(url_conf, path_conf) - download_url(url_ckpt, path_ckpt) - - path_conf = path_conf + '/?dl=1' # fix it - path_ckpt = path_ckpt + '/?dl=1' # fix it - return path_conf, path_ckpt - - else: - raise NotImplementedError - - -def load_model_from_config(config, ckpt): - print(f"Loading model from {ckpt}") - pl_sd = torch.load(ckpt, map_location="cpu") - global_step = pl_sd["global_step"] - sd = pl_sd["state_dict"] - model = instantiate_from_config(config.model) - m, u = model.load_state_dict(sd, strict=False) - model.cuda() - model.eval() - return {"model": model}, global_step - - -def get_model(mode): - path_conf, path_ckpt = download_models(mode) - config = OmegaConf.load(path_conf) - model, step = load_model_from_config(config, path_ckpt) - return model - - -def get_custom_cond(mode): - dest = "data/example_conditioning" - - if mode == "superresolution": - uploaded_img = files.upload() - filename = next(iter(uploaded_img)) - name, filetype = filename.split(".") # todo assumes just one dot in name ! - os.rename(f"{filename}", f"{dest}/{mode}/custom_{name}.{filetype}") - - elif mode == "text_conditional": - w = widgets.Text(value='A cake with cream!', disabled=True) - display(w) - - with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", 'w') as f: - f.write(w.value) - - elif mode == "class_conditional": - w = widgets.IntSlider(min=0, max=1000) - display(w) - with open(f"{dest}/{mode}/custom.txt", 'w') as f: - f.write(w.value) - - else: - raise NotImplementedError(f"cond not implemented for mode{mode}") - - -def get_cond_options(mode): - path = "data/example_conditioning" - path = os.path.join(path, mode) - onlyfiles = [f for f in sorted(os.listdir(path))] - return path, onlyfiles - - -def select_cond_path(mode): - path = "data/example_conditioning" # todo - path = os.path.join(path, mode) - onlyfiles = [f for f in sorted(os.listdir(path))] - - selected = widgets.RadioButtons( - options=onlyfiles, - description='Select conditioning:', - disabled=False - ) - display(selected) - selected_path = os.path.join(path, selected.value) - return selected_path - - -def get_cond(mode, selected_path): - example = dict() - if mode == "superresolution": - up_f = 4 - visualize_cond_img(selected_path) - - c = Image.open(selected_path) - c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0) - c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True) - c_up = rearrange(c_up, '1 c h w -> 1 h w c') - c = rearrange(c, '1 c h w -> 1 h w c') - c = 2. * c - 1. - - c = c.to(torch.device("cuda")) - example["LR_image"] = c - example["image"] = c_up - - return example - - -def visualize_cond_img(path): - display(ipyimg(filename=path)) - - -def run(model, selected_path, task, custom_steps, resize_enabled=False, classifier_ckpt=None, global_step=None): - - example = get_cond(task, selected_path) - - save_intermediate_vid = False - n_runs = 1 - masked = False - guider = None - ckwargs = None - mode = 'ddim' - ddim_use_x0_pred = False - temperature = 1. - eta = 1. - make_progrow = True - custom_shape = None - - height, width = example["image"].shape[1:3] - split_input = height >= 128 and width >= 128 - - if split_input: - ks = 128 - stride = 64 - vqf = 4 # - model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride), - "vqf": vqf, - "patch_distributed_vq": True, - "tie_braker": False, - "clip_max_weight": 0.5, - "clip_min_weight": 0.01, - "clip_max_tie_weight": 0.5, - "clip_min_tie_weight": 0.01} - else: - if hasattr(model, "split_input_params"): - delattr(model, "split_input_params") - - invert_mask = False - - x_T = None - for n in range(n_runs): - if custom_shape is not None: - x_T = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device) - x_T = repeat(x_T, '1 c h w -> b c h w', b=custom_shape[0]) - - logs = make_convolutional_sample(example, model, - mode=mode, custom_steps=custom_steps, - eta=eta, swap_mode=False , masked=masked, - invert_mask=invert_mask, quantize_x0=False, - custom_schedule=None, decode_interval=10, - resize_enabled=resize_enabled, custom_shape=custom_shape, - temperature=temperature, noise_dropout=0., - corrector=guider, corrector_kwargs=ckwargs, x_T=x_T, save_intermediate_vid=save_intermediate_vid, - make_progrow=make_progrow,ddim_use_x0_pred=ddim_use_x0_pred - ) - return logs - - -@torch.no_grad() -def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None, - mask=None, x0=None, quantize_x0=False, img_callback=None, - temperature=1., noise_dropout=0., score_corrector=None, - corrector_kwargs=None, x_T=None, log_every_t=None - ): - - ddim = DDIMSampler(model) - bs = shape[0] # dont know where this comes from but wayne - shape = shape[1:] # cut batch dim - print(f"Sampling with eta = {eta}; steps: {steps}") - samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback, - normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta, - mask=mask, x0=x0, temperature=temperature, verbose=False, - score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, x_T=x_T) - - return samples, intermediates - - -@torch.no_grad() -def make_convolutional_sample(batch, model, mode="vanilla", custom_steps=None, eta=1.0, swap_mode=False, masked=False, - invert_mask=True, quantize_x0=False, custom_schedule=None, decode_interval=1000, - resize_enabled=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None, - corrector_kwargs=None, x_T=None, save_intermediate_vid=False, make_progrow=True,ddim_use_x0_pred=False): - log = dict() - - z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key, - return_first_stage_outputs=True, - force_c_encode=not (hasattr(model, 'split_input_params') - and model.cond_stage_key == 'coordinates_bbox'), - return_original_cond=True) - - log_every_t = 1 if save_intermediate_vid else None - - if custom_shape is not None: - z = torch.randn(custom_shape) - print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}") - - z0 = None - - log["input"] = x - log["reconstruction"] = xrec - - if ismap(xc): - log["original_conditioning"] = model.to_rgb(xc) - if hasattr(model, 'cond_stage_key'): - log[model.cond_stage_key] = model.to_rgb(xc) - - else: - log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x) - if model.cond_stage_model: - log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x) - if model.cond_stage_key =='class_label': - log[model.cond_stage_key] = xc[model.cond_stage_key] - - with model.ema_scope("Plotting"): - t0 = time.time() - img_cb = None - - sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape, - eta=eta, - quantize_x0=quantize_x0, img_callback=img_cb, mask=None, x0=z0, - temperature=temperature, noise_dropout=noise_dropout, - score_corrector=corrector, corrector_kwargs=corrector_kwargs, - x_T=x_T, log_every_t=log_every_t) - t1 = time.time() - - if ddim_use_x0_pred: - sample = intermediates['pred_x0'][-1] - - x_sample = model.decode_first_stage(sample) - - try: - x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True) - log["sample_noquant"] = x_sample_noquant - log["sample_diff"] = torch.abs(x_sample_noquant - x_sample) - except: - pass - - log["sample"] = x_sample - log["time"] = t1 - t0 - - return log \ No newline at end of file diff --git a/spaces/tom-beer/hotel-recommender/README.md b/spaces/tom-beer/hotel-recommender/README.md deleted file mode 100644 index 987d694b2fd15ff63d1749956a2eca06f2460e94..0000000000000000000000000000000000000000 --- a/spaces/tom-beer/hotel-recommender/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Hotel Recommender -emoji: 👁 -colorFrom: pink -colorTo: green -sdk: gradio -sdk_version: 3.34.0 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/tomofi/MMOCR/configs/_base_/schedules/schedule_sgd_160e.py b/spaces/tomofi/MMOCR/configs/_base_/schedules/schedule_sgd_160e.py deleted file mode 100644 index 0958701a28ad8802a65caf0bb99cef02b0b021c5..0000000000000000000000000000000000000000 --- a/spaces/tomofi/MMOCR/configs/_base_/schedules/schedule_sgd_160e.py +++ /dev/null @@ -1,11 +0,0 @@ -# optimizer -optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001) -optimizer_config = dict(grad_clip=None) -# learning policy -lr_config = dict( - policy='step', - warmup='linear', - warmup_iters=500, - warmup_ratio=0.001, - step=[80, 128]) -total_epochs = 160 diff --git a/spaces/tomofi/MMOCR/tests/test_apis/test_single_gpu_test.py b/spaces/tomofi/MMOCR/tests/test_apis/test_single_gpu_test.py deleted file mode 100644 index 64fd99fe92187aedd9ab2a2dc574e693f504191b..0000000000000000000000000000000000000000 --- a/spaces/tomofi/MMOCR/tests/test_apis/test_single_gpu_test.py +++ /dev/null @@ -1,205 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import copy -import json -import os -import os.path as osp -import tempfile - -import mmcv -import numpy as np -import pytest -import torch -from mmcv import Config -from mmcv.parallel import MMDataParallel - -from mmocr.apis.test import single_gpu_test -from mmocr.datasets import build_dataloader, build_dataset -from mmocr.models import build_detector -from mmocr.utils import