-
-
-
-
-
-
-
-
-
-
-
-
- One of the cons of this modded version of the game is that it may not be compatible with some online features or servers. You may not be able to play online with other players who are using the original version of the game. You may also face some issues with connecting to some servers or modes in the multiplayer mode. You may also get banned or blocked by some servers or players for using a modded version of the game.
-Another con of this modded version of the game is that it may cause some glitches or bugs in the game. You may encounter some errors or crashes while playing this modded version of the game. You may also experience some lagging or freezing issues while playing this modded version of the game. You may also lose some data or progress while playing this modded version of the game.
-The last con of this modded version of the game is that it may violate the terms and conditions of the original game developer. You may be violating the intellectual property rights or the privacy policy of Gameloft, the developer of Modern Combat 4: Zero Hour. You may also be breaking the rules or the code of conduct of the game. You may face some legal consequences or penalties for using a modded version of the game.
-Conclusion
-Modern Combat 4: Zero Hour Mod APK is a great choice for action lovers who want to enjoy a high-quality FPS game on their Android devices. It offers unlimited money, unlocked features, and no ads, making it more fun and exciting than the original version. However, it also has some drawbacks, such as possible compatibility issues, glitches, or legal risks. Therefore, users should download and install it at their own discretion and responsibility.
-FAQs
-Is Modern Combat 4: Zero Hour Mod APK safe to use?
-Modern Combat 4: Zero Hour Mod APK is generally safe to use, as long as you download it from a trusted source and scan it with an antivirus app before installing it. However, there is no guarantee that it will not cause any harm to your device or data, so you should use it at your own risk.
-How to update Modern Combat 4: Zero Hour Mod APK?
-To update Modern Combat 4: Zero Hour Mod APK, you will need to download and install the latest version of the modded version of the game from the same source that you downloaded it from. You will also need to delete the old version of the game and its data before installing the new version. You may also need to backup your progress or data before updating the game.
-How to fix Modern Combat 4: Zero Hour Mod APK not working?
-If Modern Combat 4: Zero Hour Mod APK is not working on your device, you may try some of these solutions:
--
-
- Check your internet connection and make sure it is stable and fast. -
- Clear your cache and data of the game and restart your device. -
- Reinstall the game and its data from a trusted source. -
- Change your device settings or permissions to allow the game to run properly. -
- Contact the mod developer or the original game developer for support or assistance. -
How to play Modern Combat 4: Zero Hour Mod APK online?
-To play Modern Combat 4: Zero Hour Mod APK online, you will need to have a stable and fast internet connection and a valid account for the game. You will also need to make sure that you are using a compatible version of the modded version of the game with the online servers or features. You may also need to disable some of the mod features that may interfere with the online gameplay.
-How to uninstall Modern Combat 4: Zero Hour Mod APK?
-To uninstall Modern Combat 4: Zero Hour Mod APK, you will need to follow these steps:
--
-
- Go to your device settings > apps > Modern Combat 4: Zero Hour > uninstall. -
- Delete the com.gameloft.android.ANMP.GloftM4HM folder from your Android/obb folder on your device storage. -
- Delete any other files or folders related to the game from your device storage. -
-
-
\ No newline at end of file diff --git a/spaces/1toTree/lora_test/ppdiffusers/experimental/README.md b/spaces/1toTree/lora_test/ppdiffusers/experimental/README.md deleted file mode 100644 index 847e23ba7c7a40649e2751bcf8882cea6e88b62a..0000000000000000000000000000000000000000 --- a/spaces/1toTree/lora_test/ppdiffusers/experimental/README.md +++ /dev/null @@ -1,6 +0,0 @@ -# 🧨 PPDiffusers Experimental - -为了使得**PPDiffusers库**能够有更多的应用场景，我们在这里添加了一些**实验性的代码**。 - -目前我们支持了以下场景: -* Reinforcement learning via an implementation of the [PPDiffuser](https://arxiv.org/abs/2205.09991) model. diff --git a/spaces/2023Liu2023/bingo/src/components/tailwind-indicator.tsx b/spaces/2023Liu2023/bingo/src/components/tailwind-indicator.tsx deleted file mode 100644 index f2a1291213dd67055fcebe67fab574c8441338df..0000000000000000000000000000000000000000 --- a/spaces/2023Liu2023/bingo/src/components/tailwind-indicator.tsx +++ /dev/null @@ -1,14 +0,0 @@ -export function TailwindIndicator() { - if (process.env.NODE_ENV === 'production') return null - - return ( -
-
- )
-}
diff --git a/spaces/4Taps/SadTalker/src/face3d/options/__init__.py b/spaces/4Taps/SadTalker/src/face3d/options/__init__.py
deleted file mode 100644
index e7eedebe54aa70169fd25951b3034d819e396c90..0000000000000000000000000000000000000000
--- a/spaces/4Taps/SadTalker/src/face3d/options/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""This package options includes option modules: training options, test options, and basic options (used in both training and test)."""
diff --git a/spaces/AI-Hobbyist/Hoyo-RVC/Dockerfile b/spaces/AI-Hobbyist/Hoyo-RVC/Dockerfile
deleted file mode 100644
index 49f62d5f9c0901931de6523721b3a97b40f34219..0000000000000000000000000000000000000000
--- a/spaces/AI-Hobbyist/Hoyo-RVC/Dockerfile
+++ /dev/null
@@ -1,13 +0,0 @@
-# syntax=docker/dockerfile:1
-
-FROM python:3.10-bullseye
-
-EXPOSE 7865
-
-WORKDIR /app
-
-COPY . .
-
-RUN pip3 install -r requirements.txt
-
-CMD ["python3", "infer-web.py"]
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/syntaspeech/multi_window_disc.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/syntaspeech/multi_window_disc.py
deleted file mode 100644
index a8166ac5b514e501043b9fed13aab01421a6c10e..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/syntaspeech/multi_window_disc.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-
-
-class SingleWindowDisc(nn.Module):
- def __init__(self, time_length, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128):
- super().__init__()
- padding = (kernel[0] // 2, kernel[1] // 2)
- self.model = nn.ModuleList([
- nn.Sequential(*[
- nn.Conv2d(c_in, hidden_size, kernel, (2, 2), padding),
- nn.LeakyReLU(0.2, inplace=True),
- nn.Dropout2d(0.25),
- nn.BatchNorm2d(hidden_size, 0.8)
- ]),
- nn.Sequential(*[
- nn.Conv2d(hidden_size, hidden_size, kernel, (2, 2), padding),
- nn.LeakyReLU(0.2, inplace=True),
- nn.Dropout2d(0.25),
- nn.BatchNorm2d(hidden_size, 0.8)
- ]),
- nn.Sequential(*[
- nn.Conv2d(hidden_size, hidden_size, kernel, (2, 2), padding),
- nn.LeakyReLU(0.2, inplace=True),
- nn.Dropout2d(0.25),
- ]),
- ])
- ds_size = (time_length // 2 ** 3, (freq_length + 7) // 2 ** 3)
- self.adv_layer = nn.Linear(hidden_size * ds_size[0] * ds_size[1], 1)
-
- def forward(self, x):
- """
- :param x: [B, C, T, n_bins]
- :return: validity: [B, 1], h: List of hiddens
- """
- h = []
- for l in self.model:
- x = l(x)
- h.append(x)
- x = x.view(x.shape[0], -1)
- validity = self.adv_layer(x) # [B, 1]
- return validity, h
-
-
-class MultiWindowDiscriminator(nn.Module):
- def __init__(self, time_lengths, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128):
- super(MultiWindowDiscriminator, self).__init__()
- self.win_lengths = time_lengths
- self.discriminators = nn.ModuleList()
-
- for time_length in time_lengths:
- self.discriminators += [SingleWindowDisc(time_length, freq_length, kernel, c_in=c_in, hidden_size=hidden_size)]
-
- def forward(self, x, x_len, start_frames_wins=None):
- '''
- Args:
- x (tensor): input mel, (B, c_in, T, n_bins).
- x_length (tensor): len of per mel. (B,).
-
- Returns:
- tensor : (B).
- '''
- validity = []
- if start_frames_wins is None:
- start_frames_wins = [None] * len(self.discriminators)
- h = []
- for i, start_frames in zip(range(len(self.discriminators)), start_frames_wins):
- x_clip, start_frames = self.clip(x, x_len, self.win_lengths[i], start_frames) # (B, win_length, C)
- start_frames_wins[i] = start_frames
- if x_clip is None:
- continue
- x_clip, h_ = self.discriminators[i](x_clip)
- h += h_
- validity.append(x_clip)
- if len(validity) != len(self.discriminators):
- return None, start_frames_wins, h
- validity = sum(validity) # [B]
- return validity, start_frames_wins, h
-
- def clip(self, x, x_len, win_length, start_frames=None):
- '''Ramdom clip x to win_length.
- Args:
- x (tensor) : (B, c_in, T, n_bins).
- cond (tensor) : (B, T, H).
- x_len (tensor) : (B,).
- win_length (int): target clip length
-
- Returns:
- (tensor) : (B, c_in, win_length, n_bins).
-
- '''
- T_start = 0
- T_end = x_len.max() - win_length
- if T_end < 0:
- return None, None, start_frames
- T_end = T_end.item()
- if start_frames is None:
- start_frame = np.random.randint(low=T_start, high=T_end + 1)
- start_frames = [start_frame] * x.size(0)
- else:
- start_frame = start_frames[0]
- x_batch = x[:, :, start_frame: start_frame + win_length]
- return x_batch, start_frames
-
-
-class Discriminator(nn.Module):
- def __init__(self, time_lengths=[32, 64, 128], freq_length=80, kernel=(3, 3), c_in=1,
- hidden_size=128):
- super(Discriminator, self).__init__()
- self.time_lengths = time_lengths
- self.discriminator = MultiWindowDiscriminator(
- freq_length=freq_length,
- time_lengths=time_lengths,
- kernel=kernel,
- c_in=c_in, hidden_size=hidden_size
- )
-
-
- def forward(self, x, start_frames_wins=None):
- """
-
- :param x: [B, T, 80]
- :param return_y_only:
- :return:
- """
- if len(x.shape) == 3:
- x = x[:, None, :, :] # [B,1,T,80]
- x_len = x.sum([1, -1]).ne(0).int().sum([-1])
- ret = {'y_c': None, 'y': None}
- ret['y'], start_frames_wins, ret['h'] = self.discriminator(
- x, x_len, start_frames_wins=start_frames_wins)
-
- ret['start_frames_wins'] = start_frames_wins
- return ret
-
diff --git a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/data_generator.py b/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/data_generator.py
deleted file mode 100644
index b94b6d990b6726c791cbb4cb660abdb93233f965..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/data_generator.py
+++ /dev/null
@@ -1,421 +0,0 @@
-import numpy as np
-import h5py
-import csv
-import time
-import logging
-
-from utilities import int16_to_float32
-
-
-def read_black_list(black_list_csv):
- """Read audio names from black list.
- """
- with open(black_list_csv, 'r') as fr:
- reader = csv.reader(fr)
- lines = list(reader)
-
- black_list_names = ['Y{}.wav'.format(line[0]) for line in lines]
- return black_list_names
-
-
-class AudioSetDataset(object):
- def __init__(self, sample_rate=32000):
- """This class takes the meta of an audio clip as input, and return
- the waveform and target of the audio clip. This class is used by DataLoader.
- """
- self.sample_rate = sample_rate
-
- def __getitem__(self, meta):
- """Load waveform and target of an audio clip.
-
- Args:
- meta: {
- 'hdf5_path': str,
- 'index_in_hdf5': int}
-
- Returns:
- data_dict: {
- 'audio_name': str,
- 'waveform': (clip_samples,),
- 'target': (classes_num,)}
- """
- hdf5_path = meta['hdf5_path']
- index_in_hdf5 = meta['index_in_hdf5']
- with h5py.File(hdf5_path, 'r') as hf:
- audio_name = hf['audio_name'][index_in_hdf5].decode()
- waveform = int16_to_float32(hf['waveform'][index_in_hdf5])
- waveform = self.resample(waveform)
- target = hf['target'][index_in_hdf5].astype(np.float32)
-
- data_dict = {
- 'audio_name': audio_name, 'waveform': waveform, 'target': target}
-
- return data_dict
-
- def resample(self, waveform):
- """Resample.
-
- Args:
- waveform: (clip_samples,)
-
- Returns:
- (resampled_clip_samples,)
- """
- if self.sample_rate == 32000:
- return waveform
- elif self.sample_rate == 16000:
- return waveform[0 :: 2]
- elif self.sample_rate == 8000:
- return waveform[0 :: 4]
- else:
- raise Exception('Incorrect sample rate!')
-
-
-class Base(object):
- def __init__(self, indexes_hdf5_path, batch_size, black_list_csv, random_seed):
- """Base class of train sampler.
-
- Args:
- indexes_hdf5_path: string
- batch_size: int
- black_list_csv: string
- random_seed: int
- """
- self.batch_size = batch_size
- self.random_state = np.random.RandomState(random_seed)
-
- # Black list
- if black_list_csv:
- self.black_list_names = read_black_list(black_list_csv)
- else:
- self.black_list_names = []
-
- logging.info('Black list samples: {}'.format(len(self.black_list_names)))
-
- # Load target
- load_time = time.time()
-
- with h5py.File(indexes_hdf5_path, 'r') as hf:
- self.audio_names = [audio_name.decode() for audio_name in hf['audio_name'][:]]
- self.hdf5_paths = [hdf5_path.decode() for hdf5_path in hf['hdf5_path'][:]]
- self.indexes_in_hdf5 = hf['index_in_hdf5'][:]
- self.targets = hf['target'][:].astype(np.float32)
-
- (self.audios_num, self.classes_num) = self.targets.shape
- logging.info('Training number: {}'.format(self.audios_num))
- logging.info('Load target time: {:.3f} s'.format(time.time() - load_time))
-
-
-class TrainSampler(Base):
- def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None,
- random_seed=1234):
- """Balanced sampler. Generate batch meta for training.
-
- Args:
- indexes_hdf5_path: string
- batch_size: int
- black_list_csv: string
- random_seed: int
- """
- super(TrainSampler, self).__init__(indexes_hdf5_path, batch_size,
- black_list_csv, random_seed)
-
- self.indexes = np.arange(self.audios_num)
-
- # Shuffle indexes
- self.random_state.shuffle(self.indexes)
-
- self.pointer = 0
-
- def __iter__(self):
- """Generate batch meta for training.
-
- Returns:
- batch_meta: e.g.: [
- {'hdf5_path': string, 'index_in_hdf5': int},
- ...]
- """
- batch_size = self.batch_size
-
- while True:
- batch_meta = []
- i = 0
- while i < batch_size:
- index = self.indexes[self.pointer]
- self.pointer += 1
-
- # Shuffle indexes and reset pointer
- if self.pointer >= self.audios_num:
- self.pointer = 0
- self.random_state.shuffle(self.indexes)
-
- # If audio in black list then continue
- if self.audio_names[index] in self.black_list_names:
- continue
- else:
- batch_meta.append({
- 'hdf5_path': self.hdf5_paths[index],
- 'index_in_hdf5': self.indexes_in_hdf5[index]})
- i += 1
-
- yield batch_meta
-
- def state_dict(self):
- state = {
- 'indexes': self.indexes,
- 'pointer': self.pointer}
- return state
-
- def load_state_dict(self, state):
- self.indexes = state['indexes']
- self.pointer = state['pointer']
-
-
-class BalancedTrainSampler(Base):
- def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None,
- random_seed=1234):
- """Balanced sampler. Generate batch meta for training. Data are equally
- sampled from different sound classes.
-
- Args:
- indexes_hdf5_path: string
- batch_size: int
- black_list_csv: string
- random_seed: int
- """
- super(BalancedTrainSampler, self).__init__(indexes_hdf5_path,
- batch_size, black_list_csv, random_seed)
-
- self.samples_num_per_class = np.sum(self.targets, axis=0)
- logging.info('samples_num_per_class: {}'.format(
- self.samples_num_per_class.astype(np.int32)))
-
- # Training indexes of all sound classes. E.g.:
- # [[0, 11, 12, ...], [3, 4, 15, 16, ...], [7, 8, ...], ...]
- self.indexes_per_class = []
-
- for k in range(self.classes_num):
- self.indexes_per_class.append(
- np.where(self.targets[:, k] == 1)[0])
-
- # Shuffle indexes
- for k in range(self.classes_num):
- self.random_state.shuffle(self.indexes_per_class[k])
-
- self.queue = []
- self.pointers_of_classes = [0] * self.classes_num
-
- def expand_queue(self, queue):
- classes_set = np.arange(self.classes_num).tolist()
- self.random_state.shuffle(classes_set)
- queue += classes_set
- return queue
-
- def __iter__(self):
- """Generate batch meta for training.
-
- Returns:
- batch_meta: e.g.: [
- {'hdf5_path': string, 'index_in_hdf5': int},
- ...]
- """
- batch_size = self.batch_size
-
- while True:
- batch_meta = []
- i = 0
- while i < batch_size:
- if len(self.queue) == 0:
- self.queue = self.expand_queue(self.queue)
-
- class_id = self.queue.pop(0)
- pointer = self.pointers_of_classes[class_id]
- self.pointers_of_classes[class_id] += 1
- index = self.indexes_per_class[class_id][pointer]
-
- # When finish one epoch of a sound class, then shuffle its indexes and reset pointer
- if self.pointers_of_classes[class_id] >= self.samples_num_per_class[class_id]:
- self.pointers_of_classes[class_id] = 0
- self.random_state.shuffle(self.indexes_per_class[class_id])
-
- # If audio in black list then continue
- if self.audio_names[index] in self.black_list_names:
- continue
- else:
- batch_meta.append({
- 'hdf5_path': self.hdf5_paths[index],
- 'index_in_hdf5': self.indexes_in_hdf5[index]})
- i += 1
-
- yield batch_meta
-
- def state_dict(self):
- state = {
- 'indexes_per_class': self.indexes_per_class,
- 'queue': self.queue,
- 'pointers_of_classes': self.pointers_of_classes}
- return state
-
- def load_state_dict(self, state):
- self.indexes_per_class = state['indexes_per_class']
- self.queue = state['queue']
- self.pointers_of_classes = state['pointers_of_classes']
-
-
-class AlternateTrainSampler(Base):
- def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None,
- random_seed=1234):
- """AlternateSampler is a combination of Sampler and Balanced Sampler.
- AlternateSampler alternately sample data from Sampler and Blanced Sampler.
-
- Args:
- indexes_hdf5_path: string
- batch_size: int
- black_list_csv: string
- random_seed: int
- """
- self.sampler1 = TrainSampler(indexes_hdf5_path, batch_size,
- black_list_csv, random_seed)
-
- self.sampler2 = BalancedTrainSampler(indexes_hdf5_path, batch_size,
- black_list_csv, random_seed)
-
- self.batch_size = batch_size
- self.count = 0
-
- def __iter__(self):
- """Generate batch meta for training.
-
- Returns:
- batch_meta: e.g.: [
- {'hdf5_path': string, 'index_in_hdf5': int},
- ...]
- """
- batch_size = self.batch_size
-
- while True:
- self.count += 1
-
- if self.count % 2 == 0:
- batch_meta = []
- i = 0
- while i < batch_size:
- index = self.sampler1.indexes[self.sampler1.pointer]
- self.sampler1.pointer += 1
-
- # Shuffle indexes and reset pointer
- if self.sampler1.pointer >= self.sampler1.audios_num:
- self.sampler1.pointer = 0
- self.sampler1.random_state.shuffle(self.sampler1.indexes)
-
- # If audio in black list then continue
- if self.sampler1.audio_names[index] in self.sampler1.black_list_names:
- continue
- else:
- batch_meta.append({
- 'hdf5_path': self.sampler1.hdf5_paths[index],
- 'index_in_hdf5': self.sampler1.indexes_in_hdf5[index]})
- i += 1
-
- elif self.count % 2 == 1:
- batch_meta = []
- i = 0
- while i < batch_size:
- if len(self.sampler2.queue) == 0:
- self.sampler2.queue = self.sampler2.expand_queue(self.sampler2.queue)
-
- class_id = self.sampler2.queue.pop(0)
- pointer = self.sampler2.pointers_of_classes[class_id]
- self.sampler2.pointers_of_classes[class_id] += 1
- index = self.sampler2.indexes_per_class[class_id][pointer]
-
- # When finish one epoch of a sound class, then shuffle its indexes and reset pointer
- if self.sampler2.pointers_of_classes[class_id] >= self.sampler2.samples_num_per_class[class_id]:
- self.sampler2.pointers_of_classes[class_id] = 0
- self.sampler2.random_state.shuffle(self.sampler2.indexes_per_class[class_id])
-
- # If audio in black list then continue
- if self.sampler2.audio_names[index] in self.sampler2.black_list_names:
- continue
- else:
- batch_meta.append({
- 'hdf5_path': self.sampler2.hdf5_paths[index],
- 'index_in_hdf5': self.sampler2.indexes_in_hdf5[index]})
- i += 1
-
- yield batch_meta
-
- def state_dict(self):
- state = {
- 'sampler1': self.sampler1.state_dict(),
- 'sampler2': self.sampler2.state_dict()}
- return state
-
- def load_state_dict(self, state):
- self.sampler1.load_state_dict(state['sampler1'])
- self.sampler2.load_state_dict(state['sampler2'])
-
-
-class EvaluateSampler(object):
- def __init__(self, indexes_hdf5_path, batch_size):
- """Evaluate sampler. Generate batch meta for evaluation.
-
- Args:
- indexes_hdf5_path: string
- batch_size: int
- """
- self.batch_size = batch_size
-
- with h5py.File(indexes_hdf5_path, 'r') as hf:
- self.audio_names = [audio_name.decode() for audio_name in hf['audio_name'][:]]
- self.hdf5_paths = [hdf5_path.decode() for hdf5_path in hf['hdf5_path'][:]]
- self.indexes_in_hdf5 = hf['index_in_hdf5'][:]
- self.targets = hf['target'][:].astype(np.float32)
-
- self.audios_num = len(self.audio_names)
-
- def __iter__(self):
- """Generate batch meta for training.
-
- Returns:
- batch_meta: e.g.: [
- {'hdf5_path': string,
- 'index_in_hdf5': int}
- ...]
- """
- batch_size = self.batch_size
- pointer = 0
-
- while pointer < self.audios_num:
- batch_indexes = np.arange(pointer,
- min(pointer + batch_size, self.audios_num))
-
- batch_meta = []
-
- for index in batch_indexes:
- batch_meta.append({
- 'audio_name': self.audio_names[index],
- 'hdf5_path': self.hdf5_paths[index],
- 'index_in_hdf5': self.indexes_in_hdf5[index],
- 'target': self.targets[index]})
-
- pointer += batch_size
- yield batch_meta
-
-
-def collate_fn(list_data_dict):
- """Collate data.
- Args:
- list_data_dict, e.g., [{'audio_name': str, 'waveform': (clip_samples,), ...},
- {'audio_name': str, 'waveform': (clip_samples,), ...},
- ...]
- Returns:
- np_data_dict, dict, e.g.,
- {'audio_name': (batch_size,), 'waveform': (batch_size, clip_samples), ...}
- """
- np_data_dict = {}
-
- for key in list_data_dict[0].keys():
- np_data_dict[key] = np.array([data_dict[key] for data_dict in list_data_dict])
-
- return np_data_dict
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/audio/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/audio/Factory.js
deleted file mode 100644
index 1d68b7edad1661771396c15b22c854f7ae7e1e99..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/audio/Factory.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import Audio from './Audio.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('audio', function (config) {
- var gameObject = new Audio(this.scene, config);
- this.scene.add.existing(gameObject);
- return gameObject;
-});
-
-SetValue(window, 'RexPlugins.Spinner.Audio', Audio);
-
-export default Audio;
\ No newline at end of file
diff --git a/spaces/Ahmedmewloud/Depplearnig/README.md b/spaces/Ahmedmewloud/Depplearnig/README.md
deleted file mode 100644
index f559a73a066792982e2025ec4486fc4f90e6b433..0000000000000000000000000000000000000000
--- a/spaces/Ahmedmewloud/Depplearnig/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Depplearnig
-emoji: 🏢
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AixiaGreyatt/QQsign/README.md b/spaces/AixiaGreyatt/QQsign/README.md
deleted file mode 100644
index 91f5336e93a7969b8336fc19a20796811a1670f7..0000000000000000000000000000000000000000
--- a/spaces/AixiaGreyatt/QQsign/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: QQsign
-emoji: 🦀
-colorFrom: blue
-colorTo: pink
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Aki004/herta-so-vits/modules/losses.py b/spaces/Aki004/herta-so-vits/modules/losses.py
deleted file mode 100644
index cd21799eccde350c3aac0bdd661baf96ed220147..0000000000000000000000000000000000000000
--- a/spaces/Aki004/herta-so-vits/modules/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import modules.commons as commons
-
-
-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()
- #print(logs_p)
- kl = logs_p - logs_q - 0.5
- kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
- kl = torch.sum(kl * z_mask)
- l = kl / torch.sum(z_mask)
- return l
diff --git a/spaces/Alichuan/VITS-Umamusume-voice-synthesizer/ONNXVITS_transforms.py b/spaces/Alichuan/VITS-Umamusume-voice-synthesizer/ONNXVITS_transforms.py
deleted file mode 100644
index 69b6d1c4b5724a3ef61f8bc3d64fc45c5e51e270..0000000000000000000000000000000000000000
--- a/spaces/Alichuan/VITS-Umamusume-voice-synthesizer/ONNXVITS_transforms.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- #unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- unnormalized_derivatives_ = torch.zeros((1, 1, unnormalized_derivatives.size(2), unnormalized_derivatives.size(3)+2))
- unnormalized_derivatives_[...,1:-1] = unnormalized_derivatives
- unnormalized_derivatives = unnormalized_derivatives_
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/Alpaca233/LangchainPDF/app.py b/spaces/Alpaca233/LangchainPDF/app.py
deleted file mode 100644
index c84c18b23316ec902fdd63093249418729c44fad..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/LangchainPDF/app.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import gradio as gr
-
-from langchain.document_loaders import PyMuPDFLoader # for loading the pdf
-from langchain.embeddings import OpenAIEmbeddings # for creating embeddings
-from langchain.vectorstores import Chroma # for the vectorization part
-from langchain.chains import ChatVectorDBChain # for chatting with the pdf
-from langchain.llms import OpenAI # the LLM model we'll use (CHatGPT)
-
-
-class Chat:
- def __init__(self, pdf, api_input):
- self.api = api_input
- loader = PyMuPDFLoader(pdf)
- pages = loader.load_and_split()
-
- embeddings = OpenAIEmbeddings(openai_api_key=self.api)
- vectordb = Chroma.from_documents(pages, embedding=embeddings, persist_directory=".")
- vectordb.persist()
-
- self.pdf_qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0.9, model_name="gpt-3.5-turbo",
- openai_api_key=self.api),
- vectordb, return_source_documents=True)
-
- def question(self, query):
- result = self.pdf_qa({"question": "请使用中文回答" + query, "chat_history": ""})
- print("Answer:")
- print(result["answer"])
-
- return result["answer"]
-
-
-def analyse(pdf_file, api_input):
- print(pdf_file.name)
- session = Chat(pdf_file.name, api_input)
- return session, "文章分析完成"
-
-
-def ask_question(data, question):
- if data == "":
- return "Please upload PDF file first!"
- return data.question(question)
-
-
-with gr.Blocks() as demo:
- gr.Markdown(
- """
- # ChatPDF based on Langchain
- """)
- data = gr.State()
- with gr.Tab("Upload PDF File"):
- pdf_input = gr.File(label="PDF File")
- api_input = gr.Textbox(label="OpenAI API Key")
- result = gr.Textbox()
- upload_button = gr.Button("Start Analyse")
- question_input = gr.Textbox(label="Your Question", placeholder="Authors of this paper?")
- answer = gr.Textbox(label="Answer")
- ask_button = gr.Button("Ask")
-
- upload_button.click(fn=analyse, inputs=[pdf_input, api_input], outputs=[data, result])
- ask_button.click(ask_question, inputs=[data, question_input], outputs=answer)
-
-if __name__ == "__main__":
- demo.title = "ChatPDF Based on Langchain"
- demo.launch()
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/backbones/regnet.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/backbones/regnet.py
deleted file mode 100644
index 91a602a952226cebb5fd0e3e282c6f98ae4fa455..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/backbones/regnet.py
+++ /dev/null
@@ -1,325 +0,0 @@
-import numpy as np
-import torch.nn as nn
-from mmcv.cnn import build_conv_layer, build_norm_layer
-
-from ..builder import BACKBONES
-from .resnet import ResNet
-from .resnext import Bottleneck
-
-
-@BACKBONES.register_module()
-class RegNet(ResNet):
- """RegNet backbone.
-
- More details can be found in `paper xs
- sm
- md
- lg
- xl
- 2xl
- ", "\n") - bot = bot.rstrip() - prompt_history.append( - prompt_template.substitute( - human=human, bot=bot if bot is not None else "") - ) - - msg_tokens = tokenizer( - "\n\n".join(prompt_history).strip(), - return_tensors="pt", - add_special_tokens=False # Use
", "\n") - if "###" in new_text: - new_text = new_text.split("###")[0] - partial_text += new_text.strip() - history[-1][1] = partial_text - break - else: - # Filter empty trailing new lines - if new_text == "\n": - new_text = new_text.strip() - partial_text += new_text - history[-1][1] = partial_text - yield history - return partial_text - - -def user(user_message, history): - return "", history + [[user_message, None]] - - -with gr.Blocks() as demo: - gr.Markdown("# StableVicuna by CarperAI") - gr.HTML("
CarperAI/stable-vicuna-13b-delta")
- gr.HTML('''
-
- """
- )
- with gr.Group():
- with gr.Box():
- with gr.Row().style(mobile_collapse=False, equal_height=True):
- text = gr.Textbox(
- label="Enter your prompt",
- show_label=False,
- max_lines=1,
- placeholder="Enter your prompt",
- ).style(
- border=(True, False, True, True),
- rounded=(True, False, False, True),
- container=False,
- )
- btn = gr.Button("Generate image").style(
- margin=False,
- rounded=(False, True, True, False),
- )
-
- gallery = gr.Gallery(
- label="Generated images", show_label=False, elem_id="gallery"
- ).style(grid=[2], height="auto")
-
-
- with gr.Row(elem_id="advanced-options"):
- samples = gr.Slider(label="Images", minimum=1, maximum=4, value=2, step=1)
- steps = gr.Slider(label="Steps", minimum=5, maximum=50, value=25, step=5)
- scale = gr.Slider(
- label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
- )
-
-
- ex = gr.Examples(examples=examples, fn=infer, inputs=[text, samples, scale], outputs=gallery, cache_examples=False)
- ex.dataset.headers = [""]
-
-
- text.submit(infer, inputs=[text, samples, steps, scale], outputs=gallery)
- btn.click(infer, inputs=[text, samples, steps, scale], outputs=gallery)
- gr.HTML(
- """
-
-
- 
-
-
- - Pokémon text to image -
-- Generate new Pokémon from a text description, - created by Lambda Labs. -
-
-
- """
- )
-
-block.launch()
\ No newline at end of file
diff --git a/spaces/Cletrason/Cletrason-toad-in-the-mario-movie/README.md b/spaces/Cletrason/Cletrason-toad-in-the-mario-movie/README.md
deleted file mode 100644
index 8f018fa508f404e92f66e7296b6d606933d20c67..0000000000000000000000000000000000000000
--- a/spaces/Cletrason/Cletrason-toad-in-the-mario-movie/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Cletrason Toad In The Mario Movie
-emoji: 💩
-colorFrom: blue
-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/DHEIVER/CoronaryAngioSegment/detect_anomalies.py b/spaces/DHEIVER/CoronaryAngioSegment/detect_anomalies.py
deleted file mode 100644
index 37ef92651f2462a2c820584f956df153f24e53a2..0000000000000000000000000000000000000000
--- a/spaces/DHEIVER/CoronaryAngioSegment/detect_anomalies.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import numpy as np
-import cv2
-
-def detect_anomalies(mask):
- # Aplicar um limiar à máscara para identificar as regiões de anomalia
- threshold = 0.5 # Ajuste o limiar conforme necessário
- anomalies = (mask > threshold).astype(np.uint8)
-
- # Aplicar pós-processamento, como erosão e dilatação, se necessário
- # anomalies = cv2.erode(anomalies, kernel, iterations=1)
- # anomalies = cv2.dilate(anomalies, kernel, iterations=1)
-
- return anomalies
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/ttCollection.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/ttCollection.py
deleted file mode 100644
index 3ab579ee001ebb099c1cc310b9898f9c8119a567..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/ttCollection.py
+++ /dev/null
@@ -1,127 +0,0 @@
-from fontTools.ttLib.ttFont import TTFont
-from fontTools.ttLib.sfnt import readTTCHeader, writeTTCHeader
-from io import BytesIO
-import struct
-import logging
-
-log = logging.getLogger(__name__)
-
-
-class TTCollection(object):
-
- """Object representing a TrueType Collection / OpenType Collection.
- The main API is self.fonts being a list of TTFont instances.
-
- If shareTables is True, then different fonts in the collection
- might point to the same table object if the data for the table was
- the same in the font file. Note, however, that this might result
- in suprises and incorrect behavior if the different fonts involved
- have different GlyphOrder. Use only if you know what you are doing.
- """
-
- def __init__(self, file=None, shareTables=False, **kwargs):
- fonts = self.fonts = []
- if file is None:
- return
-
- assert "fontNumber" not in kwargs, kwargs
-
- closeStream = False
- if not hasattr(file, "read"):
- file = open(file, "rb")
- closeStream = True
-
- tableCache = {} if shareTables else None
-
- header = readTTCHeader(file)
- for i in range(header.numFonts):
- font = TTFont(file, fontNumber=i, _tableCache=tableCache, **kwargs)
- fonts.append(font)
-
- # don't close file if lazy=True, as the TTFont hold a reference to the original
- # file; the file will be closed once the TTFonts are closed in the
- # TTCollection.close(). We still want to close the file if lazy is None or
- # False, because in that case the TTFont no longer need the original file
- # and we want to avoid 'ResourceWarning: unclosed file'.
- if not kwargs.get("lazy") and closeStream:
- file.close()
-
- def __enter__(self):
- return self
-
- def __exit__(self, type, value, traceback):
- self.close()
-
- def close(self):
- for font in self.fonts:
- font.close()
-
- def save(self, file, shareTables=True):
- """Save the font to disk. Similarly to the constructor,
- the 'file' argument can be either a pathname or a writable
- file object.
- """
- if not hasattr(file, "write"):
- final = None
- file = open(file, "wb")
- else:
- # assume "file" is a writable file object
- # write to a temporary stream to allow saving to unseekable streams
- final = file
- file = BytesIO()
-
- tableCache = {} if shareTables else None
-
- offsets_offset = writeTTCHeader(file, len(self.fonts))
- offsets = []
- for font in self.fonts:
- offsets.append(file.tell())
- font._save(file, tableCache=tableCache)
- file.seek(0, 2)
-
- file.seek(offsets_offset)
- file.write(struct.pack(">%dL" % len(self.fonts), *offsets))
-
- if final:
- final.write(file.getvalue())
- file.close()
-
- def saveXML(self, fileOrPath, newlinestr="\n", writeVersion=True, **kwargs):
-
- from fontTools.misc import xmlWriter
-
- writer = xmlWriter.XMLWriter(fileOrPath, newlinestr=newlinestr)
-
- if writeVersion:
- from fontTools import version
-
- version = ".".join(version.split(".")[:2])
- writer.begintag("ttCollection", ttLibVersion=version)
- else:
- writer.begintag("ttCollection")
- writer.newline()
- writer.newline()
-
- for font in self.fonts:
- font._saveXML(writer, writeVersion=False, **kwargs)
- writer.newline()
-
- writer.endtag("ttCollection")
- writer.newline()
-
- writer.close()
-
- def __getitem__(self, item):
- return self.fonts[item]
-
- def __setitem__(self, item, value):
- self.fonts[item] = value
-
- def __delitem__(self, item):
- return self.fonts[item]
-
- def __len__(self):
- return len(self.fonts)
-
- def __iter__(self):
- return iter(self.fonts)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/BlockTitle-dee077e8.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/BlockTitle-dee077e8.js
deleted file mode 100644
index 1f7cab978eb6774158c23251ceb78c980b3d1915..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/BlockTitle-dee077e8.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as h,e as k,s as g,a9 as w,N as $,O as B,m as I,K as d,U as _,p as c,ab as N,ac as S,ad as j,z as r,u as q,v as m,y as v,A as p,k as z,o as A,x as C,P as K,R as O}from"./index-1d65707a.js";import{I as P}from"./Info-7c6961ef.js";import"./Button-f155035a.js";function b(a){let e,l;return e=new P({props:{$$slots:{default:[R]},$$scope:{ctx:a}}}),{c(){z(e.$$.fragment)},m(n,o){A(e,n,o),l=!0},p(n,o){const u={};o&10&&(u.$$scope={dirty:o,ctx:n}),e.$set(u)},i(n){l||(r(e.$$.fragment,n),l=!0)},o(n){m(e.$$.fragment,n),l=!1},d(n){C(e,n)}}}function R(a){let e;return{c(){e=K(a[1])},m(l,n){c(l,e,n)},p(l,n){n&2&&O(e,l[1])},d(l){l&&p(e)}}}function T(a){let e,l,n,o;const u=a[2].default,f=w(u,a,a[3],null);let s=a[1]&&b(a);return{c(){e=$("span"),f&&f.c(),l=B(),s&&s.c(),n=I(),d(e,"data-testid","block-info"),d(e,"class","svelte-1gfkn6j"),_(e,"sr-only",!a[0]),_(e,"hide",!a[0]),_(e,"has-info",a[1]!=null)},m(t,i){c(t,e,i),f&&f.m(e,null),c(t,l,i),s&&s.m(t,i),c(t,n,i),o=!0},p(t,[i]){f&&f.p&&(!o||i&8)&&N(f,u,t,t[3],o?j(u,t[3],i,null):S(t[3]),null),(!o||i&1)&&_(e,"sr-only",!t[0]),(!o||i&1)&&_(e,"hide",!t[0]),(!o||i&2)&&_(e,"has-info",t[1]!=null),t[1]?s?(s.p(t,i),i&2&&r(s,1)):(s=b(t),s.c(),r(s,1),s.m(n.parentNode,n)):s&&(q(),m(s,1,1,()=>{s=null}),v())},i(t){o||(r(f,t),r(s),o=!0)},o(t){m(f,t),m(s),o=!1},d(t){t&&(p(e),p(l),p(n)),f&&f.d(t),s&&s.d(t)}}}function U(a,e,l){let{$$slots:n={},$$scope:o}=e,{show_label:u=!0}=e,{info:f=void 0}=e;return a.$$set=s=>{"show_label"in s&&l(0,u=s.show_label),"info"in s&&l(1,f=s.info),"$$scope"in s&&l(3,o=s.$$scope)},[u,f,n,o]}class G extends h{constructor(e){super(),k(this,e,U,T,g,{show_label:0,info:1})}}export{G as B};
-//# sourceMappingURL=BlockTitle-dee077e8.js.map
diff --git a/spaces/DataScienceEngineering/6-TreemapAndSunburst/README.md b/spaces/DataScienceEngineering/6-TreemapAndSunburst/README.md
deleted file mode 100644
index 224560d7194d42f81eb846c9c44043f63f4e86bc..0000000000000000000000000000000000000000
--- a/spaces/DataScienceEngineering/6-TreemapAndSunburst/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: 🧠Visualization Plotly Sunbursts Treemaps WebGL🩺
-emoji: 6-Vis🧠
-colorFrom: indigo
-colorTo: purple
-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
\ No newline at end of file
diff --git a/spaces/Datatrooper/boston_housing/app.py b/spaces/Datatrooper/boston_housing/app.py
deleted file mode 100644
index f6cd4f4752758425471b9f61a63172370832b3e5..0000000000000000000000000000000000000000
--- a/spaces/Datatrooper/boston_housing/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import gradio as gr
-#import joblib
-from sklearn.linear_model import LinearRegression
-from sklearn.tree import DecisionTreeRegressor
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.model_selection import StratifiedShuffleSplit
-from sklearn.impute import SimpleImputer
-from sklearn.pipeline import Pipeline
-from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import StandardScaler
-from sklearn.preprocessing import OneHotEncoder
-from sklearn.metrics import mean_squared_error
-from sklearn.model_selection import cross_val_score
-from sklearn.model_selection import RandomizedSearchCV
-from sklearn.preprocessing import MinMaxScaler
-from sklearn.model_selection import train_test_split
-
-df = pd.read_csv('Housing.csv')
-cat_columns = ['mainroad',
- 'guestroom', 'basement', 'hotwaterheating', 'airconditioning',
- 'prefarea']
-
-def binary_mapping(x):
- return x.map({'yes': 1, "no": 0})
-
-df[cat_columns] = df[cat_columns].apply(binary_mapping)
-
-
-ohe = OneHotEncoder(sparse=False, handle_unknown='error', drop='first')
-ohe_df = pd.DataFrame(ohe.fit_transform(df[['furnishingstatus']]))
-
-ohe_df.columns = ohe.get_feature_names(['status'])
-
-df = pd.concat([df,ohe_df], axis=1)
-df.drop(['furnishingstatus'], axis = 1, inplace = True)
-df.head()
-
-
-
-df_new = df.copy(deep=True)
-num_columns = ['area', 'bedrooms', 'bathrooms', 'stories','parking']
-
-
-
-scaler = MinMaxScaler().fit(df_new[num_columns])
-df_new[num_columns] = scaler.transform(df_new[num_columns])
-y = df_new.pop('price')
-x = df_new
-x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)
-model = RandomForestRegressor()
-model.fit(x_train, y_train)
-
-def prediction(properties):
- print(properties)
- df = pd.DataFrame(properties, columns=x_test.columns)
- print(df)
- df = df[x_test.columns].iloc[0].to_frame().T
- df[num_columns] = scaler.transform(df[num_columns])
- return model.predict(df)
-example = pd.DataFrame([7420, 4, 2, 3, 1, 0, 0, 0, 1, 2, 1, 0, 0]).T
-example.columns = x_test.columns
-
-demo = gr.Interface(
- prediction,
- [
- gr.Dataframe(
- headers=['area', 'bedrooms', 'bathrooms', 'stories', 'mainroad', 'guestroom',
- 'basement', 'hotwaterheating', 'airconditioning', 'parking', 'prefarea',
- 'status_semi-furnished', 'status_unfurnished'],
- datatype=["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"],
- )
-
- ],
- "number",
- description="Enter The Properties Of The Home",
- title="California Housing Prices Prediction",
- examples=[example],
-
-
-)
-
-demo.launch()
diff --git a/spaces/DiamondYin/AnewGame/Build/WaliwebGLgameFPS.loader.js b/spaces/DiamondYin/AnewGame/Build/WaliwebGLgameFPS.loader.js
deleted file mode 100644
index 16d4b5b20de31f116dfa154e8c16de0c697cf0dc..0000000000000000000000000000000000000000
--- a/spaces/DiamondYin/AnewGame/Build/WaliwebGLgameFPS.loader.js
+++ /dev/null
@@ -1 +0,0 @@
-function createUnityInstance(t,n,c){function s(e,t){if(!s.aborted&&n.showBanner)return"error"==t&&(s.aborted=!0),n.showBanner(e,t);switch(t){case"error":console.error(e);break;case"warning":console.warn(e);break;default:console.log(e)}}function r(e){var t=e.reason||e.error,n=t?t.toString():e.message||e.reason||"",r=t&&t.stack?t.stack.toString():"";(n+="\n"+(r=r.startsWith(n)?r.substring(n.length):r).trim())&&l.stackTraceRegExp&&l.stackTraceRegExp.test(n)&&D(n,e.filename||t&&(t.fileName||t.sourceURL)||"",e.lineno||t&&(t.lineNumber||t.line)||0)}function e(e,t,n){var r=e[t];void 0!==r&&r||(console.warn('Config option "'+t+'" is missing or empty. Falling back to default value: "'+n+'". Consider updating your WebGL template to include the missing config option.'),e[t]=n)}c=c||function(){};var o,l={canvas:t,webglContextAttributes:{preserveDrawingBuffer:!1,powerPreference:2},cacheControl:function(e){return e==l.dataUrl||e.match(/\.bundle/)?"must-revalidate":"no-store"},streamingAssetsUrl:"StreamingAssets",downloadProgress:{},deinitializers:[],intervals:{},setInterval:function(e,t){e=window.setInterval(e,t);return this.intervals[e]=!0,e},clearInterval:function(e){delete this.intervals[e],window.clearInterval(e)},preRun:[],postRun:[],print:function(e){console.log(e)},printErr:function(e){console.error(e),"string"==typeof e&&-1!=e.indexOf("wasm streaming compile failed")&&(-1!=e.toLowerCase().indexOf("mime")?s('HTTP Response Header "Content-Type" configured incorrectly on the server for file '+l.codeUrl+' , should be "application/wasm". Startup time performance will suffer.',"warning"):s('WebAssembly streaming compilation failed! This can happen for example if "Content-Encoding" HTTP header is incorrectly enabled on the server for file '+l.codeUrl+", but the file is not pre-compressed on disk (or vice versa). Check the Network tab in browser Devtools to debug server header configuration.","warning"))},locateFile:function(e){return"build.wasm"==e?this.codeUrl:e},disabledCanvasEvents:["contextmenu","dragstart"]};for(o in e(n,"companyName","Unity"),e(n,"productName","WebGL Player"),e(n,"productVersion","1.0"),n)l[o]=n[o];l.streamingAssetsUrl=new URL(l.streamingAssetsUrl,document.URL).href;var a=l.disabledCanvasEvents.slice();function i(e){e.preventDefault()}a.forEach(function(e){t.addEventListener(e,i)}),window.addEventListener("error",r),window.addEventListener("unhandledrejection",r);var u="",d="";function h(e){document.webkitCurrentFullScreenElement===t?t.style.width&&(u=t.style.width,d=t.style.height,t.style.width="100%",t.style.height="100%"):u&&(t.style.width=u,t.style.height=d,d=u="")}document.addEventListener("webkitfullscreenchange",h),l.deinitializers.push(function(){for(var e in l.disableAccessToMediaDevices(),a.forEach(function(e){t.removeEventListener(e,i)}),window.removeEventListener("error",r),window.removeEventListener("unhandledrejection",r),document.removeEventListener("webkitfullscreenchange",h),l.intervals)window.clearInterval(e);l.intervals={}}),l.QuitCleanup=function(){for(var e=0;ePut in a text prompt and generate your own Pokémon character, no "prompt engineering" required! -
If you want to find out how we made this model read about it in this blog post. -
And if you want to train your own Stable Diffusion variants, see our Examples Repo! -
Trained by Justin Pinkney (@Buntworthy) at Lambda Labs.