check_argument, list_to_file, revert_sync_batchnorm - - -def build_model(cfg): - model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) - model = revert_sync_batchnorm(model) - model = MMDataParallel(model) - - return model - - -def generate_sample_dataloader(cfg, curr_dir, img_prefix='', ann_file=''): - must_keys = ['img_norm_cfg', 'ori_filename', 'img_shape', 'ori_shape'] - test_pipeline = cfg.data.test.pipeline - for key in must_keys: - if test_pipeline[1].type == 'MultiRotateAugOCR': - collect_pipeline = test_pipeline[1]['transforms'][-1] - else: - collect_pipeline = test_pipeline[-1] - if 'meta_keys' not in collect_pipeline: - continue - collect_pipeline['meta_keys'].append(key) - - img_prefix = osp.join(curr_dir, img_prefix) - ann_file = osp.join(curr_dir, ann_file) - test = copy.deepcopy(cfg.data.test.datasets[0]) - test.img_prefix = img_prefix - test.ann_file = ann_file - cfg.data.workers_per_gpu = 0 - cfg.data.test.datasets = [test] - dataset = build_dataset(cfg.data.test) - - loader_cfg = { - **dict((k, cfg.data[k]) for k in [ - 'workers_per_gpu', 'samples_per_gpu' - ] if k in cfg.data) - } - test_loader_cfg = { - **loader_cfg, - **dict(shuffle=False, drop_last=False), - **cfg.data.get('test_dataloader', {}) - } - - data_loader = build_dataloader(dataset, **test_loader_cfg) - - return data_loader - - -@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda') -@pytest.mark.parametrize('cfg_file', [ - '../configs/textrecog/sar/sar_r31_parallel_decoder_academic.py', - '../configs/textrecog/crnn/crnn_academic_dataset.py', - '../configs/textrecog/seg/seg_r31_1by16_fpnocr_academic.py' -]) -def test_single_gpu_test_recog(cfg_file): - curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) - config_file = os.path.join(curr_dir, cfg_file) - cfg = Config.fromfile(config_file) - - model = build_model(cfg) - img_prefix = 'data/ocr_toy_dataset/imgs' - ann_file = 'data/ocr_toy_dataset/label.txt' - data_loader = generate_sample_dataloader(cfg, curr_dir, img_prefix, - ann_file) - - with tempfile.TemporaryDirectory() as tmpdirname: - out_dir = osp.join(tmpdirname, 'tmp') - results = single_gpu_test(model, data_loader, out_dir=out_dir) - assert check_argument.is_type_list(results, dict) - - -@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda') -@pytest.mark.parametrize( - 'cfg_file', - ['../configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2017.py']) -def test_single_gpu_test_det(cfg_file): - curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) - config_file = os.path.join(curr_dir, cfg_file) - cfg = Config.fromfile(config_file) - - model = build_model(cfg) - img_prefix = 'data/toy_dataset/imgs' - ann_file = 'data/toy_dataset/instances_test.json' - data_loader = generate_sample_dataloader(cfg, curr_dir, img_prefix, - ann_file) - - with tempfile.TemporaryDirectory() as tmpdirname: - out_dir = osp.join(tmpdirname, 'tmp') - results = single_gpu_test(model, data_loader, out_dir=out_dir) - assert check_argument.is_type_list(results, dict) - - -def gene_sdmgr_model_dataloader(cfg, dirname, curr_dir, empty_img=False): - json_obj = { - 'file_name': - '1.jpg', - 'height': - 348, - 'width': - 348, - 'annotations': [{ - 'box': [114.0, 19.0, 230.0, 19.0, 230.0, 1.0, 114.0, 1.0], - 'text': - 'CHOEUN', - 'label': - 1 - }] - } - ann_file = osp.join(dirname, 'test.txt') - list_to_file(ann_file, [json.dumps(json_obj, ensure_ascii=False)]) - - if not empty_img: - img = np.ones((348, 348, 3), dtype=np.uint8) - img_file = osp.join(dirname, '1.jpg') - mmcv.imwrite(img, img_file) - - test = copy.deepcopy(cfg.data.test) - test.ann_file = ann_file - test.img_prefix = dirname - test.dict_file = osp.join(curr_dir, 'data/kie_toy_dataset/dict.txt') - cfg.data.workers_per_gpu = 1 - cfg.data.test = test - cfg.model.class_list = osp.join(curr_dir, - 'data/kie_toy_dataset/class_list.txt') - - dataset = build_dataset(cfg.data.test) - - loader_cfg = { - **dict((k, cfg.data[k]) for k in [ - 'workers_per_gpu', 'samples_per_gpu' - ] if k in cfg.data) - } - test_loader_cfg = { - **loader_cfg, - **dict(shuffle=False, drop_last=False), - **cfg.data.get('test_dataloader', {}) - } - - data_loader = build_dataloader(dataset, **test_loader_cfg) - model = build_model(cfg) - - return model, data_loader - - -@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda') -@pytest.mark.parametrize( - 'cfg_file', ['../configs/kie/sdmgr/sdmgr_unet16_60e_wildreceipt.py']) -def test_single_gpu_test_kie(cfg_file): - curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) - config_file = os.path.join(curr_dir, cfg_file) - cfg = Config.fromfile(config_file) - - with tempfile.TemporaryDirectory() as tmpdirname: - out_dir = osp.join(tmpdirname, 'tmp') - model, data_loader = gene_sdmgr_model_dataloader( - cfg, out_dir, curr_dir) - results = single_gpu_test( - model, data_loader, out_dir=out_dir, is_kie=True) - assert check_argument.is_type_list(results, dict) - - -@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda') -@pytest.mark.parametrize( - 'cfg_file', ['../configs/kie/sdmgr/sdmgr_novisual_60e_wildreceipt.py']) -def test_single_gpu_test_kie_novisual(cfg_file): - curr_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) - config_file = os.path.join(curr_dir, cfg_file) - cfg = Config.fromfile(config_file) - meta_keys = list(cfg.data.test.pipeline[-1]['meta_keys']) - must_keys = ['img_norm_cfg', 'ori_filename', 'img_shape'] - for key in must_keys: - meta_keys.append(key) - - cfg.data.test.pipeline[-1]['meta_keys'] = tuple(meta_keys) - - with tempfile.TemporaryDirectory() as tmpdirname: - out_dir = osp.join(tmpdirname, 'tmp') - model, data_loader = gene_sdmgr_model_dataloader( - cfg, out_dir, curr_dir, empty_img=True) - results = single_gpu_test( - model, data_loader, out_dir=out_dir, is_kie=True) - assert check_argument.is_type_list(results, dict) - - model, data_loader = gene_sdmgr_model_dataloader( - cfg, out_dir, curr_dir) - results = single_gpu_test( - model, data_loader, out_dir=out_dir, is_kie=True) - assert check_argument.is_type_list(results, dict) diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py deleted file mode 100644 index 1b695f0e19049dc91b7656d7684df151896b7727..0000000000000000000000000000000000000000 --- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py +++ /dev/null @@ -1,12 +0,0 @@ -_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' -model = dict( - roi_head=dict( - bbox_roi_extractor=dict( - type='SingleRoIExtractor', - roi_layer=dict( - _delete_=True, - type='DeformRoIPoolPack', - output_size=7, - output_channels=256), - out_channels=256, - featmap_strides=[4, 8, 16, 32]))) diff --git a/spaces/touhou-ai-experimental/research-paper/style.css b/spaces/touhou-ai-experimental/research-paper/style.css deleted file mode 100644 index edbbf951b476dc095ee32990c0e278893b365a5f..0000000000000000000000000000000000000000 --- a/spaces/touhou-ai-experimental/research-paper/style.css +++ /dev/null @@ -1,133 +0,0 @@ -/* Reset some default styling */ -body, h1, h2, p { - margin: 0; - padding: 0; -} - -body { - font-family: Arial, sans-serif; - background-color: #121212; - color: #FFFFFF; -} - -.header-title { - font-family: 'Montserrat', sans-serif; - font-size: 24px; - text-align: center; - color: #FF4500; /* Original Orange-Red Color */ - margin: 0; - padding: 20px 0; -} -.header-subtitle { - font-size: 16px; - text-align: center; - color: #FF4500; - margin: 10px 0; -} - -.highlight-orange { - color: #EE82EE; /* Original Orange-Red Color */ -} - -.highlight-violate { - color: #EE82EE; /* Violate Color */ -} -header { - display: flex; - justify-content: space-between; - align-items: center; - background-color: #FF4500; /* Original Orange-Red Color */ - padding: 10px 20px; -} - -nav ul { - list-style-type: none; - display: flex; -} - -nav li { - margin-right: 20px; -} - -nav a { - text-decoration: none; - color: #FFFFFF; -} - -section { - background-color: #1E1E1E; - border: 2px solid #FF4500; - border-radius: 10px; - padding: 20px; - margin: 20px; -} - -section:hover { - border-color: #EE82EE; -} - -h2 { - color: #FF4500; - margin-bottom: 10px; -} - -.icon { - margin-right: 10px; -} - -.footer { - text-align: center; - margin-top: 40px; -} - -.buttons { - display: flex; - justify-content: center; - align-items: center; - margin: 20px auto; -} - -.button { - background-color: #FF4500; - color: #121212; - padding: 8px 16px; - border-radius: 10px; - text-decoration: none; - font-weight: bold; - transition: background-color 0.3s; - margin-right: 10px; -} - -.button:last-child { - margin-right: 0; -} - -.button:hover { - background-color: #EE82EE; -} - -/* Media Queries for Responsive Design */ -@media (max-width: 768px) { - header { - padding: 10px; - } - - nav ul { - flex-direction: row; - } - - nav li { - margin-right: 10px; - } - - .buttons { - flex-direction: column; - align-items: center; - margin: 10px auto; - } - - .button { - margin-right: 0; - margin-bottom: 10px; - } -} diff --git a/spaces/triggah61/chingu-music/audiocraft/modules/__init__.py b/spaces/triggah61/chingu-music/audiocraft/modules/__init__.py deleted file mode 100644 index 81ba30f6466ff91b90490a4fb92f7d3d0d00144d..0000000000000000000000000000000000000000 --- a/spaces/triggah61/chingu-music/audiocraft/modules/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -# flake8: noqa -from .conv import ( - NormConv1d, - NormConv2d, - NormConvTranspose1d, - NormConvTranspose2d, - StreamableConv1d, - StreamableConvTranspose1d, - pad_for_conv1d, - pad1d, - unpad1d, -) -from .lstm import StreamableLSTM -from .seanet import SEANetEncoder, SEANetDecoder diff --git a/spaces/triggah61/chingu-music/audiocraft/modules/streaming.py b/spaces/triggah61/chingu-music/audiocraft/modules/streaming.py deleted file mode 100644 index fdbdf5e90fc0c6560873d66bf273460b38e5ed7e..0000000000000000000000000000000000000000 --- a/spaces/triggah61/chingu-music/audiocraft/modules/streaming.py +++ /dev/null @@ -1,135 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -""" -Streaming module API that should be implemented by all Streaming components, -""" - -from contextlib import contextmanager -import typing as tp -from torch import nn -import torch - - -State = tp.Dict[str, torch.Tensor] - - -class StreamingModule(nn.Module): - """Common API for streaming components. - - Each streaming component has a streaming state, which is just a dict[str, Tensor]. - By convention, the first dim of each tensor must be the batch size. - Don't use dots in the key names, as this would clash with submodules - (like in state_dict). - - If `self._is_streaming` is True, the component should use and remember - the proper state inside `self._streaming_state`. - - To set a streaming component in streaming state, use - - with module.streaming(): - ... - - This will automatically reset the streaming state when exiting the context manager. - This also automatically propagates to all streaming children module. - - Some module might also implement the `StreamingModule.flush` method, although - this one is trickier, as all parents module must be StreamingModule and implement - it as well for it to work properly. See `StreamingSequential` after. - """ - def __init__(self) -> None: - super().__init__() - self._streaming_state: State = {} - self._is_streaming = False - - def _apply_named_streaming(self, fn: tp.Any): - for name, module in self.named_modules(): - if isinstance(module, StreamingModule): - fn(name, module) - - def _set_streaming(self, streaming: bool): - def _set_streaming(name, module): - module._is_streaming = streaming - self._apply_named_streaming(_set_streaming) - - @contextmanager - def streaming(self): - """Context manager to enter streaming mode. Reset streaming state on exit. - """ - self._set_streaming(True) - try: - yield - finally: - self._set_streaming(False) - self.reset_streaming() - - def reset_streaming(self): - """Reset the streaming state. - """ - def _reset(name: str, module: StreamingModule): - module._streaming_state.clear() - - self._apply_named_streaming(_reset) - - def get_streaming_state(self) -> State: - """Return the streaming state, including that of sub-modules. - """ - state: State = {} - - def _add(name: str, module: StreamingModule): - if name: - name += "." - for key, value in module._streaming_state.items(): - state[name + key] = value - - self._apply_named_streaming(_add) - return state - - def set_streaming_state(self, state: State): - """Set the streaming state, including that of sub-modules. - """ - state = dict(state) - - def _set(name: str, module: StreamingModule): - if name: - name += "." - module._streaming_state.clear() - for key, value in list(state.items()): - # complexity is not ideal here, but probably fine. - if key.startswith(name): - local_key = key[len(name):] - if '.' not in local_key: - module._streaming_state[local_key] = value - del state[key] - - self._apply_named_streaming(_set) - assert len(state) == 0, list(state.keys()) - - def flush(self, x: tp.Optional[torch.Tensor] = None): - """Flush any remaining outputs that were waiting for completion. - Typically, for convolutions, this will add the final padding - and process the last buffer. - - This should take an optional argument `x`, which will be provided - if a module before this one in the streaming pipeline has already - spitted out a flushed out buffer. - """ - if x is None: - return None - else: - return self(x) - - -class StreamingSequential(StreamingModule, nn.Sequential): - """A streaming compatible alternative of `nn.Sequential`. - """ - def flush(self, x: tp.Optional[torch.Tensor] = None): - for module in self: - if isinstance(module, StreamingModule): - x = module.flush(x) - elif x is not None: - x = module(x) - return x diff --git a/spaces/tsi-org/LLaVA/llava/model/multimodal_projector/builder.py b/spaces/tsi-org/LLaVA/llava/model/multimodal_projector/builder.py deleted file mode 100644 index 31cd4f48e6055cd6d00a162af30b1c8139e26b57..0000000000000000000000000000000000000000 --- a/spaces/tsi-org/LLaVA/llava/model/multimodal_projector/builder.py +++ /dev/null @@ -1,51 +0,0 @@ -import torch -import torch.nn as nn -import re - - -class IdentityMap(nn.Module): - def __init__(self): - super().__init__() - - def forward(self, x, *args, **kwargs): - return x - - @property - def config(self): - return {"mm_projector_type": 'identity'} - - -class SimpleResBlock(nn.Module): - def __init__(self, channels): - super().__init__() - self.pre_norm = nn.LayerNorm(channels) - - self.proj = nn.Sequential( - nn.Linear(channels, channels), - nn.GELU(), - nn.Linear(channels, channels) - ) - def forward(self, x): - x = self.pre_norm(x) - return x + self.proj(x) - - -def build_vision_projector(config, delay_load=False, **kwargs): - projector_type = getattr(config, 'mm_projector_type', 'linear') - - if projector_type == 'linear': - return nn.Linear(config.mm_hidden_size, config.hidden_size) - - mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type) - if mlp_gelu_match: - mlp_depth = int(mlp_gelu_match.group(1)) - modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)] - for _ in range(1, mlp_depth): - modules.append(nn.GELU()) - modules.append(nn.Linear(config.hidden_size, config.hidden_size)) - return nn.Sequential(*modules) - - if projector_type == 'identity': - return IdentityMap() - - raise ValueError(f'Unknown projector type: {projector_type}') diff --git a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/nlg_utils.py b/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/nlg_utils.py deleted file mode 100644 index 6c37a835847feee3728c8691a299a3391235aa6c..0000000000000000000000000000000000000000 --- a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/nlg_utils.py +++ /dev/null @@ -1,65 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from typing import Union - - -@torch.jit.script -def nlg_softmax_prob(logit: torch.Tensor, temperature:float=1.): - assert logit.ndim == 1 and logit.shape[0] >= 1, 'Error! Invalid logit shape.' - # do temperature - probs = logit.div(temperature).softmax(dim=0) - return probs - - -@torch.jit.script -def nlg_prob_decay(prob: torch.Tensor, before_tokens: torch.Tensor, watch_len: int=10): - ''' - 概率衰减，如果多次采样到同一个词，则对已有词进行概率衰减 - :param prob: 当前 token 的概率 - :param before_tokens: 之前生成的 token id. - :param max_watch_len: 最大观察长度 - :param copy: 是否复制 - :return: - ''' - assert prob.ndim == 1, 'Error! Invalid now_token_probs shape.' - assert before_tokens.ndim == 1, 'Error! Invalid before_tokens shape.' - assert watch_len > 0, 'Error! Invalid max_watch_len value.' - - uq_ids, uq_counts = torch.unique(before_tokens[-watch_len:], sorted=False, return_counts=True) - prob[uq_ids] = prob[uq_ids] * (1 - uq_counts.type_as(prob) / watch_len) - return prob - - -@torch.jit.script -def nlg_sample(prob: torch.Tensor, top_k:int=3, top_p:float=0.9): - assert prob.ndim == 1 and prob.shape[0] >= 1, 'Error! Invalid prob shape.' - - # do top_k - topk_value, topk_idx = torch.topk(prob, top_k) - # do top_p - bools = torch.cumsum(topk_value, 0) <= top_p - # 与自己右移一位做 或 运算，可以额外包含多一个临界结果，从而使分数能大于等于top_p所需分数 - torch.logical_or(bools, torch.roll(bools, 1, 0), out=bools) - # 保证至少有一个候选对象 - bools[0] = True - topk_value = topk_value[bools] - topk_idx = topk_idx[bools] - # random choice word idx - topk_value.clamp_min_(1e-8) - word_idx = topk_idx[torch.multinomial(topk_value, 1)] - - return word_idx - - -# @torch.jit.script -# def check_early_stop(logit: torch.Tensor, n_gram=1, max_repeat): -# ''' -# 检查是否要早期停止，避免无限重复同一个符号 -# :param logit: -# :param top_k: -# :param top_p: -# :param temperature: -# :return: -# ''' -# assert logit.ndim == 1, 'Error! Invalid logit shape.' diff --git a/spaces/ucalyptus/PTI/configs/hyperparameters.py b/spaces/ucalyptus/PTI/configs/hyperparameters.py deleted file mode 100644 index ab50db62b29ef29eeb128663e4f7ff737df81ca3..0000000000000000000000000000000000000000 --- a/spaces/ucalyptus/PTI/configs/hyperparameters.py +++ /dev/null @@ -1,28 +0,0 @@ -## Architechture -lpips_type = 'alex' -first_inv_type = 'w' -optim_type = 'adam' - -## Locality regularization -latent_ball_num_of_samples = 1 -locality_regularization_interval = 1 -use_locality_regularization = False -regulizer_l2_lambda = 0.1 -regulizer_lpips_lambda = 0.1 -regulizer_alpha = 30 - -## Loss -pt_l2_lambda = 1 -pt_lpips_lambda = 1 - -## Steps -LPIPS_value_threshold = 0.06 -max_pti_steps = 350 -first_inv_steps = 450 -max_images_to_invert = 300 - -## Optimization -pti_learning_rate = 3e-4 -first_inv_lr = 5e-3 -train_batch_size = 1 -use_last_w_pivots = False diff --git a/spaces/unidiffuser-testing/unidiffuser-testing/libs/timm.py b/spaces/unidiffuser-testing/unidiffuser-testing/libs/timm.py deleted file mode 100644 index 943a227f8e057b048e2b2dcb3a16a00f9c7c7d53..0000000000000000000000000000000000000000 --- a/spaces/unidiffuser-testing/unidiffuser-testing/libs/timm.py +++ /dev/null @@ -1,112 +0,0 @@ -# code from timm 0.3.2 -import torch -import torch.nn as nn -import math -import warnings - - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - # Cut & paste from PyTorch official master until it's in a few official releases - RW - # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf - def norm_cdf(x): - # Computes standard normal cumulative distribution function - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - - with torch.no_grad(): - # Values are generated by using a truncated uniform distribution and - # then using the inverse CDF for the normal distribution. - # Get upper and lower cdf values - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - - # Uniformly fill tensor with values from [l, u], then translate to - # [2l-1, 2u-1]. - tensor.uniform_(2 * l - 1, 2 * u - 1) - - # Use inverse cdf transform for normal distribution to get truncated - # standard normal - tensor.erfinv_() - - # Transform to proper mean, std - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - - # Clamp to ensure it's in the proper range - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - r"""Fills the input Tensor with values drawn from a truncated - normal distribution. The values are effectively drawn from the - normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` - with values outside :math:`[a, b]` redrawn until they are within - the bounds. The method used for generating the random values works - best when :math:`a \leq \text{mean} \leq b`. - Args: - tensor: an n-dimensional `torch.Tensor` - mean: the mean of the normal distribution - std: the standard deviation of the normal distribution - a: the minimum cutoff value - b: the maximum cutoff value - Examples: - >>> w = torch.empty(3, 5) - >>> nn.init.trunc_normal_(w) - """ - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def drop_path(x, drop_prob: float = 0., training: bool = False): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - - This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, - the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... - See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for - changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use - 'survival rate' as the argument. - - """ - if drop_prob == 0. or not training: - return x - keep_prob = 1 - drop_prob - shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets - random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) - random_tensor.floor_() # binarize - output = x.div(keep_prob) * random_tensor - return output - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - """ - def __init__(self, drop_prob=None): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - - def forward(self, x): - return drop_path(x, self.drop_prob, self.training) - - -class Mlp(nn.Module): - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x diff --git a/spaces/vaibhavarduino/anime-plus/op/fused_act.py b/spaces/vaibhavarduino/anime-plus/op/fused_act.py deleted file mode 100644 index 8459d510d7b79684779dfe47f5b46d81c94b4a4d..0000000000000000000000000000000000000000 --- a/spaces/vaibhavarduino/anime-plus/op/fused_act.py +++ /dev/null @@ -1,86 +0,0 @@ -import os - -import torch -from torch import nn -from torch.autograd import Function -from torch.utils.cpp_extension import load - - -module_path = os.path.dirname(__file__) -fused = load( - 'fused', - sources=[ - os.path.join(module_path, 'fused_bias_act.cpp'), - os.path.join(module_path, 'fused_bias_act_kernel.cu'), - ], -) - - -class FusedLeakyReLUFunctionBackward(Function): - @staticmethod - def forward(ctx, grad_output, out, negative_slope, scale): - ctx.save_for_backward(out) - ctx.negative_slope = negative_slope - ctx.scale = scale - - empty = grad_output.new_empty(0) - - grad_input = fused.fused_bias_act( - grad_output, empty, out, 3, 1, negative_slope, scale - ) - - dim = [0] - - if grad_input.ndim > 2: - dim += list(range(2, grad_input.ndim)) - - grad_bias = grad_input.sum(dim).detach() - - return grad_input, grad_bias - - @staticmethod - def backward(ctx, gradgrad_input, gradgrad_bias): - out, = ctx.saved_tensors - gradgrad_out = fused.fused_bias_act( - gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale - ) - - return gradgrad_out, None, None, None - - -class FusedLeakyReLUFunction(Function): - @staticmethod - def forward(ctx, input, bias, negative_slope, scale): - empty = input.new_empty(0) - out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) - ctx.save_for_backward(out) - ctx.negative_slope = negative_slope - ctx.scale = scale - - return out - - @staticmethod - def backward(ctx, grad_output): - out, = ctx.saved_tensors - - grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply( - grad_output, out, ctx.negative_slope, ctx.scale - ) - - return grad_input, grad_bias, None, None - - -class FusedLeakyReLU(nn.Module): - def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): - super().__init__() - - self.bias = nn.Parameter(torch.zeros(channel)) - self.negative_slope = negative_slope - self.scale = scale - - def forward(self, input): - return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) - - -def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): - return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) diff --git a/spaces/vaibhavarduino/anime-plus/op/upfirdn2d_cpu.py b/spaces/vaibhavarduino/anime-plus/op/upfirdn2d_cpu.py deleted file mode 100644 index a0f820b4c81e03598589b1ea6b95cf9bef9b04f8..0000000000000000000000000000000000000000 --- a/spaces/vaibhavarduino/anime-plus/op/upfirdn2d_cpu.py +++ /dev/null @@ -1,60 +0,0 @@ -import os - -import torch -from torch.autograd import Function -from torch.nn import functional as F - - - -module_path = os.path.dirname(__file__) - -def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): - out = upfirdn2d_native( - input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1] - ) - - return out - - -def upfirdn2d_native( - input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 -): - _, channel, in_h, in_w = input.shape - input = input.reshape(-1, in_h, in_w, 1) - - _, in_h, in_w, minor = input.shape - kernel_h, kernel_w = kernel.shape - - out = input.view(-1, in_h, 1, in_w, 1, minor) - out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) - out = out.view(-1, in_h * up_y, in_w * up_x, minor) - - out = F.pad( - out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] - ) - out = out[ - :, - max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), - max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), - :, - ] - - out = out.permute(0, 3, 1, 2) - out = out.reshape( - [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] - ) - w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) - out = F.conv2d(out, w) - out = out.reshape( - -1, - minor, - in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, - in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, - ) - out = out.permute(0, 2, 3, 1) - out = out[:, ::down_y, ::down_x, :] - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h + down_y) // down_y - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x - - return out.view(-1, channel, out_h, out_w) diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/usage/python.