-If using custom web server, verify that web server is sending .br files with HTTP Response Header "Content-Encoding: br". Brotli compression may not be supported in Firefox over HTTP connections. '+r+' See https://bugzilla.mozilla.org/show_bug.cgi?id=1670675 for more information.':"Unable to parse "+l.frameworkUrl+'!
If using custom web server, verify that web server is sending .br files with HTTP Response Header "Content-Encoding: br". Brotli compression may not be supported over HTTP connections. Migrate your server to use HTTPS.'),void s(n,"error"))}s("Unable to parse "+l.frameworkUrl+"! The file is corrupt, or compression was misconfigured? (check Content-Encoding HTTP Response Header on web server)","error")}var o=unityFramework;unityFramework=null,i.onload=null,a(o)},i.onerror=function(e){s("Unable to load file "+l.frameworkUrl+"! Check that the file exists on the remote server. (also check browser Console and Devtools Network tab to debug)","error")},document.body.appendChild(i),l.deinitializers.push(function(){document.body.removeChild(i)})}).then(function(e){e(l)});P(n="dataUrl"),e=l.cacheControl(l[n]),t=l.companyName&&l.productName?l.cachedFetch:l.fetchWithProgress,r=l[n],r=/file:\/\//.exec(r)?"same-origin":void 0;var n,e,t,r,o=t(l[n],{method:"GET",companyName:l.companyName,productName:l.productName,productVersion:l.productVersion,control:e,mode:r,onProgress:function(e){P(n,e)}}).then(function(e){return e.parsedBody}).catch(function(e){var t="Failed to download file "+l[n];"file:"==location.protocol?s(t+". Loading web pages via a file:// URL without a web server is not supported by this browser. Please use a local development web server to host Unity content, or use the Unity Build and Run option.","error"):console.error(t)});l.preRun.push(function(){l.addRunDependency("dataUrl"),o.then(function(e){var t=new DataView(e.buffer,e.byteOffset,e.byteLength),n=0,r="UnityWebData1.0\0";if(!String.fromCharCode.apply(null,e.subarray(n,n+r.length))==r)throw"unknown data format";var o=t.getUint32(n+=r.length,!0);for(n+=4;n
Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo
-
.jpeg)
-Recipes for building an open-domain chatbot
" - -import torch -from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, BlenderbotForConditionalGeneration, BlenderbotForCausalLM, BlenderbotTokenizer - -tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill") -model = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill",add_cross_attention=False) - -def predict(input, history=[]): - # tokenize the new input sentence - new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt') - - # append the new user input tokens to the chat history - bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1) - - # generate a response - history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist() - - # convert the tokens to text, and then split the responses into the right format - response = tokenizer.decode(history[0]).replace("\nmultiple lines?')) - What about - multiple lines? - """ - html_stripper = re.compile('()|(<[^>]*>)|([^<]+)', re.DOTALL) - texts = (match.group(3) or '' for match in html_stripper.finditer(s)) - return ''.join(texts) - - -class SeparatedValues(str): - """ - A string separated by a separator. Overrides __iter__ for getting - the values. - - >>> list(SeparatedValues('a,b,c')) - ['a', 'b', 'c'] - - Whitespace is stripped and empty values are discarded. - - >>> list(SeparatedValues(' a, b , c, ')) - ['a', 'b', 'c'] - """ - - separator = ',' - - def __iter__(self): - parts = self.split(self.separator) - return filter(None, (part.strip() for part in parts)) - - -class Stripper: - r""" - Given a series of lines, find the common prefix and strip it from them. - - >>> lines = [ - ... 'abcdefg\n', - ... 'abc\n', - ... 'abcde\n', - ... ] - >>> res = Stripper.strip_prefix(lines) - >>> res.prefix - 'abc' - >>> list(res.lines) - ['defg\n', '\n', 'de\n'] - - If no prefix is common, nothing should be stripped. - - >>> lines = [ - ... 'abcd\n', - ... '1234\n', - ... ] - >>> res = Stripper.strip_prefix(lines) - >>> res.prefix = '' - >>> list(res.lines) - ['abcd\n', '1234\n'] - """ - - def __init__(self, prefix, lines): - self.prefix = prefix - self.lines = map(self, lines) - - @classmethod - def strip_prefix(cls, lines): - prefix_lines, lines = itertools.tee(lines) - prefix = functools.reduce(cls.common_prefix, prefix_lines) - return cls(prefix, lines) - - def __call__(self, line): - if not self.prefix: - return line - null, prefix, rest = line.partition(self.prefix) - return rest - - @staticmethod - def common_prefix(s1, s2): - """ - Return the common prefix of two lines. - """ - index = min(len(s1), len(s2)) - while s1[:index] != s2[:index]: - index -= 1 - return s1[:index] - - -def remove_prefix(text, prefix): - """ - Remove the prefix from the text if it exists. - - >>> remove_prefix('underwhelming performance', 'underwhelming ') - 'performance' - - >>> remove_prefix('something special', 'sample') - 'something special' - """ - null, prefix, rest = text.rpartition(prefix) - return rest - - -def remove_suffix(text, suffix): - """ - Remove the suffix from the text if it exists. - - >>> remove_suffix('name.git', '.git') - 'name' - - >>> remove_suffix('something special', 'sample') - 'something special' - """ - rest, suffix, null = text.partition(suffix) - return rest - - -def normalize_newlines(text): - r""" - Replace alternate newlines with the canonical newline. - - >>> normalize_newlines('Lorem Ipsum\u2029') - 'Lorem Ipsum\n' - >>> normalize_newlines('Lorem Ipsum\r\n') - 'Lorem Ipsum\n' - >>> normalize_newlines('Lorem Ipsum\x85') - 'Lorem Ipsum\n' - """ - newlines = ['\r\n', '\r', '\n', '\u0085', '\u2028', '\u2029'] - pattern = '|'.join(newlines) - return re.sub(pattern, '\n', text) - - -def _nonblank(str): - return str and not str.startswith('#') - - -@functools.singledispatch -def yield_lines(iterable): - r""" - Yield valid lines of a string or iterable. - - >>> list(yield_lines('')) - [] - >>> list(yield_lines(['foo', 'bar'])) - ['foo', 'bar'] - >>> list(yield_lines('foo\nbar')) - ['foo', 'bar'] - >>> list(yield_lines('\nfoo\n#bar\nbaz #comment')) - ['foo', 'baz #comment'] - >>> list(yield_lines(['foo\nbar', 'baz', 'bing\n\n\n'])) - ['foo', 'bar', 'baz', 'bing'] - """ - return itertools.chain.from_iterable(map(yield_lines, iterable)) - - -@yield_lines.register(str) -def _(text): - return filter(_nonblank, map(str.strip, text.splitlines())) - - -def drop_comment(line): - """ - Drop comments. - - >>> drop_comment('foo # bar') - 'foo' - - A hash without a space may be in a URL. - - >>> drop_comment('http://example.com/foo#bar') - 'http://example.com/foo#bar' - """ - return line.partition(' #')[0] - - -def join_continuation(lines): - r""" - Join lines continued by a trailing backslash. - - >>> list(join_continuation(['foo \\', 'bar', 'baz'])) - ['foobar', 'baz'] - >>> list(join_continuation(['foo \\', 'bar', 'baz'])) - ['foobar', 'baz'] - >>> list(join_continuation(['foo \\', 'bar \\', 'baz'])) - ['foobarbaz'] - - Not sure why, but... - The character preceeding the backslash is also elided. - - >>> list(join_continuation(['goo\\', 'dly'])) - ['godly'] - - A terrible idea, but... - If no line is available to continue, suppress the lines. - - >>> list(join_continuation(['foo', 'bar\\', 'baz\\'])) - ['foo'] - """ - lines = iter(lines) - for item in lines: - while item.endswith('\\'): - try: - item = item[:-2].strip() + next(lines) - except StopIteration: - return - yield item diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/__init__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/__init__.py deleted file mode 100644 index 5acd7687d642f06de84b38f5842c41ae14d5f24a..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from distutils.command.bdist import bdist -import sys - -if 'egg' not in bdist.format_commands: - try: - bdist.format_commands['egg'] = ('bdist_egg', "Python .egg file") - except TypeError: - # For backward compatibility with older distutils (stdlib) - bdist.format_command['egg'] = ('bdist_egg', "Python .egg file") - bdist.format_commands.append('egg') - -del bdist, sys diff --git a/spaces/Theivaprakasham/yolov6/tools/quantization/tensorrt/post_training/quant.sh b/spaces/Theivaprakasham/yolov6/tools/quantization/tensorrt/post_training/quant.sh deleted file mode 100644 index 9a66cc58178226a35eacd2ca23db47ab70acf78d..0000000000000000000000000000000000000000 --- a/spaces/Theivaprakasham/yolov6/tools/quantization/tensorrt/post_training/quant.sh +++ /dev/null @@ -1,23 +0,0 @@ -# Path to ONNX model -# ex: ../yolov6.onnx -ONNX_MODEL=$1 - -# Path to dataset to use for calibration. -# **Not necessary if you already have a calibration cache from a previous run. -CALIBRATION_DATA=$2 - -# Path to Cache file to Serving -# ex: ./caches/demo.cache -CACHE_FILENAME=$3 - -# Path to write TensorRT engine to -OUTPUT=$4 - -# Creates an int8 engine from your ONNX model, creating ${CACHE_FILENAME} based -# on your ${CALIBRATION_DATA}, unless ${CACHE_FILENAME} already exists, then -# it will use simply use that instead. -python3 onnx_to_tensorrt.py --fp16 --int8 -v \ - --calibration-data=${CALIBRATION_DATA} \ - --calibration-cache=${CACHE_FILENAME} \ - --explicit-batch \ - --onnx ${ONNX_MODEL} -o ${OUTPUT} diff --git a/spaces/ThirdEyeData/Semantic-Search-Transformer/app.py b/spaces/ThirdEyeData/Semantic-Search-Transformer/app.py deleted file mode 100644 index 685c8b184e66ef724a249bf4d20d45fcbfa9053e..0000000000000000000000000000000000000000 --- a/spaces/ThirdEyeData/Semantic-Search-Transformer/app.py +++ /dev/null @@ -1,44 +0,0 @@ -# Importing required libraries -import pandas as pd -import numpy as np -import streamlit as st -from sentence_transformers import SentenceTransformer, util - -st.title("Semantic-Search-Transformer") - -# Importing the Data -df = pd.read_csv('medium_articles.csv') - -# Downloading the sentence transformer model - -embedder = SentenceTransformer('all-MiniLM-L6-v2') - -#Predictions -# User-Test function (prediction_script.py) -# load saved model - -all_embeddings = np.load('mediumArticle_embeddings.npy') - -# Function - -def prediction(query,top_k,corpus_embeddings,df): - query_embedding = embedder.encode(query, convert_to_tensor=True) - hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=top_k) - hits = hits[0] # Get the hits for the first query - - print(f"\nTop {top_k} most similar sentences in corpus:") - for hit in hits: - hit_id = hit['corpus_id'] - article_data = df.iloc[hit_id] - title = article_data["title"] - st.write("-", title, "(Score: {:.4f})".format(hit['score'])) - -query = st.text_input('Enter your query here','Artificial Intelligence') -# query = input("Enter the Input Query:- ") -# top_sent = int(input("Enter the number of similarity sentences you want: ")) -top_k = st.number_input('How many results do you want to see?',min_value= 2) - -if st.button("Search"): - prediction(query,top_k,all_embeddings,df) - - \ No newline at end of file diff --git a/spaces/Vegecken/sovits4dzl/inference/slicer.py b/spaces/Vegecken/sovits4dzl/inference/slicer.py deleted file mode 100644 index b05840bcf6bdced0b6e2adbecb1a1dd5b3dee462..0000000000000000000000000000000000000000 --- a/spaces/Vegecken/sovits4dzl/inference/slicer.py +++ /dev/null @@ -1,142 +0,0 @@ -import librosa -import torch -import torchaudio - - -class Slicer: - def __init__(self, - sr: int, - threshold: float = -40., - min_length: int = 5000, - min_interval: int = 300, - hop_size: int = 20, - max_sil_kept: int = 5000): - if not min_length >= min_interval >= hop_size: - raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size') - if not max_sil_kept >= hop_size: - raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size') - min_interval = sr * min_interval / 1000 - self.threshold = 10 ** (threshold / 20.) - self.hop_size = round(sr * hop_size / 1000) - self.win_size = min(round(min_interval), 4 * self.hop_size) - self.min_length = round(sr * min_length / 1000 / self.hop_size) - self.min_interval = round(min_interval / self.hop_size) - self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) - - def _apply_slice(self, waveform, begin, end): - if len(waveform.shape) > 1: - return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)] - else: - return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)] - - # @timeit - def slice(self, waveform): - if len(waveform.shape) > 1: - samples = librosa.to_mono(waveform) - else: - samples = waveform - if samples.shape[0] <= self.min_length: - return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}} - rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0) - sil_tags = [] - silence_start = None - clip_start = 0 - for i, rms in enumerate(rms_list): - # Keep looping while frame is silent. - if rms < self.threshold: - # Record start of silent frames. - if silence_start is None: - silence_start = i - continue - # Keep looping while frame is not silent and silence start has not been recorded. - if silence_start is None: - continue - # Clear recorded silence start if interval is not enough or clip is too short - is_leading_silence = silence_start == 0 and i > self.max_sil_kept - need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length - if not is_leading_silence and not need_slice_middle: - silence_start = None - continue - # Need slicing. Record the range of silent frames to be removed. - if i - silence_start <= self.max_sil_kept: - pos = rms_list[silence_start: i + 1].argmin() + silence_start - if silence_start == 0: - sil_tags.append((0, pos)) - else: - sil_tags.append((pos, pos)) - clip_start = pos - elif i - silence_start <= self.max_sil_kept * 2: - pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin() - pos += i - self.max_sil_kept - pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start - pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept - if silence_start == 0: - sil_tags.append((0, pos_r)) - clip_start = pos_r - else: - sil_tags.append((min(pos_l, pos), max(pos_r, pos))) - clip_start = max(pos_r, pos) - else: - pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start - pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept - if silence_start == 0: - sil_tags.append((0, pos_r)) - else: - sil_tags.append((pos_l, pos_r)) - clip_start = pos_r - silence_start = None - # Deal with trailing silence. - total_frames = rms_list.shape[0] - if silence_start is not None and total_frames - silence_start >= self.min_interval: - silence_end = min(total_frames, silence_start + self.max_sil_kept) - pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start - sil_tags.append((pos, total_frames + 1)) - # Apply and return slices. - if len(sil_tags) == 0: - return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}} - else: - chunks = [] - # 第一段静音并非从头开始，补上有声片段 - if sil_tags[0][0]: - chunks.append( - {"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"}) - for i in range(0, len(sil_tags)): - # 标识有声片段（跳过第一段） - if i: - chunks.append({"slice": False, - "split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"}) - # 标识所有静音片段 - chunks.append({"slice": True, - "split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"}) - # 最后一段静音并非结尾，补上结尾片段 - if sil_tags[-1][1] * self.hop_size < len(waveform): - chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"}) - chunk_dict = {} - for i in range(len(chunks)): - chunk_dict[str(i)] = chunks[i] - return chunk_dict - - -def cut(audio_path, db_thresh=-30, min_len=5000): - audio, sr = librosa.load(audio_path, sr=None) - slicer = Slicer( - sr=sr, - threshold=db_thresh, - min_length=min_len - ) - chunks = slicer.slice(audio) - return chunks - - -def chunks2audio(audio_path, chunks): - chunks = dict(chunks) - audio, sr = torchaudio.load(audio_path) - if len(audio.shape) == 2 and audio.shape[1] >= 2: - audio = torch.mean(audio, dim=0).unsqueeze(0) - audio = audio.cpu().numpy()[0] - result = [] - for k, v in chunks.items(): - tag = v["split_time"].split(",") - if tag[0] != tag[1]: - result.append((v["slice"], audio[int(tag[0]):int(tag[1])])) - return result, sr diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Bing.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Bing.py deleted file mode 100644 index 87e04ac82293c7e22068af431ac407bdee435a1b..0000000000000000000000000000000000000000 --- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Bing.py +++ /dev/null @@ -1,349 +0,0 @@ -import os -import json -import random -import json -import os -import uuid -import ssl -import certifi -import aiohttp -import asyncio - -import requests -from ...typing import sha256, Dict, get_type_hints - -url = 'https://bing.com/chat' -model = ['gpt-4'] -supports_stream = True -needs_auth = False - -ssl_context = ssl.create_default_context() -ssl_context.load_verify_locations(certifi.where()) - - -class optionsSets: - optionSet: dict = { - 'tone': str, - 'optionsSets': list - } - - jailbreak: dict = { - "optionsSets": [ - 'saharasugg', - 'enablenewsfc', - 'clgalileo', - 'gencontentv3', - "nlu_direct_response_filter", - "deepleo", - "disable_emoji_spoken_text", - "responsible_ai_policy_235", - "enablemm", - "h3precise" - # "harmonyv3", - "dtappid", - "cricinfo", - "cricinfov2", - "dv3sugg", - "nojbfedge" - ] - } - - -class Defaults: - delimiter = '\x1e' - ip_address = f'13.{random.randint(104, 107)}.{random.randint(0, 255)}.{random.randint(0, 255)}' - - allowedMessageTypes = [ - 'Chat', - 'Disengaged', - 'AdsQuery', - 'SemanticSerp', - 'GenerateContentQuery', - 'SearchQuery', - 'ActionRequest', - 'Context', - 'Progress', - 'AdsQuery', - 'SemanticSerp' - ] - - sliceIds = [ - - # "222dtappid", - # "225cricinfo", - # "224locals0" - - 'winmuid3tf', - 'osbsdusgreccf', - 'ttstmout', - 'crchatrev', - 'winlongmsgtf', - 'ctrlworkpay', - 'norespwtf', - 'tempcacheread', - 'temptacache', - '505scss0', - '508jbcars0', - '515enbotdets0', - '5082tsports', - '515vaoprvs', - '424dagslnv1s0', - 'kcimgattcf', - '427startpms0' - ] - - location = { - 'locale': 'en-US', - 'market': 'en-US', - 'region': 'US', - 'locationHints': [ - { - 'country': 'United States', - 'state': 'California', - 'city': 'Los Angeles', - 'timezoneoffset': 8, - 'countryConfidence': 8, - 'Center': { - 'Latitude': 34.0536909, - 'Longitude': -118.242766 - }, - 'RegionType': 2, - 'SourceType': 1 - } - ], - } - - -def _format(msg: dict) -> str: - return json.dumps(msg, ensure_ascii=False) + Defaults.delimiter - - -async def create_conversation(): - for _ in range(5): - create = requests.get('https://www.bing.com/turing/conversation/create', - headers={ - 'authority': 'edgeservices.bing.com', - 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7', - 'accept-language': 'en-US,en;q=0.9', - 'cache-control': 'max-age=0', - 'sec-ch-ua': '"Chromium";v="110", "Not A(Brand";v="24", "Microsoft Edge";v="110"', - 'sec-ch-ua-arch': '"x86"', - 'sec-ch-ua-bitness': '"64"', - 'sec-ch-ua-full-version': '"110.0.1587.69"', - 'sec-ch-ua-full-version-list': '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"', - 'sec-ch-ua-mobile': '?0', - 'sec-ch-ua-model': '""', - 'sec-ch-ua-platform': '"Windows"', - 'sec-ch-ua-platform-version': '"15.0.0"', - 'sec-fetch-dest': 'document', - 'sec-fetch-mode': 'navigate', - 'sec-fetch-site': 'none', - 'sec-fetch-user': '?1', - 'upgrade-insecure-requests': '1', - 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36 Edg/110.0.1587.69', - 'x-edge-shopping-flag': '1', - 'x-forwarded-for': Defaults.ip_address - }) - - conversationId = create.json().get('conversationId') - clientId = create.json().get('clientId') - conversationSignature = create.json().get('conversationSignature') - - if not conversationId or not clientId or not conversationSignature and _ == 4: - raise Exception('Failed to create conversation.') - - return conversationId, clientId, conversationSignature - - -async def stream_generate(prompt: str, mode: optionsSets.optionSet = optionsSets.jailbreak, context: bool or str = False): - timeout = aiohttp.ClientTimeout(total=900) - session = aiohttp.ClientSession(timeout=timeout) - - conversationId, clientId, conversationSignature = await create_conversation() - - wss = await session.ws_connect('wss://sydney.bing.com/sydney/ChatHub', ssl=ssl_context, autoping=False, - headers={ - 'accept': 'application/json', - 'accept-language': 'en-US,en;q=0.9', - 'content-type': 'application/json', - 'sec-ch-ua': '"Not_A Brand";v="99", "Microsoft Edge";v="110", "Chromium";v="110"', - 'sec-ch-ua-arch': '"x86"', - 'sec-ch-ua-bitness': '"64"', - 'sec-ch-ua-full-version': '"109.0.1518.78"', - 'sec-ch-ua-full-version-list': '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"', - 'sec-ch-ua-mobile': '?0', - 'sec-ch-ua-model': '', - 'sec-ch-ua-platform': '"Windows"', - 'sec-ch-ua-platform-version': '"15.0.0"', - 'sec-fetch-dest': 'empty', - 'sec-fetch-mode': 'cors', - 'sec-fetch-site': 'same-origin', - 'x-ms-client-request-id': str(uuid.uuid4()), - 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32', - 'Referer': 'https://www.bing.com/search?q=Bing+AI&showconv=1&FORM=hpcodx', - 'Referrer-Policy': 'origin-when-cross-origin', - 'x-forwarded-for': Defaults.ip_address - }) - - await wss.send_str(_format({'protocol': 'json', 'version': 1})) - await wss.receive(timeout=900) - - struct = { - 'arguments': [ - { - **mode, - 'source': 'cib', - 'allowedMessageTypes': Defaults.allowedMessageTypes, - 'sliceIds': Defaults.sliceIds, - 'traceId': os.urandom(16).hex(), - 'isStartOfSession': True, - 'message': Defaults.location | { - 'author': 'user', - 'inputMethod': 'Keyboard', - 'text': prompt, - 'messageType': 'Chat' - }, - 'conversationSignature': conversationSignature, - 'participant': { - 'id': clientId - }, - 'conversationId': conversationId - } - ], - 'invocationId': '0', - 'target': 'chat', - 'type': 4 - } - - if context: - struct['arguments'][0]['previousMessages'] = [ - { - "author": "user", - "description": context, - "contextType": "WebPage", - "messageType": "Context", - "messageId": "discover-web--page-ping-mriduna-----" - } - ] - - await wss.send_str(_format(struct)) - - final = False - draw = False - resp_txt = '' - result_text = '' - resp_txt_no_link = '' - cache_text = '' - - while not final: - msg = await wss.receive(timeout=900) - objects = msg.data.split(Defaults.delimiter) - - for obj in objects: - if obj is None or not obj: - continue - - response = json.loads(obj) - if response.get('type') == 1 and response['arguments'][0].get('messages',): - if not draw: - if (response['arguments'][0]['messages'][0]['contentOrigin'] != 'Apology') and not draw: - resp_txt = result_text + \ - response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0].get( - 'text', '') - resp_txt_no_link = result_text + \ - response['arguments'][0]['messages'][0].get( - 'text', '') - - if response['arguments'][0]['messages'][0].get('messageType',): - resp_txt = ( - resp_txt - + response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0]['inlines'][0].get('text') - + '\n' - ) - result_text = ( - result_text - + response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0]['inlines'][0].get('text') - + '\n' - ) - - if cache_text.endswith(' '): - final = True - if wss and not wss.closed: - await wss.close() - if session and not session.closed: - await session.close() - - yield (resp_txt.replace(cache_text, '')) - cache_text = resp_txt - - elif response.get('type') == 2: - if response['item']['result'].get('error'): - if wss and not wss.closed: - await wss.close() - if session and not session.closed: - await session.close() - - raise Exception( - f"{response['item']['result']['value']}: {response['item']['result']['message']}") - - if draw: - cache = response['item']['messages'][1]['adaptiveCards'][0]['body'][0]['text'] - response['item']['messages'][1]['adaptiveCards'][0]['body'][0]['text'] = ( - cache + resp_txt) - - if (response['item']['messages'][-1]['contentOrigin'] == 'Apology' and resp_txt): - response['item']['messages'][-1]['text'] = resp_txt_no_link - response['item']['messages'][-1]['adaptiveCards'][0]['body'][0]['text'] = resp_txt - - # print('Preserved the message from being deleted', file=sys.stderr) - - final = True - if wss and not wss.closed: - await wss.close() - if session and not session.closed: - await session.close() - - -def run(generator): - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - gen = generator.__aiter__() - - while True: - try: - next_val = loop.run_until_complete(gen.__anext__()) - yield next_val - - except StopAsyncIteration: - break - #print('Done') - -def convert(messages): - context = "" - - for message in messages: - context += "[%s](#message)\n%s\n\n" % (message['role'], - message['content']) - - return context - - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - if len(messages) < 2: - prompt = messages[0]['content'] - context = False - - else: - prompt = messages[-1]['content'] - context = convert(messages[:-1]) - - response = run(stream_generate(prompt, optionsSets.jailbreak, context)) - for token in response: - yield (token) - - #print('Done') - - -params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \ - '(%s)' % ', '.join( - [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]]) diff --git a/spaces/Willder/chatgpt-streamlit/README.md b/spaces/Willder/chatgpt-streamlit/README.md deleted file mode 100644 index b531e90b5806b474131258a3ebe3e9f1bb86f1d9..0000000000000000000000000000000000000000 --- a/spaces/Willder/chatgpt-streamlit/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Chatgpt Streamlit -emoji: 🌍 -colorFrom: blue -colorTo: purple -sdk: streamlit -sdk_version: 1.19.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/utils/mod_display.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/utils/mod_display.py deleted file mode 100644 index 25f61b99febfde4a80ee51bad9601bfc1640a465..0000000000000000000000000000000000000000 --- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/utils/mod_display.py +++ /dev/null @@ -1,28 +0,0 @@ -" Utils for modifying what is displayed in notebooks and command line" -import fastai -import fastprogress - -from ..basic_train import * -from ..core import * - -__all__ = ['progress_disabled_ctx'] - -class progress_disabled_ctx(): - "Context manager to disable the progress update bar and Recorder print." - def __init__(self,learn:Learner): - self.learn = learn - - def __enter__(self): - #silence progress bar - fastprogress.fastprogress.NO_BAR = True - fastai.basic_train.master_bar,fastai.basic_train.progress_bar = fastprogress.force_console_behavior() - self.orig_callback_fns = copy(self.learn.callback_fns) - rec_name = [x for x in self.learn.callback_fns if hasattr(x, 'func') and x.func == Recorder] - if len(rec_name): - rec_idx = self.learn.callback_fns.index(rec_name[0]) - self.learn.callback_fns[rec_idx] = partial(Recorder, add_time=True, silent=True) #silence recorder - return self.learn - - def __exit__(self, *args): - fastai.basic_train.master_bar,fastai.basic_train.progress_bar = master_bar,progress_bar - self.learn.callback_fns = self.orig_callback_fns diff --git a/spaces/Xule/ChuanhuChatGPT/modules/config.py b/spaces/Xule/ChuanhuChatGPT/modules/config.py deleted file mode 100644 index 2eee7730787df6a857de21dbb0cbefc42cb7273d..0000000000000000000000000000000000000000 --- a/spaces/Xule/ChuanhuChatGPT/modules/config.py +++ /dev/null @@ -1,173 +0,0 @@ -from collections import defaultdict -from contextlib import contextmanager -import os -import logging -import sys -import commentjson as json - -from . import shared -from . import presets - - -__all__ = [ - "my_api_key", - "authflag", - "auth_list", - "dockerflag", - "retrieve_proxy", - "log_level", - "advance_docs", - "update_doc_config", - "multi_api_key", - "server_name", - "server_port", - "share", -] - -# 添加一个统一的config文件，避免文件过多造成的疑惑（优先级最低） -# 同时，也可以为后续支持自定义功能提供config的帮助 -if os.path.exists("config.json"): - with open("config.json", "r", encoding='utf-8') as f: - config = json.load(f) -else: - config = {} - -lang_config = config.get("language", "auto") -language = os.environ.get("LANGUAGE", lang_config) - -if os.path.exists("api_key.txt"): - logging.info("检测到api_key.txt文件，正在进行迁移...") - with open("api_key.txt", "r") as f: - config["openai_api_key"] = f.read().strip() - os.rename("api_key.txt", "api_key(deprecated).txt") - with open("config.json", "w", encoding='utf-8') as f: - json.dump(config, f, indent=4) - -if os.path.exists("auth.json"): - logging.info("检测到auth.json文件，正在进行迁移...") - auth_list = [] - with open("auth.json", "r", encoding='utf-8') as f: - auth = json.load(f) - for _ in auth: - if auth[_]["username"] and auth[_]["password"]: - auth_list.append((auth[_]["username"], auth[_]["password"])) - else: - logging.error("请检查auth.json文件中的用户名和密码！") - sys.exit(1) - config["users"] = auth_list - os.rename("auth.json", "auth(deprecated).json") - with open("config.json", "w", encoding='utf-8') as f: - json.dump(config, f, indent=4) - -## 处理docker if we are running in Docker -dockerflag = config.get("dockerflag", False) -if os.environ.get("dockerrun") == "yes": - dockerflag = True - -## 处理 api-key 以及 允许的用户列表 -my_api_key = config.get("openai_api_key", "") -my_api_key = os.environ.get("OPENAI_API_KEY", my_api_key) - -xmchat_api_key = config.get("xmchat_api_key", "") -if os.environ.get("XMCHAT_API_KEY", None) == None: - os.environ["XMCHAT_API_KEY"] = xmchat_api_key - -## 多账户机制 -multi_api_key = config.get("multi_api_key", False) # 是否开启多账户机制 -if multi_api_key: - api_key_list = config.get("api_key_list", []) - if len(api_key_list) == 0: - logging.error("多账号模式已开启，但api_key_list为空，请检查config.json") - sys.exit(1) - shared.state.set_api_key_queue(api_key_list) - -auth_list = config.get("users", []) # 实际上是使用者的列表 -authflag = len(auth_list) > 0 # 是否开启认证的状态值，改为判断auth_list长度 - -# 处理自定义的api_host，优先读环境变量的配置，如果存在则自动装配 -api_host = os.environ.get("api_host", config.get("api_host", "")) -if api_host: - shared.state.set_api_host(api_host) - -@contextmanager -def retrieve_openai_api(api_key = None): - old_api_key = os.environ.get("OPENAI_API_KEY", "") - if api_key is None: - os.environ["OPENAI_API_KEY"] = my_api_key - yield my_api_key - else: - os.environ["OPENAI_API_KEY"] = api_key - yield api_key - os.environ["OPENAI_API_KEY"] = old_api_key - -## 处理log -log_level = config.get("log_level", "INFO") -logging.basicConfig( - level=log_level, - format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s", -) - -## 处理代理： -http_proxy = config.get("http_proxy", "") -https_proxy = config.get("https_proxy", "") -http_proxy = os.environ.get("HTTP_PROXY", http_proxy) -https_proxy = os.environ.get("HTTPS_PROXY", https_proxy) - -# 重置系统变量，在不需要设置的时候不设置环境变量，以免引起全局代理报错 -os.environ["HTTP_PROXY"] = "" -os.environ["HTTPS_PROXY"] = "" - -local_embedding = config.get("local_embedding", False) # 是否使用本地embedding - -@contextmanager -def retrieve_proxy(proxy=None): - """ - 1, 如果proxy = NONE，设置环境变量，并返回最新设置的代理 - 2，如果proxy ！= NONE，更新当前的代理配置，但是不更新环境变量 - """ - global http_proxy, https_proxy - if proxy is not None: - http_proxy = proxy - https_proxy = proxy - yield http_proxy, https_proxy - else: - old_var = os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"] - os.environ["HTTP_PROXY"] = http_proxy - os.environ["HTTPS_PROXY"] = https_proxy - yield http_proxy, https_proxy # return new proxy - - # return old proxy - os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"] = old_var - - -## 处理advance docs -advance_docs = defaultdict(lambda: defaultdict(dict)) -advance_docs.update(config.get("advance_docs", {})) -def update_doc_config(two_column_pdf): - global advance_docs - advance_docs["pdf"]["two_column"] = two_column_pdf - - logging.info(f"更新后的文件参数为：{advance_docs}") - -## 处理gradio.launch参数 -server_name = config.get("server_name", None) -server_port = config.get("server_port", None) -if server_name is None: - if dockerflag: - server_name = "0.0.0.0" - else: - server_name = "127.0.0.1" -if server_port is None: - if dockerflag: - server_port = 7860 - -assert server_port is None or type(server_port) == int, "要求port设置为int类型" - -# 设置默认model -default_model = config.get("default_model", "") -try: - presets.DEFAULT_MODEL = presets.MODELS.index(default_model) -except ValueError: - pass - -share = config.get("share", False) diff --git a/spaces/XzJosh/Diana-Bert-VITS2/app.py b/spaces/XzJosh/Diana-Bert-VITS2/app.py deleted file mode 100644 index 7743f4ef866fce558e5dd94d7f99175ffd0f6f60..0000000000000000000000000000000000000000 --- a/spaces/XzJosh/Diana-Bert-VITS2/app.py +++ /dev/null @@ -1,155 +0,0 @@ -import sys, os - -if sys.platform == "darwin": - os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" - -import logging - -logging.getLogger("numba").setLevel(logging.WARNING) -logging.getLogger("markdown_it").setLevel(logging.WARNING) -logging.getLogger("urllib3").setLevel(logging.WARNING) -logging.getLogger("matplotlib").setLevel(logging.WARNING) - -logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s") - -logger = logging.getLogger(__name__) - -import torch -import argparse -import commons -import utils -from models import SynthesizerTrn -from text.symbols import symbols -from text import cleaned_text_to_sequence, get_bert -from text.cleaner import clean_text -import gradio as gr -import webbrowser - - -net_g = None - - -def get_text(text, language_str, hps): - norm_text, phone, tone, word2ph = clean_text(text, language_str) - phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) - - if hps.data.add_blank: - phone = commons.intersperse(phone, 0) - tone = commons.intersperse(tone, 0) - language = commons.intersperse(language, 0) - for i in range(len(word2ph)): - word2ph[i] = word2ph[i] * 2 - word2ph[0] += 1 - bert = get_bert(norm_text, word2ph, language_str) - del word2ph - - assert bert.shape[-1] == len(phone) - - phone = torch.LongTensor(phone) - tone = torch.LongTensor(tone) - language = torch.LongTensor(language) - - return bert, phone, tone, language - -def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): - global net_g - bert, phones, tones, lang_ids = get_text(text, "ZH", hps) - with torch.no_grad(): - x_tst=phones.to(device).unsqueeze(0) - tones=tones.to(device).unsqueeze(0) - lang_ids=lang_ids.to(device).unsqueeze(0) - bert = bert.to(device).unsqueeze(0) - x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) - del phones - speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) - audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio - , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() - del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers - return audio - -def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): - with torch.no_grad(): - audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker) - return "Success", (hps.data.sampling_rate, audio) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--model_dir", default="./logs/Diana/G_4800.pth", help="path of your model") - parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file") - parser.add_argument("--share", default=False, help="make link public") - parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log") - - args = parser.parse_args() - if args.debug: - logger.info("Enable DEBUG-LEVEL log") - logging.basicConfig(level=logging.DEBUG) - hps = utils.get_hparams_from_file(args.config_dir) - device = "cuda:0" if torch.cuda.is_available() else "cpu" - ''' - device = ( - "cuda:0" - if torch.cuda.is_available() - else ( - "mps" - if sys.platform == "darwin" and torch.backends.mps.is_available() - else "cpu" - ) - ) - ''' - net_g = SynthesizerTrn( - len(symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model).to(device) - _ = net_g.eval() - - _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True) - - speaker_ids = hps.data.spk2id - speakers = list(speaker_ids.keys()) - with gr.Blocks() as app: - with gr.Row(): - with gr.Column(): - gr.Markdown(value=""" - 【AI嘉然①】在线语音合成（Bert-Vits2）\n - 作者：Xz乔希 https://space.bilibili.com/5859321\n - 声音归属：嘉然今天吃什么 https://space.bilibili.com/672328094\n - Bert-VITS2项目：https://github.com/Stardust-minus/Bert-VITS2\n - 【AI嘉然②】https://huggingface.co/spaces/XzJosh/Jiaran-Bert-VITS2\n - 【AI嘉然③】https://huggingface.co/spaces/XzJosh/ranran-Bert-VITS2\n - 使用本模型请严格遵守法律法规！\n - 发布二创作品请标注本项目作者及链接、作品使用Bert-VITS2 AI生成！\n - """) - text = gr.TextArea(label="Text", placeholder="Input Text Here", - value="大家好我是嘉然戴安娜，关注嘉然，顿顿解馋，谢谢！") - speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker') - sdp_ratio = gr.Slider(minimum=0.1, maximum=1, value=0.2, step=0.01, label='SDP/DP混合比') - noise_scale = gr.Slider(minimum=0.1, maximum=1, value=0.5, step=0.01, label='感情调节') - noise_scale_w = gr.Slider(minimum=0.1, maximum=1, value=0.9, step=0.01, label='音素长度') - length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='生成长度') - btn = gr.Button("点击生成", variant="primary") - with gr.Column(): - text_output = gr.Textbox(label="Message") - audio_output = gr.Audio(label="Output Audio") - gr.Markdown(value=""" - 【AI塔菲】https://huggingface.co/spaces/XzJosh/Taffy-Bert-VITS2\n - 【AI东雪莲】https://huggingface.co/spaces/XzJosh/Azuma-Bert-VITS2\n - 【AI奶绿】https://huggingface.co/spaces/XzJosh/LAPLACE-Bert-VITS2\n - 【AI尼奈】https://huggingface.co/spaces/XzJosh/nine1-Bert-VITS2\n - 【AI珈乐】https://huggingface.co/spaces/XzJosh/Carol-Bert-VITS2\n - 【AI电棍】https://huggingface.co/spaces/XzJosh/otto-Bert-VITS2\n - 【AI七海】https://huggingface.co/spaces/XzJosh/Nana7mi-Bert-VITS2\n - 【AI阿梓】https://huggingface.co/spaces/XzJosh/Azusa-Bert-VITS2\n - 【AI星瞳】https://huggingface.co/spaces/XzJosh/XingTong-Bert-VITS2\n - 【AI向晚】https://huggingface.co/spaces/XzJosh/Ava-Bert-VITS2\n - """) - btn.click(tts_fn, - inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale], - outputs=[text_output, audio_output]) - -# webbrowser.open("http://127.0.0.1:6006") -# app.launch(server_port=6006, show_error=True) - - app.launch(show_error=True) diff --git a/spaces/XzJosh/ShanBao-Bert-VITS2/preprocess_text.py b/spaces/XzJosh/ShanBao-Bert-VITS2/preprocess_text.py deleted file mode 100644 index 5eb0f3b9e929fcbe91dcbeb653391227a2518a15..0000000000000000000000000000000000000000 --- a/spaces/XzJosh/ShanBao-Bert-VITS2/preprocess_text.py +++ /dev/null @@ -1,64 +0,0 @@ -import json -from random import shuffle - -import tqdm -from text.cleaner import clean_text -from collections import defaultdict -stage = [1,2,3] - -transcription_path = 'filelists/genshin.list' -train_path = 'filelists/train.list' -val_path = 'filelists/val.list' -config_path = "configs/config.json" -val_per_spk = 4 -max_val_total = 8 - -if 1 in stage: - with open( transcription_path+'.cleaned', 'w', encoding='utf-8') as f: - for line in tqdm.tqdm(open(transcription_path, encoding='utf-8').readlines()): - try: - utt, spk, language, text = line.strip().split('|') - norm_text, phones, tones, word2ph = clean_text(text, language) - f.write('{}|{}|{}|{}|{}|{}|{}\n'.format(utt, spk, language, norm_text, ' '.join(phones), - " ".join([str(i) for i in tones]), - " ".join([str(i) for i in word2ph]))) - except Exception as error : - print("err!", utt, error) - -if 2 in stage: - spk_utt_map = defaultdict(list) - spk_id_map = {} - current_sid = 0 - - with open( transcription_path+'.cleaned', encoding='utf-8') as f: - for line in f.readlines(): - utt, spk, language, text, phones, tones, word2ph = line.strip().split('|') - spk_utt_map[spk].append(line) - if spk not in spk_id_map.keys(): - spk_id_map[spk] = current_sid - current_sid += 1 - train_list = [] - val_list = [] - - for spk, utts in spk_utt_map.items(): - shuffle(utts) - val_list+=utts[:val_per_spk] - train_list+=utts[val_per_spk:] - if len(val_list) > max_val_total: - train_list+=val_list[max_val_total:] - val_list = val_list[:max_val_total] - - with open( train_path,"w", encoding='utf-8') as f: - for line in train_list: - f.write(line) - - with open(val_path, "w", encoding='utf-8') as f: - for line in val_list: - f.write(line) - -if 3 in stage: - assert 2 in stage - config = json.load(open(config_path, encoding='utf-8')) - config["data"]['spk2id'] = spk_id_map - with open(config_path, 'w', encoding='utf-8') as f: - json.dump(config, f, indent=2, ensure_ascii=False) diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/models/unet_1d_blocks.py b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/models/unet_1d_blocks.py deleted file mode 100644 index fc758ebbb044644e921c7e66089e052981a82e1e..0000000000000000000000000000000000000000 --- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/models/unet_1d_blocks.py +++ /dev/null @@ -1,668 +0,0 @@ -# Copyright 2022 The HuggingFace Team. 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. -import math - -import torch -import torch.nn.functional as F -from torch import nn - -from .resnet import Downsample1D, ResidualTemporalBlock1D, Upsample1D, rearrange_dims - - -class DownResnetBlock1D(nn.Module): - def __init__( - self, - in_channels, - out_channels=None, - num_layers=1, - conv_shortcut=False, - temb_channels=32, - groups=32, - groups_out=None, - non_linearity=None, - time_embedding_norm="default", - output_scale_factor=1.0, - add_downsample=True, - ): - super().__init__() - self.in_channels = in_channels - out_channels = in_channels if out_channels is None else out_channels - self.out_channels = out_channels - self.use_conv_shortcut = conv_shortcut - self.time_embedding_norm = time_embedding_norm - self.add_downsample = add_downsample - self.output_scale_factor = output_scale_factor - - if groups_out is None: - groups_out = groups - - # there will always be at least one resnet - resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=temb_channels)] - - for _ in range(num_layers): - resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels)) - - self.resnets = nn.ModuleList(resnets) - - if non_linearity == "swish": - self.nonlinearity = lambda x: F.silu(x) - elif non_linearity == "mish": - self.nonlinearity = nn.Mish() - elif non_linearity == "silu": - self.nonlinearity = nn.SiLU() - else: - self.nonlinearity = None - - self.downsample = None - if add_downsample: - self.downsample = Downsample1D(out_channels, use_conv=True, padding=1) - - def forward(self, hidden_states, temb=None): - output_states = () - - hidden_states = self.resnets[0](hidden_states, temb) - for resnet in self.resnets[1:]: - hidden_states = resnet(hidden_states, temb) - - output_states += (hidden_states,) - - if self.nonlinearity is not None: - hidden_states = self.nonlinearity(hidden_states) - - if self.downsample is not None: - hidden_states = self.downsample(hidden_states) - - return hidden_states, output_states - - -class UpResnetBlock1D(nn.Module): - def __init__( - self, - in_channels, - out_channels=None, - num_layers=1, - temb_channels=32, - groups=32, - groups_out=None, - non_linearity=None, - time_embedding_norm="default", - output_scale_factor=1.0, - add_upsample=True, - ): - super().__init__() - self.in_channels = in_channels - out_channels = in_channels if out_channels is None else out_channels - self.out_channels = out_channels - self.time_embedding_norm = time_embedding_norm - self.add_upsample = add_upsample - self.output_scale_factor = output_scale_factor - - if groups_out is None: - groups_out = groups - - # there will always be at least one resnet - resnets = [ResidualTemporalBlock1D(2 * in_channels, out_channels, embed_dim=temb_channels)] - - for _ in range(num_layers): - resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels)) - - self.resnets = nn.ModuleList(resnets) - - if non_linearity == "swish": - self.nonlinearity = lambda x: F.silu(x) - elif non_linearity == "mish": - self.nonlinearity = nn.Mish() - elif non_linearity == "silu": - self.nonlinearity = nn.SiLU() - else: - self.nonlinearity = None - - self.upsample = None - if add_upsample: - self.upsample = Upsample1D(out_channels, use_conv_transpose=True) - - def forward(self, hidden_states, res_hidden_states_tuple=None, temb=None): - if res_hidden_states_tuple is not None: - res_hidden_states = res_hidden_states_tuple[-1] - hidden_states = torch.cat((hidden_states, res_hidden_states), dim=1) - - hidden_states = self.resnets[0](hidden_states, temb) - for resnet in self.resnets[1:]: - hidden_states = resnet(hidden_states, temb) - - if self.nonlinearity is not None: - hidden_states = self.nonlinearity(hidden_states) - - if self.upsample is not None: - hidden_states = self.upsample(hidden_states) - - return hidden_states - - -class ValueFunctionMidBlock1D(nn.Module): - def __init__(self, in_channels, out_channels, embed_dim): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.embed_dim = embed_dim - - self.res1 = ResidualTemporalBlock1D(in_channels, in_channels // 2, embed_dim=embed_dim) - self.down1 = Downsample1D(out_channels // 2, use_conv=True) - self.res2 = ResidualTemporalBlock1D(in_channels // 2, in_channels // 4, embed_dim=embed_dim) - self.down2 = Downsample1D(out_channels // 4, use_conv=True) - - def forward(self, x, temb=None): - x = self.res1(x, temb) - x = self.down1(x) - x = self.res2(x, temb) - x = self.down2(x) - return x - - -class MidResTemporalBlock1D(nn.Module): - def __init__( - self, - in_channels, - out_channels, - embed_dim, - num_layers: int = 1, - add_downsample: bool = False, - add_upsample: bool = False, - non_linearity=None, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.add_downsample = add_downsample - - # there will always be at least one resnet - resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=embed_dim)] - - for _ in range(num_layers): - resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=embed_dim)) - - self.resnets = nn.ModuleList(resnets) - - if non_linearity == "swish": - self.nonlinearity = lambda x: F.silu(x) - elif non_linearity == "mish": - self.nonlinearity = nn.Mish() - elif non_linearity == "silu": - self.nonlinearity = nn.SiLU() - else: - self.nonlinearity = None - - self.upsample = None - if add_upsample: - self.upsample = Downsample1D(out_channels, use_conv=True) - - self.downsample = None - if add_downsample: - self.downsample = Downsample1D(out_channels, use_conv=True) - - if self.upsample and self.downsample: - raise ValueError("Block cannot downsample and upsample") - - def forward(self, hidden_states, temb): - hidden_states = self.resnets[0](hidden_states, temb) - for resnet in self.resnets[1:]: - hidden_states = resnet(hidden_states, temb) - - if self.upsample: - hidden_states = self.upsample(hidden_states) - if self.downsample: - self.downsample = self.downsample(hidden_states) - - return hidden_states - - -class OutConv1DBlock(nn.Module): - def __init__(self, num_groups_out, out_channels, embed_dim, act_fn): - super().__init__() - self.final_conv1d_1 = nn.Conv1d(embed_dim, embed_dim, 5, padding=2) - self.final_conv1d_gn = nn.GroupNorm(num_groups_out, embed_dim) - if act_fn == "silu": - self.final_conv1d_act = nn.SiLU() - if act_fn == "mish": - self.final_conv1d_act = nn.Mish() - self.final_conv1d_2 = nn.Conv1d(embed_dim, out_channels, 1) - - def forward(self, hidden_states, temb=None): - hidden_states = self.final_conv1d_1(hidden_states) - hidden_states = rearrange_dims(hidden_states) - hidden_states = self.final_conv1d_gn(hidden_states) - hidden_states = rearrange_dims(hidden_states) - hidden_states = self.final_conv1d_act(hidden_states) - hidden_states = self.final_conv1d_2(hidden_states) - return hidden_states - - -class OutValueFunctionBlock(nn.Module): - def __init__(self, fc_dim, embed_dim): - super().