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/usage/python.md deleted file mode 100644 index 2d8bb4c4e6f9a8bebdde0d1a7e6aa26266d148f0..0000000000000000000000000000000000000000 --- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/usage/python.md +++ /dev/null @@ -1,283 +0,0 @@ ---- -comments: true -description: Integrate YOLOv8 in Python. Load, use pretrained models, train, and infer images. Export to ONNX. Track objects in videos. -keywords: yolov8, python usage, object detection, segmentation, classification, pretrained models, train models, image predictions ---- - -# Python Usage - -Welcome to the YOLOv8 Python Usage documentation! This guide is designed to help you seamlessly integrate YOLOv8 into -your Python projects for object detection, segmentation, and classification. Here, you'll learn how to load and use -pretrained models, train new models, and perform predictions on images. The easy-to-use Python interface is a valuable -resource for anyone looking to incorporate YOLOv8 into their Python projects, allowing you to quickly implement advanced -object detection capabilities. Let's get started! - -For example, users can load a model, train it, evaluate its performance on a validation set, and even export it to ONNX -format with just a few lines of code. - -!!! example "Python" - - ```python - from ultralytics import YOLO - - # Create a new YOLO model from scratch - model = YOLO('yolov8n.yaml') - - # Load a pretrained YOLO model (recommended for training) - model = YOLO('yolov8n.pt') - - # Train the model using the 'coco128.yaml' dataset for 3 epochs - results = model.train(data='coco128.yaml', epochs=3) - - # Evaluate the model's performance on the validation set - results = model.val() - - # Perform object detection on an image using the model - results = model('https://ultralytics.com/images/bus.jpg') - - # Export the model to ONNX format - success = model.export(format='onnx') - ``` - -## [Train](../modes/train.md) - -Train mode is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the -specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can -accurately predict the classes and locations of objects in an image. - -!!! example "Train" - - === "From pretrained(recommended)" - ```python - from ultralytics import YOLO - - model = YOLO('yolov8n.pt') # pass any model type - model.train(epochs=5) - ``` - - === "From scratch" - ```python - from ultralytics import YOLO - - model = YOLO('yolov8n.yaml') - model.train(data='coco128.yaml', epochs=5) - ``` - - === "Resume" - ```python - model = YOLO("last.pt") - model.train(resume=True) - ``` - -[Train Examples](../modes/train.md){ .md-button .md-button--primary} - -## [Val](../modes/val.md) - -Val mode is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a -validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters -of the model to improve its performance. - -!!! example "Val" - - === "Val after training" - ```python - from ultralytics import YOLO - - model = YOLO('yolov8n.yaml') - model.train(data='coco128.yaml', epochs=5) - model.val() # It'll automatically evaluate the data you trained. - ``` - - === "Val independently" - ```python - from ultralytics import YOLO - - model = YOLO("model.pt") - # It'll use the data yaml file in model.pt if you don't set data. - model.val() - # or you can set the data you want to val - model.val(data='coco128.yaml') - ``` - -[Val Examples](../modes/val.md){ .md-button .md-button--primary} - -## [Predict](../modes/predict.md) - -Predict mode is used for making predictions using a trained YOLOv8 model on new images or videos. In this mode, the -model is loaded from a checkpoint file, and the user can provide images or videos to perform inference. The model -predicts the classes and locations of objects in the input images or videos. - -!!! example "Predict" - - === "From source" - ```python - from ultralytics import YOLO - from PIL import Image - import cv2 - - model = YOLO("model.pt") - # accepts all formats - image/dir/Path/URL/video/PIL/ndarray. 0 for webcam - results = model.predict(source="0") - results = model.predict(source="folder", show=True) # Display preds. Accepts all YOLO predict arguments - - # from PIL - im1 = Image.open("bus.jpg") - results = model.predict(source=im1, save=True) # save plotted images - - # from ndarray - im2 = cv2.imread("bus.jpg") - results = model.predict(source=im2, save=True, save_txt=True) # save predictions as labels - - # from list of PIL/ndarray - results = model.predict(source=[im1, im2]) - ``` - - === "Results usage" - ```python - # results would be a list of Results object including all the predictions by default - # but be careful as it could occupy a lot memory when there're many images, - # especially the task is segmentation. - # 1. return as a list - results = model.predict(source="folder") - - # results would be a generator which is more friendly to memory by setting stream=True - # 2. return as a generator - results = model.predict(source=0, stream=True) - - for result in results: - # Detection - result.boxes.xyxy # box with xyxy format, (N, 4) - result.boxes.xywh # box with xywh format, (N, 4) - result.boxes.xyxyn # box with xyxy format but normalized, (N, 4) - result.boxes.xywhn # box with xywh format but normalized, (N, 4) - result.boxes.conf # confidence score, (N, 1) - result.boxes.cls # cls, (N, 1) - - # Segmentation - result.masks.data # masks, (N, H, W) - result.masks.xy # x,y segments (pixels), List[segment] * N - result.masks.xyn # x,y segments (normalized), List[segment] * N - - # Classification - result.probs # cls prob, (num_class, ) - - # Each result is composed of torch.Tensor by default, - # in which you can easily use following functionality: - result = result.cuda() - result = result.cpu() - result = result.to("cpu") - result = result.numpy() - ``` - -[Predict Examples](../modes/predict.md){ .md-button .md-button--primary} - -## [Export](../modes/export.md) - -Export mode is used for exporting a YOLOv8 model to a format that can be used for deployment. In this mode, the model is -converted to a format that can be used by other software applications or hardware devices. This mode is useful when -deploying the model to production environments. - -!!! example "Export" - - === "Export to ONNX" - - Export an official YOLOv8n model to ONNX with dynamic batch-size and image-size. - ```python - from ultralytics import YOLO - - model = YOLO('yolov8n.pt') - model.export(format='onnx', dynamic=True) - ``` - - === "Export to TensorRT" - - Export an official YOLOv8n model to TensorRT on `device=0` for acceleration on CUDA devices. - ```python - from ultralytics import YOLO - - model = YOLO('yolov8n.pt') - model.export(format='onnx', device=0) - ``` - -[Export Examples](../modes/export.md){ .md-button .md-button--primary} - -## [Track](../modes/track.md) - -Track mode is used for tracking objects in real-time using a YOLOv8 model. In this mode, the model is loaded from a -checkpoint file, and the user can provide a live video stream to perform real-time object tracking. This mode is useful -for applications such as surveillance systems or self-driving cars. - -!!! example "Track" - - === "Python" - - ```python - from ultralytics import YOLO - - # Load a model - model = YOLO('yolov8n.pt') # load an official detection model - model = YOLO('yolov8n-seg.pt') # load an official segmentation model - model = YOLO('path/to/best.pt') # load a custom model - - # Track with the model - results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True) - results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True, tracker="bytetrack.yaml") - ``` - -[Track Examples](../modes/track.md){ .md-button .md-button--primary} - -## [Benchmark](../modes/benchmark.md) - -Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks provide -information on the size of the exported format, its `mAP50-95` metrics (for object detection and segmentation) -or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export -formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for -their specific use case based on their requirements for speed and accuracy. - -!!! example "Benchmark" - - === "Python" - - Benchmark an official YOLOv8n model across all export formats. - ```python - from ultralytics.yolo.utils.benchmarks import benchmark - - # Benchmark - benchmark(model='yolov8n.pt', imgsz=640, half=False, device=0) - ``` - -[Benchmark Examples](../modes/benchmark.md){ .md-button .md-button--primary} - -## Using Trainers - -`YOLO` model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits -from `BaseTrainer`. - -!!! tip "Detection Trainer Example" - - ```python - from ultralytics.yolo import v8 import DetectionTrainer, DetectionValidator, DetectionPredictor - - # trainer - trainer = DetectionTrainer(overrides={}) - trainer.train() - trained_model = trainer.best - - # Validator - val = DetectionValidator(args=...) - val(model=trained_model) - - # predictor - pred = DetectionPredictor(overrides={}) - pred(source=SOURCE, model=trained_model) - - # resume from last weight - overrides["resume"] = trainer.last - trainer = detect.DetectionTrainer(overrides=overrides) - ``` - -You can easily customize Trainers to support custom tasks or explore R&D ideas. -Learn more about Customizing `Trainers`, `Validators` and `Predictors` to suit your project needs in the Customization -Section. - -[Customization tutorials](engine.md){ .md-button .md-button--primary} \ No newline at end of file diff --git a/spaces/vasu0508/Meena_Chatbot/app.py b/spaces/vasu0508/Meena_Chatbot/app.py deleted file mode 100644 index 345223082fc38b16b530cef9c3a4022a022c05c9..0000000000000000000000000000000000000000 --- a/spaces/vasu0508/Meena_Chatbot/app.py +++ /dev/null @@ -1,114 +0,0 @@ -# -*- coding: utf-8 -*- -"""Meena_A_Multilingual_Chatbot (1).ipynb - -Automatically generated by Colaboratory. - -Original file is located at - https://colab.research.google.com/drive/1-IfUcnDUppyMArHonc_iesEcN2gSKU-j -""" - -#!pip3 install transformers -#!pip install -q translate -#!pip install polyglot - -#!pip install Pyicu - -#!pip install Morfessor -#!pip install pycld2 - -from transformers import AutoModelForCausalLM, AutoTokenizer -import torch -from translate import Translator -from polyglot.detect import Detector - -# model_name = "microsoft/DialoGPT-large" -model_name = "microsoft/DialoGPT-large" -# model_name = "microsoft/DialoGPT-small" -tokenizer = AutoTokenizer.from_pretrained(model_name) -model = AutoModelForCausalLM.from_pretrained(model_name) - -# # chatting 5 times with nucleus sampling & tweaking temperature -# step=-1 -# while(True): -# step+=1 -# # take user input -# text = input(">> You:>") -# detected_language=Detector(text,quiet=True).language.code -# translator=Translator(from_lang=detected_language,to_lang="en") -# translated_input=translator.translate(text) -# print(translated_input) -# if text.lower().find("bye")!=-1: -# print(f">> Meena:> Bye Bye!") -# break; -# # encode the input and add end of string token -# input_ids = tokenizer.encode(translated_input+tokenizer.eos_token, return_tensors="pt") -# # concatenate new user input with chat history (if there is) -# bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids -# # generate a bot response -# chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id,do_sample=True,top_p=0.9,top_k=50,temperature=0.7,num_beams=5,no_repeat_ngram_size=2) -# #print the output -# output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) -# print(output) -# translator=Translator(from_lang="en",to_lang=detected_language) -# translated_output=translator.translate(output) - -# print(f">> Meena:> {translated_output}") - -#!pip install gradio - -import gradio as gr - -with gr.Blocks() as meena: - chatbot = gr.Chatbot(label="Meena- A Multilingual Chatbot") - msg = gr.Textbox(label="You") - clear = gr.Button("Clear") - def set(chat_history_ids1): - global chat_history_ids - chat_history_ids=chat_history_ids1 - def get(): - return chat_history_ids - def set2(step1): - global step - step=step1 - def get2(): - return step - def generate_text(text,chat_history): - step=-1 - if len(chat_history)==0: - step=-1 - else: - step=get2() - step+=1 - set2(step) - print(step) - if step!=0: - chat_history_ids=get() - if text.isdigit(): - detected_language='en' - else: - detected_language=Detector(text,quiet=True).language.code - translator=Translator(from_lang=detected_language,to_lang="en") - translated_input=translator.translate(text) - # encode the input and add end of string token - input_ids=tokenizer.encode(translated_input+tokenizer.eos_token,return_tensors="pt") - # concatenate new user input with chat history (if there is) - bot_input_ids=torch.cat([chat_history_ids,input_ids],dim=-1) if step>0 else input_ids - # generate a bot response - chat_history_ids=model.generate(bot_input_ids,max_length=1000,pad_token_id=tokenizer.eos_token_id,do_sample=True,top_p=0.9,top_k=50,temperature=0.7,num_beams=5,no_repeat_ngram_size=2) - print(chat_history_ids) - set(chat_history_ids) - #print the output - output=tokenizer.decode(chat_history_ids[:,bot_input_ids.shape[-1]:][0],skip_special_tokens=True) - translator=Translator(from_lang="en",to_lang=detected_language) - translated_output=translator.translate(output) - chat_history.append((text,translated_output)) - if step==5: - set(-1) - set2(-1) - - return "",chat_history - - msg.submit(generate_text, [msg, chatbot], [msg, chatbot]) - clear.click(lambda: None, None, chatbot, queue=False) - -meena.queue().launch() \ No newline at end of file diff --git a/spaces/vialibre/edia_lmodels_en/app.py b/spaces/vialibre/edia_lmodels_en/app.py deleted file mode 100644 index 8a7ab056c44ff456829dc700a16ce9f946f98f82..0000000000000000000000000000000000000000 --- a/spaces/vialibre/edia_lmodels_en/app.py +++ /dev/null @@ -1,58 +0,0 @@ -# --- Imports libs --- -import gradio as gr -import pandas as pd -import configparser - - -# --- Imports modules --- -from modules.module_languageModel import LanguageModel - - -# --- Imports interfaces --- -from interfaces.interface_biasPhrase import interface as interface_sesgoEnFrases -from interfaces.interface_crowsPairs import interface as interface_crowsPairs - - -# --- Tool config --- -cfg = configparser.ConfigParser() -cfg.read('tool.cfg') - -LANGUAGE = cfg['INTERFACE']['language'] -LANGUAGE_MODEL = cfg['LMODEL']['language_model'] -AVAILABLE_LOGS = cfg['LOGS'].getboolean('available_logs') - - - -# --- Init classes --- -bert_lm = LanguageModel( - model_name=LANGUAGE_MODEL -) - -# --- Init Vars --- -labels = pd.read_json(f"language/{LANGUAGE}.json")["app"] - - -# --- Init App --- -INTERFACE_LIST = [ - interface_sesgoEnFrases( - language_model=bert_lm, - available_logs=AVAILABLE_LOGS, - lang=LANGUAGE), - interface_crowsPairs( - language_model=bert_lm, - available_logs=AVAILABLE_LOGS, - lang=LANGUAGE), -] - -TAB_NAMES = [ - labels["phraseExplorer"], - labels["crowsPairsExplorer"] -] - -iface = gr.TabbedInterface( - interface_list=INTERFACE_LIST, - tab_names=TAB_NAMES -) - -iface.queue(concurrency_count=8) -iface.launch(debug=False) diff --git a/spaces/victorisgeek/SwapFace2Pon/face_enhancer.py b/spaces/victorisgeek/SwapFace2Pon/face_enhancer.py deleted file mode 100644 index 9bcf2fef411285e02b32a9c37dcc1d53d2cd0f88..0000000000000000000000000000000000000000 --- a/spaces/victorisgeek/SwapFace2Pon/face_enhancer.py +++ /dev/null @@ -1,72 +0,0 @@ -import os -import cv2 -import torch -import gfpgan -from PIL import Image -from upscaler.RealESRGAN import RealESRGAN -from upscaler.codeformer import CodeFormerEnhancer - -def gfpgan_runner(img, model): - _, imgs, _ = model.enhance(img, paste_back=True, has_aligned=True) - return imgs[0] - - -def realesrgan_runner(img, model): - img = model.predict(img) - return img - - -def codeformer_runner(img, model): - img = model.enhance(img) - return img - - -supported_enhancers = { - "CodeFormer": ("./assets/pretrained_models/codeformer.onnx", codeformer_runner), - "GFPGAN": ("./assets/pretrained_models/GFPGANv1.4.pth", gfpgan_runner), - "REAL-ESRGAN 2x": ("./assets/pretrained_models/RealESRGAN_x2.pth", realesrgan_runner), - "REAL-ESRGAN 4x": ("./assets/pretrained_models/RealESRGAN_x4.pth", realesrgan_runner), - "REAL-ESRGAN 8x": ("./assets/pretrained_models/RealESRGAN_x8.pth", realesrgan_runner) -} - -cv2_interpolations = ["LANCZOS4", "CUBIC", "NEAREST"] - -def get_available_enhancer_names(): - available = [] - for name, data in supported_enhancers.items(): - path = os.path.join(os.path.abspath(os.path.dirname(__file__)), data[0]) - if os.path.exists(path): - available.append(name) - return available - - -def load_face_enhancer_model(name='GFPGAN', device="cpu"): - assert name in get_available_enhancer_names() + cv2_interpolations, f"Face enhancer {name} unavailable." - if name in supported_enhancers.keys(): - model_path, model_runner = supported_enhancers.get(name) - model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), model_path) - if name == 'CodeFormer': - model = CodeFormerEnhancer(model_path=model_path, device=device) - elif name == 'GFPGAN': - model = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=device) - elif name == 'REAL-ESRGAN 2x': - model = RealESRGAN(device, scale=2) - model.load_weights(model_path, download=False) - elif name == 'REAL-ESRGAN 4x': - model = RealESRGAN(device, scale=4) - model.load_weights(model_path, download=False) - elif name == 'REAL-ESRGAN 8x': - model = RealESRGAN(device, scale=8) - model.load_weights(model_path, download=False) - elif name == 'LANCZOS4': - model = None - model_runner = lambda img, _: cv2.resize(img, (512,512), interpolation=cv2.INTER_LANCZOS4) - elif name == 'CUBIC': - model = None - model_runner = lambda img, _: cv2.resize(img, (512,512), interpolation=cv2.INTER_CUBIC) - elif name == 'NEAREST': - model = None - model_runner = lambda img, _: cv2.resize(img, (512,512), interpolation=cv2.INTER_NEAREST) - else: - model = None - return (model, model_runner) diff --git a/spaces/vivym/image-matting-app/ppmatting/models/layers/gca_module.py b/spaces/vivym/image-matting-app/ppmatting/models/layers/gca_module.py deleted file mode 100644 index ba8654efc9bd24de2e127393ad8338d21964e4a5..0000000000000000000000000000000000000000 --- a/spaces/vivym/image-matting-app/ppmatting/models/layers/gca_module.py +++ /dev/null @@ -1,211 +0,0 @@ -# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# The gca code was heavily based on https://github.com/Yaoyi-Li/GCA-Matting -# and https://github.com/open-mmlab/mmediting - -import paddle -import paddle.nn as nn -import paddle.nn.functional as F - -from paddleseg.cvlibs import param_init - - -class GuidedCxtAtten(nn.Layer): - def __init__(self, - out_channels, - guidance_channels, - kernel_size=3, - stride=1, - rate=2): - super().