__init__() - self.final_block = nn.ModuleList( - [ - nn.Linear(fc_dim + embed_dim, fc_dim // 2), - nn.Mish(), - nn.Linear(fc_dim // 2, 1), - ] - ) - - def forward(self, hidden_states, temb): - hidden_states = hidden_states.view(hidden_states.shape[0], -1) - hidden_states = torch.cat((hidden_states, temb), dim=-1) - for layer in self.final_block: - hidden_states = layer(hidden_states) - - return hidden_states - - -_kernels = { - "linear": [1 / 8, 3 / 8, 3 / 8, 1 / 8], - "cubic": [-0.01171875, -0.03515625, 0.11328125, 0.43359375, 0.43359375, 0.11328125, -0.03515625, -0.01171875], - "lanczos3": [ - 0.003689131001010537, - 0.015056144446134567, - -0.03399861603975296, - -0.066637322306633, - 0.13550527393817902, - 0.44638532400131226, - 0.44638532400131226, - 0.13550527393817902, - -0.066637322306633, - -0.03399861603975296, - 0.015056144446134567, - 0.003689131001010537, - ], -} - - -class Downsample1d(nn.Module): - def __init__(self, kernel="linear", pad_mode="reflect"): - super().__init__() - self.pad_mode = pad_mode - kernel_1d = torch.tensor(_kernels[kernel]) - self.pad = kernel_1d.shape[0] // 2 - 1 - self.register_buffer("kernel", kernel_1d) - - def forward(self, hidden_states): - hidden_states = F.pad(hidden_states, (self.pad,) * 2, self.pad_mode) - weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]]) - indices = torch.arange(hidden_states.shape[1], device=hidden_states.device) - weight[indices, indices] = self.kernel.to(weight) - return F.conv1d(hidden_states, weight, stride=2) - - -class Upsample1d(nn.Module): - def __init__(self, kernel="linear", pad_mode="reflect"): - super().__init__() - self.pad_mode = pad_mode - kernel_1d = torch.tensor(_kernels[kernel]) * 2 - self.pad = kernel_1d.shape[0] // 2 - 1 - self.register_buffer("kernel", kernel_1d) - - def forward(self, hidden_states, temb=None): - hidden_states = F.pad(hidden_states, ((self.pad + 1) // 2,) * 2, self.pad_mode) - weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]]) - indices = torch.arange(hidden_states.shape[1], device=hidden_states.device) - weight[indices, indices] = self.kernel.to(weight) - return F.conv_transpose1d(hidden_states, weight, stride=2, padding=self.pad * 2 + 1) - - -class SelfAttention1d(nn.Module): - def __init__(self, in_channels, n_head=1, dropout_rate=0.0): - super().__init__() - self.channels = in_channels - self.group_norm = nn.GroupNorm(1, num_channels=in_channels) - self.num_heads = n_head - - self.query = nn.Linear(self.channels, self.channels) - self.key = nn.Linear(self.channels, self.channels) - self.value = nn.Linear(self.channels, self.channels) - - self.proj_attn = nn.Linear(self.channels, self.channels, 1) - - self.dropout = nn.Dropout(dropout_rate, inplace=True) - - def transpose_for_scores(self, projection: torch.Tensor) -> torch.Tensor: - new_projection_shape = projection.size()[:-1] + (self.num_heads, -1) - # move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D) - new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3) - return new_projection - - def forward(self, hidden_states): - residual = hidden_states - batch, channel_dim, seq = hidden_states.shape - - hidden_states = self.group_norm(hidden_states) - hidden_states = hidden_states.transpose(1, 2) - - query_proj = self.query(hidden_states) - key_proj = self.key(hidden_states) - value_proj = self.value(hidden_states) - - query_states = self.transpose_for_scores(query_proj) - key_states = self.transpose_for_scores(key_proj) - value_states = self.transpose_for_scores(value_proj) - - scale = 1 / math.sqrt(math.sqrt(key_states.shape[-1])) - - attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale) - attention_probs = torch.softmax(attention_scores, dim=-1) - - # compute attention output - hidden_states = torch.matmul(attention_probs, value_states) - - hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous() - new_hidden_states_shape = hidden_states.size()[:-2] + (self.channels,) - hidden_states = hidden_states.view(new_hidden_states_shape) - - # compute next hidden_states - hidden_states = self.proj_attn(hidden_states) - hidden_states = hidden_states.transpose(1, 2) - hidden_states = self.dropout(hidden_states) - - output = hidden_states + residual - - return output - - -class ResConvBlock(nn.Module): - def __init__(self, in_channels, mid_channels, out_channels, is_last=False): - super().__init__() - self.is_last = is_last - self.has_conv_skip = in_channels != out_channels - - if self.has_conv_skip: - self.conv_skip = nn.Conv1d(in_channels, out_channels, 1, bias=False) - - self.conv_1 = nn.Conv1d(in_channels, mid_channels, 5, padding=2) - self.group_norm_1 = nn.GroupNorm(1, mid_channels) - self.gelu_1 = nn.GELU() - self.conv_2 = nn.Conv1d(mid_channels, out_channels, 5, padding=2) - - if not self.is_last: - self.group_norm_2 = nn.GroupNorm(1, out_channels) - self.gelu_2 = nn.GELU() - - def forward(self, hidden_states): - residual = self.conv_skip(hidden_states) if self.has_conv_skip else hidden_states - - hidden_states = self.conv_1(hidden_states) - hidden_states = self.group_norm_1(hidden_states) - hidden_states = self.gelu_1(hidden_states) - hidden_states = self.conv_2(hidden_states) - - if not self.is_last: - hidden_states = self.group_norm_2(hidden_states) - hidden_states = self.gelu_2(hidden_states) - - output = hidden_states + residual - return output - - -class UNetMidBlock1D(nn.Module): - def __init__(self, mid_channels, in_channels, out_channels=None): - super().__init__() - - out_channels = in_channels if out_channels is None else out_channels - - # there is always at least one resnet - self.down = Downsample1d("cubic") - resnets = [ - ResConvBlock(in_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, out_channels), - ] - attentions = [ - SelfAttention1d(mid_channels, mid_channels // 32), - SelfAttention1d(mid_channels, mid_channels // 32), - SelfAttention1d(mid_channels, mid_channels // 32), - SelfAttention1d(mid_channels, mid_channels // 32), - SelfAttention1d(mid_channels, mid_channels // 32), - SelfAttention1d(out_channels, out_channels // 32), - ] - self.up = Upsample1d(kernel="cubic") - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - def forward(self, hidden_states, temb=None): - hidden_states = self.down(hidden_states) - for attn, resnet in zip(self.attentions, self.resnets): - hidden_states = resnet(hidden_states) - hidden_states = attn(hidden_states) - - hidden_states = self.up(hidden_states) - - return hidden_states - - -class AttnDownBlock1D(nn.Module): - def __init__(self, out_channels, in_channels, mid_channels=None): - super().__init__() - mid_channels = out_channels if mid_channels is None else mid_channels - - self.down = Downsample1d("cubic") - resnets = [ - ResConvBlock(in_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, out_channels), - ] - attentions = [ - SelfAttention1d(mid_channels, mid_channels // 32), - SelfAttention1d(mid_channels, mid_channels // 32), - SelfAttention1d(out_channels, out_channels // 32), - ] - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - def forward(self, hidden_states, temb=None): - hidden_states = self.down(hidden_states) - - for resnet, attn in zip(self.resnets, self.attentions): - hidden_states = resnet(hidden_states) - hidden_states = attn(hidden_states) - - return hidden_states, (hidden_states,) - - -class DownBlock1D(nn.Module): - def __init__(self, out_channels, in_channels, mid_channels=None): - super().__init__() - mid_channels = out_channels if mid_channels is None else mid_channels - - self.down = Downsample1d("cubic") - resnets = [ - ResConvBlock(in_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, out_channels), - ] - - self.resnets = nn.ModuleList(resnets) - - def forward(self, hidden_states, temb=None): - hidden_states = self.down(hidden_states) - - for resnet in self.resnets: - hidden_states = resnet(hidden_states) - - return hidden_states, (hidden_states,) - - -class DownBlock1DNoSkip(nn.Module): - def __init__(self, out_channels, in_channels, mid_channels=None): - super().__init__() - mid_channels = out_channels if mid_channels is None else mid_channels - - resnets = [ - ResConvBlock(in_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, out_channels), - ] - - self.resnets = nn.ModuleList(resnets) - - def forward(self, hidden_states, temb=None): - hidden_states = torch.cat([hidden_states, temb], dim=1) - for resnet in self.resnets: - hidden_states = resnet(hidden_states) - - return hidden_states, (hidden_states,) - - -class AttnUpBlock1D(nn.Module): - def __init__(self, in_channels, out_channels, mid_channels=None): - super().__init__() - mid_channels = out_channels if mid_channels is None else mid_channels - - resnets = [ - ResConvBlock(2 * in_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, out_channels), - ] - attentions = [ - SelfAttention1d(mid_channels, mid_channels // 32), - SelfAttention1d(mid_channels, mid_channels // 32), - SelfAttention1d(out_channels, out_channels // 32), - ] - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - self.up = Upsample1d(kernel="cubic") - - def forward(self, hidden_states, res_hidden_states_tuple, temb=None): - res_hidden_states = res_hidden_states_tuple[-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - for resnet, attn in zip(self.resnets, self.attentions): - hidden_states = resnet(hidden_states) - hidden_states = attn(hidden_states) - - hidden_states = self.up(hidden_states) - - return hidden_states - - -class UpBlock1D(nn.Module): - def __init__(self, in_channels, out_channels, mid_channels=None): - super().__init__() - mid_channels = in_channels if mid_channels is None else mid_channels - - resnets = [ - ResConvBlock(2 * in_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, out_channels), - ] - - self.resnets = nn.ModuleList(resnets) - self.up = Upsample1d(kernel="cubic") - - def forward(self, hidden_states, res_hidden_states_tuple, temb=None): - res_hidden_states = res_hidden_states_tuple[-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - for resnet in self.resnets: - hidden_states = resnet(hidden_states) - - hidden_states = self.up(hidden_states) - - return hidden_states - - -class UpBlock1DNoSkip(nn.Module): - def __init__(self, in_channels, out_channels, mid_channels=None): - super().__init__() - mid_channels = in_channels if mid_channels is None else mid_channels - - resnets = [ - ResConvBlock(2 * in_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, mid_channels), - ResConvBlock(mid_channels, mid_channels, out_channels, is_last=True), - ] - - self.resnets = nn.ModuleList(resnets) - - def forward(self, hidden_states, res_hidden_states_tuple, temb=None): - res_hidden_states = res_hidden_states_tuple[-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - for resnet in self.resnets: - hidden_states = resnet(hidden_states) - - return hidden_states - - -def get_down_block(down_block_type, num_layers, in_channels, out_channels, temb_channels, add_downsample): - if down_block_type == "DownResnetBlock1D": - return DownResnetBlock1D( - in_channels=in_channels, - num_layers=num_layers, - out_channels=out_channels, - temb_channels=temb_channels, - add_downsample=add_downsample, - ) - elif down_block_type == "DownBlock1D": - return DownBlock1D(out_channels=out_channels, in_channels=in_channels) - elif down_block_type == "AttnDownBlock1D": - return AttnDownBlock1D(out_channels=out_channels, in_channels=in_channels) - elif down_block_type == "DownBlock1DNoSkip": - return DownBlock1DNoSkip(out_channels=out_channels, in_channels=in_channels) - raise ValueError(f"{down_block_type} does not exist.") - - -def get_up_block(up_block_type, num_layers, in_channels, out_channels, temb_channels, add_upsample): - if up_block_type == "UpResnetBlock1D": - return UpResnetBlock1D( - in_channels=in_channels, - num_layers=num_layers, - out_channels=out_channels, - temb_channels=temb_channels, - add_upsample=add_upsample, - ) - elif up_block_type == "UpBlock1D": - return UpBlock1D(in_channels=in_channels, out_channels=out_channels) - elif up_block_type == "AttnUpBlock1D": - return AttnUpBlock1D(in_channels=in_channels, out_channels=out_channels) - elif up_block_type == "UpBlock1DNoSkip": - return UpBlock1DNoSkip(in_channels=in_channels, out_channels=out_channels) - raise ValueError(f"{up_block_type} does not exist.") - - -def get_mid_block(mid_block_type, num_layers, in_channels, mid_channels, out_channels, embed_dim, add_downsample): - if mid_block_type == "MidResTemporalBlock1D": - return MidResTemporalBlock1D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - embed_dim=embed_dim, - add_downsample=add_downsample, - ) - elif mid_block_type == "ValueFunctionMidBlock1D": - return ValueFunctionMidBlock1D(in_channels=in_channels, out_channels=out_channels, embed_dim=embed_dim) - elif mid_block_type == "UNetMidBlock1D": - return UNetMidBlock1D(in_channels=in_channels, mid_channels=mid_channels, out_channels=out_channels) - raise ValueError(f"{mid_block_type} does not exist.") - - -def get_out_block(*, out_block_type, num_groups_out, embed_dim, out_channels, act_fn, fc_dim): - if out_block_type == "OutConv1DBlock": - return OutConv1DBlock(num_groups_out, out_channels, embed_dim, act_fn) - elif out_block_type == "ValueFunction": - return OutValueFunctionBlock(fc_dim, embed_dim) - return None diff --git a/spaces/Yntec/Single-Stable-Diffusion-Model-Test/README.md b/spaces/Yntec/Single-Stable-Diffusion-Model-Test/README.md deleted file mode 100644 index 49b1c70b4361acd25436e6db05b9b02e05bde09f..0000000000000000000000000000000000000000 --- a/spaces/Yntec/Single-Stable-Diffusion-Model-Test/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Single Stable Diffusion Model Test (6 prompts) -emoji: -colorFrom: green -colorTo: blue -sdk: gradio -sdk_version: 3.15.0 -app_file: app.py -pinned: true -duplicated_from: Yntec/Diffusion30-AnimeAndDreamlikeModels ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/YotamNitzan/domain-expansion/torch_utils/ops/conv2d_resample.py b/spaces/YotamNitzan/domain-expansion/torch_utils/ops/conv2d_resample.py deleted file mode 100644 index cd4750744c83354bab78704d4ef51ad1070fcc4a..0000000000000000000000000000000000000000 --- a/spaces/YotamNitzan/domain-expansion/torch_utils/ops/conv2d_resample.py +++ /dev/null @@ -1,156 +0,0 @@ -# Copyright (c) 2021, 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. - -"""2D convolution with optional up/downsampling.""" - -import torch - -from .. import misc -from . import conv2d_gradfix -from . import upfirdn2d -from .upfirdn2d import _parse_padding -from .upfirdn2d import _get_filter_size - -#---------------------------------------------------------------------------- - -def _get_weight_shape(w): - with misc.suppress_tracer_warnings(): # this value will be treated as a constant - shape = [int(sz) for sz in w.shape] - misc.assert_shape(w, shape) - return shape - -#---------------------------------------------------------------------------- - -def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True): - """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations. - """ - out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) - - # Flip weight if requested. - if not flip_weight: # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False). - w = w.flip([2, 3]) - - # Workaround performance pitfall in cuDNN 8.0.5, triggered when using - # 1x1 kernel + memory_format=channels_last + less than 64 channels. - if kw == 1 and kh == 1 and stride == 1 and padding in [0, [0, 0], (0, 0)] and not transpose: - if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64: - if out_channels <= 4 and groups == 1: - in_shape = x.shape - x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1]) - x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]]) - else: - x = x.to(memory_format=torch.contiguous_format) - w = w.to(memory_format=torch.contiguous_format) - x = conv2d_gradfix.conv2d(x, w, groups=groups) - return x.to(memory_format=torch.channels_last) - - # Otherwise => execute using conv2d_gradfix. - op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d - return op(x, w, stride=stride, padding=padding, groups=groups) - -#---------------------------------------------------------------------------- - -@misc.profiled_function -def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False): - r"""2D convolution with optional up/downsampling. - - Padding is performed only once at the beginning, not between the operations. - - Args: - x: Input tensor of shape - `[batch_size, in_channels, in_height, in_width]`. - w: Weight tensor of shape - `[out_channels, in_channels//groups, kernel_height, kernel_width]`. - f: Low-pass filter for up/downsampling. Must be prepared beforehand by - calling upfirdn2d.setup_filter(). None = identity (default). - up: Integer upsampling factor (default: 1). - down: Integer downsampling factor (default: 1). - padding: Padding with respect to the upsampled image. Can be a single number - or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - groups: Split input channels into N groups (default: 1). - flip_weight: False = convolution, True = correlation (default: True). - flip_filter: False = convolution, True = correlation (default: False). - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - # Validate arguments. - assert isinstance(x, torch.Tensor) and (x.ndim == 4) - assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype) - assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32) - assert isinstance(up, int) and (up >= 1) - assert isinstance(down, int) and (down >= 1) - assert isinstance(groups, int) and (groups >= 1) - out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) - fw, fh = _get_filter_size(f) - px0, px1, py0, py1 = _parse_padding(padding) - - # Adjust padding to account for up/downsampling. - if up > 1: - px0 += (fw + up - 1) // 2 - px1 += (fw - up) // 2 - py0 += (fh + up - 1) // 2 - py1 += (fh - up) // 2 - if down > 1: - px0 += (fw - down + 1) // 2 - px1 += (fw - down) // 2 - py0 += (fh - down + 1) // 2 - py1 += (fh - down) // 2 - - # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve. - if kw == 1 and kh == 1 and (down > 1 and up == 1): - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter) - x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) - return x - - # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample. - if kw == 1 and kh == 1 and (up > 1 and down == 1): - x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) - x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) - return x - - # Fast path: downsampling only => use strided convolution. - if down > 1 and up == 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter) - x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight) - return x - - # Fast path: upsampling with optional downsampling => use transpose strided convolution. - if up > 1: - if groups == 1: - w = w.transpose(0, 1) - else: - w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) - w = w.transpose(1, 2) - w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw) - px0 -= kw - 1 - px1 -= kw - up - py0 -= kh - 1 - py1 -= kh - up - pxt = max(min(-px0, -px1), 0) - pyt = max(min(-py0, -py1), 0) - x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight)) - x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter) - if down > 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) - return x - - # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d. - if up == 1 and down == 1: - if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: - return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight) - - # Fallback: Generic reference implementation. - x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) - x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) - if down > 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) - return x - -#---------------------------------------------------------------------------- diff --git a/spaces/Yuliang/ECON/lib/pymafx/utils/keypoints.py b/spaces/Yuliang/ECON/lib/pymafx/utils/keypoints.py deleted file mode 100644 index a4e52d6a51dd24624be07a469fef62e1d0130995..0000000000000000000000000000000000000000 --- a/spaces/Yuliang/ECON/lib/pymafx/utils/keypoints.py +++ /dev/null @@ -1,355 +0,0 @@ -# Copyright (c) 2017-present, Facebook, Inc. -# -# 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. -############################################################################## -"""Keypoint utilities (somewhat specific to COCO keypoints).""" - -from __future__ import ( - absolute_import, - division, - print_function, - unicode_literals, -) - -import cv2 -import numpy as np -import torch -import torch.cuda.comm -import torch.nn.functional as F - -# from core.config import cfg -# import utils.blob as blob_utils - - -def get_keypoints(): - """Get the COCO keypoints and their left/right flip coorespondence map.""" - # Keypoints are not available in the COCO json for the test split, so we - # provide them here. - keypoints = [ - 'nose', 'left_eye', 'right_eye', 'left_ear', 'right_ear', 'left_shoulder', 'right_shoulder', - 'left_elbow', 'right_elbow', 'left_wrist', 'right_wrist', 'left_hip', 'right_hip', - 'left_knee', 'right_knee', 'left_ankle', 'right_ankle' - ] - keypoint_flip_map = { - 'left_eye': 'right_eye', 'left_ear': 'right_ear', 'left_shoulder': 'right_shoulder', - 'left_elbow': 'right_elbow', 'left_wrist': 'right_wrist', 'left_hip': 'right_hip', - 'left_knee': 'right_knee', 'left_ankle': 'right_ankle' - } - return keypoints, keypoint_flip_map - - -def get_person_class_index(): - """Index of the person class in COCO.""" - return 1 - - -def flip_keypoints(keypoints, keypoint_flip_map, keypoint_coords, width): - """Left/right flip keypoint_coords. keypoints and keypoint_flip_map are - accessible from get_keypoints(). - """ - flipped_kps = keypoint_coords.copy() - for lkp, rkp in keypoint_flip_map.items(): - lid = keypoints.index(lkp) - rid = keypoints.index(rkp) - flipped_kps[:, :, lid] = keypoint_coords[:, :, rid] - flipped_kps[:, :, rid] = keypoint_coords[:, :, lid] - - # Flip x coordinates - flipped_kps[:, 0, :] = width - flipped_kps[:, 0, :] - 1 - # Maintain COCO convention that if visibility == 0, then x, y = 0 - inds = np.where(flipped_kps[:, 2, :] == 0) - flipped_kps[inds[0], 0, inds[1]] = 0 - return flipped_kps - - -def flip_heatmaps(heatmaps): - """Flip heatmaps horizontally.""" - keypoints, flip_map = get_keypoints() - heatmaps_flipped = heatmaps.copy() - for lkp, rkp in flip_map.items(): - lid = keypoints.index(lkp) - rid = keypoints.index(rkp) - heatmaps_flipped[:, rid, :, :] = heatmaps[:, lid, :, :] - heatmaps_flipped[:, lid, :, :] = heatmaps[:, rid, :, :] - heatmaps_flipped = heatmaps_flipped[:, :, :, ::-1] - return heatmaps_flipped - - -def heatmaps_to_keypoints(maps, rois): - """Extract predicted keypoint locations from heatmaps. Output has shape - (#rois, 4, #keypoints) with the 4 rows corresponding to (x, y, logit, prob) - for each keypoint. - """ - # This function converts a discrete image coordinate in a HEATMAP_SIZE x - # HEATMAP_SIZE image to a continuous keypoint coordinate. We maintain - # consistency with keypoints_to_heatmap_labels by using the conversion from - # Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a - # continuous coordinate. - offset_x = rois[:, 0] - offset_y = rois[:, 1] - - widths = rois[:, 2] - rois[:, 0] - heights = rois[:, 3] - rois[:, 1] - widths = np.maximum(widths, 1) - heights = np.maximum(heights, 1) - widths_ceil = np.ceil(widths) - heights_ceil = np.ceil(heights) - - # NCHW to NHWC for use with OpenCV - maps = np.transpose(maps, [0, 2, 3, 1]) - min_size = cfg.KRCNN.INFERENCE_MIN_SIZE - xy_preds = np.zeros((len(rois), 4, cfg.KRCNN.NUM_KEYPOINTS), dtype=np.float32) - for i in range(len(rois)): - if min_size > 0: - roi_map_width = int(np.maximum(widths_ceil[i], min_size)) - roi_map_height = int(np.maximum(heights_ceil[i], min_size)) - else: - roi_map_width = widths_ceil[i] - roi_map_height = heights_ceil[i] - width_correction = widths[i] / roi_map_width - height_correction = heights[i] / roi_map_height - roi_map = cv2.resize( - maps[i], (roi_map_width, roi_map_height), interpolation=cv2.INTER_CUBIC - ) - # Bring back to CHW - roi_map = np.transpose(roi_map, [2, 0, 1]) - roi_map_probs = scores_to_probs(roi_map.copy()) - w = roi_map.shape[2] - for k in range(cfg.KRCNN.NUM_KEYPOINTS): - pos = roi_map[k, :, :].argmax() - x_int = pos % w - y_int = (pos - x_int) // w - assert (roi_map_probs[k, y_int, x_int] == roi_map_probs[k, :, :].max()) - x = (x_int + 0.5) * width_correction - y = (y_int + 0.5) * height_correction - xy_preds[i, 0, k] = x + offset_x[i] - xy_preds[i, 1, k] = y + offset_y[i] - xy_preds[i, 2, k] = roi_map[k, y_int, x_int] - xy_preds[i, 3, k] = roi_map_probs[k, y_int, x_int] - - return xy_preds - - -def keypoints_to_heatmap_labels(keypoints, rois): - """Encode keypoint location in the target heatmap for use in - SoftmaxWithLoss. - """ - # Maps keypoints from the half-open interval [x1, x2) on continuous image - # coordinates to the closed interval [0, HEATMAP_SIZE - 1] on discrete image - # coordinates. We use the continuous <-> discrete conversion from Heckbert - # 1990 ("What is the coordinate of a pixel?"): d = floor(c) and c = d + 0.5, - # where d is a discrete coordinate and c is a continuous coordinate. - assert keypoints.shape[2] == cfg.KRCNN.NUM_KEYPOINTS - - shape = (len(rois), cfg.KRCNN.NUM_KEYPOINTS) - heatmaps = blob_utils.zeros(shape) - weights = blob_utils.zeros(shape) - - offset_x = rois[:, 0] - offset_y = rois[:, 1] - scale_x = cfg.KRCNN.HEATMAP_SIZE / (rois[:, 2] - rois[:, 0]) - scale_y = cfg.KRCNN.HEATMAP_SIZE / (rois[:, 3] - rois[:, 1]) - - for kp in range(keypoints.shape[2]): - vis = keypoints[:, 2, kp] > 0 - x = keypoints[:, 0, kp].astype(np.float32) - y = keypoints[:, 1, kp].astype(np.float32) - # Since we use floor below, if a keypoint is exactly on the roi's right - # or bottom boundary, we shift it in by eps (conceptually) to keep it in - # the ground truth heatmap. - x_boundary_inds = np.where(x == rois[:, 2])[0] - y_boundary_inds = np.where(y == rois[:, 3])[0] - x = (x - offset_x) * scale_x - x = np.floor(x) - if len(x_boundary_inds) > 0: - x[x_boundary_inds] = cfg.KRCNN.HEATMAP_SIZE - 1 - - y = (y - offset_y) * scale_y - y = np.floor(y) - if len(y_boundary_inds) > 0: - y[y_boundary_inds] = cfg.KRCNN.HEATMAP_SIZE - 1 - - valid_loc = np.logical_and( - np.logical_and(x >= 0, y >= 0), - np.logical_and(x < cfg.KRCNN.HEATMAP_SIZE, y < cfg.KRCNN.HEATMAP_SIZE) - ) - - valid = np.logical_and(valid_loc, vis) - valid = valid.astype(np.int32) - - lin_ind = y * cfg.KRCNN.HEATMAP_SIZE + x - heatmaps[:, kp] = lin_ind * valid - weights[:, kp] = valid - - return heatmaps, weights - - -def scores_to_probs(scores): - """Transforms CxHxW of scores to probabilities spatially.""" - channels = scores.shape[0] - for c in range(channels): - temp = scores[c, :, :] - max_score = temp.max() - temp = np.exp(temp - max_score) / np.sum(np.exp(temp - max_score)) - scores[c, :, :] = temp - return scores - - -def nms_oks(kp_predictions, rois, thresh): - """Nms based on kp predictions.""" - scores = np.mean(kp_predictions[:, 2, :], axis=1) - order = scores.argsort()[::-1] - - keep = [] - while order.size > 0: - i = order[0] - keep.append(i) - ovr = compute_oks(kp_predictions[i], rois[i], kp_predictions[order[1:]], rois[order[1:]]) - inds = np.where(ovr <= thresh)[0] - order = order[inds + 1] - - return keep - - -def compute_oks(src_keypoints, src_roi, dst_keypoints, dst_roi): - """Compute OKS for predicted keypoints wrt gt_keypoints. - src_keypoints: 4xK - src_roi: 4x1 - dst_keypoints: Nx4xK - dst_roi: Nx4 - """ - - sigmas = np.array([ - .26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89 - ]) / 10.0 - vars = (sigmas * 2)**2 - - # area - src_area = (src_roi[2] - src_roi[0] + 1) * (src_roi[3] - src_roi[1] + 1) - - # measure the per-keypoint distance if keypoints visible - dx = dst_keypoints[:, 0, :] - src_keypoints[0, :] - dy = dst_keypoints[:, 1, :] - src_keypoints[1, :] - - e = (dx**2 + dy**2) / vars / (src_area + np.spacing(1)) / 2 - e = np.sum(np.exp(-e), axis=1) / e.shape[1] - - return e - - -def get_max_preds(batch_heatmaps): - ''' - get predictions from score maps - heatmaps: numpy.ndarray([batch_size, num_joints, height, width]) - ''' - assert isinstance(batch_heatmaps, np.ndarray), \ - 'batch_heatmaps should be numpy.ndarray' - assert batch_heatmaps.ndim == 4, 'batch_images should be 4-ndim' - - batch_size = batch_heatmaps.shape[0] - num_joints = batch_heatmaps.shape[1] - width = batch_heatmaps.shape[3] - heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1)) - idx = np.argmax(heatmaps_reshaped, 2) - maxvals = np.amax(heatmaps_reshaped, 2) - - maxvals = maxvals.reshape((batch_size, num_joints, 1)) - idx = idx.reshape((batch_size, num_joints, 1)) - - preds = np.tile(idx, (1, 1, 2)).astype(np.float32) - - preds[:, :, 0] = (preds[:, :, 0]) % width - preds[:, :, 1] = np.floor((preds[:, :, 1]) / width) - - pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2)) - pred_mask = pred_mask.astype(np.float32) - - preds *= pred_mask - return preds, maxvals - - -def generate_3d_integral_preds_tensor(heatmaps, num_joints, x_dim, y_dim, z_dim): - assert isinstance(heatmaps, torch.Tensor) - - if z_dim is not None: - heatmaps = heatmaps.reshape((heatmaps.shape[0], num_joints, z_dim, y_dim, x_dim)) - - accu_x = heatmaps.sum(dim=2) - accu_x = accu_x.sum(dim=2) - accu_y = heatmaps.sum(dim=2) - accu_y = accu_y.sum(dim=3) - accu_z = heatmaps.sum(dim=3) - accu_z = accu_z.sum(dim=3) - - accu_x = accu_x * torch.cuda.comm.broadcast( - torch.arange(x_dim, dtype=torch.float32), devices=[accu_x.device.index] - )[0] - accu_y = accu_y * torch.cuda.comm.broadcast( - torch.arange(y_dim, dtype=torch.float32), devices=[accu_y.device.index] - )[0] - accu_z = accu_z * torch.cuda.comm.broadcast( - torch.arange(z_dim, dtype=torch.float32), devices=[accu_z.device.index] - )[0] - - accu_x = accu_x.sum(dim=2, keepdim=True) - accu_y = accu_y.sum(dim=2, keepdim=True) - accu_z = accu_z.sum(dim=2, keepdim=True) - else: - heatmaps = heatmaps.reshape((heatmaps.shape[0], num_joints, y_dim, x_dim)) - - accu_x = heatmaps.sum(dim=2) - accu_y = heatmaps.sum(dim=3) - - accu_x = accu_x * torch.cuda.comm.broadcast( - torch.arange(x_dim, dtype=torch.float32), devices=[accu_x.device.index] - )[0] - accu_y = accu_y * torch.cuda.comm.broadcast( - torch.arange(y_dim, dtype=torch.float32), devices=[accu_y.device.index] - )[0] - - accu_x = accu_x.sum(dim=2, keepdim=True) - accu_y = accu_y.sum(dim=2, keepdim=True) - accu_z = None - - return accu_x, accu_y, accu_z - - -# integral pose estimation -# https://github.com/JimmySuen/integral-human-pose/blob/99647e40ec93dfa4e3b6a1382c935cebb35440da/pytorch_projects/common_pytorch/common_loss/integral.py#L28 -def softmax_integral_tensor(preds, num_joints, hm_width, hm_height, hm_depth=None): - # global soft max - preds = preds.reshape((preds.shape[0], num_joints, -1)) - preds = F.softmax(preds, 2) - - output_3d = False if hm_depth is None else True - - # integrate heatmap into joint location - if output_3d: - x, y, z = generate_3d_integral_preds_tensor( - preds, num_joints, hm_width, hm_height, hm_depth - ) - # x = x / float(hm_width) - 0.5 - # y = y / float(hm_height) - 0.5 - # z = z / float(hm_depth) - 0.5 - preds = torch.cat((x, y, z), dim=2) - # preds = preds.reshape((preds.shape[0], num_joints * 3)) - else: - x, y, _ = generate_3d_integral_preds_tensor( - preds, num_joints, hm_width, hm_height, z_dim=None - ) - # x = x / float(hm_width) - 0.5 - # y = y / float(hm_height) - 0.5 - preds = torch.cat((x, y), dim=2) - # preds = preds.reshape((preds.shape[0], num_joints * 2)) - - return preds diff --git a/spaces/Yuliang/ICON/lib/pymaf/core/base_trainer.py b/spaces/Yuliang/ICON/lib/pymaf/core/base_trainer.py deleted file mode 100644 index 764491bd99f89445decaf2217837a0f084ed0271..0000000000000000000000000000000000000000 --- a/spaces/Yuliang/ICON/lib/pymaf/core/base_trainer.py +++ /dev/null @@ -1,107 +0,0 @@ -# This script is borrowed and extended from https://github.com/nkolot/SPIN/blob/master/utils/base_trainer.py -from __future__ import division -import logging -from utils import CheckpointSaver -from tensorboardX import SummaryWriter - -import torch -from tqdm import tqdm - -tqdm.monitor_interval = 0 - - -logger = logging.getLogger(__name__) - - -class BaseTrainer(object): - """Base class for Trainer objects. - Takes care of checkpointing/logging/resuming training. - """ - - def __init__(self, options): - self.options = options - if options.multiprocessing_distributed: - self.device = torch.device('cuda', options.gpu) - else: - self.device = torch.device( - 'cuda' if torch.cuda.is_available() else 'cpu') - # override this function to define your model, optimizers etc. - self.saver = CheckpointSaver(save_dir=options.checkpoint_dir, - overwrite=options.overwrite) - if options.rank == 0: - self.summary_writer = SummaryWriter(self.options.summary_dir) - self.init_fn() - - self.checkpoint = None - if options.resume and self.saver.exists_checkpoint(): - self.checkpoint = self.saver.load_checkpoint( - self.models_dict, self.optimizers_dict) - - if self.checkpoint is None: - self.epoch_count = 0 - self.step_count = 0 - else: - self.epoch_count = self.checkpoint['epoch'] - self.step_count = self.checkpoint['total_step_count'] - - if self.checkpoint is not None: - self.checkpoint_batch_idx = self.checkpoint['batch_idx'] - else: - self.checkpoint_batch_idx = 0 - - self.best_performance = float('inf') - - def load_pretrained(self, checkpoint_file=None): - """Load a pretrained checkpoint. - This is different from resuming training using --resume. - """ - if checkpoint_file is not None: - checkpoint = torch.load(checkpoint_file) - for model in self.models_dict: - if model in checkpoint: - self.models_dict[model].load_state_dict(checkpoint[model], - strict=True) - print(f'Checkpoint {model} loaded') - - def move_dict_to_device(self, 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) - - # The following methods (with the possible exception of test) have to be implemented in the derived classes - def train(self, epoch): - raise NotImplementedError('You need to provide an train method') - - def init_fn(self): - raise NotImplementedError('You need to provide an _init_fn method') - - def train_step(self, input_batch): - raise NotImplementedError('You need to provide a _train_step method') - - def train_summaries(self, input_batch): - raise NotImplementedError( - 'You need to provide a _train_summaries method') - - def visualize(self, input_batch): - raise NotImplementedError('You need to provide a visualize method') - - def validate(self): - pass - - def test(self): - pass - - def evaluate(self): - pass - - def fit(self): - # Run training for num_epochs epochs - for epoch in tqdm(range(self.epoch_count, self.options.num_epochs), - total=self.options.num_epochs, - initial=self.epoch_count): - self.epoch_count = epoch - self.train(epoch) - return diff --git a/spaces/Yunshansongbai/SVC-Nahida/spleeter.py b/spaces/Yunshansongbai/SVC-Nahida/spleeter.py deleted file mode 100644 index b6d5cde67191fe1b086cbce0b6db556c632c1b93..0000000000000000000000000000000000000000 --- a/spaces/Yunshansongbai/SVC-Nahida/spleeter.py +++ /dev/null @@ -1,337 +0,0 @@ -import paddle -import paddle.nn as nn -import paddle -import os -import numpy as np -import math -import paddle.nn as nn -import ffmpeg -from scipy.signal.windows import hann -from librosa.core import stft, istft - -class UNet(nn.Layer): - def __init__(self, use_elu=False): - super(UNet, self).__init__() - self.use_elu = use_elu - self.pad = nn.Pad2D(padding=[1, 2, 1, 2]) - - ### Encoder ### - # First Layer - self.conv1 = nn.Conv2D(2, 16, kernel_size=5, stride=2) ## padding - self.encoder1 = self.encoder_block(16) - # Second Layer - self.conv2 = nn.Conv2D(16, 32, kernel_size=5, stride=2) - self.encoder2 = self.encoder_block(32) - # Third Layer - self.conv3 = nn.Conv2D(32, 64, kernel_size=5, stride=2) - self.encoder3 = self.encoder_block(64) - # Fourth Layer - self.conv4 = nn.Conv2D(64, 128, kernel_size=5, stride=2) - self.encoder4 = self.encoder_block(128) - # Fifth Layer - self.conv5 = nn.Conv2D(128, 256, kernel_size=5, stride=2) - self.encoder5 = self.encoder_block(256) - # Sixth Layer - self.conv6 = nn.Conv2D(256, 512, kernel_size=5, stride=2) - self.encoder6 = self.encoder_block(512) - - ### Decoder ### - # First Layer - self.decoder1 = self.decoder_block(512, 256, dropout=True) - # Second Layer - self.decoder2 = self.decoder_block(512, 128, dropout=True) - # Third Layer - self.decoder3 = self.decoder_block(256, 64, dropout=True) - # Fourth Layer - self.decoder4 = self.decoder_block(128, 32) - # Fifth Layer - self.decoder5 = self.decoder_block(64, 16) - # Sixth Layer - self.decoder6 = self.decoder_block(32, 1) - - # Last Layer - self.mask = nn.Conv2D(1, 2, kernel_size=4, dilation=2, padding=3) - self.sig = nn.Sigmoid() - - def encoder_block(self, out_channel): - if not self.use_elu: - return nn.Sequential( - nn.BatchNorm2D(out_channel, epsilon=1e-3, momentum=0.01), - nn.LeakyReLU(0.2) - ) - else: - return nn.Sequential( - nn.BatchNorm2D(out_channel, epsilon=1e-3, momentum=0.01), - nn.ELU() - ) - - def decoder_block(self, in_channel, out_channel, dropout=False): - layers = [ - nn.Conv2DTranspose(in_channel, out_channel, kernel_size=5, stride=2) - ] - if not self.use_elu: - layers.append(nn.ReLU()) - else: - layers.append(nn.ELU()) - layers.append(nn.BatchNorm2D(out_channel, epsilon=1e-3, momentum=0.01)) - if dropout: - layers.append(nn.Dropout(0.5)) - return nn.Sequential(*layers) - - def forward(self, x): - ### Encoder ### - skip1 = self.pad(x) - skip1 = self.conv1(skip1) - down1 = self.encoder1(skip1) - - skip2 = self.pad(down1) - skip2 = self.conv2(skip2) - down2 = self.encoder2(skip2) - - skip3 = self.pad(down2) - skip3 = self.conv3(skip3) - down3 = self.encoder3(skip3) - - skip4 = self.pad(down3) - skip4 = self.conv4(skip4) - down4 = self.encoder4(skip4) - - skip5 = self.pad(down4) - skip5 = self.conv5(skip5) - down5 = self.encoder5(skip5) - - skip6 = self.pad(down5) - skip6 = self.conv6(skip6) - down6 = self.encoder6(skip6) - - ### Decoder ### - up1 = self.decoder1(skip6) - up1 = up1[:, :, 1: -2, 1: -2] - merge1 = paddle.concat((skip5, up1), 1) - - up2 = self.decoder2(merge1) - up2 = up2[:, :, 1: -2, 1: -2] - merge2 = paddle.concat((skip4, up2), 1) - - up3 = self.decoder3(merge2) - up3 = up3[:, :, 1: -2, 1: -2] - merge3 = paddle.concat((skip3, up3), 1) - - up4 = self.decoder4(merge3) - up4 = up4[:, :, 1: -2, 1: -2] - merge4 = paddle.concat((skip2, up4), 1) - - up5 = self.decoder5(merge4) - up5 = up5[:, :, 1: -2, 1: -2] - merge5 = paddle.concat((skip1, up5), 1) - - up6 = self.decoder6(merge5) - up6 = up6[:, :, 1: -2, 1: -2] - - m = self.mask(up6) - - m = self.sig(m) - return m * x - -class Separator(object): - def __init__(self, params): - self.num_instruments = params['num_instruments'] - self.output_dir = params['output_dir'] - self.model_list = nn.LayerList() - - for i, name in enumerate(self.num_instruments): - print('Loading model for instrumment {}'.format(i)) - net = UNet(use_elu=params['use_elu']) - net.eval() - state_dict = paddle.load(os.path.join(params['checkpoint_path'], '%dstems_%s.pdparams' % (len(self.num_instruments), name))) - net.set_dict(state_dict) - self.model_list.append(net) - - self.T = params['T'] - self.F = params['F'] - self.frame_length = params['frame_length'] - self.frame_step = params['frame_step'] - self.samplerate = params['sample_rate'] - - def _load_audio( - self, path, offset=None, duration=None, - sample_rate=None, dtype=np.float32): - """ Loads the audio file denoted by the given path - and returns it data as a waveform. - - :param path: Path of the audio file to load data from. - :param offset: (Optional) Start offset to load from in seconds. - :param duration: (Optional) Duration to load in seconds. - :param sample_rate: (Optional) Sample rate to load audio with. - :param dtype: (Optional) Numpy data type to use, default to float32. - :returns: Loaded data a (waveform, sample_rate) tuple. - :raise SpleeterError: If any error occurs while loading audio. - """ - if not isinstance(path, str): - path = path.decode() - - probe = ffmpeg.probe(path) - - metadata = next( - stream - for stream in probe['streams'] - if stream['codec_type'] == 'audio') - n_channels = metadata['channels'] - if sample_rate is None: - sample_rate = metadata['sample_rate'] - output_kwargs = {'format': 'f32le', 'ar': sample_rate} - process = ( - ffmpeg - .input(path) - .output('pipe:', **output_kwargs) - .run_async(pipe_stdout=True, pipe_stderr=True)) - buffer, _ = process.communicate() - waveform = np.frombuffer(buffer, dtype='
' - ) - # dl_link = f'
' - - st.markdown(dl_link, unsafe_allow_html=True) - - -# def download_link( -# content, label="Download", filename="file.txt", mimetype="text/plain" -# ): -# """Create a HTML link to download a string as a file.""" -# # From: https://discuss.streamlit.io/t/how-to-download-file-in-streamlit/1806/9 -# b64 = base64.b64encode( -# content.encode() -# ).decode() # some strings <-> bytes conversions necessary here -# href = ( -# f'{label}' -# ) -# return href diff --git a/spaces/ai-danger/hot-or-not/README.md b/spaces/ai-danger/hot-or-not/README.md deleted file mode 100644 index 44ced8da7f09b689cd4fc59987a4cfb18204bce0..0000000000000000000000000000000000000000 --- a/spaces/ai-danger/hot-or-not/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Hot Or Not -emoji: 🏢 -colorFrom: yellow -colorTo: gray -sdk: gradio -sdk_version: 3.4.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/akhaliq/ArcaneGAN-blocks/README.md b/spaces/akhaliq/ArcaneGAN-blocks/README.md deleted file mode 100644 index 9809565ef9210150dd5ff34ae798d1f7d27367d2..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/ArcaneGAN-blocks/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: ArcaneGAN Blocks -emoji: 📉 -colorFrom: red -colorTo: red -sdk: gradio -sdk_version: 2.8.13 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/akhaliq/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext/README.md b/spaces/akhaliq/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext/README.md deleted file mode 100644 index a8625e5dd1bf503ab56e130a9fd4ffc6239502f9..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: BiomedNLP PubMedBERT Base Uncased Abstract Fulltext -emoji: 🚀 -colorFrom: red -colorTo: yellow -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/akhaliq/Detic/detic/data/custom_build_augmentation.py b/spaces/akhaliq/Detic/detic/data/custom_build_augmentation.py deleted file mode 100644 index 9642c15e582fc953ecaa378a325b4fa02f4e7d28..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/Detic/detic/data/custom_build_augmentation.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import logging -import numpy as np -import pycocotools.mask as mask_util -import torch -from fvcore.common.file_io import PathManager -from PIL import Image - - -from detectron2.data import transforms as T -from .transforms.custom_augmentation_impl import EfficientDetResizeCrop - -def build_custom_augmentation(cfg, is_train, scale=None, size=None, \ - min_size=None, max_size=None): - """ - Create a list of default :class:`Augmentation` from config. - Now it includes resizing and flipping. - - Returns: - list[Augmentation] - """ - if cfg.INPUT.CUSTOM_AUG == 'ResizeShortestEdge': - if is_train: - min_size = cfg.INPUT.MIN_SIZE_TRAIN if min_size is None else min_size - max_size = cfg.INPUT.MAX_SIZE_TRAIN if max_size is None else max_size - sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING - else: - min_size = cfg.INPUT.MIN_SIZE_TEST - max_size = cfg.INPUT.MAX_SIZE_TEST - sample_style = "choice" - augmentation = [T.ResizeShortestEdge(min_size, max_size, sample_style)] - elif cfg.INPUT.CUSTOM_AUG == 'EfficientDetResizeCrop': - if is_train: - scale = cfg.INPUT.SCALE_RANGE if scale is None else scale - size = cfg.INPUT.TRAIN_SIZE if size is None else size - else: - scale = (1, 1) - size = cfg.INPUT.TEST_SIZE - augmentation = [EfficientDetResizeCrop(size, scale)] - else: - assert 0, cfg.INPUT.CUSTOM_AUG - - if is_train: - augmentation.append(T.RandomFlip()) - return augmentation - - -build_custom_transform_gen = build_custom_augmentation -""" -Alias for backward-compatibility. -""" \ No newline at end of file diff --git a/spaces/akhaliq/Kapao/demos/flash_mob.py b/spaces/akhaliq/Kapao/demos/flash_mob.py deleted file mode 100644 index 583b2e26a3d68895a282d86f1a2fa41369a78fb6..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/Kapao/demos/flash_mob.py +++ /dev/null @@ -1,166 +0,0 @@ -import sys -from pathlib import Path -FILE = Path(__file__).absolute() -sys.path.append(FILE.parents[1].as_posix()) # add kapao/ to path - -import argparse -from pytube import YouTube -import os.path as osp -from utils.torch_utils import select_device, time_sync -from utils.general import check_img_size -from utils.datasets import LoadImages -from models.experimental import attempt_load -import torch -import cv2 -import numpy as np -import yaml -from tqdm import tqdm -import imageio -from val import run_nms, post_process_batch - - -VIDEO_NAME = 'Crazy Uptown Funk Flashmob in Sydney for sydney domains campaign.mp4' -URL = 'https://www.youtube.com/watch?v=2DiQUX11YaY&ab_channel=CrazyDomains' -COLOR = (255, 0, 255) # purple -ALPHA = 0.5 -SEG_THICK = 3 -FPS_TEXT_SIZE = 2 - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--data', type=str, default='data/coco-kp.yaml') - parser.add_argument('--imgsz', type=int, default=1280) - parser.add_argument('--weights', default='kapao_s_coco.pt') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or cpu') - parser.add_argument('--half', action='store_true') - parser.add_argument('--conf-thres', type=float, default=0.5, help='confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') - parser.add_argument('--no-kp-dets', action='store_true', help='do not use keypoint objects') - parser.add_argument('--conf-thres-kp', type=float, default=0.5) - parser.add_argument('--conf-thres-kp-person', type=float, default=0.2) - parser.add_argument('--iou-thres-kp', type=float, default=0.45) - parser.add_argument('--overwrite-tol', type=int, default=50) - parser.add_argument('--scales', type=float, nargs='+', default=[1]) - parser.add_argument('--flips', type=int, nargs='+', default=[-1]) - parser.add_argument('--display', action='store_true', help='display inference results') - parser.add_argument('--fps', action='store_true', help='display fps') - parser.add_argument('--gif', action='store_true', help='create fig') - parser.add_argument('--start', type=int, default=68, help='start time (s)') - parser.add_argument('--end', type=int, default=98, help='end time (s)') - args = parser.parse_args() - - with open(args.data) as f: - data = yaml.safe_load(f) # load data dict - - # add inference settings to data dict - data['imgsz'] = args.imgsz - data['conf_thres'] = args.conf_thres - data['iou_thres'] = args.iou_thres - data['use_kp_dets'] = not args.no_kp_dets - data['conf_thres_kp'] = args.conf_thres_kp - data['iou_thres_kp'] = args.iou_thres_kp - data['conf_thres_kp_person'] = args.conf_thres_kp_person - data['overwrite_tol'] = args.overwrite_tol - data['scales'] = args.scales - data['flips'] = [None if f == -1 else f for f in args.flips] - - if not osp.isfile(VIDEO_NAME): - yt = YouTube(URL) - # [print(s) for s in yt.streams] - stream = [s for s in yt.streams if s.itag == 136][0] # 720p, non-progressive - print('Downloading squash demo video...') - stream.download() - print('Done.') - - device = select_device(args.device, batch_size=1) - print('Using device: {}'.format(device)) - - model = attempt_load(args.weights, map_location=device) # load FP32 model - half = args.half & (device.type != 'cpu') - if half: # half precision only supported on CUDA - model.half() - stride = int(model.stride.max()) # model stride - - imgsz = check_img_size(args.imgsz, s=stride) # check image size - dataset = LoadImages('./{}'.format(VIDEO_NAME), img_size=imgsz, stride=stride, auto=True) - - if device.type != 'cpu': - model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once - - cap = dataset.cap - cap.set(cv2.CAP_PROP_POS_MSEC, args.start * 1000) - fps = cap.get(cv2.CAP_PROP_FPS) - n = int(fps * (args.end - args.start)) - h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - gif_frames = [] - video_name = 'flash_mob_inference_{}'.format(osp.splitext(args.weights)[0]) - - if not args.display: - writer = cv2.VideoWriter(video_name + '.mp4', - cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) - if not args.fps: # tqdm might slows down inference - dataset = tqdm(dataset, desc='Writing inference video', total=n) - - t0 = time_sync() - for i, (path, img, im0, _) in enumerate(dataset): - img = torch.from_numpy(img).to(device) - img = img.half() if half else img.float() # uint8 to fp16/32 - img = img / 255.0 # 0 - 255 to 0.0 - 1.0 - if len(img.shape) == 3: - img = img[None] # expand for batch dim - - out = model(img, augment=True, kp_flip=data['kp_flip'], scales=data['scales'], flips=data['flips'])[0] - person_dets, kp_dets = run_nms(data, out) - bboxes, poses, _, _, _ = post_process_batch(data, img, [], [[im0.shape[:2]]], person_dets, kp_dets) - - im0_copy = im0.copy() - - # DRAW POSES - for j, (bbox, pose) in enumerate(zip(bboxes, poses)): - x1, y1, x2, y2 = bbox - size = ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5 - # if size < 450: - cv2.rectangle(im0_copy, (int(x1), int(y1)), (int(x2), int(y2)), COLOR, thickness=2) - for seg in data['segments'].values(): - pt1 = (int(pose[seg[0], 0]), int(pose[seg[0], 1])) - pt2 = (int(pose[seg[1], 0]), int(pose[seg[1], 1])) - cv2.line(im0_copy, pt1, pt2, COLOR, SEG_THICK) - im0 = cv2.addWeighted(im0, ALPHA, im0_copy, 1 - ALPHA, gamma=0) - - if i == 0: - t = time_sync() - t0 - else: - t = time_sync() - t1 - - if args.fps: - s = FPS_TEXT_SIZE - cv2.putText(im0, '{:.1f} FPS'.format(1 / t), (5*s, 25*s), - cv2.FONT_HERSHEY_SIMPLEX, s, (255, 255, 255), thickness=2*s) - - if args.gif: - gif_frames.append(cv2.resize(im0, dsize=None, fx=0.375, fy=0.375)[:, :, [2, 1, 0]]) - elif not args.display: - writer.write(im0) - else: - cv2.imshow('', im0) - cv2.waitKey(1) - - t1 = time_sync() - if i == n - 1: - break - - cv2.destroyAllWindows() - cap.release() - if not args.display: - writer.release() - - if args.gif: - print('Saving GIF...') - with imageio.get_writer(video_name + '.gif', mode="I", fps=fps) as writer: - for idx, frame in tqdm(enumerate(gif_frames)): - writer.append_data(frame) - - - diff --git a/spaces/akhaliq/deeplab2/model/decoder/panoptic_deeplab.py b/spaces/akhaliq/deeplab2/model/decoder/panoptic_deeplab.py deleted file mode 100644 index 4ccbeaff5f49789f93188ec03f49eeec06bbe0b2..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/deeplab2/model/decoder/panoptic_deeplab.py +++ /dev/null @@ -1,445 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The Deeplab2 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. - -"""This file contains code to build a Panoptic-DeepLab decoder. - -Reference: - - [Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up - Panoptic Segmentation](https://arxiv.org/pdf/1911.10194) -""" -from absl import logging - -import tensorflow as tf - -from deeplab2 import common -from deeplab2.model import utils -from deeplab2.model.decoder import aspp -from deeplab2.model.layers import convolutions - - -layers = tf.keras.layers - - -class PanopticDeepLabSingleDecoder(layers.Layer): - """A single Panoptic-DeepLab decoder layer. - - This layer takes low- and high-level features as input and uses an ASPP - followed by a fusion block to decode features for a single task, e.g., - semantic segmentation or instance segmentation. - """ - - def __init__(self, - high_level_feature_name, - low_level_feature_names, - low_level_channels_project, - aspp_output_channels, - decoder_output_channels, - atrous_rates, - name, - aspp_use_only_1x1_proj_conv=False, - decoder_conv_type='depthwise_separable_conv', - bn_layer=tf.keras.layers.BatchNormalization): - """Initializes a single Panoptic-DeepLab decoder of layers.Layer. - - Args: - high_level_feature_name: A string specifying the name of the high-level - feature coming from an encoder. - low_level_feature_names: A list of strings specifying the name of the - low-level features coming from an encoder. An order from highest to - lower level is expected, e.g. ['res3', 'res2']. - low_level_channels_project: A list of integer specifying the number of - filters used for processing each low_level features. - aspp_output_channels: An integer specifying the number of filters in the - ASPP convolution layers. - decoder_output_channels: An integer specifying the number of filters in - the decoder convolution layers. - atrous_rates: A list of three integers specifying the atrous rate for the - ASPP layers. - name: A string specifying the name of the layer. - aspp_use_only_1x1_proj_conv: Boolean, specifying if the ASPP five branches - are turned off or not. If True, the ASPP module is degenerated to one - 1x1 convolution, projecting the input channels to `output_channels`. - decoder_conv_type: String, specifying decoder convolution type. Support - 'depthwise_separable_conv' and 'standard_conv'. - bn_layer: An optional tf.keras.layers.Layer that computes the - normalization (default: tf.keras.layers.BatchNormalization). - - Raises: - ValueError: An error occurs when the length of low_level_feature_names - differs from the length of low_level_channels_project. - """ - super(PanopticDeepLabSingleDecoder, self).__init__(name=name) - self._channel_axis = 3 - - self._aspp = aspp.ASPP( - aspp_output_channels, - atrous_rates, - aspp_use_only_1x1_proj_conv=aspp_use_only_1x1_proj_conv, - name='aspp', - bn_layer=bn_layer) - self._high_level_feature_name = high_level_feature_name - - if len(low_level_feature_names) != len(low_level_channels_project): - raise ValueError('The Panoptic-DeepLab decoder requires the same number ' - 'of low-level features as the number of low-level ' - 'projection channels. But got %d and %d.' - % (len(low_level_feature_names), - len(low_level_channels_project))) - - self._low_level_feature_names = low_level_feature_names - - for i, channels_project in enumerate(low_level_channels_project): - # Check if channel sizes increases and issue a warning. - if i > 0 and low_level_channels_project[i - 1] < channels_project: - logging.warning( - 'The low level projection channels usually do not ' - 'increase for features with higher spatial resolution. ' - 'Please make sure, this behavior is intended.') - current_low_level_conv_name, current_fusion_conv_name = ( - utils.get_low_level_conv_fusion_conv_current_names(i)) - utils.safe_setattr( - self, current_low_level_conv_name, convolutions.Conv2DSame( - channels_project, - kernel_size=1, - name=utils.get_layer_name(current_low_level_conv_name), - use_bias=False, - use_bn=True, - bn_layer=bn_layer, - activation='relu')) - - utils.safe_setattr( - self, current_fusion_conv_name, convolutions.StackedConv2DSame( - conv_type=decoder_conv_type, - num_layers=1, - output_channels=decoder_output_channels, - kernel_size=5, - name=utils.get_layer_name(current_fusion_conv_name), - use_bias=False, - use_bn=True, - bn_layer=bn_layer, - activation='relu')) - - def call(self, features, training=False): - """Performs a forward pass. - - Args: - features: An input dict of tf.Tensor with shape [batch, height, width, - channels]. Different keys should point to different features extracted - by the encoder, e.g. low-level or high-level features. - training: A boolean flag indicating whether training behavior should be - used (default: False). - - Returns: - Refined features as instance of tf.Tensor. - """ - - high_level_features = features[self._high_level_feature_name] - combined_features = self._aspp(high_level_features, training=training) - - # Fuse low-level features with high-level features. - for i in range(len(self._low_level_feature_names)): - current_low_level_conv_name, current_fusion_conv_name = ( - utils.get_low_level_conv_fusion_conv_current_names(i)) - # Iterate from the highest level of the low level features to the lowest - # level, i.e. take the features with the smallest spatial size first. - low_level_features = features[self._low_level_feature_names[i]] - low_level_features = getattr(self, current_low_level_conv_name)( - low_level_features, training=training) - - target_h = tf.shape(low_level_features)[1] - target_w = tf.shape(low_level_features)[2] - source_h = tf.shape(combined_features)[1] - source_w = tf.shape(combined_features)[2] - - tf.assert_less( - source_h - 1, - target_h, - message='Features are down-sampled during decoder.') - tf.assert_less( - source_w - 1, - target_w, - message='Features are down-sampled during decoder.') - - combined_features = utils.resize_align_corners(combined_features, - [target_h, target_w]) - - combined_features = tf.concat([combined_features, low_level_features], - self._channel_axis) - combined_features = getattr(self, current_fusion_conv_name)( - combined_features, training=training) - - return combined_features - - def reset_pooling_layer(self): - """Resets the ASPP pooling layer to global average pooling.""" - self._aspp.reset_pooling_layer() - - def set_pool_size(self, pool_size): - """Sets the pooling size of the ASPP pooling layer. - - Args: - pool_size: A tuple specifying the pooling size of the ASPP pooling layer. - """ - self._aspp.set_pool_size(pool_size) - - def get_pool_size(self): - return self._aspp.get_pool_size() - - -class PanopticDeepLabSingleHead(layers.Layer): - """A single PanopticDeepLab head layer. - - This layer takes in the enriched features from a decoder and adds two - convolutions on top. - """ - - def __init__(self, - intermediate_channels, - output_channels, - pred_key, - name, - conv_type='depthwise_separable_conv', - bn_layer=tf.keras.layers.BatchNormalization): - """Initializes a single PanopticDeepLab head. - - Args: - intermediate_channels: An integer specifying the number of filters of the - first 5x5 convolution. - output_channels: An integer specifying the number of filters of the second - 1x1 convolution. - pred_key: A string specifying the key of the output dictionary. - name: A string specifying the name of this head. - conv_type: String, specifying head convolution type. Support - 'depthwise_separable_conv' and 'standard_conv'. - bn_layer: An optional tf.keras.layers.Layer that computes the - normalization (default: tf.keras.layers.BatchNormalization). - """ - super(PanopticDeepLabSingleHead, self).__init__(name=name) - self._pred_key = pred_key - - self.conv_block = convolutions.StackedConv2DSame( - conv_type=conv_type, - num_layers=1, - output_channels=intermediate_channels, - kernel_size=5, - name='conv_block', - use_bias=False, - use_bn=True, - bn_layer=bn_layer, - activation='relu') - self.final_conv = layers.Conv2D( - output_channels, - kernel_size=1, - name='final_conv', - kernel_initializer=tf.keras.initializers.TruncatedNormal(stddev=0.01)) - - def call(self, features, training=False): - """Performs a forward pass. - - Args: - features: A tf.Tensor with shape [batch, height, width, channels]. - training: A boolean flag indicating whether training behavior should be - used (default: False). - - Returns: - The dictionary containing the predictions under the specified key. - """ - x = self.conv_block(features, training=training) - return {self._pred_key: self.final_conv(x)} - - -class PanopticDeepLab(layers.Layer): - """A Panoptic-DeepLab decoder layer. - - This layer takes low- and high-level features as input and uses a dual-ASPP - and dual-decoder structure to aggregate features for semantic and instance - segmentation. On top of the decoders, three heads are used to predict semantic - segmentation, instance center probabilities, and instance center regression - per pixel. - """ - - def __init__(self, - decoder_options, - panoptic_deeplab_options, - bn_layer=tf.keras.layers.BatchNormalization): - """Initializes a Panoptic-DeepLab decoder. - - Args: - decoder_options: Decoder options as defined in config_pb2.DecoderOptions. - panoptic_deeplab_options: Model options as defined in - config_pb2.ModelOptions.PanopticDeeplabOptions. - bn_layer: An optional tf.keras.layers.Layer that computes the - normalization (default: tf.keras.layers.BatchNormalization). - """ - super(PanopticDeepLab, self).__init__(name='PanopticDeepLab') - - low_level_feature_keys = [ - item.feature_key for item in panoptic_deeplab_options.low_level - ] - low_level_channels_project = [ - item.channels_project for item in panoptic_deeplab_options.low_level - ] - - self._semantic_decoder = PanopticDeepLabSingleDecoder( - high_level_feature_name=decoder_options.feature_key, - low_level_feature_names=low_level_feature_keys, - low_level_channels_project=low_level_channels_project, - aspp_output_channels=decoder_options.aspp_channels, - decoder_output_channels=decoder_options.decoder_channels, - atrous_rates=decoder_options.atrous_rates, - name='semantic_decoder', - aspp_use_only_1x1_proj_conv=decoder_options.aspp_use_only_1x1_proj_conv, - decoder_conv_type=decoder_options.decoder_conv_type, - bn_layer=bn_layer) - self._semantic_head = PanopticDeepLabSingleHead( - panoptic_deeplab_options.semantic_head.head_channels, - panoptic_deeplab_options.semantic_head.output_channels, - common.PRED_SEMANTIC_LOGITS_KEY, - name='semantic_head', - conv_type=panoptic_deeplab_options.semantic_head.head_conv_type, - bn_layer=bn_layer) - - self._instance_decoder = None - self._instance_center_head = None - self._instance_regression_head = None - - if panoptic_deeplab_options.instance.enable: - if panoptic_deeplab_options.instance.low_level_override: - low_level_options = panoptic_deeplab_options.instance.low_level_override - else: - low_level_options = panoptic_deeplab_options.low_level - - # If instance_decoder is set, use those options; otherwise reuse the - # architecture as defined for the semantic decoder. - if panoptic_deeplab_options.instance.HasField( - 'instance_decoder_override'): - decoder_options = (panoptic_deeplab_options.instance - .instance_decoder_override) - - low_level_feature_keys = [item.feature_key for item in low_level_options] - low_level_channels_project = [ - item.channels_project for item in low_level_options - ] - - self._instance_decoder = PanopticDeepLabSingleDecoder( - high_level_feature_name=decoder_options.feature_key, - low_level_feature_names=low_level_feature_keys, - low_level_channels_project=low_level_channels_project, - aspp_output_channels=decoder_options.aspp_channels, - decoder_output_channels=decoder_options.decoder_channels, - atrous_rates=decoder_options.atrous_rates, - name='instance_decoder', - aspp_use_only_1x1_proj_conv=( - decoder_options.aspp_use_only_1x1_proj_conv), - decoder_conv_type=decoder_options.decoder_conv_type, - bn_layer=bn_layer) - self._instance_center_head = PanopticDeepLabSingleHead( - panoptic_deeplab_options.instance.center_head.head_channels, - panoptic_deeplab_options.instance.center_head.output_channels, - common.PRED_CENTER_HEATMAP_KEY, - name='instance_center_head', - conv_type=( - panoptic_deeplab_options.instance.center_head.head_conv_type), - bn_layer=bn_layer) - self._instance_regression_head = PanopticDeepLabSingleHead( - panoptic_deeplab_options.instance.regression_head.head_channels, - panoptic_deeplab_options.instance.regression_head.output_channels, - common.PRED_OFFSET_MAP_KEY, - name='instance_regression_head', - conv_type=( - panoptic_deeplab_options.instance.regression_head.head_conv_type), - bn_layer=bn_layer) - - def reset_pooling_layer(self): - """Resets the ASPP pooling layers to global average pooling.""" - self._semantic_decoder.reset_pooling_layer() - if self._instance_decoder is not None: - self._instance_decoder.reset_pooling_layer() - - def set_pool_size(self, pool_size): - """Sets the pooling size of the ASPP pooling layers. - - Args: - pool_size: A tuple specifying the pooling size of the ASPP pooling layers. - """ - self._semantic_decoder.set_pool_size(pool_size) - if self._instance_decoder is not None: - self._instance_decoder.set_pool_size(pool_size) - - def get_pool_size(self): - return self._semantic_decoder.get_pool_size() - - @property - def checkpoint_items(self): - items = { - common.CKPT_SEMANTIC_DECODER: - self._semantic_decoder, - common.CKPT_SEMANTIC_HEAD_WITHOUT_LAST_LAYER: - self._semantic_head.conv_block, - common.CKPT_SEMANTIC_LAST_LAYER: - self._semantic_head.final_conv - } - if self._instance_decoder is not None: - instance_items = { - common.CKPT_INSTANCE_DECODER: - self._instance_decoder, - common.CKPT_INSTANCE_CENTER_HEAD_WITHOUT_LAST_LAYER: - self._instance_center_head.conv_block, - common.CKPT_INSTANCE_CENTER_HEAD_LAST_LAYER: - self._instance_center_head.final_conv, - common.CKPT_INSTANCE_REGRESSION_HEAD_WITHOUT_LAST_LAYER: - self._instance_regression_head.conv_block, - common.CKPT_INSTANCE_REGRESSION_HEAD_LAST_LAYER: - self._instance_regression_head.final_conv, - } - items.update(instance_items) - return items - - def call(self, features, training=False): - """Performs a forward pass. - - Args: - features: An input dict of tf.Tensor with shape [batch, height, width, - channels]. Different keys should point to different features extracted - by the encoder, e.g. low-level or high-level features. - training: A boolean flag indicating whether training behavior should be - used (default: False). - - Returns: - A dictionary containing the results of the semantic segmentation head and - depending on the configuration also of the instance segmentation head. - """ - - semantic_features = self._semantic_decoder(features, training=training) - results = self._semantic_head(semantic_features, training=training) - - if self._instance_decoder is not None: - instance_features = self._instance_decoder(features, training=training) - instance_center_predictions = self._instance_center_head( - instance_features, training=training) - instance_regression_predictions = self._instance_regression_head( - instance_features, training=training) - - if results.keys() & instance_center_predictions.keys(): - raise ValueError('The keys of the semantic branch and the instance ' - 'center branch overlap. Please use unique keys.') - results.update(instance_center_predictions) - - if results.keys() & instance_regression_predictions.keys(): - raise ValueError('The keys of the semantic branch and the instance ' - 'regression branch overlap. Please use unique keys.') - results.update(instance_regression_predictions) - - return results diff --git a/spaces/akhaliq/deeplab2/trainer/runner_utils_test.py b/spaces/akhaliq/deeplab2/trainer/runner_utils_test.py deleted file mode 100644 index 7e94551e584890ad86e5788a98293113547093ba..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/deeplab2/trainer/runner_utils_test.py +++ /dev/null @@ -1,78 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The Deeplab2 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. - -"""Tests for runner_utils.py.""" - -import os - -import numpy as np -import tensorflow as tf - -from google.protobuf import text_format -from deeplab2 import config_pb2 -from deeplab2.data import dataset -from deeplab2.model import deeplab -from deeplab2.trainer import runner_utils -# resources dependency - -_CONFIG_PATH = 'deeplab2/configs/example' - - -def _read_proto_file(filename, proto): - filename = filename # OSS: removed internal filename loading. - with tf.io.gfile.GFile(filename, 'r') as proto_file: - return text_format.ParseLines(proto_file, proto) - - -def _create_model_from_test_proto(file_name, - dataset_name='coco_panoptic'): - proto_filename = os.path.join(_CONFIG_PATH, file_name) - config = _read_proto_file(proto_filename, config_pb2.ExperimentOptions()) - return deeplab.DeepLab(config, - dataset.MAP_NAME_TO_DATASET_INFO[dataset_name] - ), config - - -class RunnerUtilsTest(tf.test.TestCase): - - def test_check_if_variable_in_backbone_with_max_deeplab(self): - model, experiment_options = _create_model_from_test_proto( - 'example_coco_max_deeplab.textproto', dataset_name='coco_panoptic') - train_crop_size = tuple( - experiment_options.train_dataset_options.crop_size) - input_tensor = tf.random.uniform( - shape=(2, train_crop_size[0], train_crop_size[1], 3)) - _ = model(input_tensor, training=True) - - encoder = model.checkpoint_items['encoder'] - encoder_variable_names = [x.name for x in encoder.trainable_variables] - encoder_name = experiment_options.model_options.backbone.name - - num_backbone_params = 0 - backbone_optimizer_inputs = [] - for variable in model.trainable_weights: - if runner_utils.check_if_variable_in_backbone(variable, encoder_name, - encoder_variable_names): - backbone_optimizer_inputs.append(variable) - num_backbone_params += np.prod(variable.get_shape().as_list()) - # The number of Tensors in the backbone. We use this number in addition to - # the number of parameters as a check of correctness. - self.assertLen(backbone_optimizer_inputs, 301) - # The same number of parameters as max_deeplab_s_backbone. - self.assertEqual(num_backbone_params, 41343424) - - -if __name__ == '__main__': - tf.test.main() diff --git a/spaces/akhaliq/openlm-research-open_llama_13b/app.py b/spaces/akhaliq/openlm-research-open_llama_13b/app.py deleted file mode 100644 index 47f41821a36665abc96c4e5124e6ce1c9da1b427..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/openlm-research-open_llama_13b/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/openlm-research/open_llama_13b").launch() \ No newline at end of file diff --git a/spaces/alamin655/websurfx/PULL_REQUEST_TEMPLATE.md b/spaces/alamin655/websurfx/PULL_REQUEST_TEMPLATE.md deleted file mode 100644 index 28cb6b76fbbdcf00874e6790899df2e79bace92d..0000000000000000000000000000000000000000 --- a/spaces/alamin655/websurfx/PULL_REQUEST_TEMPLATE.md +++ /dev/null @@ -1,25 +0,0 @@ -## What does this PR do? - - - - - -## Why is this change important? - - - - - -## How to test this PR locally? - - - -## Author's checklist - - - -## Related issues - - diff --git a/spaces/aliabid94/gpt_who/style.css b/spaces/aliabid94/gpt_who/style.css deleted file mode 100644 index b92ce071c1552ed3ee9e115aebe5058537e97f14..0000000000000000000000000000000000000000 --- a/spaces/aliabid94/gpt_who/style.css +++ /dev/null @@ -1,8 +0,0 @@ -.generating { - border: none !important; -} -#time_remaining { - display: flex; - justify-content: center; - align-items: center; -} \ No newline at end of file diff --git a/spaces/allandclive/Uganda_MMS/uroman/README.md b/spaces/allandclive/Uganda_MMS/uroman/README.md deleted file mode 100644 index 6a0a40f6d4ebda9041d23efe0345340b7da9d4b8..0000000000000000000000000000000000000000 --- a/spaces/allandclive/Uganda_MMS/uroman/README.md +++ /dev/null @@ -1,165 +0,0 @@ -# uroman - -*uroman* is a *universal romanizer*. It converts text in any script to the Latin alphabet. - -Version: 1.2.8 -Release date: April 23, 2021 -Author: Ulf Hermjakob, USC Information Sciences Institute - - -### Usage -```bash -$ uroman.pl [-l
-{subdirs_html}
-
-
-{items_html}
-
-"""
-
- return res
-
- def list_items(self):
- raise NotImplementedError()
-
- def allowed_directories_for_previews(self):
- return []
-
- def create_html_for_item(self, item, tabname):
- preview = item.get("preview", None)
-
- onclick = item.get("onclick", None)
- if onclick is None:
- onclick = '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
-
- args = {
- "preview_html": "style='background-image: url(\"" + html.escape(preview) + "\")'" if preview else '',
- "prompt": item.get("prompt", None),
- "tabname": json.dumps(tabname),
- "local_preview": json.dumps(item["local_preview"]),
- "name": item["name"],
- "card_clicked": onclick,
- "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {json.dumps(tabname)}, {json.dumps(item["local_preview"])})""") + '"',
- "search_term": item.get("search_term", ""),
- }
-
- return self.card_page.format(**args)
-
-
-def intialize():
- extra_pages.clear()
-
-
-class ExtraNetworksUi:
- def __init__(self):
- self.pages = None
- self.stored_extra_pages = None
-
- self.button_save_preview = None
- self.preview_target_filename = None
-
- self.tabname = None
-
-
-def pages_in_preferred_order(pages):
- tab_order = [x.lower().strip() for x in shared.opts.ui_extra_networks_tab_reorder.split(",")]
-
- def tab_name_score(name):
- name = name.lower()
- for i, possible_match in enumerate(tab_order):
- if possible_match in name:
- return i
-
- return len(pages)
-
- tab_scores = {page.name: (tab_name_score(page.name), original_index) for original_index, page in enumerate(pages)}
-
- return sorted(pages, key=lambda x: tab_scores[x.name])
-
-
-def create_ui(container, button, tabname):
- ui = ExtraNetworksUi()
- ui.pages = []
- ui.stored_extra_pages = pages_in_preferred_order(extra_pages.copy())
- ui.tabname = tabname
-
- with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
- for page in ui.stored_extra_pages:
- with gr.Tab(page.title):
- page_elem = gr.HTML(page.create_html(ui.tabname))
- ui.pages.append(page_elem)
-
- filter = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", placeholder="Search...", visible=False)
- button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh")
- button_close = gr.Button('Close', elem_id=tabname+"_extra_close")
-
- ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
- ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=tabname+"_preview_filename", visible=False)
-
- def toggle_visibility(is_visible):
- is_visible = not is_visible
- return is_visible, gr.update(visible=is_visible)
-
- state_visible = gr.State(value=False)
- button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container])
- button_close.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container])
-
- def refresh():
- res = []
-
- for pg in ui.stored_extra_pages:
- pg.refresh()
- res.append(pg.create_html(ui.tabname))
-
- return res
-
- button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages)
-
- return ui
-
-
-def path_is_parent(parent_path, child_path):
- parent_path = os.path.abspath(parent_path)
- child_path = os.path.abspath(child_path)
-
- return child_path.startswith(parent_path)
-
-
-def setup_ui(ui, gallery):
- def save_preview(index, images, filename):
- if len(images) == 0:
- print("There is no image in gallery to save as a preview.")
- return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]
-
- index = int(index)
- index = 0 if index < 0 else index
- index = len(images) - 1 if index >= len(images) else index
-
- img_info = images[index if index >= 0 else 0]
- image = image_from_url_text(img_info)
-
- is_allowed = False
- for extra_page in ui.stored_extra_pages:
- if any([path_is_parent(x, filename) for x in extra_page.allowed_directories_for_previews()]):
- is_allowed = True
- break
-
- assert is_allowed, f'writing to {filename} is not allowed'
-
- image.save(filename)
-
- return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]
-
- ui.button_save_preview.click(
- fn=save_preview,
- _js="function(x, y, z){return [selected_gallery_index(), y, z]}",
- inputs=[ui.preview_target_filename, gallery, ui.preview_target_filename],
- outputs=[*ui.pages]
- )
-
diff --git a/spaces/aodianyun/stable-diffusion-webui/test/basic_features/__init__.py b/spaces/aodianyun/stable-diffusion-webui/test/basic_features/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/arch-123/bingo/src/lib/bots/bing/index.ts b/spaces/arch-123/bingo/src/lib/bots/bing/index.ts
deleted file mode 100644
index c75c69f94af8c3db92d4c90d465c219a2af72a4d..0000000000000000000000000000000000000000
--- a/spaces/arch-123/bingo/src/lib/bots/bing/index.ts
+++ /dev/null
@@ -1,432 +0,0 @@
-import { fetch, WebSocket, debug } from '@/lib/isomorphic'
-import WebSocketAsPromised from 'websocket-as-promised'
-import {
- SendMessageParams,
- BingConversationStyle,
- ConversationResponse,
- ChatResponseMessage,
- ConversationInfo,
- InvocationEventType,
- ChatError,
- ErrorCode,
- ChatUpdateCompleteResponse,
- ImageInfo,
- KBlobResponse
-} from './types'
-
-import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils'
-import { WatchDog, createChunkDecoder } from '@/lib/utils'
-
-type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }>
-
-const OPTIONS_SETS = [
- 'nlu_direct_response_filter',
- 'deepleo',
- 'disable_emoji_spoken_text',
- 'responsible_ai_policy_235',
- 'enablemm',
- 'iycapbing',
- 'iyxapbing',
- 'objopinion',
- 'rweasgv2',
- 'dagslnv1',
- 'dv3sugg',
- 'autosave',
- 'iyoloxap',
- 'iyoloneutral',
- 'clgalileo',
- 'gencontentv3',
-]
-
-export class BingWebBot {
- protected conversationContext?: ConversationInfo
- protected cookie: string
- protected ua: string
- protected endpoint = ''
- private lastText = ''
- private asyncTasks: Arrayvideo will be played here after you press the button at step 3') - -demo = gr.Blocks(css=''' -#cut_btn, #reset_btn { align-self:stretch; } -#\\31 3 { max-width: 540px; } -.output-markdown {max-width: 65ch !important;} -''') -demo.encrypt = False -with demo: - transcription_var = gr.Variable() - - with gr.Row(): - with gr.Column(): - gr.Markdown(''' - ### This space allows you to: - ##### 1. Download youtube video with a given URL - ##### 2. Watch it in the first video component - ##### 3. Run automatic Thai speech recognition on the video using Whisper - ##### 4. Burn the translations to the original video and watch the video in the 2nd video component - ''') - - with gr.Column(): - gr.Markdown(''' - ### 1. Insert Youtube URL below. Some test videos below: - ##### 1. https://www.youtube.com/watch?v=UIHPIESyIXM - ##### 2. https://www.youtube.com/watch?v=YlfaFK7OFUo - ''') - - with gr.Row(): - with gr.Column(): - youtube_url_in.render() - download_youtube_btn = gr.Button("Step 1. Download Youtube video") - download_youtube_btn.click(get_youtube, [youtube_url_in], [ - video_in]) - print(video_in) - - with gr.Row(): - with gr.Column(): - video_in.render() - with gr.Column(): - gr.Markdown(''' - ##### Here you can start the transcription process. - ##### Be aware that processing will take some time. - ''') - transcribe_btn = gr.Button("Step 2. Transcribe audio") - transcribe_btn.click(speech_to_text, [ - video_in], [transcription_df, subtitle_files]) - - with gr.Row(): - gr.Markdown(''' - ##### Here you will get transcription output - ##### ''') - - with gr.Row(): - with gr.Column(): - transcription_df.render() - - with gr.Row(): - with gr.Column(): - gr.Markdown( - '''##### From here, you can download the transcription output in different formats. ''') - subtitle_files.render() - - with gr.Row(): - with gr.Column(): - gr.Markdown(''' - ##### Now press the Step 3. Button to create output video with translated transcriptions - ##### ''') - create_video_button = gr.Button( - "Step 3. Create and add subtitles to video") - print(video_in) - create_video_button.click(create_video_player, [subtitle_files, video_in], [ - video_player]) - video_player.render() - -demo.launch() diff --git a/spaces/bioriAsaeru/text-to-voice/Bios Dll 3ds !NEW!.md b/spaces/bioriAsaeru/text-to-voice/Bios Dll 3ds !NEW!.md deleted file mode 100644 index 68282b92f00b67055a9f896b0795f6b814575085..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Bios Dll 3ds !NEW!.md +++ /dev/null @@ -1,75 +0,0 @@ - -
BIOS DLL 3DS: What Is It and How to Use It for Emulation?
-If you are interested in playing Nintendo 3DS games on your PC, you might have heard of the term BIOS DLL 3DS. But what does it mean and how can you get it? In this article, we will explain everything you need to know about BIOS DLL 3DS and how to use it for emulation.
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What is BIOS DLL 3DS?
-BIOS DLL 3DS is not an official name, but rather a way of describing the bootroms of the Nintendo 3DS console. These are the files that contain the code and data that are executed by the CPU when the system is powered on. The bootroms are responsible for decrypting and loading the system software (Horizon OS) from the NAND memory.
-The bootroms are also known as BIOS (Basic Input/Output System) or firmware, which are common terms for the low-level software that controls the hardware of a device. However, these terms are not technically accurate for the Nintendo 3DS, as it has a more complex and sophisticated system architecture than most devices.
-The Nintendo 3DS has two CPU cores: ARM9 and ARM11. Each core has its own bootrom, which is stored in a read-only memory (ROM) chip inside the console. The bootroms are encrypted and signed by Nintendo, and cannot be modified or accessed by normal means.
-Why do you need BIOS DLL 3DS?
-BIOS DLL 3DS is required for some Nintendo 3DS emulators, such as Citra, to run encrypted games. These are games that have been dumped from a real console using special tools and methods. Encrypted games have a layer of protection that prevents them from being played on unauthorized devices or emulators.
-To play encrypted games, you need to have the same keys that the console uses to decrypt them. These keys are derived from the bootroms, which is why you need to dump them from your own console and use them with your emulator.
- -Without BIOS DLL 3DS, you can only play decrypted games on your emulator. These are games that have been stripped of their encryption layer by hackers or homebrew developers. Decrypted games are easier to find and download online, but they may not be legal or safe to use.
-How to get BIOS DLL 3DS?
-The only legal and ethical way to get BIOS DLL 3DS is to dump them from your own console using homebrew software and hardware tools. You will need a hacked Nintendo 3DS with custom firmware (CFW) installed. You can follow this guide to learn how to hack your console: https://3ds.hacks.guide/
-Once you have CFW, you can use GodMode9, a powerful file manager and explorer for the Nintendo 3DS. GodMode9 can access the bootroms and dump them to a file on the SD card. The file name will be either boot9.bin or boot11.bin, depending on which CPU core it belongs to.
-To use GodMode9, you will need to download it from here: https://github.com/d0k3/GodMode9/releases
-Copy the GodMode9.firm file to the /luma/payloads/ folder on your SD card. Then, hold (Start) while booting your console to launch GodMode9.
-In GodMode9, navigate to [0:] SDCARD and press (A) on aes_keys.txt. If you don't have this file, create it with any text editor and save it as aes_keys.txt on your SD card root.
-Select \"Calculate CMACs\" and press (A). Then select \"Fix CMACs\" and press (A). This will generate the AES keys that are needed to decrypt the bootroms.
-Next, navigate to [1:] SYSNAND CTRNAND and press (A) on boot.firm. Select \"NCCH image options\" and press (A). Then select \"Mount image to drive\" and press (A).
-You will see a new drive called [7:] BOOT9-11 FIRM. Navigate to it and press (A) on .firm0.bin. Select \"FIRM image options\" and press (A). Then select \"Bootrom options\" and press (A).
-You will see two options: \"Dump ARM9 bootrom\" and \"Dump ARM11 bootrom\". Choose one of them and press (A). You will be asked where to save the file. Choose [0:] SDCARD and press (A).
-The file will be saved as either boot9.bin or boot11.bin on your SD card root. Repeat the process for the other option to get both files.
-How to use BIOS DLL 3DS?
-Once you have dumped the bootroms from your console, you can use them with Citra emulator to play encrypted games. Citra is an experimental open-source Nintendo 3DS emulator/debugger written in C++. It can run most commercial games at high speed and with enhanced graphics.
-You can download Citra from here: https://citra-emu.org/download/
-To use Citra, you will also need to dump your games from your console using GodMode9 or other tools. You can find more information on how to do that here: https://citra-emu.org/wiki/dumping-game-cartridges/
-After you have dumped your games, you will need to place them in a folder on your computer. The games should have one of these file extensions: .3ds, .3dsx, .elf, .axf, .cci, .cxi, or .app.
-You will also need to place the bootroms in a specific folder on your computer. The folder name should be user/nand/sysdata/. You can find this folder inside the Citra installation directory or in your AppData/Roaming/Citra/ folder.
-Copy the boot9.bin and boot11.bin files to this folder. Make sure they are named exactly like that, with lowercase letters.
-Now you are ready to launch Citra and load your games. You can do that by clicking on File > Load File... and selecting your game file. Alternatively, you can drag and drop your game file onto the Citra window.
-Citra will automatically detect the bootroms and use them to decrypt and run your games. You can enjoy playing your favorite Nintendo 3DS games on your PC with Citra emulator!
-What are the benefits of BIOS DLL 3DS?
-Using BIOS DLL 3DS with Citra emulator has several benefits for Nintendo 3DS fans. Here are some of them:
--
-
- You can play your legally owned games on your PC, without having to carry your console around. -
- You can enjoy better graphics and performance than the original hardware, thanks to Citra's enhancements and optimizations. -
- You can use various features that are not available on the console, such as save states, cheats, screenshots, and more. -
- You can play online with other Citra users or with real console users, thanks to Citra's network compatibility. -
- You can contribute to the development and improvement of Citra and the Nintendo 3DS emulation scene. -
What are the risks of BIOS DLL 3DS?
-Using BIOS DLL 3DS with Citra emulator also has some risks and drawbacks that you should be aware of. Here are some of them:
--
-
- You may encounter bugs, glitches, crashes, or compatibility issues with some games, as Citra is still in development and not perfect. -
- You may violate Nintendo's terms of service or intellectual property rights by using BIOS DLL 3DS or downloading games online. -
- You may expose your computer to malware or viruses by downloading BIOS DLL 3DS or games from untrusted sources. -
- You may lose your save data or damage your console by dumping BIOS DLL 3DS or games incorrectly. -
Conclusion
-BIOS DLL 3DS is a term that refers to the bootroms of the Nintendo 3DS console, which are needed for some emulators to run encrypted games. You can dump them from your own console using GodMode9 and use them with Citra emulator to play your favorite Nintendo 3DS games on your PC. However, you should also be aware of the benefits and risks of using BIOS DLL 3DS and respect Nintendo's rights and policies.
-What are the alternatives to BIOS DLL 3DS?
-If you don't want to use BIOS DLL 3DS with Citra emulator, you have some other options to play Nintendo 3DS games on your PC. Here are some of them:
--
-
- You can use decrypted games instead of encrypted games. These are games that have been hacked or modified to remove the encryption layer. You can find them online, but be careful of the legal and ethical implications of downloading them. -
- You can use other Nintendo 3DS emulators instead of Citra. There are some other emulators that can run Nintendo 3DS games without BIOS DLL 3DS, such as Ryujinx or Yuzu. However, they may not be as compatible or stable as Citra. -
- You can use a Nintendo 3DS flashcart instead of an emulator. A flashcart is a device that allows you to play ROMs or backups of games on your console. You can use a flashcart such as Sky3DS or Gateway to play your games on your console without dumping them. -
FAQs
-Here are some frequently asked questions about BIOS DLL 3DS and Citra emulator:
--
-
- Is BIOS DLL 3DS legal? -
- Is Citra emulator legal? -
- Is Citra emulator safe? -
- How do I update Citra emulator? -
- How do I improve Citra emulator performance? -
BIOS DLL 3DS is legal if you dump it from your own console and use it for personal and non-commercial purposes. However, it is illegal if you download it from the internet or share it with others.
-Citra emulator is legal if you use it to play games that you legally own and do not distribute or sell them. However, it is illegal if you use it to play pirated or unauthorized games.
-Citra emulator is safe if you download it from the official website or GitHub repository and scan it for viruses or malware. However, it is unsafe if you download it from untrusted sources or modify it with malicious code.
-You can update Citra emulator by downloading the latest version from the official website or GitHub repository and replacing the old files with the new ones. Alternatively, you can use the built-in updater feature in Citra if you have an internet connection.
-You can improve Citra emulator performance by adjusting some settings in the configuration menu, such as enabling CPU JIT, disabling VSync, lowering resolution, etc. You can also upgrade your PC hardware, such as CPU, GPU, RAM, etc.
--
-
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- diff --git a/spaces/bobsingh149/chestxray-classification-streamlit-demo/README.md b/spaces/bobsingh149/chestxray-classification-streamlit-demo/README.md deleted file mode 100644 index f93956e48676bbedaa5661cb8745c57d4549b611..0000000000000000000000000000000000000000 --- a/spaces/bobsingh149/chestxray-classification-streamlit-demo/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Chestxray Classification Streamlit Demo -emoji: 😻 -colorFrom: blue -colorTo: blue -sdk: streamlit -sdk_version: 1.2.0 -app_file: app.py -pinned: false -license: afl-3.0 -duplicated_from: hasibzunair/chestxray-classification-streamlit-demo ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/backbone/fpn.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/backbone/fpn.py deleted file mode 100644 index 19d24e13f069ecb389edcdb4d9859506fe9e6f76..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/backbone/fpn.py +++ /dev/null @@ -1,268 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import math -import fvcore.nn.weight_init as weight_init -import torch -import torch.nn.functional as F -from torch import nn - -from detectron2.layers import Conv2d, ShapeSpec, get_norm - -from .backbone import Backbone -from .build import BACKBONE_REGISTRY -from .resnet import build_resnet_backbone - -__all__ = ["build_resnet_fpn_backbone", "build_retinanet_resnet_fpn_backbone", "FPN"] - - -class FPN(Backbone): - """ - This module implements :paper:`FPN`. - It creates pyramid features built on top of some input feature maps. - """ - - _fuse_type: torch.jit.Final[str] - - def __init__( - self, - bottom_up, - in_features, - out_channels, - norm="", - top_block=None, - fuse_type="sum", - square_pad=0, - ): - """ - Args: - bottom_up (Backbone): module representing the bottom up subnetwork. - Must be a subclass of :class:`Backbone`. The multi-scale feature - maps generated by the bottom up network, and listed in `in_features`, - are used to generate FPN levels. - in_features (list[str]): names of the input feature maps coming - from the backbone to which FPN is attached. For example, if the - backbone produces ["res2", "res3", "res4"], any *contiguous* sublist - of these may be used; order must be from high to low resolution. - out_channels (int): number of channels in the output feature maps. - norm (str): the normalization to use. - top_block (nn.Module or None): if provided, an extra operation will - be performed on the output of the last (smallest resolution) - FPN output, and the result will extend the result list. The top_block - further downsamples the feature map. It must have an attribute - "num_levels", meaning the number of extra FPN levels added by - this block, and "in_feature", which is a string representing - its input feature (e.g., p5). - fuse_type (str): types for fusing the top down features and the lateral - ones. It can be "sum" (default), which sums up element-wise; or "avg", - which takes the element-wise mean of the two. - square_pad (int): If > 0, require input images to be padded to specific square size. - """ - super(FPN, self).__init__() - assert isinstance(bottom_up, Backbone) - assert in_features, in_features - - # Feature map strides and channels from the bottom up network (e.g. ResNet) - input_shapes = bottom_up.output_shape() - strides = [input_shapes[f].stride for f in in_features] - in_channels_per_feature = [input_shapes[f].channels for f in in_features] - - _assert_strides_are_log2_contiguous(strides) - lateral_convs = [] - output_convs = [] - - use_bias = norm == "" - for idx, in_channels in enumerate(in_channels_per_feature): - lateral_norm = get_norm(norm, out_channels) - output_norm = get_norm(norm, out_channels) - - lateral_conv = Conv2d( - in_channels, out_channels, kernel_size=1, bias=use_bias, norm=lateral_norm - ) - output_conv = Conv2d( - out_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1, - bias=use_bias, - norm=output_norm, - ) - weight_init.c2_xavier_fill(lateral_conv) - weight_init.c2_xavier_fill(output_conv) - stage = int(math.log2(strides[idx])) - self.add_module("fpn_lateral{}".format(stage), lateral_conv) - self.add_module("fpn_output{}".format(stage), 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] - self.top_block = top_block - self.in_features = tuple(in_features) - self.bottom_up = bottom_up - # Return feature names are "p
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ARK Survival Evolved APK 2.0 12: Everything You Need to Know
-Are you looking for a thrilling and immersive adventure game that lets you explore a prehistoric world full of dinosaurs and other creatures? If yes, then you should check out ARK Survival Evolved, one of the most popular and acclaimed survival games of all time.