__init__() - - self.kernel_size = kernel_size - self.rate = rate - self.stride = stride - self.guidance_conv = nn.Conv2D( - in_channels=guidance_channels, - out_channels=guidance_channels // 2, - kernel_size=1) - - self.out_conv = nn.Sequential( - nn.Conv2D( - in_channels=out_channels, - out_channels=out_channels, - kernel_size=1, - bias_attr=False), - nn.BatchNorm(out_channels)) - - self.init_weight() - - def init_weight(self): - param_init.xavier_uniform(self.guidance_conv.weight) - param_init.constant_init(self.guidance_conv.bias, value=0.0) - param_init.xavier_uniform(self.out_conv[0].weight) - param_init.constant_init(self.out_conv[1].weight, value=1e-3) - param_init.constant_init(self.out_conv[1].bias, value=0.0) - - def forward(self, img_feat, alpha_feat, unknown=None, softmax_scale=1.): - - img_feat = self.guidance_conv(img_feat) - img_feat = F.interpolate( - img_feat, scale_factor=1 / self.rate, mode='nearest') - - # process unknown mask - unknown, softmax_scale = self.process_unknown_mask(unknown, img_feat, - softmax_scale) - - img_ps, alpha_ps, unknown_ps = self.extract_feature_maps_patches( - img_feat, alpha_feat, unknown) - - self_mask = self.get_self_correlation_mask(img_feat) - - # split tensors by batch dimension; tuple is returned - img_groups = paddle.split(img_feat, 1, axis=0) - img_ps_groups = paddle.split(img_ps, 1, axis=0) - alpha_ps_groups = paddle.split(alpha_ps, 1, axis=0) - unknown_ps_groups = paddle.split(unknown_ps, 1, axis=0) - scale_groups = paddle.split(softmax_scale, 1, axis=0) - groups = (img_groups, img_ps_groups, alpha_ps_groups, unknown_ps_groups, - scale_groups) - - y = [] - - for img_i, img_ps_i, alpha_ps_i, unknown_ps_i, scale_i in zip(*groups): - # conv for compare - similarity_map = self.compute_similarity_map(img_i, img_ps_i) - - gca_score = self.compute_guided_attention_score( - similarity_map, unknown_ps_i, scale_i, self_mask) - - yi = self.propagate_alpha_feature(gca_score, alpha_ps_i) - - y.append(yi) - - y = paddle.concat(y, axis=0) # back to the mini-batch - y = paddle.reshape(y, alpha_feat.shape) - - y = self.out_conv(y) + alpha_feat - - return y - - def extract_feature_maps_patches(self, img_feat, alpha_feat, unknown): - - # extract image feature patches with shape: - # (N, img_h*img_w, img_c, img_ks, img_ks) - img_ks = self.kernel_size - img_ps = self.extract_patches(img_feat, img_ks, self.stride) - - # extract alpha feature patches with shape: - # (N, img_h*img_w, alpha_c, alpha_ks, alpha_ks) - alpha_ps = self.extract_patches(alpha_feat, self.rate * 2, self.rate) - - # extract unknown mask patches with shape: (N, img_h*img_w, 1, 1) - unknown_ps = self.extract_patches(unknown, img_ks, self.stride) - unknown_ps = unknown_ps.squeeze(axis=2) # squeeze channel dimension - unknown_ps = unknown_ps.mean(axis=[2, 3], keepdim=True) - - return img_ps, alpha_ps, unknown_ps - - def extract_patches(self, x, kernel_size, stride): - n, c, _, _ = x.shape - x = self.pad(x, kernel_size, stride) - x = F.unfold(x, [kernel_size, kernel_size], strides=[stride, stride]) - x = paddle.transpose(x, (0, 2, 1)) - x = paddle.reshape(x, (n, -1, c, kernel_size, kernel_size)) - - return x - - def pad(self, x, kernel_size, stride): - left = (kernel_size - stride + 1) // 2 - right = (kernel_size - stride) // 2 - pad = (left, right, left, right) - return F.pad(x, pad, mode='reflect') - - def compute_guided_attention_score(self, similarity_map, unknown_ps, scale, - self_mask): - # scale the correlation with predicted scale factor for known and - # unknown area - unknown_scale, known_scale = scale[0] - out = similarity_map * ( - unknown_scale * paddle.greater_than(unknown_ps, - paddle.to_tensor([0.])) + - known_scale * paddle.less_equal(unknown_ps, paddle.to_tensor([0.]))) - # mask itself, self-mask only applied to unknown area - out = out + self_mask * unknown_ps - gca_score = F.softmax(out, axis=1) - - return gca_score - - def propagate_alpha_feature(self, gca_score, alpha_ps): - - alpha_ps = alpha_ps[0] # squeeze dim 0 - if self.rate == 1: - gca_score = self.pad(gca_score, kernel_size=2, stride=1) - alpha_ps = paddle.transpose(alpha_ps, (1, 0, 2, 3)) - out = F.conv2d(gca_score, alpha_ps) / 4. - else: - out = F.conv2d_transpose( - gca_score, alpha_ps, stride=self.rate, padding=1) / 4. - - return out - - def compute_similarity_map(self, img_feat, img_ps): - img_ps = img_ps[0] # squeeze dim 0 - # convolve the feature to get correlation (similarity) map - img_ps_normed = img_ps / paddle.clip(self.l2_norm(img_ps), 1e-4) - img_feat = F.pad(img_feat, (1, 1, 1, 1), mode='reflect') - similarity_map = F.conv2d(img_feat, img_ps_normed) - - return similarity_map - - def get_self_correlation_mask(self, img_feat): - _, _, h, w = img_feat.shape - self_mask = F.one_hot( - paddle.reshape(paddle.arange(h * w), (h, w)), - num_classes=int(h * w)) - - self_mask = paddle.transpose(self_mask, (2, 0, 1)) - self_mask = paddle.reshape(self_mask, (1, h * w, h, w)) - - return self_mask * (-1e4) - - def process_unknown_mask(self, unknown, img_feat, softmax_scale): - - n, _, h, w = img_feat.shape - - if unknown is not None: - unknown = unknown.clone() - unknown = F.interpolate( - unknown, scale_factor=1 / self.rate, mode='nearest') - unknown_mean = unknown.mean(axis=[2, 3]) - known_mean = 1 - unknown_mean - unknown_scale = paddle.clip( - paddle.sqrt(unknown_mean / known_mean), 0.1, 10) - known_scale = paddle.clip( - paddle.sqrt(known_mean / unknown_mean), 0.1, 10) - softmax_scale = paddle.concat([unknown_scale, known_scale], axis=1) - else: - unknown = paddle.ones([n, 1, h, w]) - softmax_scale = paddle.reshape( - paddle.to_tensor([softmax_scale, softmax_scale]), (1, 2)) - softmax_scale = paddle.expand(softmax_scale, (n, 2)) - - return unknown, softmax_scale - - @staticmethod - def l2_norm(x): - x = x**2 - x = x.sum(axis=[1, 2, 3], keepdim=True) - return paddle.sqrt(x) diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/point_sample.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/point_sample.py deleted file mode 100644 index 267f4b3c56630acd85f9bdc630b7be09abab0aba..0000000000000000000000000000000000000000 --- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/point_sample.py +++ /dev/null @@ -1,336 +0,0 @@ -# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend # noqa - -from os import path as osp - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.modules.utils import _pair -from torch.onnx.operators import shape_as_tensor - - -def bilinear_grid_sample(im, grid, align_corners=False): - """Given an input and a flow-field grid, computes the output using input - values and pixel locations from grid. Supported only bilinear interpolation - method to sample the input pixels. - - Args: - im (torch.Tensor): Input feature map, shape (N, C, H, W) - grid (torch.Tensor): Point coordinates, shape (N, Hg, Wg, 2) - align_corners {bool}: If set to True, the extrema (-1 and 1) are - considered as referring to the center points of the input’s - corner pixels. If set to False, they are instead considered as - referring to the corner points of the input’s corner pixels, - making the sampling more resolution agnostic. - Returns: - torch.Tensor: A tensor with sampled points, shape (N, C, Hg, Wg) - """ - n, c, h, w = im.shape - gn, gh, gw, _ = grid.shape - assert n == gn - - x = grid[:, :, :, 0] - y = grid[:, :, :, 1] - - if align_corners: - x = ((x + 1) / 2) * (w - 1) - y = ((y + 1) / 2) * (h - 1) - else: - x = ((x + 1) * w - 1) / 2 - y = ((y + 1) * h - 1) / 2 - - x = x.view(n, -1) - y = y.view(n, -1) - - x0 = torch.floor(x).long() - y0 = torch.floor(y).long() - x1 = x0 + 1 - y1 = y0 + 1 - - wa = ((x1 - x) * (y1 - y)).unsqueeze(1) - wb = ((x1 - x) * (y - y0)).unsqueeze(1) - wc = ((x - x0) * (y1 - y)).unsqueeze(1) - wd = ((x - x0) * (y - y0)).unsqueeze(1) - - # Apply default for grid_sample function zero padding - im_padded = F.pad(im, pad=[1, 1, 1, 1], mode='constant', value=0) - padded_h = h + 2 - padded_w = w + 2 - # save points positions after padding - x0, x1, y0, y1 = x0 + 1, x1 + 1, y0 + 1, y1 + 1 - - # Clip coordinates to padded image size - x0 = torch.where(x0 < 0, torch.tensor(0), x0) - x0 = torch.where(x0 > padded_w - 1, torch.tensor(padded_w - 1), x0) - x1 = torch.where(x1 < 0, torch.tensor(0), x1) - x1 = torch.where(x1 > padded_w - 1, torch.tensor(padded_w - 1), x1) - y0 = torch.where(y0 < 0, torch.tensor(0), y0) - y0 = torch.where(y0 > padded_h - 1, torch.tensor(padded_h - 1), y0) - y1 = torch.where(y1 < 0, torch.tensor(0), y1) - y1 = torch.where(y1 > padded_h - 1, torch.tensor(padded_h - 1), y1) - - im_padded = im_padded.view(n, c, -1) - - x0_y0 = (x0 + y0 * padded_w).unsqueeze(1).expand(-1, c, -1) - x0_y1 = (x0 + y1 * padded_w).unsqueeze(1).expand(-1, c, -1) - x1_y0 = (x1 + y0 * padded_w).unsqueeze(1).expand(-1, c, -1) - x1_y1 = (x1 + y1 * padded_w).unsqueeze(1).expand(-1, c, -1) - - Ia = torch.gather(im_padded, 2, x0_y0) - Ib = torch.gather(im_padded, 2, x0_y1) - Ic = torch.gather(im_padded, 