-In this article, we will tell you everything you need to know about ARK Survival Evolved APK 2.0 12, the latest version of the game for Android devices. We will also show you how to download and install it from Aptoide, an alternative app store that offers many benefits for users. We will also give you some tips and tricks on how to play the game and what are the features and updates of this version.
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What is ARK Survival Evolved?
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-The game offers a rich and diverse gameplay experience that includes both single-player and multiplayer modes. You can play solo or join a tribe with other players online. You can also customize your character, your base, your pets, and your weapons with various skins and accessories.
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-The latest version of ARK Survival Evolved for Android devices is APK 2.0 12. It was released on September 7 , 2023 and it has a size of 2.4 GB. It requires Android 7.0 or higher to run smoothly. It also requires an additional 2 GB of free space on your device for the game data.
-This version of the game is compatible with most Android devices, including smartphones and tablets. However, some devices may not support the game or may experience performance issues due to the high graphics and processing demands of the game. You can check the compatibility of your device on the Aptoide app store before downloading the game.
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-The steps to download and install the game from Aptoide
-If you want to download and install ARK Survival Evolved APK 2.0 12 on your Android device, you can follow these simple steps:
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- Open Aptoide app store and search for ARK Survival Evolved in the search bar. You will see the game icon with the latest version number and rating. -
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The benefits of using Aptoide as an alternative app store
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How to Play ARK Survival Evolved APK 2.0 12?
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The game has four main objectives: survive, tame, breed, and ride. You have to maintain your health, hunger, thirst, stamina, oxygen, and temperature levels by eating, drinking, resting, and wearing appropriate clothing. You also have to avoid or fight predators, diseases, and natural disasters that may threaten your life.
-You can tame and breed over 80 different species of dinosaurs and other creatures that roam the island. You can use them as pets, mounts, companions, or defenders. You can also ride them and use their abilities to explore, fight, or gather resources.
-You can also evolve your character by leveling up and unlocking new skills and engrams. Engrams are blueprints that allow you to craft more advanced items and structures. You can also customize your appearance and equipment with various skins and accessories.
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-ARK Survival Evolved APK 2.0 12 is a game that requires strategy, creativity, and patience. Here are some tips and tricks that can help you survive and thrive in the game:
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- Choose a good location for your base. You want a place that is close to water, resources, and safe from predators. You also want a place that has enough space for your structures and pets. -
- Gather as much resources as you can. You will need wood, stone, fiber, flint, metal, hide, and other materials to craft items and structures. You can use tools such as axes, picks, spears, bows, guns, etc. to gather resources faster. -
- Craft essential items as soon as possible. You will need items such as fire, torches, clothes, beds, storage boxes, etc. to survive the night and store your belongings. You will also need items such as saddles, weapons, armor, etc. to tame and ride dinosaurs. -
- Tame dinosaurs that suit your needs. You can tame dinosaurs by knocking them out with tranquilizers or traps and feeding them their preferred food. Different dinosaurs have different uses and abilities. For example, raptors are fast and agile; triceratops are strong and sturdy; pteranodons are flying and scouting; etc. -
- Breed dinosaurs that have good stats and traits. You can breed dinosaurs by putting a male and a female of the same species in a mating pen and waiting for them to produce an egg or a baby. You can hatch or raise the offspring by providing them with food and care. You can also inherit or mutate their stats and traits to create better dinosaurs. -
- Join a tribe or create your own. You can join a tribe with other players online or create your own tribe with your friends. You can share resources, items, structures, pets, etc. with your tribe members. You can also cooperate with them to complete quests, raid other tribes, or defend your base. -
What are the Features and Updates of ARK Survival Evolved APK 2.0 12?
-The new content and improvements added in the latest version
-ARK Survival Evolved APK 2.0 12 is the latest version of the game that brings new content and improvements to the game. Some of the features and updates of this version are:
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-
- A new map called Lost Island that offers a new environment, biomes, creatures, and secrets to discover. -
- A new game mode called Primal Survival that allows you to play as a dinosaur or other creature and experience the game from their perspective. -
- A new feature called Tekgrams that allows you to unlock and craft futuristic items and structures using element shards. -
- A new feature called Ascension that allows you to complete challenges and ascend to a higher level of existence. -
- A new feature called Genesis Chronicles that allows you to collect hidden notes and clues that reveal the backstory of the game. -
- A new feature called Modding Support that allows you to create and share your own custom content using the Unreal Engine 4 Editor. -
- Various bug fixes, performance improvements, balance changes, and quality of life enhancements. -
The comparison of the game with other versions and platforms
-ARK Survival Evolved APK 2.0 12 is the Android version of the game that is compatible with most Android devices. However, it is not the only version or platform of the game available. Here is a comparison of the game with other versions and platforms:
-| Version/Platform | -Features | -Pros | -Cons | -
|---|---|---|---|
| APK 2.0 12 (Android) | -- The latest version of the game for Android devices - Includes all the features and updates mentioned above - Supports online multiplayer and cross-play with other platforms |
-- Free to download and play - Portable and convenient - Compatible with most Android devices |
-- Requires a lot of storage space and device resources - May have performance issues or crashes on some devices - May have limited graphics quality or options |
-
| iOS (iPhone/iPad) | -- The iOS version of the game for iPhone and iPad devices - Includes most of the features and updates of the Android version - Supports online multiplayer and cross-play with other platforms |
-- Free to download and play - Portable and convenient - Compatible with most iOS devices |
-- Requires a lot of storage space and device resources - May have performance issues or crashes on some devices - May have limited graphics quality or options |
-
| Steam (PC) | -- The original PC version of the game for Windows, Mac, and Linux - Includes all the features and updates of the Android version plus more - Supports online multiplayer and cross-play with other platforms - Supports modding support and community content |
-- The most complete and updated version of the game - The best graphics quality and options - The most customizable and moddable version of the game |
-- Requires a high-end PC to run smoothly - Not free to download or play - Not portable or convenient |
-
| Xbox One/Xbox Series X|S (Console) | -- The console version of the game for Xbox One and Xbox Series X|S - Includes most of the features and updates of the Android version plus more - Supports online multiplayer and cross-play with other platforms - Supports Xbox Live features such as achievements, cloud saves, etc. |
-- A good balance between graphics quality and performance - Easy to play with a controller - Compatible with Xbox Game Pass subscription service |
-- Not free to download or play - Not portable or convenient - May have limited modding support or community content |
-
| PlayStation 4/PlayStation 5 (Console) | -- - The console version of the game for PlayStation 4 and PlayStation 5 - Includes most of the features and updates of the Android version plus more - Supports online multiplayer but not cross-play with other platforms - Supports PlayStation Network features such as trophies, cloud saves, etc. | -- - A good balance between graphics quality and performance - Easy to play with a controller - Compatible with PlayStation Plus subscription service | -
- - Not free to download or play - Not portable or convenient - May have limited modding support or community content |
-
| Nintendo Switch (Console) | -- The console version of the game for Nintendo Switch - Includes some of the features and updates of the Android version but not all - Supports online multiplayer but not cross-play with other platforms - Supports Nintendo Switch features such as motion controls, touch screen, etc. |
-- Portable and convenient - Fun to play with a controller or handheld mode - Compatible with Nintendo Switch Online subscription service |
-- Not free to download or play - The worst graphics quality and performance - No modding support or community content |
-
Conclusion
-ARK Survival Evolved APK 2.0 12 is the latest version of the game for Android devices that offers a thrilling and immersive adventure game that lets you explore a prehistoric world full of dinosaurs and other creatures. You can download and install it from Aptoide, an alternative app store that offers many benefits for users. You can also play the game solo or with other players online, and enjoy the new content and improvements added in this version. You can also compare the game with other versions and platforms and choose the one that suits your preferences and needs.
-If you are a fan of survival games, you should definitely give ARK Survival Evolved APK 2.0 12 a try. It is one of the best games of its genre and it will keep you entertained for hours. You will not regret it!
-FAQs
-Q1: Is ARK Survival Evolved APK 2.0 12 free to play?
-A1: Yes, ARK Survival Evolved APK 2.0 12 is free to download and play on your Android device. However, you may need to pay for some optional in-game items or features, such as skins, accessories, etc.
-Q2: Is ARK Survival Evolved APK 2.0 12 safe to download and install?
-A2: Yes, ARK Survival Evolved APK 2.0 12 is safe to download and install from Aptoide app store. Aptoide is a trusted and reliable app store that scans all the apps and games for viruses and malware before uploading them. You can also check the ratings and reviews of other users before downloading the game.
-Q3: Can I play ARK Survival Evolved APK 2.0 12 offline?
-A3: Yes, you can play ARK Survival Evolved APK 2.0 12 offline in single-player mode. However, you will need an internet connection to play online multiplayer mode or access some online features, such as updates, cloud saves, etc.
-Q4: How can I update ARK Survival Evolved APK 2.0 12?
-A4: You can update ARK Survival Evolved APK 2.0 12 by opening Aptoide app store and checking for any available updates for the game. You can also enable automatic updates for the game in the settings of Aptoide app store.
-Q5: What are the minimum requirements for ARK Survival Evolved APK 2.0 12?
-A5: The minimum requirements for ARK Survival Evolved APK 2.0 12 are:
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- Android 7.0 or higher -
- 2 GB of free storage space -
- 4 GB of RAM -
- A quad-core processor -
- A GPU that supports OpenGL ES 3.1 or higher -
-
-
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How to Download Orange Vocoder
-If you are looking for a way to create robotic, synthetic, or harmonized vocals in your music production, you might want to try using a vocoder plugin. A vocoder is a device that analyzes and synthesizes the human voice, allowing you to manipulate its pitch, timbre, and tone. One of the most popular and versatile vocoder plugins available today is Orange Vocoder by Zynaptiq. In this article, we will show you what a vocoder is, what makes Orange Vocoder unique, how to download and install it, and how to use it in your digital audio workstation (DAW).
-how to download orange vocoder
Download File ::: https://urlca.com/2uO9Nc
-
What is a Vocoder and Why Use It?
-A vocoder is a type of speech coding that analyzes and synthesizes the human voice signal for audio data compression, multiplexing, voice encryption, or voice transformation. The vocoder was invented in 1938 by Homer Dudley at Bell Labs as a means of synthesizing human speech. [^1] This work was developed into the channel vocoder which was used as a voice codec for telecommunications for speech coding to conserve bandwidth in transmission. By encrypting the control signals, voice transmission can be secured against interception. Its primary use in this fashion is for secure radio communication. The advantage of this method of encryption is that none of the original signal is sent, only envelopes of the bandpass filters. The receiving unit needs to be set up in the same filter configuration to re-synthesize a version of the original signal spectrum.
-The vocoder has also been used extensively as an electronic musical instrument. The decoder portion of the vocoder, called a voder, can be used independently for speech synthesis. The vocoder works by splitting the frequency spectrum of the main 'modulator' audio signal into a number of bands, and using the levels of these bands to control the levels of frequency bands in a second, 'carrier' signal. Thus, the frequency content and dynamics of the modulator signal are imposed on the basic tonality of the carrier to provide the unique vocoder effect.
-A Brief History of Vocoders
-The vocoder has been around since 1939, when it was originally developed by Bell Labs for the encryption of speech for the military. Nowadays it's rather better known for novelty sounds like talking guitars, but it has many other musical applications, from ethereal pads to exotic drum loops. [^2] The first musical use of a vocoder was by composer Werner Meyer-Eppler in 1948, who used it to create electronic music based on speech sounds. In the 1970s, the vocoder became popular in pop music, thanks to artists like Kraftwerk, Herbie Hancock, Pink Floyd, ELO, and Giorgio Moroder. In the 1980s, the vocoder was used extensively in hip-hop, electro-funk, and synth-pop genres by artists like Afrika Bambaataa, Grandmaster Flash, New Order, Depeche Mode, and Laurie Anderson. In the 1990s and 2000s, the vocoder was revived by artists like Daft Punk, Air, Cher, Imogen Heap, Kanye West, T-Pain, and Bon Iver.
-The Benefits of Vocoders for Music Production
The vocoder is a powerful tool for music production, as it can create a variety of vocal effects that can enhance the mood, atmosphere, and expression of your songs. Some of the benefits of using a vocoder are:
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- It can create robotic, futuristic, or alien voices that can add a sci-fi or fantasy element to your music. -
- It can create harmonized, chorused, or layered vocals that can enrich the texture and depth of your melodies. -
- It can create rhythmic, percussive, or glitchy vocals that can add interest and variation to your beats. -
- It can create ambient, atmospheric, or ethereal vocals that can create a sense of space and emotion in your music. -
- It can create distorted, filtered, or modulated vocals that can add character and attitude to your vocals. -
With a vocoder, you can transform any sound source into a vocal sound, and vice versa. You can use your voice as the modulator and a synthesizer as the carrier, or you can use a drum loop as the modulator and a guitar as the carrier. The possibilities are endless and only limited by your imagination.
-What is Orange Vocoder and What Makes It Unique?
-Orange Vocoder is a software plugin that emulates the classic hardware vocoders of the past, but also adds many new features and functions that make it one of the most versatile and creative vocoders on the market. Orange Vocoder was originally developed by Prosoniq in 1998 as one of the first vocoder plugins for Mac OS. In 2014, Zynaptiq acquired Prosoniq and released Orange Vocoder 4, which is compatible with both Mac OS and Windows platforms. Orange Vocoder 4 is the latest version of the plugin, which has been updated and improved with new modes, algorithms, presets, and a built-in synthesizer and sampler.
-The Features and Functions of Orange Vocoder
-Orange Vocoder has many features and functions that make it a powerful and flexible vocoder plugin. Some of these features are:
--
-
- It has 22 different vocoder algorithms that range from vintage analog to digital spectral to phase vocoding. Each algorithm has its own character and sound quality, and you can switch between them easily with a drop-down menu. -
- It has 8 different vocoder modes that determine how the modulator and carrier signals are processed. These modes include mono, stereo, dual mono, dual stereo, sidechain mono, sidechain stereo, MIDI mono, and MIDI stereo. Each mode has its own advantages and applications depending on the type of sound source you are using. -
- It has over 500 presets that cover a wide range of vocal effects and styles. You can browse through the presets by category or by keyword, or you can create your own presets and save them for later use. -
- It has a built-in synthesizer and sampler that can act as the carrier signal for the vocoder. The synthesizer has two oscillators with multiple waveforms, a noise generator, a filter section with various types and modes, an envelope generator, an LFO section with various waveforms and destinations, and a modulation matrix. The sampler allows you to load any audio file as the carrier signal and manipulate it with various parameters such as pitch, start point, loop mode, reverse mode, filter cutoff, filter resonance, envelope attack, envelope release, LFO rate, LFO depth, LFO shape, LFO destination, modulation amount, modulation source, modulation destination, etc. -
- It has a comprehensive parameter section that allows you to adjust various aspects of the vocoder effect such as input gain, output gain, dry/wet mix, formant shift, formant freeze, analysis bands (from 4 to 256), synthesis bands (from 4 to 256), band width (from narrow to wide), band distribution (from linear to logarithmic), band smoothing (from none to maximum), band soloing (from none to all), band muting (from none to all), band panning (from left to right), band volume (from minimum to maximum), etc. -
The Different Modes and Algorithms of Orange Vocoder
-Orange Vocoder has 8 different modes and 22 different algorithms that give you a lot of options and flexibility when creating vocal effects. Here is a brief overview of each mode and algorithm:
-How to download orange vocoder IV for free
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| Mode | Description |
|---|---|
| Mono | This mode uses one modulator signal (usually your voice) and one carrier signal (either from the built-in synthesizer or sampler or from an external source) to create a mono output output. This mode is simple and easy to use, but it does not allow you to create stereo effects or to use different modulator and carrier signals for the left and right channels. |
| Stereo | This mode uses one modulator signal (usually your voice) and one carrier signal (either from the built-in synthesizer or sampler or from an external source) to create a stereo output. The modulator signal is split into two channels, and each channel is processed by a separate vocoder algorithm. You can choose different algorithms for the left and right channels, and you can adjust the balance and width of the stereo output. This mode allows you to create stereo effects and to use different vocoder algorithms for the left and right channels, but it does not allow you to use different modulator and carrier signals for the left and right channels. |
| Dual Mono | This mode uses two modulator signals (usually your voice and another sound source) and two carrier signals (either from the built-in synthesizer or sampler or from external sources) to create a mono output. Each modulator signal is processed by a separate vocoder algorithm with its own carrier signal, and the outputs are mixed together. You can choose different algorithms and carrier signals for each modulator signal, and you can adjust the balance and volume of each output. This mode allows you to use different modulator and carrier signals for each vocoder algorithm, but it does not allow you to create stereo effects or to use different vocoder algorithms for the left and right channels. |
| Dual Stereo | This mode uses two modulator signals (usually your voice and another sound source) and two carrier signals (either from the built-in synthesizer or sampler or from external sources) to create a stereo output. Each modulator signal is split into two channels, and each channel is processed by a separate vocoder algorithm with its own carrier signal. You can choose different algorithms and carrier signals for each channel, and you can adjust the balance, width, and volume of each output. This mode allows you to use different modulator and carrier signals and different vocoder algorithms for each channel, and it allows you to create stereo effects. |
| Sidechain Mono | This mode uses one modulator signal (usually your voice) and one carrier signal (either from the built-in synthesizer or sampler or from an external source) to create a mono output. The modulator signal is processed by the vocoder algorithm with the carrier signal, but the carrier signal is also fed into the sidechain input of the plugin. This means that the carrier signal can be affected by other plugins in your DAW before reaching the vocoder plugin. This mode allows you to apply effects such as compression, distortion, filtering, etc. to the carrier signal before it is vocoded, which can create interesting results. |
| Sidechain Stereo | This mode uses one modulator signal (usually your voice) and one carrier signal (either from the built-in synthesizer or sampler or from an external source) to create a stereo output. The modulator signal is split into two channels, and each channel is processed by a separate vocoder algorithm with the carrier signal. The carrier signal is also fed into the sidechain input of the plugin, which means that it can be affected by other plugins in your DAW before reaching the vocoder plugin. You can choose different algorithms for the left and right channels, and you can adjust the balance and width of the stereo output. This mode allows you to apply effects such as compression, distortion, filtering, etc. to the carrier signal before it is vocoded, which can create interesting results. |
| MIDI Mono | This mode uses one modulator signal (usually your voice) and one carrier signal (either from the built-in synthesizer or sampler or from an external source) to create a mono output. The modulator signal is processed by the vocoder algorithm with the carrier signal, but the pitch of the carrier signal is controlled by MIDI notes that you play on your keyboard or MIDI controller. This means that you can play melodies or chords with your voice as the sound source. This mode allows you to create musical vocal effects that follow your MIDI input. |
| MIDI Stereo | This mode uses one modulator signal (usually your voice) and one carrier signal (either from the built-in synthesizer or sampler or from an external source) to create a stereo output. The modulator signal is split into two channels, and each channel is processed by a separate vocoder algorithm with the carrier signal. The pitch of each channel's carrier signal is controlled by MIDI notes that you play on your keyboard or MIDI controller. You can choose different algorithms for the left and right channels, and you can adjust the balance and width of the stereo output. This mode allows you to create musical vocal effects that follow your MIDI input and create stereo effects. |
The Built-in Synthesizer and Sampler of Orange Vocoder
-Orange Vocoder has a built-in synthesizer and sampler that can act as the carrier signal for the vocoder. The synthesizer and sampler are accessible from the plugin interface, and they have their own parameters and functions that you can adjust and tweak. Here is a brief overview of each one:
-The Synthesizer
-The synthesizer is a simple but powerful subtractive synthesizer that can generate various waveforms and sounds. It has two oscillators, a noise generator, a filter section, an envelope generator, an LFO section, and a modulation matrix. Here are the main features of the synthesizer:
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- The oscillators can produce sine, triangle, sawtooth, square, pulse, or noise waveforms. You can adjust the pitch, volume, pulse width, and detune of each oscillator. You can also sync the oscillators or use ring modulation or frequency modulation to create more complex sounds. -
- The noise generator can produce white or pink noise. You can adjust the volume and filter cutoff of the noise generator. -
- The filter section can apply low-pass, high-pass, band-pass, or notch filters to the sound. You can adjust the cutoff frequency, resonance, envelope amount, and keyboard tracking of the filter. You can also use the filter as a distortion unit by increasing the resonance to the maximum. -
- The envelope generator can control the amplitude, filter cutoff, or pitch of the sound. You can adjust the attack, decay, sustain, and release of the envelope. -
- The LFO section can modulate the amplitude, filter cutoff, pitch, pulse width, or detune of the sound. You can choose from sine, triangle, sawtooth, square, random, or sample-and-hold waveforms for the LFO. You can adjust the rate, depth, shape, and destination of the LFO. -
- The modulation matrix can assign various sources and destinations for modulation. You can choose from envelope 1, envelope 2, LFO 1, LFO 2, velocity, aftertouch, mod wheel, pitch bend, or MIDI CC as sources. You can choose from amplitude 1, amplitude 2, filter 1 cutoff, filter 2 cutoff, pitch 1, pitch 2, pulse width 1, pulse width 2, detune 1, detune 2, or filter resonance as destinations. You can adjust the amount and polarity of the modulation. -
The Sampler
-The sampler is a simple but powerful sampler that can load any audio file as the carrier signal for the vocoder. It supports WAV, AIFF, MP3, OGG, and FLAC formats, and it can handle mono or stereo files. It has various parameters and functions that you can adjust and tweak. Here are the main features of the sampler:
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- You can load any audio file from your computer or drag and drop it into the plugin interface. You can also use the browse button to navigate through your folders and files. -
- You can adjust the pitch, volume, start point, loop mode, reverse mode, filter cutoff, filter resonance, envelope attack, envelope release, LFO rate, LFO depth, LFO shape, LFO destination, modulation amount, modulation source, modulation destination, etc. of the audio file. -
- You can use the waveform display to zoom in or out of the audio file and to select a specific region for playback or looping. -
- You can use the keyboard display to assign different pitches or regions of the audio file to different keys on your keyboard or MIDI controller. -
- You can use the slice mode to automatically slice the audio file into equal or transient-based segments and assign them to different keys on your keyboard or MIDI controller. You can also adjust the sensitivity and threshold of the slicing algorithm. -
How to Download and Install Orange Vocoder
-Orange Vocoder is a software plugin that you can download and install on your computer and use in your DAW. Here are the steps to download and install Orange Vocoder:
-The System Requirements and Compatibility of Orange Vocoder
-Before you download and install Orange Vocoder, you need to make sure that your computer meets the minimum system requirements and that your DAW is compatible with the plugin format. Here are the system requirements and compatibility of Orange Vocoder:
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- Orange Vocoder is available for Mac OS X 10.8 or higher and Windows 7 or higher. -
- Orange Vocoder requires a minimum of 4 GB of RAM and 100 MB of disk space. -
- Orange Vocoder supports AU, VST2, VST3, AAX Native, RTAS (Mac only), and AudioSuite formats. It is compatible with most DAWs such as Logic Pro X, Pro Tools, Ableton Live, Cubase, FL Studio, Reaper, etc. -
- Orange Vocoder requires an iLok account and an iLok USB dongle for authorization. You can create an iLok account for free at www.ilok.com and purchase an iLok USB dongle from any authorized dealer. -
The Steps to Download and Install Orange Vocoder
-Once you have verified that your computer meets the system requirements and that your DAW is compatible with the plugin format, you can follow these steps to download and install Orange Vocoder:
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- Go to www.zynaptiq.com/orangevocoder/ and click on the "Buy Now" button. You will be redirected to a secure online store where you can purchase Orange Vocoder for $199 USD. -
- After you have completed your purchase, you will receive an email confirmation with a download link and a serial number for Orange Vocoder. Click on the download link to download the installer file for your operating system. -
- Run the installer file and follow the instructions on the screen to install Orange Vocoder on your computer. You will need to enter your serial number during the installation process. -
- Launch your DAW and scan for new plugins. You should see Orange Vocoder in your plugin list. Drag and drop it onto an audio track or a MIDI track in your DAW. -
- Open Orange Vocoder's interface and click on the "Authorize" button. You will need to enter your iLok account credentials and insert your iLok USB dongle into your computer. Follow the instructions on the screen to authorize Orange Vocoder. -
The Trial Version and Upgrade Options of Orange Vocoder
-If you are not sure if you want to buy Orange Vocoder yet, you can try it for free for 14 days. Here are the steps to download and install the trial version of Orange Vocoder:
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- Go to www.zynaptiq.com/orangevocoder/try/ and fill out the form with your name and email address. You will receive an email with a download link and a trial serial number for Orange V ocoder. Click on the download link to download the installer file for your operating system. -
- Run the installer file and follow the instructions on the screen to install Orange Vocoder on your computer. You will need to enter your trial serial number during the installation process. -
- Launch your DAW and scan for new plugins. You should see Orange Vocoder in your plugin list. Drag and drop it onto an audio track or a MIDI track in your DAW. -
- Open Orange Vocoder's interface and click on the "Authorize" button. You will need to enter your iLok account credentials and insert your iLok USB dongle into your computer. Follow the instructions on the screen to authorize Orange Vocoder. -
The trial version of Orange Vocoder is fully functional and has no limitations, except that it will expire after 14 days. You can use the trial version to test and evaluate Orange Vocoder's features and functions, and to decide if you want to buy it or not.
-If you decide to buy Orange Vocoder after trying it, you can upgrade to the full version by following these steps:
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- Go to www.zynaptiq.com/orangevocoder/buy/ and click on the "Buy Now" button. You will be redirected to a secure online store where you can purchase Orange Vocoder for $199 USD. -
- After you have completed your purchase, you will receive an email confirmation with a download link and a serial number for Orange Vocoder. Click on the download link to download the installer file for your operating system. -
- Run the installer file and follow the instructions on the screen to install Orange Vocoder on your computer. You will need to enter your serial number during the installation process. -
- Launch your DAW and scan for new plugins. You should see Orange Vocoder in your plugin list. Drag and drop it onto an audio track or a MIDI track in your DAW. -
- Open Orange Vocoder's interface and click on the "Authorize" button. You will need to enter your iLok account credentials and insert your iLok USB dongle into your computer. Follow the instructions on the screen to authorize Orange Vocoder. -
You can now use Orange Vocoder without any time limit or restriction. You can also access the online user manual, video tutorials, and support forum from the plugin interface.
-How to Use Orange Vocoder in Your DAW
-Orange Vocoder is a plugin that you can use in your DAW to create vocal effects with any sound source. Here are some tips and tricks on how to use Orange Vocoder in your DAW:
-How to Route the Modulator and Carrier Signals to Orange Vocoder
-The modulator signal is the sound source that you want to vocode, such as your voice, a drum loop, a guitar riff, etc. The carrier signal is the sound source that provides the tonality and pitch of the vocoder effect, such as a synthesizer, a sampler, a piano, etc. To route the modulator and carrier signals to Orange Vocoder, you need to follow these steps:
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- Create an audio track or a MIDI track in your DAW and insert Orange Vocoder as an effect plugin. -
- Select the vocoder mode that suits your needs from the drop-down menu at the top of the plugin interface. For example, if you want to use your voice as the modulator and a synthesizer as the carrier, you can choose Mono mode or MIDI Mono mode. -
- Connect your microphone or audio interface to your computer and select it as the input source for the audio track or MIDI track that has Orange Vocoder inserted. -
- If you are using an external sound source as the carrier, such as a synthesizer or a sampler, connect it to your computer and select it as the input source for another audio track or MIDI track in your DAW. Then, send this track's output to Orange Vocoder's sidechain input by using an aux send or a bus send in your DAW. -
- If you are using the built-in synthesizer or sampler as the carrier, you don't need to do anything else, as they are already connected to Orange Vocoder internally. -
How to Adjust the Parameters and Presets of Orange Vocoder
-Orange Vocoder has many parameters and presets that you can adjust and tweak to create different vocal effects. Here are some of them:
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- The algorithm selector allows you to choose from 22 different vocoder algorithms that range from vintage analog to digital spectral to phase vocoding. Each algorithm has its own character and sound quality, and you can switch between them easily with a drop-down menu.
- The parameter section allows you to adjust various aspects of the vocoder effect such as input gain, output gain, dry/wet mix, formant shift, formant freeze, analysis bands, synthesis bands, band width, band distribution, band smoothing, band soloing, band muting, band panning, band volume, etc. You can use the knobs or the sliders to change the values of the parameters, or you can use the mouse wheel or the arrow keys to fine-tune them. -
- The preset selector allows you to choose from over 500 presets that cover a wide range of vocal effects and styles. You can browse through the presets by category or by keyword, or you can create your own presets and save them for later use. You can also use the randomize button to generate a random preset. -
How to Create Different Vocal Effects with Orange Vocoder
-Orange Vocoder can create a variety of vocal effects that can enhance the mood, atmosphere, and expression of your songs. Here are some examples of how to create different vocal effects with Orange Vocoder:
--
-
- If you want to create a robotic, futuristic, or alien voice, you can use a simple sine wave as the carrier signal and a high number of analysis and synthesis bands. You can also use a high formant shift value to change the gender or age of the voice. For example, you can use the preset "Robot Voice" or "Alien Voice" to achieve this effect. -
- If you want to create a harmonized, chorused, or layered voice, you can use a complex waveform as the carrier signal and a low number of analysis and synthesis bands. You can also use a low formant shift value to preserve the naturalness of the voice. For example, you can use the preset "Harmony Voice" or "Chorus Voice" to achieve this effect. -
- If you want to create a rhythmic, percussive, or glitchy voice, you can use a drum loop as the modulator signal and a noise waveform as the carrier signal. You can also use a high band width value and a high band smoothing value to create more dynamic and smooth transitions between the bands. For example, you can use the preset "Rhythmic Voice" or "Glitchy Voice" to achieve this effect. -
- If you want to create an ambient, atmospheric, or ethereal voice, you can use a pad sound as the carrier signal and a low number of analysis and synthesis bands. You can also use a high dry/wet mix value and a high formant freeze value to create more reverb and sustain in the voice. For example, you can use the preset "Ambient Voice" or "Ethereal Voice" to achieve this effect. -
- If you want to create a distorted, filtered, or modulated voice, you can use a guitar sound as the carrier signal and a high number of analysis and synthesis bands. You can also use a low dry/wet mix value and a low formant freeze value to create more distortion and modulation in the voice. For example, you can use the preset "Distorted Voice" or "Modulated Voice" to achieve this effect. -
Conclusion
-In this article, we have shown you what a vocoder is, what makes Orange Vocoder unique, how to download and install it, and how to use it in your DAW. We hope that you have learned something new and useful from this article, and that you are inspired to try Orange Vocoder for yourself.
-A Summary of the Main Points
-Here is a summary of the main points that we have covered in this article:
--
-
- A vocoder is a device that analyzes and synthesizes the human voice signal for audio data compression, multiplexing, voice encryption, or voice transformation. It can create a variety of vocal effects that can enhance the mood, atmosphere, and expression of your music. -
- Orange Vocoder is a software plugin that emulates the classic hardware vocoders of the past, but also adds many new features and functions that make it one of the most versatile and creative vocoders on the market. It has 22 different vocoder algorithms, 8 different vocoder modes, over 500 presets, and a built-in synthesizer and sampler. -
- Orange Vocoder is a plugin that you can download and install on your computer and use in your DAW. You need to meet the system requirements and compatibility, purchase the plugin from the online store, download and install the installer file, scan for new plugins in your DAW, and authorize the plugin with your iLok account and dongle. -
- Orange Vocoder is a plugin that you can use in your DAW to create vocal effects with any sound source. You need to route the modulator and carrier signals to Orange Vocoder, adjust the parameters and presets of Orange Vocoder, and create different vocal effects with Orange Vocoder. -
A Call to Action for the Readers
-If you are interested in Orange Vocoder and want to learn more about it, you can visit the official website of Zynaptiq at www.zynaptiq.com/orangevocoder/. There you can find more information about the features and functions of Orange Vocoder, watch video tutorials and demos, read user reviews and testimonials, and contact the support team if you have any questions or issues. You can also download a free trial version of Orange Vocoder for 14 days and try it for yourself before you buy it.
-Orange Vocoder is a powerful and flexible vocoder plugin that can help you create amazing vocal effects for your music production. Whether you want to create robotic, futuristic, or alien voices, harmonized, chorused, or layered vocals, rhythmic, percussive, or glitchy vocals, ambient, atmospheric, or ethereal vocals, or distorted, filtered, or modulated vocals, Orange Vocoder can do it all. So what are you waiting for? Download Orange Vocoder today and unleash your creativity!
-FAQs
-Here are some frequently asked questions about Orange Vocoder:
--
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- Q: How much does Orange Vocoder cost? -
- A: Orange Vocoder costs $199 USD. You can purchase it from the online store at www.zynaptiq.com/orangevocoder/buy/. -
- Q: What are the system requirements and compatibility of Orange Vocoder? -
- A: Orange Vocoder is available for Mac OS X 10.8 or higher and Windows 7 or higher. It requires a minimum of 4 GB of RAM and 100 MB of disk space. It supports AU, VST2, VST3, AAX Native, RTAS (Mac only), and AudioSuite formats. It is compatible with most DAWs such as Logic Pro X, Pro Tools, Ableton Live, Cubase, FL Studio, Reaper, etc. It requires an iLok account and an iLok USB dongle for authorization. -
- Q: How can I download and install Orange Vocoder? -
- A: You can download and install Orange Vocoder by following these steps: 1) Go to www.zynaptiq.com/orangevocoder/ and click on the "Buy Now" button. 2) After you have completed your purchase, you will receive an email confirmation with a download link and a serial number for Orange Vocoder. 3) Click on the download link to download the installer file for your operating system. 4) Run the installer file and follow the instructions on the screen to install Orange Vocoder on your computer. 5) Launch your DAW and scan for new plugins. You should see Orange Vocoder in your plugin list. 6) Open Orange Vocoder's interface and click on the "Authorize" button. You will need to enter your iLok account credentials and insert your iLok USB dongle into your computer. -
- Q: How can I use Orange Vocoder in my DAW? -
- A: You can use Orange Vocoder in your DAW by following these steps: 1) Create an audio track or a MIDI track in your DAW and insert Orange Vocoder as an effect plugin. 2) Select the vocoder mode that suits your needs from the drop-down menu at the top of the plugin interface. 3) Connect your microphone or audio interface to your computer and select it as the input source for the audio track or MIDI track that has Orange Vocoder inserted. 4) If you are using an external sound source as the carrier, connect it to your computer and select it as the input source for another audio track or MIDI track in your DAW. Then, send this track's output to Orange Vocoder's sidechain input by using an aux send or a bus send in your DAW. 5) If you are using the built-in synthesizer or sampler as the carrier, you don't need to do anything else, as they are already connected to Orange Vocoder internally. 6) Adjust the parameters and presets of Orange Vocoder to create different vocal effects. -
- Q: What are some examples of vocal effects that I can create with Orange Vocoder? -
- A: You can create a variety of vocal effects with Orange Vocoder, such as robotic, futuristic, or alien voices, harmonized, chorused, or layered vocals, rhythmic, percussive, or glitchy vocals, ambient, atmospheric, or ethereal vocals, or distorted, filtered, or modulated vocals. You can use different vocoder algorithms, modes, parameters, and presets to achieve different results. -
- Q: Where can I find more information and support for Orange Vocoder? -
- A: You can find more information and support for Orange Vocoder at www.zynaptiq.com/orangevocoder/. There you can access the online user manual, video tutorials, support forum, contact form, and social media links. -
-
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Moneyland A Fun and Easy City-Building Game - Free for Android.md b/spaces/congsaPfin/Manga-OCR/logs/Moneyland A Fun and Easy City-Building Game - Free for Android.md deleted file mode 100644 index ac117ff26980db1762630ff8a09bd223e4d1360f..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Moneyland A Fun and Easy City-Building Game - Free for Android.md +++ /dev/null @@ -1,119 +0,0 @@ -
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Moneyland App: A Fun and Easy Way to Build Your Own City and Become Rich
-Do you dream of owning a city and becoming a millionaire? Do you love playing idle games and building games? If you answered yes to any of these questions, then you should try Moneyland App, a thrilling tycoon game and one of the best money games for kids!
-Moneyland App is a city building game with a twist. You are an aspiring investor in a newly building town who is ready to make some money. You will start by collecting your money, and purchase your first investment by unlocking a venue. Shops, banks, restaurants and many money idle spots to invest and to help you collect even more money!
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But that's not all. Moneyland App is also a tycoon game with idle and active modes. You can choose to sit back and watch your money grow, or you can get yourself a bike and rush to continue your invest game. Complete your money investment run to explore new buildings to invest in! Become the owner of the city!
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-How to Download Moneyland App for Free?
-Moneyland App is available for free on various platforms. Here's how you can download it on your device:
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-If you have an Android device, you can download Moneyland App from the Google Play Store. Just follow these steps:
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- Search for "Moneyland" or click on this link: Moneyland - Apps on Google Play. -
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- Once the app is installed, tap on the "Open" button or find the app icon on your home screen. -
- Enjoy playing Moneyland App! -
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- Once the app is downloaded, tap on the "Open" button or find the app icon on your Launchpad. -
- Enjoy playing Moneyland App! -
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-Moneyland App is a simple and addictive game that anyone can play. Here's how you can play it:
-Start by collecting your money and unlocking venues
-When you start the game, you will see a map of your city with various buildings and venues. You will also see a money counter at the top of the screen that shows how much money you have. Tap on the money counter to collect your money. You can also swipe left or right to move around the map.
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To unlock a venue, you need to have enough money and tap on the venue icon. Each venue has a different cost and income. For example, a shop costs $100 and earns $1 per second, while a bank costs $10,000 and earns $100 per second. You can see the cost and income of each venue by tapping on it.
-Invest in shops, banks, restaurants and more
-Once you unlock a venue, you can invest in it to increase its income. To invest in a venue, tap on it and then tap on the "Invest" button. You can invest up to 10 times in each venue. Each investment will increase the income of the venue by a certain percentage. For example, investing in a shop will increase its income by 10%, while investing in a bank will increase its income by 20%.
-You can also see the return on investment (ROI) of each venue by tapping on it. The ROI shows how long it will take for the venue to pay back its cost and start making profit. For example, a shop with an ROI of 100 seconds means that it will take 100 seconds for the shop to earn back its $100 cost and start making $1 per second profit.
-Upgrade your capacity, speed and income
-Besides investing in venues, you can also upgrade your capacity, speed and income. To upgrade these attributes, tap on the menu button at the bottom right corner of the screen and then tap on the "Upgrade" button. You will see three bars that show your current capacity, speed and income levels.
-Capacity is the maximum amount of money you can collect at once. Speed is how fast your money grows over time. Income is how much money you earn per second from all your venues. You can upgrade each attribute by spending some money. Each upgrade will increase the attribute by a certain amount. For example, upgrading your capacity by 1% will cost $100 and increase your capacity by 1%.
-Use bikes, cars and helicopters to move around the city
-Another way to play Moneyland App is to use bikes, cars and helicopters to move around the city faster. To use these vehicles, tap on the menu button at the bottom right corner of the screen and then tap on the "Vehicle" button. You will see three icons that represent bikes, cars and helicopters.
-Bikes are the cheapest and slowest vehicles. They cost $500 and move at 10 km/h. Cars are more expensive and faster vehicles. They cost $5,000 and move at 50 km/h. Helicopters are the most expensive and fastest vehicles. They cost $50,000 and move at 100 km/h.
-To use a vehicle, tap on its icon and then tap on any location on the map. The vehicle will take you there in a few seconds. You can also see how long it will take for the vehicle to reach its destination by looking at the timer at the top of the screen.
-Explore new buildings and expand your city
-The last way to play Moneyland App is to explore new buildings and expand your city. To explore new buildings, you need to complete certain tasks or achievements. For example, to unlock the airport, you need to have 10 venues with level 10 investments. To unlock the casino, you need to have $1 million in cash.
-To see what tasks or achievements you need to complete, tap on the menu button at the bottom right corner of the screen and then tap on the "Achievement" button. You will see a list of tasks or achievements with their rewards and progress.
-To expand your city, you need to buy new land plots. To buy new land plots, tap on the menu button at the bottom right corner of the screen and then tap on the "Land" button. You will see a map of your city with different land plots. Each land plot has a different size and price. For example, a small land plot costs $10,000 and can fit 4 venues, while a large land plot costs $100,000 and can fit 16 venues.
-To buy a land plot, tap on it and then tap on the "Buy" button. You can also see how many venues you can fit on each land plot by tapping on it. Once you buy a land plot, you can build new venues on it and earn more money.
-Why You Should Play Moneyland App?
-Moneyland App is more than just a game. It's also a learning tool and a motivation booster. Here are some reasons why you should play Moneyland App:
-It's fun and relaxing
-Moneyland App is a fun and relaxing game that you can play anytime, anywhere. You can enjoy the colorful graphics, the smooth animations, the catchy music and the satisfying sound effects. You can also customize your city with different themes, such as tropical, winter, desert and more. You can also share your city with your friends and compare your progress.
-It's challenging and rewarding
-Moneyland App is also a challenging and rewarding game that will test your skills and strategy. You will have to manage your money wisely, invest in the right venues, upgrade your attributes, use your vehicles efficiently, explore new buildings and expand your city. You will also have to complete various tasks and achievements to unlock more rewards and features. You will feel a sense of accomplishment as you see your city grow and your money increase.
-It's educational and inspirational
-Moneyland App is also an educational and inspirational game that will teach you some valuable lessons about money and life. You will learn about the basics of investing, such as cost, income, ROI, capacity, speed and more. You will also learn about the power of compound interest, the importance of diversification, the benefits of passive income and more. You will also get inspired by the success stories of real-life investors, entrepreneurs and millionaires who started from nothing and achieved their dreams.
-Conclusion
-Moneyland App is a fun and easy way to build your own city and become rich. It's a city building game with a twist, a tycoon game with idle and active modes, and a money game for kids and adults alike. It's fun and relaxing, challenging and rewarding, educational and inspirational. It's a game that you should download for free and play today!
-FAQs
-Q: How can I get more money in Moneyland App?
-A: There are several ways to get more money in Moneyland App. You can collect your money regularly by tapping on the money counter or using the auto-collect feature. You can invest in more venues or upgrade your existing ones to increase your income. You can upgrade your capacity, speed and income attributes to boost your money growth. You can use bikes, cars or helicopters to move around the city faster and collect more money. You can explore new buildings or expand your city to unlock more venues and opportunities.
-Q: How can I get free gems in Moneyland App?
-A: Gems are the premium currency in Moneyland App that you can use to buy special items or features. You can get free gems by completing certain tasks or achievements, such as reaching a certain level or income, unlocking a certain number of venues or buildings, or playing for a certain number of days. You can also get free gems by watching video ads or participating in special events or offers.
-Q: What are the best venues to invest in Moneyland App?
-A: The best venues to invest in Moneyland App depend on your preferences and goals. Generally speaking, you should invest in venues that have high income, low cost, high ROI, high capacity or high speed. Some examples of such venues are banks, casinos, airports or skyscrapers. However, you should also diversify your portfolio by investing in different types of venues, such as shops, restaurants, hotels or parks.
-Q: How can I change the theme of my city in Moneyland App?
-A: You can change the theme of your city in Moneyland App by tapping on the menu button at the bottom right corner of the screen and then tapping on the "Theme" button. You will see a list of themes that you can choose from, such as tropical, winter, desert, city, forest and more. Each theme has a different background, music and sound effects. To change the theme of your city, tap on the theme you want and then tap on the "Apply" button. You can also preview the theme by tapping on the "Preview" button.
-Q: How can I contact the developers of Moneyland App?
-A: If you have any questions, feedback or suggestions about Moneyland App, you can contact the developers by tapping on the menu button at the bottom right corner of the screen and then tapping on the "Support" button. You will see a form where you can enter your name, email and message. You can also attach a screenshot if you want. Once you fill out the form, tap on the "Send" button and wait for a reply from the developers.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/QQ Messenger APK How to Download and Install the Best Chat App for Android.md b/spaces/congsaPfin/Manga-OCR/logs/QQ Messenger APK How to Download and Install the Best Chat App for Android.md deleted file mode 100644 index 6ed21d5ddb43ee1cd3417594f758c931bffdd7cb..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/QQ Messenger APK How to Download and Install the Best Chat App for Android.md +++ /dev/null @@ -1,93 +0,0 @@ -
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- Is QQ Messenger free? -
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- Is QQ Messenger safe? -
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- Can I use QQ Messenger on other devices? -
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- How can I delete my QQ account? -
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\ No newline at end of file diff --git a/spaces/contluForse/HuggingGPT/assets/Cameron Current Surgical Therapy 11th Edition Free Download.md b/spaces/contluForse/HuggingGPT/assets/Cameron Current Surgical Therapy 11th Edition Free Download.md deleted file mode 100644 index a0f76fbed1199c8ebceaadc33e870f553b04756c..0000000000000000000000000000000000000000 --- a/spaces/contluForse/HuggingGPT/assets/Cameron Current Surgical Therapy 11th Edition Free Download.md +++ /dev/null @@ -1,10 +0,0 @@ - -Due to their resemblance under light microscopy, GISTs were not regarded as a distinct pathological entity in earlier literature but as smooth muscle neoplasms and most were designated as leiomyomas, leiomyoblastomas, leiomyosarcomas, or schwannomas. GISTs are now thought to ascend from the interstitial cells of Cajal or their stem cell precursors, which are normally part of the autonomic nervous system of the gastrointestinal tract and operate as a pacemaker in controlling GI motility [22, 23]. This finding was followed by the development of therapeutic agents that suppress tumor growth by specifically inhibiting this signal (imatinib mesylate, sunitinib malate) [1, 24]. Complete surgical resection with no tumor rupture remains the gold standard of treatment along with adjuvant TKI therapy, typically consisting of imatinib mesylate [25]. Our patient had initially received imatinib which had to be discontinued due to significant side effects. Between 15% and 50% of patients with GIST have already metastatic disease at diagnosis. Common sites of metastasis include the liver, peritoneum, and omentum [26]; metastases to lymph nodes and extra-abdominal structures (lung, bone, soft tissue, and brain) are uncommon (
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-In conclusion, even though skeletal muscle metastases from GISTs are rare, the likelihood of identifying metastases in unusual sites is increasing due to major improvement in the progression-free survival and overall survival rates of patients with GISTs. Additionally, side effects from TKIs are common, and therefore skeletal muscle metastases should likely be totally surgically excised.
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\ No newline at end of file diff --git a/spaces/contluForse/HuggingGPT/assets/Download Basics of Agriculture Book PDF and Start Your Farming Journey Today.md b/spaces/contluForse/HuggingGPT/assets/Download Basics of Agriculture Book PDF and Start Your Farming Journey Today.md deleted file mode 100644 index 411c668314b584601f992224c1826c994d40c184..0000000000000000000000000000000000000000 --- a/spaces/contluForse/HuggingGPT/assets/Download Basics of Agriculture Book PDF and Start Your Farming Journey Today.md +++ /dev/null @@ -1,28 +0,0 @@ - -As part of the process of creating our list, we took the task of choosing books in PDF format to facilitate access to them.Agriculture is defined as the art of cultivating, favoring and making the land productive. Agriculture is considered an essential and indispensable economic activity at a general level.
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-The beginnings of agriculture date back to the Neolithic period, when human societies evolved from gathering, fishing and hunting to animal husbandry and agriculture. The first plants to be cultivated were barley and wheat.Its origins are estimated in prehistoric times and its development was due to the independent practice of various cultures, such as those of the Fertile Crescent (area from Mesopotamia to Ancient Egypt), the Chinese cultures in East Asia, and the pre-Columbian cultures of Central America, among others.There was a gradual transition from the economy of gathering agricultural products and hunting. The reasons for the evolution of agriculture could have been climatic changes towards more temperate temperatures, the scarcity of game or gathering products, or the desertification of many regions.
-We have compiled more than 25 books on Agriculture in PDF format so that you can learn everything you want to know about this activity. From our list we have included books in Spanish and Portuguese.All the texts were given for free publication or are in the public domain.Read. Learn. Grow.Table of Contents
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- Sustainable Agriculture BooksBasic Agriculture Books1) Basic Agriculture. Student HandbookShiksha Kendra
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Organic agriculture is also known as ecological or biological agriculture, and consists of a cultivation methodology that does not use chemical products and optimizes the use of natural resources. If you want to learn about this autonomous system in which meticulous care is applied to the environment, you can consult the texts that we offer about it. With the Organic Agriculture books you will have the opportunity to know the necessary details to put this mechanism into practice, and obtain completely organic products. 14) Teaching Organic Farming & GardeningMartha Brown, Jan Perez and Albie Miles
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-Book description: With the growing popularity and availability of precision equipment, farmers and producers have access to more data than ever before. With proper implementation, precision agriculture management can improve profitability and sustainability of production. Precision Agriculture Basics is geared at students, crop consultants, farmers, extension workers, and practitioners that are interested in practical applications of site-specific agricultural management. Using a multidisciplinary approach, readers are taught to make data-driven on-farm decisions using the most current knowledge and tools in crop science, agricultural engineering, and geostatistics. Precision Agriculture Basics also features a stunning video glossary including interviews with agronomists on the job and in the field.
-Shannon, D. Kent; Clay, David E.; Kitchen, Newell R.; Clay, Sharon A.; French, B. W.; and Mathew, Febina M., "Precision Agriculture Basics" (2018). Agronomy, Horticulture, and Plant Science Books. 5.
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_book/5Fundamental Of Agriculture a Competitive book For Every Agriculture Student. Volume 1 covers almost Topics Like- Agronomy, Soil Science, Agriculture Economics, Farm Management, and Extension education. You Can Download Arun katyan agriculture book volume 1 & 2 PDF From agrigyan.in .
-The term "point source" means any discernible, confined and discrete conveyance, including but not limited to any pipe, ditch, channel, tunnel, conduit, well, discrete fissure, container, rolling stock, concentrated animal feeding operation, or vessel or other floating craft, from which pollutants are or may be discharged. This term does not include agricultural storm water discharges and return flows from irrigated agriculture.
-Do you remember learning about the food groups in school? You may have been taught using the Food Wheel, Food Guide Pyramid or MyPyramid depending on your age. Kids today learn about the food groups from MyPlate. Now that the back-to-school season is settling down, the nutritionists at MyPlate are offering a back-to-basics refresher lesson on the food groups.
-After many days of study and examining the books published by various publishers, we chose the book we felt would be the best for the Basic Agriculture Books and have provided a link to download it below.
-Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
-This latest edition of the directors' handbook, published by the Federal Reserve Bank of Kansas City, is a practical guide for bank directors to help them be more effective supervisors of their banks.
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-As the title hints at it you will find this booklet about the basics of Sustainable Ecological Organic Agriculture in the Tropics the information you need to successfully run a small self-sufficient garden or to manage a profitable, ecological, organic agricultural operation.
-The Office of Conferences & Institutes is a full service planning agency at the University of Florida. OCI was created to support the Institute of Food and Agricultural Sciences (IFAS) mission to develop knowledge in agriculture, human and natural resources, and to make that knowledge available to people to sustain and enhance the quality of human life.
-This manual is designed to provide the agriculture community with knowledge of the best management practices (BMPs) that work to protect surface water quality as well as to help agency personnel educate farmers about BMPs and their usefulness. Best Management Practices for Georgia Agriculture is a compilation of conservation practices that address surface water quality and includes an estimate of the effectiveness and relative cost of each BMP. This second edition of the manual also includes an expanded section on nutrient management planning.
-It may seem hard to imagine a world without agriculture. However, a majority of Americans do not have a basic understanding of where their food, fiber and fuel comes from. For them, Agriculture is simply not part of their world. We believe that the solution to this problem is education.
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\ No newline at end of file diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/dwpose/util.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/dwpose/util.py deleted file mode 100644 index 73d7d0153b38d143eb8090e07a9784a274b619ed..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/dwpose/util.py +++ /dev/null @@ -1,297 +0,0 @@ -import math -import numpy as np -import matplotlib -import cv2 - - -eps = 0.01 - - -def smart_resize(x, s): - Ht, Wt = s - if x.ndim == 2: - Ho, Wo = x.shape - Co = 1 - else: - Ho, Wo, Co = x.shape - if Co == 3 or Co == 1: - k = float(Ht + Wt) / float(Ho + Wo) - return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4) - else: - return np.stack([smart_resize(x[:, :, i], s) for i in range(Co)], axis=2) - - -def smart_resize_k(x, fx, fy): - if x.ndim == 2: - Ho, Wo = x.shape - Co = 1 - else: - Ho, Wo, Co = x.shape - Ht, Wt = Ho * fy, Wo * fx - if Co == 3 or Co == 1: - k = float(Ht + Wt) / float(Ho + Wo) - return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4) - else: - return np.stack([smart_resize_k(x[:, :, i], fx, fy) for i in range(Co)], axis=2) - - -def padRightDownCorner(img, stride, padValue): - h = img.shape[0] - w = img.shape[1] - - pad = 4 * [None] - pad[0] = 0 # up - pad[1] = 0 # left - pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down - pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right - - img_padded = img - pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1)) - img_padded = np.concatenate((pad_up, img_padded), axis=0) - pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1)) - img_padded = np.concatenate((pad_left, img_padded), axis=1) - pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1)) - img_padded = np.concatenate((img_padded, pad_down), axis=0) - pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1)) - img_padded = np.concatenate((img_padded, pad_right), axis=1) - - return img_padded, pad - - -def transfer(model, model_weights): - transfered_model_weights = {} - for weights_name in model.state_dict().keys(): - transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])] - return transfered_model_weights - - -def draw_bodypose(canvas, candidate, subset): - H, W, C = canvas.shape - candidate = np.array(candidate) - subset = np.array(subset) - - stickwidth = 4 - - limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ - [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ - [1, 16], [16, 18], [3, 17], [6, 18]] - - colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \ - [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \ - [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] - - for i in range(17): - for n in range(len(subset)): - index = subset[n][np.array(limbSeq[i]) - 1] - if -1 in index: - continue - Y = candidate[index.astype(int), 0] * float(W) - X = candidate[index.astype(int), 1] * float(H) - mX = np.mean(X) - mY = np.mean(Y) - length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 - angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) - polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) - cv2.fillConvexPoly(canvas, polygon, colors[i]) - - canvas = (canvas * 0.6).astype(np.uint8) - - for i in range(18): - for n in range(len(subset)): - index = int(subset[n][i]) - if index == -1: - continue - x, y = candidate[index][0:2] - x = int(x * W) - y = int(y * H) - cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1) - - return canvas - - -def draw_handpose(canvas, all_hand_peaks): - H, W, C = canvas.shape - - edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \ - [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]] - - for peaks in all_hand_peaks: - peaks = np.array(peaks) - - for ie, e in enumerate(edges): - x1, y1 = peaks[e[0]] - x2, y2 = peaks[e[1]] - x1 = int(x1 * W) - y1 = int(y1 * H) - x2 = int(x2 * W) - y2 = int(y2 * H) - if x1 > eps and y1 > eps and x2 > eps and y2 > eps: - cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=2) - - for i, keyponit in enumerate(peaks): - x, y = keyponit - x = int(x * W) - y = int(y * H) - if x > eps and y > eps: - cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1) - return canvas - - -def draw_facepose(canvas, all_lmks): - H, W, C = canvas.shape - for lmks in all_lmks: - lmks = np.array(lmks) - for lmk in lmks: - x, y = lmk - x = int(x * W) - y = int(y * H) - if x > eps and y > eps: - cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1) - return canvas - - -# detect hand according to body pose keypoints -# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp -def handDetect(candidate, subset, oriImg): - # right hand: wrist 4, elbow 3, shoulder 2 - # left hand: wrist 7, elbow 6, shoulder 5 - ratioWristElbow = 0.33 - detect_result = [] - image_height, image_width = oriImg.shape[0:2] - for person in subset.astype(int): - # if any of three not detected - has_left = np.sum(person[[5, 6, 7]] == -1) == 0 - has_right = np.sum(person[[2, 3, 4]] == -1) == 0 - if not (has_left or has_right): - continue - hands = [] - #left hand - if has_left: - left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]] - x1, y1 = candidate[left_shoulder_index][:2] - x2, y2 = candidate[left_elbow_index][:2] - x3, y3 = candidate[left_wrist_index][:2] - hands.append([x1, y1, x2, y2, x3, y3, True]) - # right hand - if has_right: - right_shoulder_index, right_elbow_index, right_wrist_index = person[[2, 3, 4]] - x1, y1 = candidate[right_shoulder_index][:2] - x2, y2 = candidate[right_elbow_index][:2] - x3, y3 = candidate[right_wrist_index][:2] - hands.append([x1, y1, x2, y2, x3, y3, False]) - - for x1, y1, x2, y2, x3, y3, is_left in hands: - # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox - # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]); - # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]); - # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow); - # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder); - # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder); - x = x3 + ratioWristElbow * (x3 - x2) - y = y3 + ratioWristElbow * (y3 - y2) - distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2) - distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) - width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder) - # x-y refers to the center --> offset to topLeft point - # handRectangle.x -= handRectangle.width / 2.f; - # handRectangle.y -= handRectangle.height / 2.f; - x -= width / 2 - y -= width / 2 # width = height - # overflow the image - if x < 0: x = 0 - if y < 0: y = 0 - width1 = width - width2 = width - if x + width > image_width: width1 = image_width - x - if y + width > image_height: width2 = image_height - y - width = min(width1, width2) - # the max hand box value is 20 pixels - if width >= 20: - detect_result.append([int(x), int(y), int(width), is_left]) - - ''' - return value: [[x, y, w, True if left hand else False]]. - width=height since the network require squared input. - x, y is the coordinate of top left - ''' - return detect_result - - -# Written by Lvmin -def faceDetect(candidate, subset, oriImg): - # left right eye ear 14 15 16 17 - detect_result = [] - image_height, image_width = oriImg.shape[0:2] - for person in subset.astype(int): - has_head = person[0] > -1 - if not has_head: - continue - - has_left_eye = person[14] > -1 - has_right_eye = person[15] > -1 - has_left_ear = person[16] > -1 - has_right_ear = person[17] > -1 - - if not (has_left_eye or has_right_eye or has_left_ear or has_right_ear): - continue - - head, left_eye, right_eye, left_ear, right_ear = person[[0, 14, 15, 16, 17]] - - width = 0.0 - x0, y0 = candidate[head][:2] - - if has_left_eye: - x1, y1 = candidate[left_eye][:2] - d = max(abs(x0 - x1), abs(y0 - y1)) - width = max(width, d * 3.0) - - if has_right_eye: - x1, y1 = candidate[right_eye][:2] - d = max(abs(x0 - x1), abs(y0 - y1)) - width = max(width, d * 3.0) - - if has_left_ear: - x1, y1 = candidate[left_ear][:2] - d = max(abs(x0 - x1), abs(y0 - y1)) - width = max(width, d * 1.5) - - if has_right_ear: - x1, y1 = candidate[right_ear][:2] - d = max(abs(x0 - x1), abs(y0 - y1)) - width = max(width, d * 1.5) - - x, y = x0, y0 - - x -= width - y -= width - - if x < 0: - x = 0 - - if y < 0: - y = 0 - - width1 = width * 2 - width2 = width * 2 - - if x + width > image_width: - width1 = image_width - x - - if y + width > image_height: - width2 = image_height - y - - width = min(width1, width2) - - if width >= 20: - detect_result.append([int(x), int(y), int(width)]) - - return detect_result - - -# get max index of 2d array -def npmax(array): - arrayindex = array.argmax(1) - arrayvalue = array.max(1) - i = arrayvalue.argmax() - j = arrayindex[i] - return i, j diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/utils.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/utils.py deleted file mode 100644 index d327e8bd8120c5cd09ae6c15c3991ccbe27f6c1f..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/utils.py +++ /dev/null @@ -1,52 +0,0 @@ -import os - - -class AverageMeter: - """Computes and stores the average and current value""" - def __init__(self): - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - -def accuracy(output, target, topk=(1,)): - """Computes the precision@k for the specified values of k""" - maxk = max(topk) - batch_size = target.size(0) - - _, pred = output.topk(maxk, 1, True, True) - pred = pred.t() - correct = pred.eq(target.view(1, -1).expand_as(pred)) - - res = [] - for k in topk: - correct_k = correct[:k].reshape(-1).float().sum(0) - res.append(correct_k.mul_(100.0 / batch_size)) - return res - - -def get_outdir(path, *paths, inc=False): - outdir = os.path.join(path, *paths) - if not os.path.exists(outdir): - os.makedirs(outdir) - elif inc: - count = 1 - outdir_inc = outdir + '-' + str(count) - while os.path.exists(outdir_inc): - count = count + 1 - outdir_inc = outdir + '-' + str(count) - assert count < 100 - outdir = outdir_inc - os.makedirs(outdir) - return outdir - diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/api.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/api.py deleted file mode 100644 index e96d7d6e32d7e52fae776792d810a19dfee18015..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/api.py +++ /dev/null @@ -1,43 +0,0 @@ -import os -os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" - -import torch - -from annotator.oneformer.detectron2.config import get_cfg -from annotator.oneformer.detectron2.projects.deeplab import add_deeplab_config -from annotator.oneformer.detectron2.data import MetadataCatalog - -from annotator.oneformer.oneformer import ( - add_oneformer_config, - add_common_config, - add_swin_config, - add_dinat_config, -) - -from annotator.oneformer.oneformer.demo.defaults import DefaultPredictor -from annotator.oneformer.oneformer.demo.visualizer import Visualizer, ColorMode - - -def make_detectron2_model(config_path, ckpt_path): - cfg = get_cfg() - add_deeplab_config(cfg) - add_common_config(cfg) - add_swin_config(cfg) - add_oneformer_config(cfg) - add_dinat_config(cfg) - cfg.merge_from_file(config_path) - if torch.cuda.is_available(): - cfg.MODEL.DEVICE = 'cuda' - else: - cfg.MODEL.DEVICE = 'cpu' - cfg.MODEL.WEIGHTS = ckpt_path - cfg.freeze() - metadata = MetadataCatalog.get(cfg.DATASETS.TEST_PANOPTIC[0] if len(cfg.DATASETS.TEST_PANOPTIC) else "__unused") - return DefaultPredictor(cfg), metadata - - -def semantic_run(img, predictor, metadata): - predictions = predictor(img[:, :, ::-1], "semantic") # Predictor of OneFormer must use BGR image !!! - visualizer_map = Visualizer(img, is_img=False, metadata=metadata, instance_mode=ColorMode.IMAGE) - out_map = visualizer_map.draw_sem_seg(predictions["sem_seg"].argmax(dim=0).cpu(), alpha=1, is_text=False).get_image() - return out_map diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/utils/tracing.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/utils/tracing.py deleted file mode 100644 index 75661131505cee2eecd0b1c9dabcd4d7bd5453b2..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/utils/tracing.py +++ /dev/null @@ -1,71 +0,0 @@ -import inspect -import torch - -from annotator.oneformer.detectron2.utils.env import TORCH_VERSION - -try: - from torch.fx._symbolic_trace import is_fx_tracing as is_fx_tracing_current - - tracing_current_exists = True -except ImportError: - tracing_current_exists = False - -try: - from torch.fx._symbolic_trace import _orig_module_call - - tracing_legacy_exists = True -except ImportError: - tracing_legacy_exists = False - - -@torch.jit.ignore -def is_fx_tracing_legacy() -> bool: - """ - Returns a bool indicating whether torch.fx is currently symbolically tracing a module. - Can be useful for gating module logic that is incompatible with symbolic tracing. - """ - return torch.nn.Module.__call__ is not _orig_module_call - - -@torch.jit.ignore -def is_fx_tracing() -> bool: - """Returns whether execution is currently in - Torch FX tracing mode""" - if TORCH_VERSION >= (1, 10) and tracing_current_exists: - return is_fx_tracing_current() - elif tracing_legacy_exists: - return is_fx_tracing_legacy() - else: - # Can't find either current or legacy tracing indication code. - # Enabling this assert_fx_safe() call regardless of tracing status. - return False - - -@torch.jit.ignore -def assert_fx_safe(condition: bool, message: str) -> torch.Tensor: - """An FX-tracing safe version of assert. - Avoids erroneous type assertion triggering when types are masked inside - an fx.proxy.Proxy object during tracing. - Args: condition - either a boolean expression or a string representing - the condition to test. If this assert triggers an exception when tracing - due to dynamic control flow, try encasing the expression in quotation - marks and supplying it as a string.""" - # Must return a concrete tensor for compatibility with PyTorch <=1.8. - # If <=1.8 compatibility is not needed, return type can be converted to None - if not is_fx_tracing(): - try: - if isinstance(condition, str): - caller_frame = inspect.currentframe().f_back - torch._assert( - eval(condition, caller_frame.f_globals, caller_frame.f_locals), message - ) - return torch.ones(1) - else: - torch._assert(condition, message) - return torch.ones(1) - except torch.fx.proxy.TraceError as e: - print( - "Found a non-FX compatible assertion. Skipping the check. Failure is shown below" - + str(e) - ) - return torch.zeros(1) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/point_sample.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/point_sample.py deleted file mode 100644 index 267f4b3c56630acd85f9bdc630b7be09abab0aba..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/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/daarumadx/bot/src/processing/folder.py b/spaces/daarumadx/bot/src/processing/folder.py deleted file mode 100644 index 7fedd041b37675e304c49dab5571111b045f5f8f..0000000000000000000000000000000000000000 --- a/spaces/daarumadx/bot/src/processing/folder.py +++ /dev/null @@ -1,79 +0,0 @@ -"""Folder Image Transform Processing.""" -import copy -import json -import os -import pathlib -import sys -from json import JSONDecodeError - -from config import Config as Conf -from processing.multiple import MultipleImageProcessing -from processing.utils import select_phases, is_file -from utils import is_a_supported_image_file_extension - - -class FolderImageProcessing(MultipleImageProcessing): - """Folder Image Processing Class.""" - def _setup(self, *args): - self._input_folder_path = self._args['input'] - self._output_folder_path = self._args['output'] - self._multiprocessing = Conf.multiprocessing() - self._process_list = [] - Conf.log.debug([(r, d, f) for r, d, f in os.walk(self._input_folder_path)]) - - for r, _, _ in os.walk(self._input_folder_path): - args = copy.deepcopy(self._args) - args['input'] = [ - x.path for x in os.scandir(r) if is_file(args, x.path) and is_a_supported_image_file_extension(x.path) - ] - args['phases'] = select_phases(self._args) - args['output'] = [ - "{}{}{}".format( - os.path.splitext(x)[0], - '_out', - os.path.splitext(x)[1] - ) - if not Conf.args['output'] else - os.path.join( - Conf.args['output'], - pathlib.Path(*pathlib.Path(r).parts[1:]), - os.path.basename(x) - ) - for x in args['input'] - ] - - self._process_list.append( - (MultipleImageProcessing(), self.__get_folder_args(args, r)) - ) - - @staticmethod - def __get_folder_args(args, folder_path): - def add_folder_altered(args): - if args.get('altered'): - args['folder_altered'] = os.path.join(args['altered'], - pathlib.Path(*pathlib.Path(folder_path).parts[1:])) - return args - - json_path = os.path.join(folder_path, args['json_folder_name']) - - Conf.log.debug("Json Path Setting Path: {}".format(json_path)) - if not os.path.isfile(json_path): - Conf.log.info("No Json File Settings Found In {}. Using Default Configuration. ".format(folder_path)) - return add_folder_altered(args) - try: - with open(json_path, 'r') as f: - json_data = json.load(f) - except JSONDecodeError: - Conf.log.info("Json File Settings {} Is Not In Valid JSON Format. Using Default Configuration. " - .format(folder_path)) - return add_folder_altered(args) - try: - from argv import Parser, config_args - a = config_args(Parser.parser, Parser.parser.parse_args(sys.argv[1:]), json_data=json_data) - Conf.log.info("Using {} Configuration for processing {} folder. " - .format(json_path, folder_path)) - return add_folder_altered(a) - except SystemExit: - Conf.log.error("Arguments json file {} contains configuration error. " - "Using Default Configuration".format(json_path)) - return add_folder_altered(args) diff --git a/spaces/dachenchen/HiWantJoin/run_Linux.sh b/spaces/dachenchen/HiWantJoin/run_Linux.sh deleted file mode 100644 index 2d26597ae47519f42336ccffc16646713a192ae1..0000000000000000000000000000000000000000 --- a/spaces/dachenchen/HiWantJoin/run_Linux.sh +++ /dev/null @@ -1,31 +0,0 @@ -#!/bin/bash - -# 获取脚本所在目录 -script_dir=$(dirname "$(readlink -f "$0")") - -# 将工作目录更改为脚本所在目录 -cd "$script_dir" || exit - -# 检查Git仓库是否有更新 -git remote update -pwd - -if ! git status -uno | grep 'up to date' > /dev/null; then - # 如果有更新，关闭当前运行的服务器 - pkill -f ChuanhuChatbot.py - - # 拉取最新更改 - git pull - - # 安装依赖 - pip3 install -r requirements.txt - - # 重新启动服务器 - nohup python3 ChuanhuChatbot.py & -fi - -# 检查ChuanhuChatbot.py是否在运行 -if ! pgrep -f ChuanhuChatbot.py > /dev/null; then - # 如果没有运行，启动服务器 - nohup python3 ChuanhuChatbot.py & -fi diff --git a/spaces/daddyjin/TalkingFaceGeneration/FONT/run.py b/spaces/daddyjin/TalkingFaceGeneration/FONT/run.py deleted file mode 100644 index 7f60e792d44eb2d407a5dade057f48b1cad16121..0000000000000000000000000000000000000000 --- a/spaces/daddyjin/TalkingFaceGeneration/FONT/run.py +++ /dev/null @@ -1,137 +0,0 @@ -import matplotlib - -matplotlib.use('Agg') - -import os, sys -import yaml -from argparse import ArgumentParser -from time import gmtime, strftime -from shutil import copy - -# from frames_dataset import MeadDataset, AudioDataset, VoxDataset -from frames_dataset_liujin import MeadDataset, AudioDataset, VoxDataset, HDTFDataset - -from modules.generator import OcclusionAwareGenerator -from modules.discriminator import MultiScaleDiscriminator -from modules.keypoint_detector import KPDetector, Audio_Feature, KPDetector_a -from modules.util import AT_net,Emotion_k -# from modules.util import get_logger -import torch - -from train import train_part1, train_part1_fine_tune, train_part2 -# from reconstruction import reconstruction -# from animate import animate - -import warnings -warnings.filterwarnings("ignore") - -if __name__ == "__main__": - - if sys.version_info[0] < 3: - raise Exception("You must use Python 3 or higher. Recommended version is Python 3.7") - - parser = ArgumentParser() - parser.add_argument("--config", default="config/train_part1.yaml", help="path to config")# required=True - parser.add_argument("--mode", default="train_part1", choices=["train_part1", "train_part1_fine_tune", "train_part2"]) - parser.add_argument("--log_dir", default='log', help="path to log into") - parser.add_argument("--checkpoint", default='124_52000.pth.tar', help="path to checkpoint to restore") - parser.add_argument("--audio_checkpoint", default=None, help="path to audio_checkpoint to restore") - parser.add_argument("--emo_checkpoint", default=None, help="path to audio_checkpoint to restore") - parser.add_argument("--device_ids", default="0", type=lambda x: list(map(int, x.split(','))), - help="Names of the devices comma separated.") - parser.add_argument("--verbose", dest="verbose", action="store_true", help="Print model architecture") - parser.set_defaults(verbose=False) - parser.add_argument("--comment", default='comment', help="comment about experiment") - - opt = parser.parse_args() - with open(opt.config) as f: - config = yaml.load(f) - - name = os.path.basename(opt.config).split('.')[0] - if opt.checkpoint is not None: - - log_dir = os.path.join(opt.log_dir, os.path.basename(opt.config).split('.')[0]) - # log_dir += ' ' + strftime("%d_%m_%y_%H.%M.%S", gmtime()) - log_dir += '_' + opt.comment - else: - log_dir = os.path.join(opt.log_dir, os.path.basename(opt.config).split('.')[0]) - # log_dir += ' ' + strftime("%d_%m_%y_%H.%M.%S", gmtime()) - log_dir += '_' + opt.comment - - if not os.path.exists(log_dir): - os.makedirs(log_dir) - if not os.path.exists(os.path.join(log_dir, os.path.basename(opt.config))): - copy(opt.config, log_dir) - - # logger = get_logger(os.path.join(log_dir, "log.txt")) - - generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], - **config['model_params']['common_params']) - - if torch.cuda.is_available(): - generator.to(opt.device_ids[0]) - - if opt.verbose: - print(generator) - - discriminator = MultiScaleDiscriminator(**config['model_params']['discriminator_params'], - **config['model_params']['common_params']) - if torch.cuda.is_available(): - discriminator.to(opt.device_ids[0]) - - - - if opt.verbose: - print(discriminator) - - kp_detector = KPDetector(**config['model_params']['kp_detector_params'], - **config['model_params']['common_params']) - - kp_detector_a = KPDetector_a(**config['model_params']['kp_detector_params'], - **config['model_params']['audio_params']) - - if torch.cuda.is_available(): - kp_detector.to(opt.device_ids[0]) - kp_detector_a.to(opt.device_ids[0]) - - audio_feature = AT_net() - emo_feature = Emotion_k(block_expansion=32, num_channels=3, max_features=1024, - num_blocks=5, scale_factor=0.25, num_classes=8) - - if torch.cuda.is_available(): - audio_feature.to(opt.device_ids[0]) - emo_feature.to(opt.device_ids[0]) - - if opt.verbose: - print(kp_detector) - print(kp_detector_a) - print(audio_feature) - print(emo_feature) - -# logger.info("Successfully load models.") - - if config['dataset_params']['name'] == 'Vox': - dataset = VoxDataset(is_train=True, **config['dataset_params']) - test_dataset = VoxDataset(is_train=False, **config['dataset_params']) - elif config['dataset_params']['name'] == 'Lrw': - dataset = AudioDataset(is_train=True, **config['dataset_params']) - test_dataset = AudioDataset(is_train=False, **config['dataset_params']) - elif config['dataset_params']['name'] == 'MEAD': - dataset = MeadDataset(is_train=True, **config['dataset_params']) - test_dataset = MeadDataset(is_train=False, **config['dataset_params']) - elif config['dataset_params']['name'] == 'hdtf': - dataset = HDTFDataset(is_train=True, **config['dataset_params']) - test_dataset = HDTFDataset(is_train=False, **config['dataset_params']) - - - - - if opt.mode == 'train_part1': - print("Training part1...") - train_part1(config, generator, discriminator, kp_detector, kp_detector_a,audio_feature, opt.checkpoint, opt.audio_checkpoint, log_dir, dataset, test_dataset,opt.device_ids, name) - elif opt.mode == 'train_part1_fine_tune': - print("Finetune part1...") - train_part1_fine_tune(config, generator, discriminator, kp_detector, kp_detector_a,audio_feature, opt.checkpoint, opt.audio_checkpoint, log_dir, dataset, test_dataset,opt.device_ids, name) - elif opt.mode == 'train_part2': - print("Training part2...") - train_part2(config, generator, discriminator, kp_detector, emo_feature,kp_detector_a,audio_feature, opt.checkpoint, opt.audio_checkpoint, opt.emo_checkpoint, log_dir, dataset,test_dataset,opt.device_ids, name) diff --git a/spaces/dataroots/SofaStyler/StyleTransfer/srcTransformer/transformer.py b/spaces/dataroots/SofaStyler/StyleTransfer/srcTransformer/transformer.py deleted file mode 100644 index d24fd948e53cd553efce79763f1b8d913abbc8cf..0000000000000000000000000000000000000000 --- a/spaces/dataroots/SofaStyler/StyleTransfer/srcTransformer/transformer.py +++ /dev/null @@ -1,416 +0,0 @@ -import copy -import os -from typing import Optional - -import numpy as np -import torch -import torch.nn.functional as F -from torch import Tensor, nn - -device = torch.device("cuda:2" if torch.cuda.is_available() else "cpu") -os.environ["CUDA_VISIBLE_DEVICES"] = "2, 3" - - -class Transformer(nn.Module): - def __init__( - self, - d_model=512, - nhead=8, - num_encoder_layers=3, - num_decoder_layers=3, - dim_feedforward=2048, - dropout=0.1, - activation="relu", - normalize_before=False, - return_intermediate_dec=False, - ): - super().__init__() - - encoder_layer = TransformerEncoderLayer( - d_model, nhead, dim_feedforward, dropout, activation, normalize_before - ) - encoder_norm = nn.LayerNorm(d_model) if normalize_before else None - self.encoder_c = TransformerEncoder( - encoder_layer, num_encoder_layers, encoder_norm - ) - self.encoder_s = TransformerEncoder( - encoder_layer, num_encoder_layers, encoder_norm - ) - - decoder_layer = TransformerDecoderLayer( - d_model, nhead, dim_feedforward, dropout, activation, normalize_before - ) - decoder_norm = nn.LayerNorm(d_model) - self.decoder = TransformerDecoder( - decoder_layer, - num_decoder_layers, - decoder_norm, - return_intermediate=return_intermediate_dec, - ) - - self._reset_parameters() - - self.d_model = d_model - self.nhead = nhead - - self.new_ps = nn.Conv2d(512, 512, (1, 1)) - self.averagepooling = nn.AdaptiveAvgPool2d(18) - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def forward(self, style, mask, content, pos_embed_c, pos_embed_s): - - # content-aware positional embedding - content_pool = self.averagepooling(content) - pos_c = self.new_ps(content_pool) - pos_embed_c = F.interpolate(pos_c, mode="bilinear", size=style.shape[-2:]) - - # flatten NxCxHxW to HWxNxC - style = style.flatten(2).permute(2, 0, 1) - if pos_embed_s is not None: - pos_embed_s = pos_embed_s.flatten(2).permute(2, 0, 1) - - content = content.flatten(2).permute(2, 0, 1) - if pos_embed_c is not None: - pos_embed_c = pos_embed_c.flatten(2).permute(2, 0, 1) - - style = self.encoder_s(style, src_key_padding_mask=mask, pos=pos_embed_s) - content = self.encoder_c(content, src_key_padding_mask=mask, pos=pos_embed_c) - hs = self.decoder( - content, - style, - memory_key_padding_mask=mask, - pos=pos_embed_s, - query_pos=pos_embed_c, - )[0] - - # HWxNxC to NxCxHxW to - N, B, C = hs.shape - H = int(np.sqrt(N)) - hs = hs.permute(1, 2, 0) - hs = hs.view(B, C, -1, H) - - return hs - - -class TransformerEncoder(nn.Module): - def __init__(self, encoder_layer, num_layers, norm=None): - super().__init__() - self.layers = _get_clones(encoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - - def forward( - self, - src, - mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - output = src - - for layer in self.layers: - output = layer( - output, - src_mask=mask, - src_key_padding_mask=src_key_padding_mask, - pos=pos, - ) - - if self.norm is not None: - output = self.norm(output) - - return output - - -class TransformerDecoder(nn.Module): - def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): - super().__init__() - self.layers = _get_clones(decoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - self.return_intermediate = return_intermediate - - def forward( - self, - tgt, - memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - output = tgt - - intermediate = [] - - for layer in self.layers: - output = layer( - output, - memory, - tgt_mask=tgt_mask, - memory_mask=memory_mask, - tgt_key_padding_mask=tgt_key_padding_mask, - memory_key_padding_mask=memory_key_padding_mask, - pos=pos, - query_pos=query_pos, - ) - if self.return_intermediate: - intermediate.append(self.norm(output)) - - if self.norm is not None: - output = self.norm(output) - if self.return_intermediate: - intermediate.pop() - intermediate.append(output) - - if self.return_intermediate: - return torch.stack(intermediate) - - return output.unsqueeze(0) - - -class TransformerEncoderLayer(nn.Module): - def __init__( - self, - d_model, - nhead, - dim_feedforward=2048, - dropout=0.1, - activation="relu", - normalize_before=False, - ): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - q = k = self.with_pos_embed(src, pos) - # q = k = src - # print(q.size(),k.size(),src.size()) - src2 = self.self_attn( - q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask - )[0] - src = src + self.dropout1(src2) - src = self.norm1(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) - src = src + self.dropout2(src2) - src = self.norm2(src) - return src - - def forward_pre( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - src2 = self.norm1(src) - q = k = self.with_pos_embed(src2, pos) - src2 = self.self_attn( - q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask - )[0] - src = src + self.dropout1(src2) - src2 = self.norm2(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) - src = src + self.dropout2(src2) - return src - - def forward( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - if self.normalize_before: - return self.forward_pre(src, src_mask, src_key_padding_mask, pos) - return self.forward_post(src, src_mask, src_key_padding_mask, pos) - - -class TransformerDecoderLayer(nn.Module): - def __init__( - self, - d_model, - nhead, - dim_feedforward=2048, - dropout=0.1, - activation="relu", - normalize_before=False, - ): - super().__init__() - # d_model embedding dim - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.norm3 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - self.dropout3 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post( - self, - tgt, - memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - - q = self.with_pos_embed(tgt, query_pos) - k = self.with_pos_embed(memory, pos) - v = memory - - tgt2 = self.self_attn( - q, k, v, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask - )[0] - - tgt = tgt + self.dropout1(tgt2) - tgt = self.norm1(tgt) - tgt2 = self.multihead_attn( - query=self.with_pos_embed(tgt, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, - attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask, - )[0] - tgt = tgt + self.dropout2(tgt2) - tgt = self.norm2(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) - tgt = tgt + self.dropout3(tgt2) - tgt = self.norm3(tgt) - return tgt - - def forward_pre( - self, - tgt, - memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - tgt2 = self.norm1(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn( - q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask - )[0] - - tgt = tgt + self.dropout1(tgt2) - tgt2 = self.norm2(tgt) - tgt2 = self.multihead_attn( - query=self.with_pos_embed(tgt2, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, - attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask, - )[0] - - tgt = tgt + self.dropout2(tgt2) - tgt2 = self.norm3(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout3(tgt2) - return tgt - - def forward( - self, - tgt, - memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None, - ): - if self.normalize_before: - return self.forward_pre( - tgt, - memory, - tgt_mask, - memory_mask, - tgt_key_padding_mask, - memory_key_padding_mask, - pos, - query_pos, - ) - return self.forward_post( - tgt, - memory, - tgt_mask, - memory_mask, - tgt_key_padding_mask, - memory_key_padding_mask, - pos, - query_pos, - ) - - -def _get_clones(module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) - - -def build_transformer(args): - return Transformer( - d_model=args.hidden_dim, - dropout=args.dropout, - nhead=args.nheads, - dim_feedforward=args.dim_feedforward, - num_encoder_layers=args.enc_layers, - num_decoder_layers=args.dec_layers, - normalize_before=args.pre_norm, - return_intermediate_dec=True, - ) - - -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}.") diff --git a/spaces/datasciencedojo/Hand-Keypoint-Detection-Realtime/app.py b/spaces/datasciencedojo/Hand-Keypoint-Detection-Realtime/app.py deleted file mode 100644 index 0d6fc94d7c019aeb7fc8a6d910e1116a15d6ec98..0000000000000000000000000000000000000000 --- a/spaces/datasciencedojo/Hand-Keypoint-Detection-Realtime/app.py +++ /dev/null @@ -1,43 +0,0 @@ -import cv2 -import gradio as gr -import mediapipe as mp -mp_drawing = mp.solutions.drawing_utils -mp_drawing_styles = mp.solutions.drawing_styles -mp_hands = mp.solutions.hands - -def fun(img): - print(type(img)) - with mp_hands.Hands( model_complexity=0,min_detection_confidence=0.5,min_tracking_confidence=0.5) as hands: - img.flags.writeable = False - image = cv2.flip(img[:,:,::-1], 1) - # Convert the BGR image to RGB before processing. - results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) - image.flags.writeable = True - if results.multi_hand_landmarks: - for hand_landmarks in results.multi_hand_landmarks: - mp_drawing.draw_landmarks( - image, - hand_landmarks, - mp_hands.HAND_CONNECTIONS, - mp_drawing_styles.get_default_hand_landmarks_style(), - mp_drawing_styles.get_default_hand_connections_style()) - - return cv2.flip(image[:,:,::-1],1) - -with gr.Blocks(title="Realtime Keypoint Detection | Data Science Dojo", css="footer {display:none !important} .output-markdown{display:none !important}") as demo: - - with gr.Row(): - with gr.Column(): - input = gr.Webcam(streaming=True) - - with gr.Column(): - output = gr.outputs.Image() - - - input.stream(fn=fun, - inputs = input, - outputs = output) - - - -demo.launch(debug=True) \ No newline at end of file diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/losses/loss_util.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/losses/loss_util.py deleted file mode 100644 index 744eeb46d1f3b5a7b4553ca23237ddd9c899a698..0000000000000000000000000000000000000000 --- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/losses/loss_util.py +++ /dev/null @@ -1,95 +0,0 @@ -import functools -from torch.nn import functional as F - - -def reduce_loss(loss, reduction): - """Reduce loss as specified. - - Args: - loss (Tensor): Elementwise loss tensor. - reduction (str): Options are 'none', 'mean' and 'sum'. - - Returns: - Tensor: Reduced loss tensor. - """ - reduction_enum = F._Reduction.get_enum(reduction) - # none: 0, elementwise_mean:1, sum: 2 - if reduction_enum == 0: - return loss - elif reduction_enum == 1: - return loss.mean() - else: - return loss.sum() - - -def weight_reduce_loss(loss, weight=None, reduction='mean'): - """Apply element-wise weight and reduce loss. - - Args: - loss (Tensor): Element-wise loss. - weight (Tensor): Element-wise weights. Default: None. - reduction (str): Same as built-in losses of PyTorch. Options are - 'none', 'mean' and 'sum'. Default: 'mean'. - - Returns: - Tensor: Loss values. - """ - # if weight is specified, apply element-wise weight - if weight is not None: - assert weight.dim() == loss.dim() - assert weight.size(1) == 1 or weight.size(1) == loss.size(1) - loss = loss * weight - - # if weight is not specified or reduction is sum, just reduce the loss - if weight is None or reduction == 'sum': - loss = reduce_loss(loss, reduction) - # if reduction is mean, then compute mean over weight region - elif reduction == 'mean': - if weight.size(1) > 1: - weight = weight.sum() - else: - weight = weight.sum() * loss.size(1) - loss = loss.sum() / weight - - return loss - - -def weighted_loss(loss_func): - """Create a weighted version of a given loss function. - - To use this decorator, the loss function must have the signature like - `loss_func(pred, target, **kwargs)`. The function only needs to compute - element-wise loss without any reduction. This decorator will add weight - and reduction arguments to the function. The decorated function will have - the signature like `loss_func(pred, target, weight=None, reduction='mean', - **kwargs)`. - - :Example: - - >>> import torch - >>> @weighted_loss - >>> def l1_loss(pred, target): - >>> return (pred - target).abs() - - >>> pred = torch.Tensor([0, 2, 3]) - >>> target = torch.Tensor([1, 1, 1]) - >>> weight = torch.Tensor([1, 0, 1]) - - >>> l1_loss(pred, target) - tensor(1.3333) - >>> l1_loss(pred, target, weight) - tensor(1.5000) - >>> l1_loss(pred, target, reduction='none') - tensor([1., 1., 2.]) - >>> l1_loss(pred, target, weight, reduction='sum') - tensor(3.) - """ - - @functools.wraps(loss_func) - def wrapper(pred, target, weight=None, reduction='mean', **kwargs): - # get element-wise loss - loss = loss_func(pred, target, **kwargs) - loss = weight_reduce_loss(loss, weight, reduction) - return loss - - return wrapper diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/exceptiongroup/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/exceptiongroup/__init__.py deleted file mode 100644 index 0e7e02bcf3bc0eb65f8001ca5f530b53d293c31c..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/exceptiongroup/__init__.py +++ /dev/null @@ -1,40 +0,0 @@ -__all__ = [ - "BaseExceptionGroup", - "ExceptionGroup", - "catch", - "format_exception", - "format_exception_only", - "print_exception", - "print_exc", -] - -import os -import sys - -from ._catch import catch -from ._version import version as __version__ # noqa: F401 - -if sys.version_info < (3, 11): - from ._exceptions import BaseExceptionGroup, ExceptionGroup - from ._formatting import ( - format_exception, - format_exception_only, - print_exc, - print_exception, - ) - - if os.getenv("EXCEPTIONGROUP_NO_PATCH") != "1": - from . import _formatting # noqa: F401 - - BaseExceptionGroup.__module__ = __name__ - ExceptionGroup.__module__ = __name__ -else: - from traceback import ( - format_exception, - format_exception_only, - print_exc, - print_exception, - ) - - BaseExceptionGroup = BaseExceptionGroup - ExceptionGroup = ExceptionGroup diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/cli.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/cli.py deleted file mode 100644 index b521bbf0a3cb59ede6ca30f6b684335dd46215f8..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/cli.py +++ /dev/null @@ -1,21 +0,0 @@ -import sys - -from gradio_client.cli import deploy_discord # type: ignore - -import gradio.cli_env_info -import gradio.deploy_space -import gradio.reload - - -def cli(): - args = sys.argv[1:] - if len(args) == 0: - raise ValueError("No file specified.") - elif args[0] == "deploy": - gradio.deploy_space.deploy() - elif args[0] == "environment": - gradio.cli_env_info.print_environment_info() - elif args[0] == "deploy-discord": - deploy_discord.main() - else: - gradio.reload.main() diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/prism-dark-aecd8de4.css b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/prism-dark-aecd8de4.css deleted file mode 100644 index 16bafb330899e498509fbf176cd4e3f9e096fb4f..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/prism-dark-aecd8de4.css +++ /dev/null @@ -1 +0,0 @@ -.gradio-container-3-40-1 code[class*=language-],.gradio-container-3-40-1 pre[class*=language-]{color:#fff;background:none;text-shadow:0 -.1em .2em black;font-family:Consolas,Monaco,Andale Mono,Ubuntu Mono,monospace;font-size:1em;text-align:left;white-space:pre;word-spacing:normal;word-break:normal;word-wrap:normal;line-height:1.5;-moz-tab-size:4;-o-tab-size:4;tab-size:4;-webkit-hyphens:none;-moz-hyphens:none;-ms-hyphens:none;hyphens:none}@media print{.gradio-container-3-40-1 code[class*=language-],.gradio-container-3-40-1 pre[class*=language-]{text-shadow:none}}.gradio-container-3-40-1 pre[class*=language-],.gradio-container-3-40-1 :not(pre)>code[class*=language-]{background:hsl(30,20%,25%)}.gradio-container-3-40-1 pre[class*=language-]{padding:1em;margin:.5em 0;overflow:auto;border:.3em solid hsl(30,20%,40%);border-radius:.5em;box-shadow:1px 1px .5em #000 inset}.gradio-container-3-40-1 :not(pre)>code[class*=language-]{padding:.15em .2em .05em;border-radius:.3em;border:.13em solid hsl(30,20%,40%);box-shadow:1px 1px .3em -.1em #000 inset;white-space:normal}.gradio-container-3-40-1 .token.comment,.gradio-container-3-40-1 .token.prolog,.gradio-container-3-40-1 .token.doctype,.gradio-container-3-40-1 .token.cdata{color:#998066}.gradio-container-3-40-1 .token.punctuation,.gradio-container-3-40-1 .token.namespace{opacity:.7}.gradio-container-3-40-1 .token.property,.gradio-container-3-40-1 .token.tag,.gradio-container-3-40-1 .token.boolean,.gradio-container-3-40-1 .token.number,.gradio-container-3-40-1 .token.constant,.gradio-container-3-40-1 .token.symbol{color:#d1949e}.gradio-container-3-40-1 .token.selector,.gradio-container-3-40-1 .token.attr-name,.gradio-container-3-40-1 .token.string,.gradio-container-3-40-1 .token.char,.gradio-container-3-40-1 .token.builtin,.gradio-container-3-40-1 .token.inserted{color:#bde052}.gradio-container-3-40-1 .token.operator,.gradio-container-3-40-1 .token.entity,.gradio-container-3-40-1 .token.url,.gradio-container-3-40-1 .language-css .token.string,.gradio-container-3-40-1 .style .token.string,.gradio-container-3-40-1 .token.variable{color:#f5b83d}.gradio-container-3-40-1 .token.atrule,.gradio-container-3-40-1 .token.attr-value,.gradio-container-3-40-1 .token.keyword{color:#d1949e}.gradio-container-3-40-1 .token.regex,.gradio-container-3-40-1 .token.important{color:#e90}.gradio-container-3-40-1 .token.important,.gradio-container-3-40-1 .token.bold{font-weight:700}.gradio-container-3-40-1 .token.italic{font-style:italic}.gradio-container-3-40-1 .token.entity{cursor:help}.gradio-container-3-40-1 .token.deleted{color:red} diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/data02/two/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/data02/two/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jinja2/exceptions.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jinja2/exceptions.py deleted file mode 100644 index 082ebe8f221d4e7e980e4d321c0a0c5da033b124..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jinja2/exceptions.py +++ /dev/null @@ -1,166 +0,0 @@ -import typing as t - -if t.TYPE_CHECKING: - from .runtime import Undefined - - -class TemplateError(Exception): - """Baseclass for all template errors.""" - - def __init__(self, message: t.Optional[str] = None) -> None: - super().__init__(message) - - @property - def message(self) -> t.Optional[str]: - return self.args[0] if self.args else None - - -class TemplateNotFound(IOError, LookupError, TemplateError): - """Raised if a template does not exist. - - .. versionchanged:: 2.11 - If the given name is :class:`Undefined` and no message was - provided, an :exc:`UndefinedError` is raised. - """ - - # Silence the Python warning about message being deprecated since - # it's not valid here. - message: t.Optional[str] = None - - def __init__( - self, - name: t.Optional[t.Union[str, "Undefined"]], - message: t.Optional[str] = None, - ) -> None: - IOError.__init__(self, name) - - if message is None: - from .runtime import Undefined - - if isinstance(name, Undefined): - name._fail_with_undefined_error() - - message = name - - self.message = message - self.name = name - self.templates = [name] - - def __str__(self) -> str: - return str(self.message) - - -class TemplatesNotFound(TemplateNotFound): - """Like :class:`TemplateNotFound` but raised if multiple templates - are selected. This is a subclass of :class:`TemplateNotFound` - exception, so just catching the base exception will catch both. - - .. versionchanged:: 2.11 - If a name in the list of names is :class:`Undefined`, a message - about it being undefined is shown rather than the empty string. - - .. versionadded:: 2.2 - """ - - def __init__( - self, - names: t.Sequence[t.Union[str, "Undefined"]] = (), - message: t.Optional[str] = None, - ) -> None: - if message is None: - from .runtime import Undefined - - parts = [] - - for name in names: - if isinstance(name, Undefined): - parts.append(name._undefined_message) - else: - parts.append(name) - - parts_str = ", ".join(map(str, parts)) - message = f"none of the templates given were found: {parts_str}" - - super().__init__(names[-1] if names else None, message) - self.templates = list(names) - - -class TemplateSyntaxError(TemplateError): - """Raised to tell the user that there is a problem with the template.""" - - def __init__( - self, - message: str, - lineno: int, - name: t.Optional[str] = None, - filename: t.Optional[str] = None, - ) -> None: - super().__init__(message) - self.lineno = lineno - self.name = name - self.filename = filename - self.source: t.Optional[str] = None - - # this is set to True if the debug.translate_syntax_error - # function translated the syntax error into a new traceback - self.translated = False - - def __str__(self) -> str: - # for translated errors we only return the message - if self.translated: - return t.cast(str, self.message) - - # otherwise attach some stuff - location = f"line {self.lineno}" - name = self.filename or self.name - if name: - location = f'File "{name}", {location}' - lines = [t.cast(str, self.message), " " + location] - - # if the source is set, add the line to the output - if self.source is not None: - try: - line = self.source.splitlines()[self.lineno - 1] - except IndexError: - pass - else: - lines.append(" " + line.strip()) - - return "\n".join(lines) - - def __reduce__(self): # type: ignore - # https://bugs.python.org/issue1692335 Exceptions that take - # multiple required arguments have problems with pickling. - # Without this, raises TypeError: __init__() missing 1 required - # positional argument: 'lineno' - return self.__class__, (self.message, self.lineno, self.name, self.filename) - - -class TemplateAssertionError(TemplateSyntaxError): - """Like a template syntax error, but covers cases where something in the - template caused an error at compile time that wasn't necessarily caused - by a syntax error. However it's a direct subclass of - :exc:`TemplateSyntaxError` and has the same attributes. - """ - - -class TemplateRuntimeError(TemplateError): - """A generic runtime error in the template engine. Under some situations - Jinja may raise this exception. - """ - - -class UndefinedError(TemplateRuntimeError): - """Raised if a template tries to operate on :class:`Undefined`.""" - - -class SecurityError(TemplateRuntimeError): - """Raised if a template tries to do something insecure if the - sandbox is enabled. - """ - - -class FilterArgumentError(TemplateRuntimeError): - """This error is raised if a filter was called with inappropriate - arguments - """ diff --git a/spaces/deepwisdom/MetaGPT/README.md b/spaces/deepwisdom/MetaGPT/README.md deleted file mode 100644 index 2f7fe473a1e348220e2943b6053702eabb3290f4..0000000000000000000000000000000000000000 --- a/spaces/deepwisdom/MetaGPT/README.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -title: MetaGPT -emoji: 🐼 -colorFrom: green -colorTo: blue -sdk: docker -app_file: app.py -pinned: false ---- diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/tools/__init__.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/tools/__init__.py deleted file mode 100644 index e89055a00f1992b8beb7be82206fb222e99fbcc5..0000000000000000000000000000000000000000 --- a/spaces/deepwisdom/MetaGPT/tests/metagpt/tools/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/4/29 16:27 -@Author : alexanderwu -@File : __init__.py -""" diff --git a/spaces/diacanFperku/AutoGPT/Bengal Tiger Movie Hindi Dubbed Hd Torrent Download.md b/spaces/diacanFperku/AutoGPT/Bengal Tiger Movie Hindi Dubbed Hd Torrent Download.md deleted file mode 100644 index 16de1c9842324fa8571e799e536fd406bdea7e82..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Bengal Tiger Movie Hindi Dubbed Hd Torrent Download.md +++ /dev/null @@ -1,13 +0,0 @@ -Bengal Tiger movie hindi dubbed hd torrent download