2, x1_y0) - Id = torch.gather(im_padded, 2, x1_y1) - - return (Ia * wa + Ib * wb + Ic * wc + Id * wd).reshape(n, c, gh, gw) - - -def is_in_onnx_export_without_custom_ops(): - from annotator.uniformer.mmcv.ops import get_onnxruntime_op_path - ort_custom_op_path = get_onnxruntime_op_path() - return torch.onnx.is_in_onnx_export( - ) and not osp.exists(ort_custom_op_path) - - -def normalize(grid): - """Normalize input grid from [-1, 1] to [0, 1] - Args: - grid (Tensor): The grid to be normalize, range [-1, 1]. - Returns: - Tensor: Normalized grid, range [0, 1]. - """ - - return (grid + 1.0) / 2.0 - - -def denormalize(grid): - """Denormalize input grid from range [0, 1] to [-1, 1] - Args: - grid (Tensor): The grid to be denormalize, range [0, 1]. - Returns: - Tensor: Denormalized grid, range [-1, 1]. - """ - - return grid * 2.0 - 1.0 - - -def generate_grid(num_grid, size, device): - """Generate regular square grid of points in [0, 1] x [0, 1] coordinate - space. - - Args: - num_grid (int): The number of grids to sample, one for each region. - size (tuple(int, int)): The side size of the regular grid. - device (torch.device): Desired device of returned tensor. - - Returns: - (torch.Tensor): A tensor of shape (num_grid, size[0]*size[1], 2) that - contains coordinates for the regular grids. - """ - - affine_trans = torch.tensor([[[1., 0., 0.], [0., 1., 0.]]], device=device) - grid = F.affine_grid( - affine_trans, torch.Size((1, 1, *size)), align_corners=False) - grid = normalize(grid) - return grid.view(1, -1, 2).expand(num_grid, -1, -1) - - -def rel_roi_point_to_abs_img_point(rois, rel_roi_points): - """Convert roi based relative point coordinates to image based absolute - point coordinates. - - Args: - rois (Tensor): RoIs or BBoxes, shape (N, 4) or (N, 5) - rel_roi_points (Tensor): Point coordinates inside RoI, relative to - RoI, location, range (0, 1), shape (N, P, 2) - Returns: - Tensor: Image based absolute point coordinates, shape (N, P, 2) - """ - - with torch.no_grad(): - assert rel_roi_points.size(0) == rois.size(0) - assert rois.dim() == 2 - assert rel_roi_points.dim() == 3 - assert rel_roi_points.size(2) == 2 - # remove batch idx - if rois.size(1) == 5: - rois = rois[:, 1:] - abs_img_points = rel_roi_points.clone() - # To avoid an error during exporting to onnx use independent - # variables instead inplace computation - xs = abs_img_points[:, :, 0] * (rois[:, None, 2] - rois[:, None, 0]) - ys = abs_img_points[:, :, 1] * (rois[:, None, 3] - rois[:, None, 1]) - xs += rois[:, None, 0] - ys += rois[:, None, 1] - abs_img_points = torch.stack([xs, ys], dim=2) - return abs_img_points - - -def get_shape_from_feature_map(x): - """Get spatial resolution of input feature map considering exporting to - onnx mode. - - Args: - x (torch.Tensor): Input tensor, shape (N, C, H, W) - Returns: - torch.Tensor: Spatial resolution (width, height), shape (1, 1, 2) - """ - if torch.onnx.is_in_onnx_export(): - img_shape = shape_as_tensor(x)[2:].flip(0).view(1, 1, 2).to( - x.device).float() - else: - img_shape = torch.tensor(x.shape[2:]).flip(0).view(1, 1, 2).to( - x.device).float() - return img_shape - - -def abs_img_point_to_rel_img_point(abs_img_points, img, spatial_scale=1.): - """Convert image based absolute point coordinates to image based relative - coordinates for sampling. - - Args: - abs_img_points (Tensor): Image based absolute point coordinates, - shape (N, P, 2) - img (tuple/Tensor): (height, width) of image or feature map. - spatial_scale (float): Scale points by this factor. Default: 1. - - Returns: - Tensor: Image based relative point coordinates for sampling, - shape (N, P, 2) - """ - - assert (isinstance(img, tuple) and len(img) == 2) or \ - (isinstance(img, torch.Tensor) and len(img.shape) == 4) - - if isinstance(img, tuple): - h, w = img - scale = torch.tensor([w, h], - dtype=torch.float, - device=abs_img_points.device) - scale = scale.view(1, 1, 2) - else: - scale = get_shape_from_feature_map(img) - - return abs_img_points / scale * spatial_scale - - -def rel_roi_point_to_rel_img_point(rois, - rel_roi_points, - img, - spatial_scale=1.): - """Convert roi based relative point coordinates to image based absolute - point coordinates. - - Args: - rois (Tensor): RoIs or BBoxes, shape (N, 4) or (N, 5) - rel_roi_points (Tensor): Point coordinates inside RoI, relative to - RoI, location, range (0, 1), shape (N, P, 2) - img (tuple/Tensor): (height, width) of image or feature map. - spatial_scale (float): Scale points by this factor. Default: 1. - - Returns: - Tensor: Image based relative point coordinates for sampling, - shape (N, P, 2) - """ - - abs_img_point = rel_roi_point_to_abs_img_point(rois, rel_roi_points) - rel_img_point = abs_img_point_to_rel_img_point(abs_img_point, img, - spatial_scale) - - return rel_img_point - - -def point_sample(input, points, align_corners=False, **kwargs): - """A wrapper around :func:`grid_sample` to support 3D point_coords tensors - Unlike :func:`torch.nn.functional.grid_sample` it assumes point_coords to - lie inside ``[0, 1] x [0, 1]`` square. - - Args: - input (Tensor): Feature map, shape (N, C, H, W). - points (Tensor): Image based absolute point coordinates (normalized), - range [0, 1] x [0, 1], shape (N, P, 2) or (N, Hgrid, Wgrid, 2). - align_corners (bool): Whether align_corners. Default: False - - Returns: - Tensor: Features of `point` on `input`, shape (N, C, P) or - (N, C, Hgrid, Wgrid). - """ - - add_dim = False - if points.dim() == 3: - add_dim = True - points = points.unsqueeze(2) - if is_in_onnx_export_without_custom_ops(): - # If custom ops for onnx runtime not compiled use python - # implementation of grid_sample function to make onnx graph - # with supported nodes - output = bilinear_grid_sample( - input, denormalize(points), align_corners=align_corners) - else: - output = F.grid_sample( - input, denormalize(points), align_corners=align_corners, **kwargs) - if add_dim: - output = output.squeeze(3) - return output - - -class SimpleRoIAlign(nn.Module): - - def __init__(self, output_size, spatial_scale, aligned=True): - """Simple RoI align in PointRend, faster than standard RoIAlign. - - Args: - output_size (tuple[int]): h, w - spatial_scale (float): scale the input boxes by this number - aligned (bool): if False, use the legacy implementation in - MMDetection, align_corners=True will be used in F.grid_sample. - If True, align the results more perfectly. - """ - - super(SimpleRoIAlign, self).__init__() - self.output_size = _pair(output_size) - self.spatial_scale = float(spatial_scale) - # to be consistent with other RoI ops - self.use_torchvision = False - self.aligned = aligned - - def forward(self, features, rois): - num_imgs = features.size(0) - num_rois = rois.size(0) - rel_roi_points = generate_grid( - num_rois, self.output_size, device=rois.device) - - if torch.onnx.is_in_onnx_export(): - rel_img_points = rel_roi_point_to_rel_img_point( - rois, rel_roi_points, features, self.spatial_scale) - rel_img_points = rel_img_points.reshape(num_imgs, -1, - *rel_img_points.shape[1:]) - point_feats = point_sample( - features, rel_img_points, align_corners=not self.aligned) - point_feats = point_feats.transpose(1, 2) - else: - point_feats = [] - for batch_ind in range(num_imgs): - # unravel batch dim - feat = features[batch_ind].unsqueeze(0) - inds = (rois[:, 0].long() == batch_ind) - if inds.any(): - rel_img_points = rel_roi_point_to_rel_img_point( - rois[inds], rel_roi_points[inds], feat, - self.spatial_scale).unsqueeze(0) - point_feat = point_sample( - feat, rel_img_points, align_corners=not self.aligned) - point_feat = point_feat.squeeze(0).transpose(0, 1) - point_feats.append(point_feat) - - point_feats = torch.cat(point_feats, dim=0) - - channels = features.size(1) - roi_feats = point_feats.reshape(num_rois, channels, *self.output_size) - - return roi_feats - - def __repr__(self): - format_str = self.__class__.__name__ - format_str += '(output_size={}, spatial_scale={}'.format( - self.output_size, self.spatial_scale) - return format_str diff --git a/spaces/wahaha/u2net_portrait/U-2-Net/u2net_portrait_demo.py b/spaces/wahaha/u2net_portrait/U-2-Net/u2net_portrait_demo.py deleted file mode 100644 index 516272a61d6533b8ebf8e466dfa3bda2d9c4e9a3..0000000000000000000000000000000000000000 --- a/spaces/wahaha/u2net_portrait/U-2-Net/u2net_portrait_demo.py +++ /dev/null @@ -1,175 +0,0 @@ -import cv2 -import torch -from model import U2NET -from torch.autograd import Variable -import numpy as np -from glob import glob -import os - -def detect_single_face(face_cascade,img): - # Convert into grayscale - gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - - # Detect faces - faces = face_cascade.detectMultiScale(gray, 1.1, 4) - if(len(faces)==0): - print("Warming: no face detection, the portrait u2net will run on the whole image!") - return None - - # filter to keep the largest face - wh = 0 - idx = 0 - for i in range(0,len(faces)): - (x,y,w,h) = faces[i] - if(wh