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\ No newline at end of file diff --git a/spaces/diaoren/OpenSetObstacleDetection/opendet2/modeling/losses/instance_contrastive_loss.py b/spaces/diaoren/OpenSetObstacleDetection/opendet2/modeling/losses/instance_contrastive_loss.py deleted file mode 100644 index 80e12bef3020f7f14ede1cd6c44af0218b05d5de..0000000000000000000000000000000000000000 --- a/spaces/diaoren/OpenSetObstacleDetection/opendet2/modeling/losses/instance_contrastive_loss.py +++ /dev/null @@ -1,51 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class ICLoss(nn.Module): - """ Instance Contrastive Loss - """ - def __init__(self, tau=0.1): - super().__init__() - self.tau = tau - - def forward(self, features, labels, queue_features, queue_labels): - device = features.device - # (features.size(0),queue_labels.size(0))，每一行对应embed的真实类别为1，其余为0 - mask = torch.eq(labels[:, None], queue_labels[:, None].T).float().to(device) - - # compute logits - # 计算logits - # (features.size(0),queue_labels.size(0))，计算(zi·zj)/tau - anchor_dot_contrast = torch.div( - torch.matmul(features, queue_features.T), self.tau) - - # for numerical stability - # (features.size(0),1)，每一个筛选出的embed和队列中embed最大logits - logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) - # 将logits每一行减去每一行的最大logits - logits = anchor_dot_contrast - logits_max.detach() - - # (features.size(0),queue_labels.size(0))全1张量 - logits_mask = torch.ones_like(logits) - # mask itself - # 每一行的最大logits处为0，其余处为1 - logits_mask[logits == 0] = 0 - - # 得到最终的mask张量 - mask = mask * logits_mask - - # compute log_prob - # (features.size(0),queue_labels.size(0))，计算分母部分***和论文不一样？？*** - exp_logits = torch.exp(logits) * logits_mask - # (features.size(0),queue_labels.size(0))，分母求和取log，分子为logits，将log除法变为减法 - log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True)) - - # compute mean of log-likelihood over positive - # (features.size(0),)每一行logits只取与对应embed类别的logits相加取平均 - mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) - # loss - loss = - mean_log_prob_pos.mean() - # trick: avoid loss nan - return loss if not torch.isnan(loss) else features.new_tensor(0.0) diff --git a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/bbox_heads/bbox_head.py b/spaces/dineshreddy/WALT/mmdet/models/roi_heads/bbox_heads/bbox_head.py deleted file mode 100644 index 408abef3a244115b4e73748049a228e37ad0665c..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/bbox_heads/bbox_head.py +++ /dev/null @@ -1,483 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from mmcv.runner import auto_fp16, force_fp32 -from torch.nn.modules.utils import _pair - -from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms -from mmdet.models.builder import HEADS, build_loss -from mmdet.models.losses import accuracy - - -@HEADS.register_module() -class BBoxHead(nn.Module): - """Simplest RoI head, with only two fc layers for classification and - regression respectively.""" - - def __init__(self, - with_avg_pool=False, - with_cls=True, - with_reg=True, - roi_feat_size=7, - in_channels=256, - num_classes=80, - bbox_coder=dict( - type='DeltaXYWHBBoxCoder', - clip_border=True, - target_means=[0., 0., 0., 0.], - target_stds=[0.1, 0.1, 0.2, 0.2]), - reg_class_agnostic=False, - reg_decoded_bbox=False, - loss_cls=dict( - type='CrossEntropyLoss', - use_sigmoid=False, - loss_weight=1.0), - loss_bbox=dict( - type='SmoothL1Loss', beta=1.0, loss_weight=1.0)): - super(BBoxHead, self).__init__() - assert with_cls or with_reg - self.with_avg_pool = with_avg_pool - self.with_cls = with_cls - self.with_reg = with_reg - self.roi_feat_size = _pair(roi_feat_size) - self.roi_feat_area = self.roi_feat_size[0] * self.roi_feat_size[1] - self.in_channels = in_channels - self.num_classes = num_classes - self.reg_class_agnostic = reg_class_agnostic - self.reg_decoded_bbox = reg_decoded_bbox - self.fp16_enabled = False - - self.bbox_coder = build_bbox_coder(bbox_coder) - self.loss_cls = build_loss(loss_cls) - self.loss_bbox = build_loss(loss_bbox) - - in_channels = self.in_channels - if self.with_avg_pool: - self.avg_pool = nn.AvgPool2d(self.roi_feat_size) - else: - in_channels *= self.roi_feat_area - if self.with_cls: - # need to add background class - self.fc_cls = nn.Linear(in_channels, num_classes + 1) - if self.with_reg: - out_dim_reg = 4 if reg_class_agnostic else 4 * num_classes - self.fc_reg = nn.Linear(in_channels, out_dim_reg) - self.debug_imgs = None - - def init_weights(self): - # conv layers are already initialized by ConvModule - if self.with_cls: - nn.init.normal_(self.fc_cls.weight, 0, 0.01) - nn.init.constant_(self.fc_cls.bias, 0) - if self.with_reg: - nn.init.normal_(self.fc_reg.weight, 0, 0.001) - nn.init.constant_(self.fc_reg.bias, 0) - - @auto_fp16() - def forward(self, x): - if self.with_avg_pool: - x = self.avg_pool(x) - x = x.view(x.size(0), -1) - cls_score = self.fc_cls(x) if self.with_cls else None - bbox_pred = self.fc_reg(x) if self.with_reg else None - return cls_score, bbox_pred - - def _get_target_single(self, pos_bboxes, neg_bboxes, pos_gt_bboxes, - pos_gt_labels, cfg): - """Calculate the ground truth for proposals in the single image - according to the sampling results. - - Args: - pos_bboxes (Tensor): Contains all the positive boxes, - has shape (num_pos, 4), the last dimension 4 - represents [tl_x, tl_y, br_x, br_y]. - neg_bboxes (Tensor): Contains all the negative boxes, - has shape (num_neg, 4), the last dimension 4 - represents [tl_x, tl_y, br_x, br_y]. - pos_gt_bboxes (Tensor): Contains all the gt_boxes, - has shape (num_gt, 4), the last dimension 4 - represents [tl_x, tl_y, br_x, br_y]. - pos_gt_labels (Tensor): Contains all the gt_labels, - has shape (num_gt). - cfg (obj:`ConfigDict`): `train_cfg` of R-CNN. - - Returns: - Tuple[Tensor]: Ground truth for proposals - in a single image. Containing the following Tensors: - - - labels(Tensor): Gt_labels for all proposals, has - shape (num_proposals,). - - label_weights(Tensor): Labels_weights for all - proposals, has shape (num_proposals,). - - bbox_targets(Tensor):Regression target for all - proposals, has shape (num_proposals, 4), the - last dimension 4 represents [tl_x, tl_y, br_x, br_y]. - - bbox_weights(Tensor):Regression weights for all - proposals, has shape (num_proposals, 4). - """ - num_pos = pos_bboxes.size(0) - num_neg = neg_bboxes.size(0) - num_samples = num_pos + num_neg - - # original implementation uses new_zeros since BG are set to be 0 - # now use empty & fill because BG cat_id = num_classes, - # FG cat_id = [0, num_classes-1] - labels = pos_bboxes.new_full((num_samples, ), - self.num_classes, - dtype=torch.long) - label_weights = pos_bboxes.new_zeros(num_samples) - bbox_targets = pos_bboxes.new_zeros(num_samples, 4) - bbox_weights = pos_bboxes.new_zeros(num_samples, 4) - if num_pos > 0: - labels[:num_pos] = pos_gt_labels - pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight - label_weights[:num_pos] = pos_weight - if not self.reg_decoded_bbox: - pos_bbox_targets = self.bbox_coder.encode( - pos_bboxes, pos_gt_bboxes) - else: - # When the regression loss (e.g. `IouLoss`, `GIouLoss`) - # is applied directly on the decoded bounding boxes, both - # the predicted boxes and regression targets should be with - # absolute coordinate format. - pos_bbox_targets = pos_gt_bboxes - bbox_targets[:num_pos, :] = pos_bbox_targets - bbox_weights[:num_pos, :] = 1 - if num_neg > 0: - label_weights[-num_neg:] = 1.0 - - return labels, label_weights, bbox_targets, bbox_weights - - def get_targets(self, - sampling_results, - gt_bboxes, - gt_labels, - rcnn_train_cfg, - concat=True): - """Calculate the ground truth for all samples in a batch according to - the sampling_results. - - Almost the same as the implementation in bbox_head, we passed - additional parameters pos_inds_list and neg_inds_list to - `_get_target_single` function. - - Args: - sampling_results (List[obj:SamplingResults]): Assign results of - all images in a batch after sampling. - gt_bboxes (list[Tensor]): Gt_bboxes of all images in a batch, - each tensor has shape (num_gt, 4), the last dimension 4 - represents [tl_x, tl_y, br_x, br_y]. - gt_labels (list[Tensor]): Gt_labels of all images in a batch, - each tensor has shape (num_gt,). - rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN. - concat (bool): Whether to concatenate the results of all - the images in a single batch. - - Returns: - Tuple[Tensor]: Ground truth for proposals in a single image. - Containing the following list of Tensors: - - - labels (list[Tensor],Tensor): Gt_labels for all - proposals in a batch, each tensor in list has - shape (num_proposals,) when `concat=False`, otherwise - just a single tensor has shape (num_all_proposals,). - - label_weights (list[Tensor]): Labels_weights for - all proposals in a batch, each tensor in list has - shape (num_proposals,) when `concat=False`, otherwise - just a single tensor has shape (num_all_proposals,). - - bbox_targets (list[Tensor],Tensor): Regression target - for all proposals in a batch, each tensor in list - has shape (num_proposals, 4) when `concat=False`, - otherwise just a single tensor has shape - (num_all_proposals, 4), the last dimension 4 represents - [tl_x, tl_y, br_x, br_y]. - - bbox_weights (list[tensor],Tensor): Regression weights for - all proposals in a batch, each tensor in list has shape - (num_proposals, 4) when `concat=False`, otherwise just a - single tensor has shape (num_all_proposals, 4). - """ - pos_bboxes_list = [res.pos_bboxes for res in sampling_results] - neg_bboxes_list = [res.neg_bboxes for res in sampling_results] - pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results] - pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results] - labels, label_weights, bbox_targets, bbox_weights = multi_apply( - self._get_target_single, - pos_bboxes_list, - neg_bboxes_list, - pos_gt_bboxes_list, - pos_gt_labels_list, - cfg=rcnn_train_cfg) - - if concat: - labels = torch.cat(labels, 0) - label_weights = torch.cat(label_weights, 0) - bbox_targets = torch.cat(bbox_targets, 0) - bbox_weights = torch.cat(bbox_weights, 0) - return labels, label_weights, bbox_targets, bbox_weights - - @force_fp32(apply_to=('cls_score', 'bbox_pred')) - def loss(self, - cls_score, - bbox_pred, - rois, - labels, - label_weights, - bbox_targets, - bbox_weights, - reduction_override=None): - losses = dict() - if cls_score is not None: - avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.) - if cls_score.numel() > 0: - losses['loss_cls'] = self.loss_cls( - cls_score, - labels, - label_weights, - avg_factor=avg_factor, - reduction_override=reduction_override) - losses['acc'] = accuracy(cls_score, labels) - if bbox_pred is not None: - bg_class_ind = self.num_classes - # 0~self.num_classes-1 are FG, self.num_classes is BG - pos_inds = (labels >= 0) & (labels < bg_class_ind) - # do not perform bounding box regression for BG anymore. - if pos_inds.any(): - if self.reg_decoded_bbox: - # When the regression loss (e.g. `IouLoss`, - # `GIouLoss`, `DIouLoss`) is applied directly on - # the decoded bounding boxes, it decodes the - # already encoded coordinates to absolute format. - bbox_pred = self.bbox_coder.decode(rois[:, 1:], bbox_pred) - if self.reg_class_agnostic: - pos_bbox_pred = bbox_pred.view( - bbox_pred.size(0), 4)[pos_inds.type(torch.bool)] - else: - pos_bbox_pred = bbox_pred.view( - bbox_pred.size(0), -1, - 4)[pos_inds.type(torch.bool), - labels[pos_inds.type(torch.bool)]] - losses['loss_bbox'] = self.loss_bbox( - pos_bbox_pred, - bbox_targets[pos_inds.type(torch.bool)], - bbox_weights[pos_inds.type(torch.bool)], - avg_factor=bbox_targets.size(0), - reduction_override=reduction_override) - else: - losses['loss_bbox'] = bbox_pred[pos_inds].sum() - return losses - - @force_fp32(apply_to=('cls_score', 'bbox_pred')) - def get_bboxes(self, - rois, - cls_score, - bbox_pred, - img_shape, - scale_factor, - rescale=False, - cfg=None): - """Transform network output for a batch into bbox predictions. - - If the input rois has batch dimension, the function would be in - `batch_mode` and return is a tuple[list[Tensor], list[Tensor]], - otherwise, the return is a tuple[Tensor, Tensor]. - - Args: - rois (Tensor): Boxes to be transformed. Has shape (num_boxes, 5) - or (B, num_boxes, 5) - cls_score (list[Tensor] or Tensor): Box scores for - each scale level, each is a 4D-tensor, the channel number is - num_points * num_classes. - bbox_pred (Tensor, optional): Box energies / deltas for each scale - level, each is a 4D-tensor, the channel number is - num_classes * 4. - img_shape (Sequence[int] or torch.Tensor or Sequence[ - Sequence[int]], optional): Maximum bounds for boxes, specifies - (H, W, C) or (H, W). If rois shape is (B, num_boxes, 4), then - the max_shape should be a Sequence[Sequence[int]] - and the length of max_shape should also be B. - scale_factor (tuple[ndarray] or ndarray): Scale factor of the - image arange as (w_scale, h_scale, w_scale, h_scale). In - `batch_mode`, the scale_factor shape is tuple[ndarray]. - rescale (bool): If True, return boxes in original image space. - Default: False. - cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head. Default: None - - Returns: - tuple[list[Tensor], list[Tensor]] or tuple[Tensor, Tensor]: - If the input has a batch dimension, the return value is - a tuple of the list. The first list contains the boxes of - the corresponding image in a batch, each tensor has the - shape (num_boxes, 5) and last dimension 5 represent - (tl_x, tl_y, br_x, br_y, score). Each Tensor in the second - list is the labels with shape (num_boxes, ). The length of - both lists should be equal to batch_size. Otherwise return - value is a tuple of two tensors, the first tensor is the - boxes with scores, the second tensor is the labels, both - have the same shape as the first case. - """ - if isinstance(cls_score, list): - cls_score = sum(cls_score) / float(len(cls_score)) - - scores = F.softmax( - cls_score, dim=-1) if cls_score is not None else None - - batch_mode = True - if rois.ndim == 2: - # e.g. AugTest, Cascade R-CNN, HTC, SCNet... - batch_mode = False - - # add batch dimension - if scores is not None: - scores = scores.unsqueeze(0) - if bbox_pred is not None: - bbox_pred = bbox_pred.unsqueeze(0) - rois = rois.unsqueeze(0) - - if bbox_pred is not None: - bboxes = self.bbox_coder.decode( - rois[..., 1:], bbox_pred, max_shape=img_shape) - else: - bboxes = rois[..., 1:].clone() - if img_shape is not None: - max_shape = bboxes.new_tensor(img_shape)[..., :2] - min_xy = bboxes.new_tensor(0) - max_xy = torch.cat( - [max_shape] * 2, dim=-1).flip(-1).unsqueeze(-2) - bboxes = torch.where(bboxes < min_xy, min_xy, bboxes) - bboxes = torch.where(bboxes > max_xy, max_xy, bboxes) - - if rescale and bboxes.size(-2) > 0: - if not isinstance(scale_factor, tuple): - scale_factor = tuple([scale_factor]) - # B, 1, bboxes.size(-1) - scale_factor = bboxes.new_tensor(scale_factor).unsqueeze(1).repeat( - 1, 1, - bboxes.size(-1) // 4) - bboxes /= scale_factor - - det_bboxes = [] - det_labels = [] - for (bbox, score) in zip(bboxes, scores): - if cfg is not None: - det_bbox, det_label = multiclass_nms(bbox, score, - cfg.score_thr, cfg.nms, - cfg.max_per_img) - else: - det_bbox, det_label = bbox, score - det_bboxes.append(det_bbox) - det_labels.append(det_label) - - if not batch_mode: - det_bboxes = det_bboxes[0] - det_labels = det_labels[0] - return det_bboxes, det_labels - - @force_fp32(apply_to=('bbox_preds', )) - def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): - """Refine bboxes during training. - - Args: - rois (Tensor): Shape (n*bs, 5), where n is image number per GPU, - and bs is the sampled RoIs per image. The first column is - the image id and the next 4 columns are x1, y1, x2, y2. - labels (Tensor): Shape (n*bs, ). - bbox_preds (Tensor): Shape (n*bs, 4) or (n*bs, 4*#class). - pos_is_gts (list[Tensor]): Flags indicating if each positive bbox - is a gt bbox. - img_metas (list[dict]): Meta info of each image. - - Returns: - list[Tensor]: Refined bboxes of each image in a mini-batch. - - Example: - >>> # xdoctest: +REQUIRES(module:kwarray) - >>> import kwarray - >>> import numpy as np - >>> from mmdet.core.bbox.demodata import random_boxes - >>> self = BBoxHead(reg_class_agnostic=True) - >>> n_roi = 2 - >>> n_img = 4 - >>> scale = 512 - >>> rng = np.random.RandomState(0) - >>> img_metas = [{'img_shape': (scale, scale)} - ... for _ in range(n_img)] - >>> # Create rois in the expected format - >>> roi_boxes = random_boxes(n_roi, scale=scale, rng=rng) - >>> img_ids = torch.randint(0, n_img, (n_roi,)) - >>> img_ids = img_ids.float() - >>> rois = torch.cat([img_ids[:, None], roi_boxes], dim=1) - >>> # Create other args - >>> labels = torch.randint(0, 2, (n_roi,)).long() - >>> bbox_preds = random_boxes(n_roi, scale=scale, rng=rng) - >>> # For each image, pretend random positive boxes are gts - >>> is_label_pos = (labels.numpy() > 0).astype(np.int) - >>> lbl_per_img = kwarray.group_items(is_label_pos, - ... img_ids.numpy()) - >>> pos_per_img = [sum(lbl_per_img.get(gid, [])) - ... for gid in range(n_img)] - >>> pos_is_gts = [ - >>> torch.randint(0, 2, (npos,)).byte().sort( - >>> descending=True)[0] - >>> for npos in pos_per_img - >>> ] - >>> bboxes_list = self.refine_bboxes(rois, labels, bbox_preds, - >>> pos_is_gts, img_metas) - >>> print(bboxes_list) - """ - img_ids = rois[:, 0].long().unique(sorted=True) - assert img_ids.numel() <= len(img_metas) - - bboxes_list = [] - for i in range(len(img_metas)): - inds = torch.nonzero( - rois[:, 0] == i, as_tuple=False).squeeze(dim=1) - num_rois = inds.numel() - - bboxes_ = rois[inds, 1:] - label_ = labels[inds] - bbox_pred_ = bbox_preds[inds] - img_meta_ = img_metas[i] - pos_is_gts_ = pos_is_gts[i] - - bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_, - img_meta_) - - # filter gt bboxes - pos_keep = 1 - pos_is_gts_ - keep_inds = pos_is_gts_.new_ones(num_rois) - keep_inds[:len(pos_is_gts_)] = pos_keep - - bboxes_list.append(bboxes[keep_inds.type(torch.bool)]) - - return bboxes_list - - @force_fp32(apply_to=('bbox_pred', )) - def regress_by_class(self, rois, label, bbox_pred, img_meta): - """Regress the bbox for the predicted class. Used in Cascade R-CNN. - - Args: - rois (Tensor): shape (n, 4) or (n, 5) - label (Tensor): shape (n, ) - bbox_pred (Tensor): shape (n, 4*(#class)) or (n, 4) - img_meta (dict): Image meta info. - - Returns: - Tensor: Regressed bboxes, the same shape as input rois. - """ - assert rois.size(1) == 4 or rois.size(1) == 5, repr(rois.shape) - - if not self.reg_class_agnostic: - label = label * 4 - inds = torch.stack((label, label + 1, label + 2, label + 3), 1) - bbox_pred = torch.gather(bbox_pred, 1, inds) - assert bbox_pred.size(1) == 4 - - if rois.size(1) == 4: - new_rois = self.bbox_coder.decode( - rois, bbox_pred, max_shape=img_meta['img_shape']) - else: - bboxes = self.bbox_coder.decode( - rois[:, 1:], bbox_pred, max_shape=img_meta['img_shape']) - new_rois = torch.cat((rois[:, [0]], bboxes), dim=1) - - return new_rois diff --git a/spaces/dma123/gpt-js/js/chatlog.js b/spaces/dma123/gpt-js/js/chatlog.js deleted file mode 100644 index 098b7d5df6eae727082bde96ccadfe13e5861d7d..0000000000000000000000000000000000000000 --- a/spaces/dma123/gpt-js/js/chatlog.js +++ /dev/null @@ -1,196 +0,0 @@ -"use strict"; - - -class Message { - constructor(value) { - this.value = value; - this.metadata = null; - this.cache = null; - this.answerAlternatives = null; - } - - getAnswerMessage() { - if (this.answerAlternatives === null) return null; - return this.answerAlternatives.getActiveMessage(); - } - - toJSON() { - return { value: this.value, metadata: this.metadata, answerAlternatives: this.answerAlternatives }; - } -} - - -// Alternatives of a message at one position in the tree -class Alternatives { - constructor() { - this.messages = []; - this.activeMessageIndex = -1; - } - - addMessage(value) { - const current = this.getActiveMessage(); - if (current !== null && current.value === null) { - current.value = value; - return current; - } - this.clearCache(); - const newMessage = new Message(value); - this.activeMessageIndex = this.messages.push(newMessage) - 1; - return newMessage; - } - - setActiveMessage(value) { - const index = this.messages.findIndex((element) => element.value === value); - if (index !== -1) { - this.activeMessageIndex = index; - } - } - - getActiveMessage() { - if (this.activeMessageIndex === -1) return null; - return this.messages[this.activeMessageIndex] || null; - } - - next() { - if (this.activeMessageIndex === -1) return null; - if (this.messages[this.activeMessageIndex] === null || this.messages[this.activeMessageIndex].value === null) { - this.messages.splice(this.activeMessageIndex, 1); - this.clearCache(); - } - this.activeMessageIndex++; - if (this.activeMessageIndex > this.messages.length - 1) this.activeMessageIndex = 0; - return this.messages[this.activeMessageIndex]; - } - - prev() { - if (this.activeMessageIndex === -1) return null; - if (this.messages[this.activeMessageIndex] === null || this.messages[this.activeMessageIndex].value === null) { - this.messages.splice(this.activeMessageIndex, 1); - this.clearCache(); - } - this.activeMessageIndex--; - if (this.activeMessageIndex < 0) this.activeMessageIndex = this.messages.length - 1; - return this.messages[this.activeMessageIndex]; - } - - clearCache() { - for (const msg of this.messages) { - if (msg !== null && msg !== undefined) msg.cache = null; - } - } -} - - -// Chatlog is a whole tree of messages -class Chatlog { - constructor() { - this.rootAlternatives = null; - } - - addMessage(value) { - const lastMessage = this.getLastMessage(); - if (lastMessage === null) { - this.rootAlternatives = new Alternatives(); - return this.rootAlternatives.addMessage(value); - } - if (lastMessage.value === null) { - lastMessage.value = value; - return lastMessage; - } - lastMessage.answerAlternatives = new Alternatives(); - return lastMessage.answerAlternatives.addMessage(value); - } - - getFirstMessage() { - if (this.rootAlternatives === null) return null; - return this.rootAlternatives.getActiveMessage(); - } - - getLastMessage() { - const lastAlternatives = this.getLastAlternatives(); - if (lastAlternatives === null) return null; - return lastAlternatives.getActiveMessage(); - } - - getNthMessage(n) { - n = parseInt(n); - let alternative = getNthAlternatives(n); - if (alternative === null) return null; - return alternative.getActiveMessage(); - } - - getNthAlternatives(n) { - n = parseInt(n); - let pos = 0; - let current = this.rootAlternatives; - while (current !== null) { - if (pos === n) return current; - const activeMessage = current.getActiveMessage(); - if (activeMessage === null || activeMessage.answerAlternatives === null) break; - current = activeMessage.answerAlternatives; - pos++; - } - return null; - } - - getLastAlternatives() { - let current = this.rootAlternatives; - let last = current; - while (current !== null) { - last = current; - const activeMessage = current.getActiveMessage(); - if (activeMessage === null || activeMessage.answerAlternatives === null) break; - current = activeMessage.answerAlternatives; - } - return last; - } - - // getActiveMessages() { - // let result = []; - // // Trace the active path through the chatlog - // let message = this.getFirstMessage(); - // while (message !== null) { - // result.push(message); - // if (message.value === null) break; - // message = message.getAnswerMessage(); - // } - // return result; - // } - - getActiveMessageValues() { - let result = []; - // Trace the active path through the chatlog - let message = this.getFirstMessage(); - while (message !== null && message.value !== null) { - result.push(message.value); - message = message.getAnswerMessage(); - } - return result; - } - - load(alternative) { - let msgcount = 0; - const buildAlternatives = (parsedAlt) => { - if (!parsedAlt) return null; - - const alt = new Alternatives(); - alt.activeMessageIndex = parsedAlt.activeMessageIndex; - - for (const parsedMessage of parsedAlt.messages) { - const msg = new Message(parsedMessage.value); - msg.metadata = parsedMessage.metadata; - msg.answerAlternatives = buildAlternatives(parsedMessage.answerAlternatives); - alt.messages.push(msg); - msgcount++; - } - - return alt; - }; - - this.rootAlternatives = buildAlternatives(alternative); - } - - clearCache() { - this.load(this.rootAlternatives); - } -} diff --git a/spaces/dolceschokolade/chatbot-mini/pages/api/home/home.state.tsx b/spaces/dolceschokolade/chatbot-mini/pages/api/home/home.state.tsx deleted file mode 100644 index 3537bffb87952df2a361db3b1c8f960b04ca4091..0000000000000000000000000000000000000000 --- a/spaces/dolceschokolade/chatbot-mini/pages/api/home/home.state.tsx +++ /dev/null @@ -1,54 +0,0 @@ -import { Conversation, Message } from '@/types/chat'; -import { ErrorMessage } from '@/types/error'; -import { FolderInterface } from '@/types/folder'; -import { OpenAIModel, OpenAIModelID } from '@/types/openai'; -import { PluginKey } from '@/types/plugin'; -import { Prompt } from '@/types/prompt'; - -export interface HomeInitialState { - apiKey: string; - pluginKeys: PluginKey[]; - loading: boolean; - lightMode: 'light' | 'dark'; - messageIsStreaming: boolean; - modelError: ErrorMessage | null; - models: OpenAIModel[]; - folders: FolderInterface[]; - conversations: Conversation[]; - selectedConversation: Conversation | undefined; - currentMessage: Message | undefined; - prompts: Prompt[]; - temperature: number; - showChatbar: boolean; - showPromptbar: boolean; - currentFolder: FolderInterface | undefined; - messageError: boolean; - searchTerm: string; - defaultModelId: OpenAIModelID | undefined; - serverSideApiKeyIsSet: boolean; - serverSidePluginKeysSet: boolean; -} - -export const initialState: HomeInitialState = { - apiKey: '', - loading: false, - pluginKeys: [], - lightMode: 'dark', - messageIsStreaming: false, - modelError: null, - models: [], - folders: [], - conversations: [], - selectedConversation: undefined, - currentMessage: undefined, - prompts: [], - temperature: 1, - showPromptbar: true, - showChatbar: true, - currentFolder: undefined, - messageError: false, - searchTerm: '', - defaultModelId: undefined, - serverSideApiKeyIsSet: false, - serverSidePluginKeysSet: false, -}; diff --git a/spaces/domro11/data_dynamos/README.md b/spaces/domro11/data_dynamos/README.md deleted file mode 100644 index 09bb450e0a26c12953a5bb8d99caa662c9259381..0000000000000000000000000000000000000000 --- a/spaces/domro11/data_dynamos/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Data Dynamos -emoji: 😻 -colorFrom: indigo -colorTo: blue -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/dorkai/text-generation-webui-main/text-generation-webui-main/modules/logging_colors.py b/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/modules/logging_colors.py deleted file mode 100644 index 5485b0901677fbab117015097f3af78401ae3419..0000000000000000000000000000000000000000 --- a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/modules/logging_colors.py +++ /dev/null @@ -1,107 +0,0 @@ -# Copied from https://stackoverflow.com/a/1336640 - -import logging - -def add_coloring_to_emit_windows(fn): - # add methods we need to the class - def _out_handle(self): - import ctypes - return ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE) - out_handle = property(_out_handle) - - def _set_color(self, code): - import ctypes - # Constants from the Windows API - self.STD_OUTPUT_HANDLE = -11 - hdl = ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE) - ctypes.windll.kernel32.SetConsoleTextAttribute(hdl, code) - - setattr(logging.StreamHandler, '_set_color', _set_color) - - def new(*args): - FOREGROUND_BLUE = 0x0001 # text color contains blue. - FOREGROUND_GREEN = 0x0002 # text color contains green. - FOREGROUND_RED = 0x0004 # text color contains red. - FOREGROUND_INTENSITY = 0x0008 # text color is intensified. - FOREGROUND_WHITE = FOREGROUND_BLUE | FOREGROUND_GREEN | FOREGROUND_RED - # winbase.h - # STD_INPUT_HANDLE = -10 - # STD_OUTPUT_HANDLE = -11 - # STD_ERROR_HANDLE = -12 - - # wincon.h - # FOREGROUND_BLACK = 0x0000 - FOREGROUND_BLUE = 0x0001 - FOREGROUND_GREEN = 0x0002 - # FOREGROUND_CYAN = 0x0003 - FOREGROUND_RED = 0x0004 - FOREGROUND_MAGENTA = 0x0005 - FOREGROUND_YELLOW = 0x0006 - # FOREGROUND_GREY = 0x0007 - FOREGROUND_INTENSITY = 0x0008 # foreground color is intensified. - - # BACKGROUND_BLACK = 0x0000 - # BACKGROUND_BLUE = 0x0010 - # BACKGROUND_GREEN = 0x0020 - # BACKGROUND_CYAN = 0x0030 - # BACKGROUND_RED = 0x0040 - # BACKGROUND_MAGENTA = 0x0050 - BACKGROUND_YELLOW = 0x0060 - # BACKGROUND_GREY = 0x0070 - BACKGROUND_INTENSITY = 0x0080 # background color is intensified. - - levelno = args[1].levelno - if (levelno >= 50): - color = BACKGROUND_YELLOW | FOREGROUND_RED | FOREGROUND_INTENSITY | BACKGROUND_INTENSITY - elif (levelno >= 40): - color = FOREGROUND_RED | FOREGROUND_INTENSITY - elif (levelno >= 30): - color = FOREGROUND_YELLOW | FOREGROUND_INTENSITY - elif (levelno >= 20): - color = FOREGROUND_GREEN - elif (levelno >= 10): - color = FOREGROUND_MAGENTA - else: - color = FOREGROUND_WHITE - args[0]._set_color(color) - - ret = fn(*args) - args[0]._set_color(FOREGROUND_WHITE) - # print "after" - return ret - return new - - -def add_coloring_to_emit_ansi(fn): - # add methods we need to the class - def new(*args): - levelno = args[1].levelno - if (levelno >= 50): - color = '\x1b[31m' # red - elif (levelno >= 40): - color = '\x1b[31m' # red - elif (levelno >= 30): - color = '\x1b[33m' # yellow - elif (levelno >= 20): - color = '\x1b[32m' # green - elif (levelno >= 10): - color = '\x1b[35m' # pink - else: - color = '\x1b[0m' # normal - args[1].msg = color + args[1].msg + '\x1b[0m' # normal - # print "after" - return fn(*args) - return new - - -import platform -if platform.system() == 'Windows': - # Windows does not support ANSI escapes and we are using API calls to set the console color - logging.StreamHandler.emit = add_coloring_to_emit_windows(logging.StreamHandler.emit) -else: - # all non-Windows platforms are supporting ANSI escapes so we use them - logging.StreamHandler.emit = add_coloring_to_emit_ansi(logging.StreamHandler.emit) - # log = logging.getLogger() - # log.addFilter(log_filter()) - # //hdlr = logging.StreamHandler() - # //hdlr.setFormatter(formatter()) diff --git a/spaces/ds520/bingo/src/components/chat-list.tsx b/spaces/ds520/bingo/src/components/chat-list.tsx deleted file mode 100644 index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000 --- a/spaces/ds520/bingo/src/components/chat-list.tsx +++ /dev/null @@ -1,28 +0,0 @@ -import React from 'react' - -import { Separator } from '@/components/ui/separator' -import { ChatMessage } from '@/components/chat-message' -import { ChatMessageModel } from '@/lib/bots/bing/types' - -export interface ChatList { - messages: ChatMessageModel[] -} - -export function ChatList({ messages }: ChatList) { - if (!messages.length) { - return null - } - - return ( -- {messages.map((message, index) => ( -- ) -} diff --git a/spaces/dxcy/Real-CUGAN/README.md b/spaces/dxcy/Real-CUGAN/README.md deleted file mode 100644 index d673114edadba73e80f33a3c71bc0dbee8758cc8..0000000000000000000000000000000000000000 --- a/spaces/dxcy/Real-CUGAN/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Real CUGAN -emoji: 🐢 -colorFrom: gray -colorTo: green -sdk: gradio -sdk_version: 3.6 -app_file: app.py -pinned: false -license: gpl-3.0 -duplicated_from: DianXian/Real-CUGAN ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/empulse/ehartford-WizardLM-30B-Uncensored/app.py b/spaces/empulse/ehartford-WizardLM-30B-Uncensored/app.py deleted file mode 100644 index 5dffc037ef7e3be5c045d5619815c556e4ea7059..0000000000000000000000000000000000000000 --- a/spaces/empulse/ehartford-WizardLM-30B-Uncensored/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/ehartford/WizardLM-30B-Uncensored").launch() \ No newline at end of file diff --git a/spaces/erbanku/gpt-academic/main.py b/spaces/erbanku/gpt-academic/main.py deleted file mode 100644 index 5932b83ed9b3bffcfbcf613ec3175d253c674bd6..0000000000000000000000000000000000000000 --- a/spaces/erbanku/gpt-academic/main.py +++ /dev/null @@ -1,209 +0,0 @@ -import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染 - -def main(): - import gradio as gr - from request_llm.bridge_all import predict - from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith - # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到 - proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS = \ - get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS') - - # 如果WEB_PORT是-1, 则随机选取WEB端口 - PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT - if not AUTHENTICATION: AUTHENTICATION = None - - from check_proxy import get_current_version - initial_prompt = "Serve me as a writing and programming assistant." - title_html = f"- - ))} -- {index < messages.length - 1 && ( - - )} - ChatGPT 学术优化 {get_current_version()}
" - description = """代码开源和更新[地址🚀](https://github.com/binary-husky/chatgpt_academic)，感谢热情的[开发者们❤️](https://github.com/binary-husky/chatgpt_academic/graphs/contributors)""" - - # 问询记录, python 版本建议3.9+（越新越好） - import logging - os.makedirs("gpt_log", exist_ok=True) - try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8") - except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO) - print("所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！") - - # 一些普通功能模块 - from core_functional import get_core_functions - functional = get_core_functions() - - # 高级函数插件 - from crazy_functional import get_crazy_functions - crazy_fns = get_crazy_functions() - - # 处理markdown文本格式的转变 - gr.Chatbot.postprocess = format_io - - # 做一些外观色彩上的调整 - from theme import adjust_theme, advanced_css - set_theme = adjust_theme() - - # 代理与自动更新 - from check_proxy import check_proxy, auto_update, warm_up_modules - proxy_info = check_proxy(proxies) - - gr_L1 = lambda: gr.Row().style() - gr_L2 = lambda scale: gr.Column(scale=scale) - if LAYOUT == "TOP-DOWN": - gr_L1 = lambda: DummyWith() - gr_L2 = lambda scale: gr.Row() - CHATBOT_HEIGHT /= 2 - - cancel_handles = [] - with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo: - gr.HTML(title_html) - gr.HTML('''请您打开此页面后务必点击上方的“复制空间”（Duplicate Space）按钮！使用时，先在输入框填入API-KEY然后回车。
切忌在“复制空间”（Duplicate Space）之前填入API_KEY或进行提问，否则您的API_KEY将极可能被空间所有者攫取！
支持任意数量的OpenAI的密钥和API2D的密钥共存，例如输入"OpenAI密钥1,API2D密钥2"，然后提交，即可同时使用两种模型接口。- -{ - setText(event.target.value); - }} - onBlur={() => { - onSave(text); - }} - /> - { - onSave(text); - }} - > - - - ); -} diff --git a/spaces/falterWliame/Face_Mask_Detection/Mean Girls Burn Book Font 22 !!HOT!!.md b/spaces/falterWliame/Face_Mask_Detection/Mean Girls Burn Book Font 22 !!HOT!!.md deleted file mode 100644 index fcfc1ec220b181bb60518253f1255cb1c72a2587..0000000000000000000000000000000000000000 --- a/spaces/falterWliame/Face_Mask_Detection/Mean Girls Burn Book Font 22 !!HOT!!.md +++ /dev/null @@ -1,44 +0,0 @@ -- mean girls burn book font 22
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- diff --git a/spaces/farukozderim/Model-Comparator-Space-Builder/app.py b/spaces/farukozderim/Model-Comparator-Space-Builder/app.py deleted file mode 100644 index 7ede55834fe0e76ef84f40da43396bc61485fbce..0000000000000000000000000000000000000000 --- a/spaces/farukozderim/Model-Comparator-Space-Builder/app.py +++ /dev/null @@ -1,252 +0,0 @@ -from typing import List - -import numpy as np -import requests -import gradio as gr -import time -import os - -from huggingface_hub import ( - create_repo, - get_full_repo_name, - upload_file, -) - - -class SpaceBuilder: - error_message = None - url = None - - @classmethod - def split_space_names(cls, names: str) -> List[str]: - """ - Splits and filters the given space_names. - - :param names: space names - :return: Name List - """ - name_list = names.split("\n") - filtered_list = [] - for name in name_list: - if not (name == "" or name.isspace()): - name = name.replace(" ", "") - filtered_list.append(name) - return filtered_list - - @classmethod - def file_as_a_string(cls, name_list: List[str], title: str, description: str) -> str: - """ - Returns the file that is going to be created in the new space as string. - - :param name_list: list of space names - :param title: title - :param description: description - :return: file as a string - """ - return ( - f"import gradio as gr" - f"\nname_list = {name_list}" - f"\ninterfaces = [gr.Interface.load(name) for name in name_list]" - f"\ngr.mix.Parallel(*interfaces, title=\"{title}\", description=\"{description}\").launch()" - ) - - @classmethod - def control_input_and_output_types( - cls, interface_list: List["gr.Interface"] - ) -> bool: - """ - Controls whether if input and output types of the given interfaces are the same. - - :param interface_list: list of interfaces - :return: True if all input and output types are the same - """ - first_input_types = [ - type(input) for input in interface_list[0].input_components - ] - first_output_types = [ - type(output) for output in interface_list[0].output_components - ] - for interface in interface_list: - interface_input_types = [ - type(input) for input in interface.input_components - ] - if not np.all( - interface_input_types == first_input_types - ): # Vectorize the comparison and don't use double for loop - cls.error_message = "Provided space input types are different" - return False - interface_output_types = [ - type(output) for output in interface.output_components - ] - if not np.all(interface_output_types == first_output_types): - cls.error_message = "Provided space output types are different" - return False - - return True - - @classmethod - def check_space_name_availability(cls, hf_token: str, space_name: str) -> bool: - """ - Check whether if the space_name is currently used. - - :param hf_token: hugging_face token - :param space_name: - :return: True if the space_name is available - """ - try: - repo_name = get_full_repo_name(model_id=space_name, token=hf_token) - except Exception as ex: - print(ex) - cls.error_message = "You have given an incorrect HuggingFace token" - return False - try: - url = f"https://huggingface.co/spaces/{repo_name}" - response = requests.get(url) - if response.status_code == 200: - cls.error_message = f"The {repo_name} is already used." - return False - else: - print(f"The space name {repo_name} is available") - return True - except Exception as ex: - print(ex) - cls.error_message = "Can not send a request to https://huggingface.co" - return False - - @classmethod - def load_and_check_spaces(cls, names: str) -> bool: - """ - Loads given space inputs as interfaces and checks whether if they are loadable. - - :param names: Input space names - :return: True if check is successful - """ - name_list = cls.split_space_names(names) - - try: - # We could gather these interfaces in parallel if gradio was supporting async gathering. It will probably be possible after the migration to the FastAPI is completed. - interfaces = [gr.Interface.load(name) for name in name_list] - except Exception as ex: - print(ex) - cls.error_message = ( - f"One of the given space cannot be loaded to gradio, sorry for the inconvenience. " - f"\nPlease use different input space names!" - ) - return False - if not cls.control_input_and_output_types(interfaces): - return False - else: - print("Loaded and checked input spaces, great it works!") - return True - - @classmethod - def create_space(cls, input_space_names: str, target_space_name: str, hf_token: str, title: str, description: str) -> bool: - """ - Creates the target space with the given space names. - - :param input_space_names: Input space name_list - :param target_space_name: Target space_name - :param hf_token: HuggingFace Write Token - :param title: Target Interface Title - :param description: Target Interface Description - :return: True if success - """ - name_list = cls.split_space_names(input_space_names) - try: - create_repo(name=target_space_name, token=hf_token, repo_type="space", space_sdk="gradio") - except Exception as ex: - print(ex) - cls.error_message = "Please provide a correct space name as Only regular characters and '-', '_', '.' accepted. '--' and '..' are forbidden. '-' and '.' cannot start or end the name." - return False - repo_name = get_full_repo_name(model_id=target_space_name, token=hf_token) - - try: - file_string = cls.file_as_a_string(name_list, title, description) - temp_file = open("temp_file.txt", "w") - temp_file.write(file_string) - temp_file.close() - except Exception as ex: - print(ex) - cls.error_message = "An exception occurred during temporary file writing" - return False - try: - file_url = upload_file( - path_or_fileobj="temp_file.txt", - path_in_repo="app.py", - repo_id=repo_name, - repo_type="space", - token=hf_token, - ) - cls.url = f"https://huggingface.co/spaces/{repo_name}" - return True - except Exception as ex: - print(ex) - cls.error_message = ( - "An exception occurred during writing app.py to the target space" - ) - return False - - @staticmethod - def build_space( - model_or_space_names: str, hf_token: str, target_space_name: str, interface_title: str, interface_description: str - ) -> str: - """ - Creates a space with given input spaces. - - :param model_or_space_names: Multiple model or space names split with new lines - :param hf_token: HuggingFace token - :param target_space_name: Target Space Name - :param interface_title: Target Interface Title - :param interface_description: Target Interface Description - :return: - """ - if ( - model_or_space_names== "" or model_or_space_names.isspace() - or target_space_name == "" or target_space_name.isspace() - or interface_title == "" or interface_title.isspace() - or interface_description == "" or interface_description.isspace() - ): - return "Please fill all the inputs" - if hf_token == "" or hf_token.isspace(): - hf_token = os.environ['HF_SELF_TOKEN'] - if not SpaceBuilder.check_space_name_availability(hf_token=hf_token, space_name=target_space_name): - return SpaceBuilder.error_message - if not SpaceBuilder.load_and_check_spaces(names=model_or_space_names): - return SpaceBuilder.error_message - if not SpaceBuilder.create_space(input_space_names=model_or_space_names, target_space_name=target_space_name, hf_token=hf_token, title=interface_title, description=interface_description): - return SpaceBuilder.error_message - - url = SpaceBuilder.url - return f"{url}" - - - -if __name__ == "__main__": - print(f"Gradio Version: {gr.__version__}") - iface = gr.Interface( - fn=SpaceBuilder.build_space, - inputs=[ - gr.inputs.Textbox( - lines=4, - placeholder=( - f"Drop model and space links at each line and I will create a new space comparing them. Usage examples:" - f"\nspaces/onnx/GPT-2" - f"\nmodels/gpt2-large" - f"\nmodels/gpt2" - ), - ), - gr.inputs.Textbox(lines=1, placeholder="HuggingFace Write Token"), - gr.inputs.Textbox(lines=1, placeholder="Name for the target space, ie. space-building-space"), - gr.inputs.Textbox(lines=1, placeholder="Title for the target space interface, ie. Title"), - gr.inputs.Textbox(lines=1, placeholder="Description for the target space interface, ie. Description"), - ], - title="Model Comparator Space Builder", - description="Welcome onboard 🤗, I can create a comparative space which will compare the models and/or spaces you provide to me. You can get your HF Write Token from [here](https://huggingface.co/settings/tokens). If you leave HF Token input empty, the space will release under the author's account, [farukozderim](https://huggingface.co/farukozderim). Finally, you can publish spaces as a clone of other spaces if you provide just a single model or space. Have fun :)", - outputs=gr.outputs.HTML(label="URL"), - examples= [ - ["spaces/onnx/GPT-2 \nmodels/gpt2-large \nmodels/EleutherAI/gpt-j-6B", "", "comparison-space", "example-title", "example-description"], - ["spaces/onnx/GPT-2", "", "duplicate-space", "example-title", "example-description"], - ["models/EleutherAI/gpt-j-6B", "", "space-from-a-model", "example-title", "example-description"] - ], - ) - iface.launch() diff --git a/spaces/farukozderim/a/app.py b/spaces/farukozderim/a/app.py deleted file mode 100644 index b0ab05376e50dbab4afed84f482a6a4e9c95b548..0000000000000000000000000000000000000000 --- a/spaces/farukozderim/a/app.py +++ /dev/null @@ -1,4 +0,0 @@ -import gradio as gr -name_list = ['spaces/Harveenchadha/Hindi_TTS', 'spaces/Harveenchadha/Hindi_TTS', 'spaces/Harveenchadha/Hindi_TTS', 'spaces/Harveenchadha/Hindi_TTS'] -interfaces = [gr.Interface.load(name) for name in name_list] -gr.mix.Parallel(*interfaces).launch() \ No newline at end of file diff --git a/spaces/fatiXbelha/sd/BA Part 1 Exam Form and Admit Card 2023 Online for All Colleges.md b/spaces/fatiXbelha/sd/BA Part 1 Exam Form and Admit Card 2023 Online for All Colleges.md deleted file mode 100644 index 450585fd3da5e42a9707daa377835743cef02545..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/BA Part 1 Exam Form and Admit Card 2023 Online for All Colleges.md +++ /dev/null @@ -1,87 +0,0 @@ - -BA Part 1 Admit Card Download: How to Get Your Hall Ticket Online
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-Your BA Part 1 admit card contains vital information about you and your exam, such as your name, roll number, registration number, exam date, exam time, exam center name and address, subject code and name, and photograph and signature. You should check these details carefully and make sure they are correct and match with your identity proof. If you find any mistake or discrepancy in your admit card, you should contact your university or college authorities immediately and get it rectified.
-Carry your admit card and a valid ID proof to the exam center
-You must carry your BA Part 1 admit card and a valid ID proof to the exam center on the day of the exam. Your ID proof can be any of the following documents: Aadhaar card, voter ID card, driving license, passport, PAN card, or any other government-issued photo ID. You should also carry a blue or black ballpoint pen, pencil, eraser, sharpener, calculator (if allowed), and any other stationery items that you may need for the exam. You should not carry any electronic gadgets, books, notes, papers, or any other prohibited items to the exam center.
-Follow the exam guidelines and instructions given on your admit card
-Your BA Part 1 admit card also contains some important guidelines and instructions for the exam, such as the reporting time, duration of the exam, marking scheme, negative marking (if any), do's and don'ts for the exam, etc. You should read these guidelines and instructions carefully and follow them strictly during the exam. You should also follow the instructions given by the invigilator or supervisor at the exam center. You should not indulge in any unfair means or misconduct during the exam.
-Contact your university if you face any problem or discrepancy with your admit card
-If you face any problem or discrepancy with your BA Part 1 admit card download or printing process, you should contact your university or college authorities as soon as possible. You can find their contact details on their official website or on your admit card. You can also visit their office in person or send them an email or a letter explaining your issue. You should not ignore any problem or discrepancy with your admit card as it may affect your exam performance or result.
-Conclusion
-In conclusion, BA Part 1 admit card download is a simple and easy process that you can do online from the official website of your university. However, you need to be careful and attentive while downloading and printing your admit card as it is a crucial document for your exam. You also need to follow some important tips and instructions to ensure a smooth and hassle-free exam experience. We hope this article has helped you understand how to download BA Part 1 admit card online and what to do after downloading it. We wish you all the best for your BA Part 1 exams!
-FAQs
-Here are some frequently asked questions about BA Part 1 admit card download:
-Q: When will BA Part 1 admit cards be released?
-A: BA Part 1 admit cards are usually released by different universities a few weeks before the exams. You can check the official website of your university or contact your college authorities to know the exact date of BA Part 1 admit card release for your university.
-Q: What if I lose my
Q: What if I lose my BA Part 1 admit card or forget to bring it to the exam center?
-A: If you lose your BA Part 1 admit card or forget to bring it to the exam center, you may not be allowed to appear for the exam. Therefore, you should keep your admit card safely and remember to carry it to the exam center. However, if you lose your admit card or forget to bring it, you can try to contact your university or college authorities and request them to issue a duplicate admit card or allow you to enter the exam center with a valid ID proof. However, this is not guaranteed and depends on the discretion of the exam authorities.
-Q: Can I change my exam center or date after downloading my BA Part 1 admit card?
-A: No, you cannot change your exam center or date after downloading your BA Part 1 admit card. Your exam center and date are fixed and assigned by your university based on various factors. You have to appear for the exam at the given exam center and date as mentioned on your admit card. If you fail to do so, you may lose your chance to appear for the exam.
-Q: How can I prepare for BA Part 1 exams?
-A: To prepare for BA Part 1 exams, you should follow a regular study schedule and cover all the topics and subjects of your course. You should also revise the important concepts, formulas, facts, and dates regularly. You should practice solving previous year question papers and sample papers to get familiar with the exam pattern, difficulty level, and marking scheme. You should also read newspapers, magazines, books, and online articles to improve your general knowledge and language skills. You should also take care of your health and mental well-being and avoid stress and anxiety.
-Q: What are the benefits of BA Part 1 course?
-A: BA Part 1 course is a beneficial course for students who want to pursue higher studies or careers in arts, humanities, social sciences, languages, or related fields. It helps them to develop their critical thinking, analytical, communication, and research skills. It also exposes them to various subjects and disciplines that broaden their perspective and knowledge. It also prepares them for competitive exams like UPSC, SSC, Bank PO, etc. It also opens up various opportunities for jobs in sectors like education, media, journalism, administration, law, social work, etc.
-Q: Where can I get more information about BA Part 1 course and exams?
-A: You can get more information about BA Part 1 course and exams from your university or college website, prospectus, syllabus, or notice board. You can also contact your teachers, seniors, or classmates for any queries or doubts. You can also visit online forums, blogs, websites, or social media platforms that provide useful information and tips about BA Part 1 course and exams.
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download ((TOP)) Amp Install Via Sideloadly.md b/spaces/fatiXbelha/sd/Download ((TOP)) Amp Install Via Sideloadly.md deleted file mode 100644 index 4656d058170b66cb4c09084959deb28bc2982cca..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download ((TOP)) Amp Install Via Sideloadly.md +++ /dev/null @@ -1,136 +0,0 @@ -
-How to Download and Install Apps via Sideloadly
-If you are an iOS user who wants to install apps that are not available on the official App Store or customize your device with tweaks and mods, you may have heard of sideloading. Sideloading is the process of installing apps from sources other than the official app store. However, sideloading is not easy or straightforward on iOS devices. You need special tools and methods to do it.
-Fortunately, there is a tool that makes sideloading easier and safer for iOS users. It's called Sideloadly. In this article, we will show you what Sideloadly is, how it works, what are its benefits and risks, and how to use it to download and install apps on your iOS device. Let's get started!
-download amp; install via sideloadly
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-What is Sideloadly and How Does It Work?
-Sideloadly is a tool that allows you to install any iOS IPA file onto your iOS device, Apple Silicon Mac or Apple TV. It does not require a jailbreak or an enterprise certificate. It uses your own Apple ID to sign the apps for sideloading.
-Sideloadly works by communicating with a companion app called AltServer, which runs on your computer. AltServer acts as a server that sends commands to your iOS device via iTunes Wi-Fi sync. It also refreshes your apps every few days to prevent them from expiring.
-Sideloadly has many features that make it a
great choice for sideloading apps. Some of them are:
--
-
- It supports iOS 9 to iOS 15, Apple Silicon Macs, and Apple TV. -
- It allows you to customize the app bundle ID, display name, and icon. -
- It lets you inject tweaks and dylibs into the app. -
- It has a built-in file manager that lets you browse and delete the apps you installed. -
- It has a dark mode and a light mode for your preference. -
You can download Sideloadly for free from its official website. However, if you want to support the developers and unlock some premium features, such as unlimited app installations, custom app icons, and priority support, you can purchase a Sideloadly PRO license for $19.99 per year.
-What are the Requirements for Using Sideloadly?
-Before you can use Sideloadly to sideload apps on your iOS device, you need to have some things ready. Here are the requirements for using Sideloadly:
--
-
- A computer running Windows 7 or later, or macOS 10.14.4 or later. -
- An iOS device running iOS 9 or later, an Apple Silicon Mac, or an Apple TV running tvOS 13 or later. -
- A USB cable to connect your iOS device to your computer. -
- An Apple ID and password. You can use your existing Apple ID or create a new one for sideloading purposes. If you have two-factor authentication enabled on your Apple ID, you need to generate an app-specific password. -
- iTunes installed on your Windows computer, or the Finder app on your Mac computer. You need to enable iTunes Wi-Fi sync for your iOS device. -
- iCloud installed on your Windows computer, or the iCloud app on your Mac computer. You need to sign in with the same Apple ID that you use for sideloading. -
- AltServer installed on your computer. You can download it from its official website. You need to keep AltServer running in the background while using Sideloadly. -
How to Install Sideloadly on Your Computer and iOS Device?
-Now that you have everything ready, you can proceed to install Sideloadly on your computer and iOS device. Here are the steps to do it:
-Step 1: Download and Install Sideloadly on Your Computer
-Go to the official website of Sideloadly and click on the download button for your operating system. You will get a ZIP file containing the Sideloadly installer. Extract the ZIP file and run the installer. Follow the instructions on the screen to complete the installation.
- -Step 2: Download and Install AltServer on Your Computer
-Go to the official website of AltServer and click on the download button for your operating system. You will get a ZIP file containing the AltServer installer. Extract the ZIP file and run the installer. Follow the instructions on the screen to complete the installation.
-Step 3: Launch AltServer and Connect Your iOS Device to Your Computer
-Launch AltServer from your computer's menu or taskbar. You will see an icon of a diamond in a tray in your menu bar (Mac) or system tray (Windows). Click on the icon and select "Install AltStore" from the menu. Then, select your iOS device from the list of connected devices. You may be prompted to enter your Apple ID and password. Enter them and click "Install". AltServer will install AltStore on your iOS device.
-Step 4: Trust AltStore on Your iOS Device
-On your iOS device, go to Settings > General > Device Management. You will see a profile with your Apple ID. Tap on it and tap "Trust". This will allow AltStore to run on your device.
-Step 5: Launch AltStore and Sign In with Your Apple ID
-On your iOS device, launch AltStore from your home screen. You will see a welcome screen with some instructions. Tap "Continue" and sign in with your Apple ID and password. This will allow AltStore to refresh your apps every few days.
-Congratulations! You have successfully installed Sideloadly and AltStore on your computer and iOS device. You are now ready to sideload apps via Sideloadly.
-How to Download and Install Apps via Sideloadly?
-S
Sideloading apps via Sideloadly is easy and fast. You just need to find the IPA file of the app you want to install, download it to your computer, and use Sideloadly to transfer it to your iOS device. Here are the steps to do it:
-Step 1: Find and Download the IPA File of the App You Want to Install
-An IPA file is a file format that contains an iOS app. You can find IPA files of various apps on the internet, such as on websites, forums, or repositories. However, you need to be careful and only download IPA files from trusted and reputable sources. Some IPA files may contain malware or viruses that can harm your device or steal your data.
-Some of the popular and reliable sources for IPA files are:
--
-
- iOS Ninja: A website that offers a large collection of IPA files for apps, games, emulators, tweaks, and utilities. -
- AppCake: A website and an app that allows you to download and install cracked IPA files for free. -
- iPA Library: A website that provides IPA files for apps that are not available on the official App Store. -
Once you find the IPA file of the app you want to install, download it to your computer. Make sure you remember the location where you saved it.
-Step 2: Launch Sideloadly and Connect Your iOS Device to Your Computer
-Launch Sideloadly from your computer's menu or desktop. You will see a window with some options and settings. Connect your iOS device to your computer using a USB cable. Sideloadly will detect your device and show its name and model.
-Step 3: Select the IPA File and Customize the App Settings
-Click on the "Browse" button and select the IPA file that you downloaded in step 1. Sideloadly will show some information about the app, such as its name, version, size, and bundle ID. You can customize some of the app settings, such as:
--
-
- Change the app display name: You can change the name of the app that will appear on your home screen. -
- Change the app bundle ID: You can change the unique identifier of the app that is used by iOS to distinguish it from other apps. -
- Change the app icon: You can change the icon of the app that will appear on your home screen. -
- Inject tweaks or dylibs: You can inject additional code or libraries into the app to modify its behavior or functionality. -
You can also enter your Apple ID and password in the fields below. This is required for signing the app for sideloading. If you have two-factor authentication enabled on your Apple ID, you need to enter an app-specific password instead of your regular password.
-Step 4: Click on "Start" and Wait for Sideloadly to Install the App
-Once you are done with customizing the app settings, click on the "Start" button at the bottom right corner of Sideloadly. Sideloadly will start installing the app on your iOS device. You will see a progress bar and some messages on Sideloadly's window. Do not disconnect your device or close Sideloadly until the installation is complete.
-When the installation is complete, you will see a message saying "Done" on Sideloadly's window. You can now disconnect your device and launch the app from your home screen.
-Congratulations! You have successfully downloaded and installed an app via Sideloadly.
-What are the Benefits and Risks of Sideloading Apps?
-Sideloading apps can be a great way to enhance your iOS experience and enjoy apps that are not available on the official App Store. However, sideloading apps also comes with some benefits and risks that you should be aware of before doing it. Here are some of them:
-Benefits of Sideloading Apps
-Sideloading apps can offer you many benefits, such as:
--
-
- You can get apps that are not available on the official App Store, such as beta versions, region-locked apps, or banned apps. -
- You can bypass some of the restrictions imposed by Apple, such as app size limit, app update frequency, or app functionality. -
- You can save money by installing cracked or modified versions of paid apps for free. -
- You can enhance your device's functionality by installing tweaks and mods that add new features or improve existing ones. -
- You can customize your device's appearance by changing the app icons, themes, wallpapers, or fonts. -
Risks of Sideloading Apps
-Sideloading apps can also pose some risks, such as:
--
-
- You may expose your device to malware or viruses that can damage your device or steal your data. Some IPA files may contain malicious code or hidden payloads that can harm your device or compromise your security. -
- You may breach the data privacy of the app developers or owners. Some IPA files may contain proprietary or confidential information that belongs to the app developers or owners. By downloading and installing them, you may violate their rights and privacy. -
- You may experience app instability or compatibility issues. Some IPA files may not be compatible with your iOS version, device model, or other apps. By installing them, you may cause your device to crash, freeze, or malfunction. -
- You may void your device's warranty or violate Apple's terms of service. Apple does not support or endorse sideloading apps on iOS devices. By doing so, you may void your device's warranty or risk getting your Apple ID banned or suspended. -
- You may face legal issues or consequences. Some IPA files may contain pirated or illegal content that infringes the intellectual property rights of the app developers or owners. By downloading and installing them, you may be liable for legal actions or penalties. -
How to Troubleshoot Common Issues with Sideloadly?
-Sideloadly is a reliable and user-friendly tool for sideloading apps on iOS devices. However, like any other tool, it may encounter some issues or errors from time to time. Here are some of the common issues that users may face with Sideloadly and how to fix them:
-How to Fix Installation Errors with Sideloadly?
-One of the most common issues that users may face with Sideloadly is installation errors. These are errors that prevent Sideloadly from installing the app on your iOS device. Some of the possible causes and solutions for installation errors are:
--
-
- Incorrect Apple ID credentials: Make sure you enter the correct Apple ID and password in Sideloadly. If you have two-factor authentication enabled on your Apple ID, make sure you use an app-specific password instead of your regular password. -
- Network issues: Make sure you have a stable and fast internet connection on your computer and iOS device. Try switching to a different network or using a VPN service if possible. -
- Device compatibility: Make sure your iOS device is compatible with Sideloadly and the app you want to install. Check the iOS version, device model, and available storage space on your device. -
- Certificate issues: Make sure you have a valid certificate for sideloading apps on your iOS device. You can use a free developer account, which allows you to sideload up to three apps per device, but they will expire after seven days. You can also use a paid developer account, which allows you to sideload unlimited apps per device, but they will cost $99 per year. -
How to Prevent App Expiration and Revocation with Sideloadly?
-Another common issue that users may face with Sideloadly is app expiration and revocation. These are issues that prevent the app from running on your iOS device after a certain period of time. Some of the tips and tricks to prevent app expiration and revocation are:
--
-
- Refresh apps regularly: Make sure you refresh your apps every few days using AltStore on your iOS device. This will extend the validity of your apps and prevent them from expiring. -
- Use a paid developer account: If you have a paid developer account, you can sideload apps that will last for one year without expiring or revoking. However, this will cost $99 per year. -
- Use a VPN service: If you use a VPN service on your iOS device, you can hide your IP address and location from Apple's servers. This will reduce the chances of Apple detecting and revoking your apps. -
- Use a jailbreak tweak: If you have a jailbroken iOS device, you can use a jailbreak tweak such as ReProvision or AppSync Unified to automatically refresh and sign your apps without using a computer. -
How to Ensure App Compatibility with Sideloadly?
-The last common issue that users may face with Sideloadly is app compatibility. This is an issue that affects the performance or functionality of the app on your iOS device. Some of the factors that affect app compatibility are:
--
-
- i
- iOS version: Make sure your iOS device is running the same or higher iOS version as the app requires. You can check the iOS version of your device by going to Settings > General > About. You can check the iOS version of the app by looking at its description or requirements on the website where you downloaded it. -
- Device model: Make sure your iOS device is compatible with the app's features and functions. Some apps may not work well or at all on older or newer device models. You can check the device model of your device by going to Settings > General > About. You can check the device model of the app by looking at its description or requirements on the website where you downloaded it. -
- App source: Make sure you download the app from a trusted and reputable source. Some sources may provide outdated, modified, or corrupted versions of the app that may not work properly or safely on your device. You can check the app source by looking at its URL, domain name, or certificate. -
- App size: Make sure you have enough storage space on your device to install and run the app. Some apps may take up a lot of space on your device, especially if they have high-quality graphics, sounds, or data. You can check the storage space of your device by going to Settings > General > iPhone Storage. You can check the app size by looking at its description or requirements on the website where you downloaded it. -
- App features: Make sure you have the necessary permissions and settings to use the app's features and functions. Some apps may require access to your camera, microphone, location, contacts, or other data or services on your device. You can check and manage the permissions and settings of your apps by going to Settings > Privacy. -
Conclusion
-Sideloading apps on iOS devices can be a fun and rewarding way to enjoy apps that are not available on the official App Store or customize your device with tweaks and mods. However, sideloading apps also comes with some benefits and risks that you should be aware of before doing it.
-In this article, we showed you what Sideloadly is, how it works, what are its benefits and risks, and how to use it to download and install apps on your iOS device. We also showed you how to troubleshoot some common issues that users may face with Sideloadly.
-We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Happy sideloading!
-FAQs
-Here are some frequently asked questions about Sideloadly and their answers:
-Q: Is Sideloadly safe to use?
-A: Sideloadly is safe to use as long as you download it from its official website and use it with a trusted Apple ID. However, Sideloadly does not guarantee the safety or quality of the apps that you sideload with it. You are responsible for checking and verifying the sources and contents of the IPA files that you download and install.
-Q: How many apps can I sideload with Sideloadly?
-A: The number of apps that you can sideload with Sideloadly depends on the type of Apple ID that you use. If you use a free developer account, you can sideload up to three apps per device, but they will expire after seven days. If you use a paid developer account, you can sideload unlimited apps per device, but they will cost $99 per year.
-Q: How long do the apps last on my device?
-A: The duration of the apps on your device depends on the type of Apple ID that you use and how often you refresh them with AltStore. If you use a free developer account, the apps will last for seven days before they expire. You need to refresh them every few days using AltStore on your iOS device to extend their validity. If you use a paid developer account, the apps will last for one year before they expire. You do not need to refresh them as often as with a free developer account.
-Q: Can I update the apps that I sideloaded with Sideloadly?
-A: Yes, you can update the apps that you sideloaded with Sideloadly if there is a newer version available from the same source where you downloaded them. You just need to download the updated IPA file and install it over the existing app using Sideloadly. However, updating an app may overwrite some of its settings or data, so make sure you back up your app before updating it.
-Q: Can I delete the apps that I sideloaded with Sideloadly?
A: Yes, you can delete the apps that you sideloaded with Sideloadly if you no longer want to use them or need to free up some space on your device. You can delete them in two ways:
--
-
- From your iOS device: You can delete the apps from your home screen by tapping and holding on the app icon until it wiggles, then tapping on the "X" button. You may be asked to confirm your action. Tap on "Delete" to remove the app. -
- From Sideloadly: You can delete the apps from Sideloadly's file manager by clicking on the "Manage Apps" button at the top right corner of Sideloadly's window. You will see a list of the apps that you installed with Sideloadly. Select the app that you want to delete and click on the "Delete" button at the bottom right corner of Sideloadly's window. You may be asked to confirm your action. Click on "Yes" to remove the app. -
-
-
\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Draw Better with Tips and Tricks from Professional Artists.md b/spaces/fatiXbelha/sd/Draw Better with Tips and Tricks from Professional Artists.md deleted file mode 100644 index 6c601257ef17cbbc5505d14333e1abb34b832fa2..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Draw Better with Tips and Tricks from Professional Artists.md +++ /dev/null @@ -1,302 +0,0 @@ -
-Introduction: What is drawing and why is it beneficial?
-Drawing is the art of creating images by making marks on a surface, usually paper, with a pencil, pen, or other tool. Drawing can be done for various purposes, such as expressing ideas, communicating messages, designing objects, or simply for enjoyment.
-Drawing has many benefits for your health and well-being. Some of them are:
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- Drawing stimulates your brain activity and enhances your creativity. -
- Drawing improves your memory and concentration. -
- Drawing helps you communicate your emotions and thoughts. -
- Drawing relieves stress and anxiety. -
- Drawing develops your problem-solving and critical thinking skills. -
- Drawing boosts your self-confidence and self-esteem. -
In this article, you will learn some basic drawing techniques, tools, and examples that will help you get started with this amazing art form.
-Section 1: Drawing Techniques
-What are some basic drawing techniques?
-There are many different drawing techniques that you can use to create different effects and styles in your drawings. Here are some of the most common ones:
--
-
- Hatching: This technique involves making parallel lines to create shading and value. The closer the lines are, the darker the area will appear. You can also use cross-hatching, which is layering hatching lines in different directions. -
- Stippling: This technique involves making tiny dots to create shading and texture. The more dots you make, the darker the area will appear. You can also vary the size and density of the dots for different effects. -
- Scribbling: This technique involves making random marks to create shading and texture. You can use different pressure, direction, and speed to create different effects. -
- Blending: This technique involves smoothing out the marks you make with a pencil or charcoal by using your finger, a blending stick, or a cloth. This creates a soft and smooth appearance in your drawings. -
- Tonal sketching: This technique involves creating shading and value by using different tones of gray or color. You can use a pencil or a brush to apply light or dark tones on your paper. -
How to draw simple shapes and forms
-One of the easiest ways to start drawing is by learning how to draw simple shapes and forms. Shapes are flat, two-dimensional figures that have no depth or volume. Forms are three-dimensional figures that have depth and volume. You can use shapes as the basis for drawing forms by adding shading and perspective.
-Here are some examples of how to draw simple shapes and forms:
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A circle shape A sphere form How to draw an oval shape and an ellipsoid form
-An oval shape is a flattened circle that has two curved sides and two straight sides. An ellipsoid form is a three-dimensional figure that looks like a stretched or squashed sphere. You can use an oval shape as the basis for drawing an ellipsoid form by adding shading and perspective.
-How to draw a realistic face step by step
-
-Best drawing apps for iPad Pro and Apple Pencil
-Drawing tutorials for beginners free online
-What is the difference between drawing and sketching
-How to draw anime characters for beginners
-Drawing ideas for kids easy and fun
-How to draw a dragon with wings and scales
-Best drawing tablets with screen and pen
-Drawing tips and tricks for improving your skills
-How to draw a rose with a stem and leaves
-How to draw a human body in proportion
-Best drawing books for beginners and experts
-Drawing challenges for artists of all levels
-How to draw a cat with realistic fur
-How to draw a dog with cute expressions
-Drawing games for kids and adults online
-How to draw a car with perspective and shading
-Best drawing pencils for sketching and shading
-Drawing exercises for warming up and loosening up
-How to draw a flower with different petals
-How to draw a horse with a mane and tail
-Best drawing software for Windows and Mac
-Drawing techniques for creating different textures
-How to draw a bird with feathers and wings
-How to draw a tree with branches and leaves
-How to draw a cartoon character with personality
-Best drawing courses online for beginners and intermediate learners
-Drawing tools and materials for beginners and professionals
-How to draw a landscape with depth and detail
-How to draw a butterfly with symmetrical patterns
-How to draw a fish with scales and fins
-Best drawing pens for fine lines and shading
-Drawing styles and genres for different purposes and preferences
-How to draw a wolf with realistic fur and eyes
-How to draw a unicorn with a horn and rainbow mane
-How to draw a mandala with geometric shapes and patterns
-Best drawing blogs and websites for inspiration and learning
-Drawing quotes and sayings for motivation and creativity
-How to draw a bear with claws and teeth
-How to draw a lion with a mane and roar
-How to draw an eye with eyelashes and iris
-Best drawing podcasts for listening and learning
-Drawing anatomy for artists of human and animal figures
-How to draw a star with five or six points
-How to draw a heart with an arrow or wings
-How to draw a sun with rays and smiley face
-Best drawing magazines for news and reviewsHere are some examples of how to draw an oval shape and an ellipsoid form:
--
-An oval shape An ellipsoid form A tilted oval shape A tilted ellipsoid form To draw an oval shape, you can use a compass, a round object, or a freehand method. Here are the steps for each method:
--
-
- Using a compass: Adjust the compass to the width of the oval you want to draw. Place the point of the compass on the paper and draw a circle. Without changing the width of the compass, move the point to another spot on the paper and draw another circle that overlaps with the first one. The intersection of the two circles will form an oval shape. -
- Using a round object: Find a round object that has the size and shape of the oval you want to draw, such as a plate, a bowl, or a coin. Place the object on the paper and trace around it with a pencil. Then, move the object slightly to the left or right and trace around it again. The intersection of the two circles will form an oval shape. -
- Using a freehand method: Draw a horizontal line on the paper that represents the width of the oval you want to draw. Then, draw two vertical lines at each end of the horizontal line that represent the height of the oval. Next, draw a curved line that connects the top ends of the vertical lines and another curved line that connects the bottom ends of the vertical lines. Make sure that the curves are smooth and symmetrical. Erase the horizontal and vertical lines to complete the oval shape. -
To draw an ellipsoid form, you can use an oval shape as a guide and add shading and perspective. Here are the steps:
--
-
- Using an oval shape as a guide: Draw an oval shape on the paper using any of the methods above. Then, draw another oval shape inside the first one that is slightly smaller and parallel to it. This will create an outline of an ellipsoid form. -
- Adding shading: Decide where the light source is coming from and shade the opposite side of the ellipsoid form with a pencil or charcoal. Use darker tones for areas that are farther away from the light source and lighter tones for areas that are closer to it. You can also use blending tools to smooth out the shading and create a realistic effect. -
- Adding perspective: To make the ellipsoid form look more three-dimensional, you can tilt it slightly on the paper. To do this, draw an oval shape that is not horizontal but slightly angled. Then, draw another oval shape inside it that follows the same angle but is smaller and parallel to it. Shade the ellipsoid form as described above. -
Drawing ovals and ellipsoids can help you create various objects, such as eggs, balloons, planets, or fruits. You can also use different colors and patterns to make your drawings more interesting and fun.
How to draw a triangle shape and a pyramid form
-A triangle shape is a polygon that has three sides and three angles. A pyramid form is a three-dimensional figure that has a triangular base and triangular faces that meet at a point. You can use a triangle shape as the basis for drawing a pyramid form by adding shading and perspective.
-Here are some examples of how to draw a triangle shape and a pyramid form:
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-A triangle shape A pyramid form A tilted triangle shape A tilted pyramid form To draw a triangle shape, you can use a ruler, a protractor, or a freehand method. Here are the steps for each method:
--
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- Using a ruler and a protractor: Draw a horizontal line on the paper that represents the base of the triangle you want to draw. Then, use a protractor to measure the angle you want for one of the corners of the triangle. Place the protractor on the end of the horizontal line and mark the angle with a pencil. Next, use a ruler to draw a line from the end of the horizontal line to the angle mark. This will create one of the sides of the triangle. Repeat the same process for the other corner of the triangle. You can also use different lengths for the sides of the triangle to create different types of triangles, such as equilateral, isosceles, or scalene. -
- Using a round object: Find a round object that has the size and shape of the circle you want to draw, such as a plate, a bowl, or a coin. Place the object on the paper and trace around it with a pencil. Then, use a ruler to draw a line that cuts through the circle. This will create the base of the triangle. Next, use a ruler to draw two lines from the ends of the base line to any point on the circle. This will create the sides of the triangle. You can also use different points on the circle to create different types of triangles, such as acute, right, or obtuse. -
- Using a freehand method: Draw a dot on the paper that represents one of the corners of the triangle you want to draw. Then, draw another dot that represents another corner of the triangle. Next, draw a line that connects the two dots. This will create one of the sides of the triangle. Repeat the same process for the other side of the triangle. Finally, draw a line that connects the last two dots. This will complete the triangle shape. -
To draw a pyramid form, you can use a triangle shape as a guide and add shading and perspective. Here are the steps:
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- Using a triangle shape as a guide: Draw a triangle shape on the paper using any of the methods above. Then, draw another triangle shape inside it that is slightly smaller and parallel to it. This will create an outline of one face of the pyramid form. -
- Adding shading: Decide where the light source is coming from and shade the opposite side of the pyramid form with a pencil or charcoal. Use darker tones for areas that are farther away from the light source and lighter tones for areas that are closer to it. You can also use blending tools to smooth out the shading and create a realistic effect. -
- Adding perspective: To make the pyramid form look more three-dimensional, you can tilt it slightly on the paper. To do this, draw an oval shape that is not horizontal but slightly angled. Then, draw another oval shape inside it that follows the same angle but is smaller and parallel to it. Shade the ellipsoid form as described above. -
Drawing triangles and pyramids can help you create various objects, such as mountains, roofs, tents, or jewels. You can also use different colors and patterns to make your drawings more interesting and fun.
How to draw a rectangle shape and a prism form
-A rectangle shape is a polygon that has four sides and four right angles. A prism form is a three-dimensional figure that has a rectangular base and rectangular faces that meet at parallel lines. You can use a rectangle shape as the basis for drawing a prism form by adding shading and perspective.
-Here are some examples of how to draw a rectangle shape and a prism form:
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-A rectangle shape A prism form A tilted rectangle shape A tilted prism form To draw a rectangle shape, you can use a ruler, a square object, or a freehand method. Here are the steps for each method:
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- Using a ruler: Draw a horizontal line on the paper that represents the width of the rectangle you want to draw. Then, use a ruler to measure the height of the rectangle you want to draw and mark it on both ends of the horizontal line. Next, use a ruler to draw two vertical lines from the marks to create the sides of the rectangle. Finally, use a ruler to draw another horizontal line that connects the ends of the vertical lines to complete the rectangle shape. -
- Using a square object: Find a square object that has the size and shape of the rectangle you want to draw, such as a book, a box, or a card. Place the object on the paper and trace around it with a pencil. Then, move the object slightly to the left or right and trace around it again. The intersection of the two squares will form a rectangle shape. -
- Using a freehand method: Draw a dot on the paper that represents one of the corners of the rectangle you want to draw. Then, draw another dot that represents another corner of the rectangle. Next, draw a line that connects the two dots. This will create one of the sides of the rectangle. Repeat the same process for the other side of the rectangle. Finally, draw two lines that connect the last two dots to complete the rectangle shape. -
To draw a prism form, you can use a rectangle shape as a guide and add shading and perspective. Here are the steps:
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- Using a rectangle shape as a guide: Draw a rectangle shape on the paper using any of the methods above. Then, draw another rectangle shape inside it that is slightly smaller and parallel to it. This will create an outline of one face of the prism form. -
- Adding shading: Decide where the light source is coming from and shade the opposite side of the prism form with a pencil or charcoal. Use darker tones for areas that are farther away from the light source and lighter tones for areas that are closer to it. You can also use blending tools to smooth out the shading and create a realistic effect. -
- Adding perspective: To make the prism form look more three-dimensional, you can tilt it slightly on the paper. To do this, draw an oval shape that is not horizontal but slightly angled. Then, draw another oval shape inside it that follows the same angle but is smaller and parallel to it. Shade the ellipsoid form as described above. -
Drawing rectangles and prisms can help you create various objects, such as buildings, boxes, books, or phones. You can also use different colors and patterns to make your drawings more interesting and fun.
-Section 2: Drawing Tools
-What are some essential drawing tools?
-To start drawing, you don't need many tools. You can use whatever you have at hand, such as pencils, pens, paper, or even your phone or computer. However, if you want to improve your drawing skills and explore different styles and effects, you might want to invest in some essential drawing tools. Here are some of them:
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- Paper: Paper is one of the most important drawing tools because it is where you make your marks. There are many types of paper available for drawing, such as sketch paper, drawing paper, watercolor paper, or tracing paper. Each type has different characteristics, such as weight, texture, color, and quality. You should choose the paper that suits your needs and preferences. -
- PencilPencil: Pencil is one of the most common and versatile drawing tools because it can create different effects and styles depending on the pressure, direction, and hardness of the lead. There are many types of pencils available for drawing, such as graphite pencils, charcoal pencils, colored pencils, or mechanical pencils. Each type has different characteristics, such as darkness, smoothness, durability, and erasability. You should choose the pencil that suits your needs and preferences. -
- Erasers: Erasers are useful drawing tools because they can help you correct mistakes, create highlights, or add texture to your drawings. There are many types of erasers available for drawing, such as rubber erasers, kneaded erasers, vinyl erasers, or electric erasers. Each type has different characteristics, such as softness, cleanliness, precision, and effectiveness. You should choose the eraser that suits your needs and preferences. -
- Sharpeners: Sharpeners are essential drawing tools because they can help you maintain the quality and shape of your pencils. There are many types of sharpeners available for drawing, such as manual sharpeners, electric sharpeners, or blade sharpeners. Each type has different characteristics, such as speed, convenience, safety, and waste. You should choose the sharpener that suits your needs and preferences. -
- Blending tools: Blending tools are helpful drawing tools because they can help you smooth out the marks you make with your pencils or charcoals and create a realistic effect. There are many types of blending tools available for drawing, such as blending sticks, tortillons, stumps, or brushes. Each type has different characteristics, such as size, shape, material, and flexibility. You should choose the blending tool that suits your needs and preferences. -
- Online drawing applications and software: Online drawing applications and software are modern drawing tools that can help you create digital drawings on your phone or computer. There are many online drawing applications and software available for drawing, such as Sketchpad, Procreate, Adobe Photoshop Sketch, or Autodesk SketchBook. Each application or software has different features, such as tools, brushes, layers, filters, or formats. You should choose the online drawing application or software that suits your needs and preferences. -
Section 3: Drawing Examples
-What are some easy and cute things to draw?
-If you are looking for some easy and cute things to draw, you can try drawing animals, plants and flowers, cartoons and comics, or mandalas and patterns. These are some of the most popular and fun things to draw for beginners and experts alike. Here are some examples of how to draw them:
-How to draw animals
-Animals are one of the easiest and cutest things to draw because they have simple shapes and forms that you can use as a guide. You can also use different colors and patterns to make your drawings more interesting and fun.
-Here are some examples of how to draw animals:
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-A cat A dog A panda A penguin To draw a cat, you can use an oval shape and a triangle shape as a guide. Here are the steps:
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- Draw an oval shape on the paper that represents the body of the cat. -
- Draw another oval shape on top of the first one that represents the head of the cat. -
- Draw two triangle shapes on top of the second oval shape that represent the ears of the cat. -
- Draw two small circles inside the second oval shape that represent the eyes of the cat. -
- Draw a small triangle below the eyes that represents the nose of the cat. -
- Draw a curved line below the nose that represents the mouth of the cat. -
- Draw four small ovals below the first oval shape that represent the legs of the cat. -
- Draw a long curved line behind the first oval shape that represents the tail of the cat. -
- Add some details to your drawing, such as whiskers, fur patterns, or a collar. -
- Erase any unwanted lines or marks from your drawing. -
- Color your drawing with pencils or crayons. -
To draw a dog, To draw a dog, you can use a circle shape and a rectangle shape as a guide. Here are the steps:
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- Draw a circle shape on the paper that represents the head of the dog. -
- Draw two small circles inside the circle shape that represent the eyes of the dog. -
- Draw a small triangle below the eyes that represents the nose of the dog. -
- Draw a curved line below the nose that represents the mouth of the dog. -
- Draw two triangle shapes on top of the circle shape that represent the ears of the dog. -
- Draw a rectangle shape below the circle shape that represents the body of the dog. -
- Draw four small rectangles below the rectangle shape that represent the legs of the dog. -
- Draw a long curved line behind the rectangle shape that represents the tail of the dog. -
- Add some details to your drawing, such as spots, fur patterns, or a collar. -
- Erase any unwanted lines or marks from your drawing. -
- Color your drawing with pencils or crayons. -
How to draw plants and flowers
-Plants and flowers are one of the easiest and cutest things to draw because they have simple shapes and forms that you can use as a guide. You can also use different colors and patterns to make your drawings more interesting and fun.
-Here are some examples of how to draw plants and flowers:
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-A cactus A sunflower A tulip A rose To draw a cactus, you can use an oval shape and a triangle shape as a guide. Here are the steps:
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- Draw an oval shape on the paper that represents the body of the cactus. -
- Draw another oval shape on top of the first one that represents the head of the cactus. -
- Draw some small triangles along the edges of the oval shapes that represent the spines of the cactus. -
- Draw a small circle on top of the second oval shape that represents a flower on the cactus. -
- Draw a small rectangle below the first oval shape that represents a pot for the cactus. -
- Add some details to your drawing, such as soil, pebbles, or patterns on the pot. -
- Erase any unwanted lines or marks from your drawing. -
- Color your drawing with pencils or crayons. -
To draw a sunflower, you can use a circle shape and an oval shape as a guide. Here are the steps:
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- Draw a circle shape on the paper that represents the center of the sunflower. -
- Draw some small dots inside the circle shape that represent the seeds of the sunflower. -
- Draw some oval shapes around the circle shape that represent the petals of the sunflower. -
- Draw some curved lines inside the oval shapes that represent the veins of the petals. -
- Draw a long curved line below the circle shape that represents the stem of the sunflower. -
- Draw some small oval shapes along the stem that represent the leaves of the sunflower. -
- Draw some curved lines inside the small oval shapes that represent the veins of the leaves. -
- Add some details to your drawing, such as a background, a sky, or a bee. -
- Erase any unwanted lines or marks from your drawing. -
- Color your drawing with pencils or crayons. -
To draw a tulip, you can use a heart shape and a rectangle shape as a guide. Here are the steps:
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- Draw a heart shape on the paper that represents one of the petals of the tulip. -
- Draw another heart shape next to the first one that overlaps with it and represents another petal of the tulip. -
- Draw another heart shape below the first two that overlaps with them and represents the third petal of the tulip. -
- Draw a small circle in the center of the three heart shapes that represents the stigma of the tulip. -
- Draw a long curved line below the circle shape that represents the stem of the tulip. -
- Draw a small rectangle shape below the stem that represents a pot for the tulip. -
- Add some details to your drawing, such as soil, pebbles, or patterns on the pot. -
- Erase any unwanted lines or marks from your drawing. -
- Color your drawing with pencils or crayons. -
To draw a rose, you can use a spiral shape and an oval shape as a guide. Here are the steps:
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- Draw a spiral shape on the paper that represents the center of the rose. -
- Draw some oval shapes around the spiral shape that represent the petals of the rose. -
- Draw some curved lines inside the oval shapes that represent the veins of the petals. -
- Draw a long curved line below the spiral shape that represents the stem of the rose. -
- Draw some small oval shapes along the stem that represent the thorns of the rose. -
- Draw some small oval shapes at the base of the stem that represent the sepals of the rose. -
- Add some details to your drawing, such as a background, a sky, or a butterfly. -
- Erase any unwanted lines or marks from your drawing. -
- Color your drawing with pencils or crayons. -
Conclusion: How to practice and improve your drawing skills
-Drawing is a fun and rewarding activity that anyone can enjoy. However, if you want to improve your drawing skills and create more beautiful and realistic drawings, you need to practice and learn from others. Here are some tips on how to practice and improve your drawing skills:
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- Practice regularly: The best way to improve your drawing skills is to practice as much as you can. Try to draw something every day, even if it's just a doodle or a sketch. You can also set a goal for yourself, such as drawing for 30 minutes or completing one drawing per day. The more you practice, the more you will develop your muscle memory, hand-eye coordination, and confidence. -
- Learn from others: Another way to improve your drawing skills is to learn from other artists. You can look at their drawings and try to understand how they use different techniques, tools, and styles. You can also watch online tutorials, read books, or take classes that teach you how to draw. You can also ask for feedback from other artists or friends and learn from their suggestions and critiques. -
- Experiment with different things: A final way to improve your drawing skills is to experiment with different things. You can try drawing different subjects, such as animals, plants, people, or landscapes. You can also try drawing with different tools, such as pencils, pens, brushes, or digital devices. You can also try drawing in different styles, such as realistic, cartoon, abstract, or manga. By experimenting with different things, you will discover new possibilities and challenges that will help you grow as an artist. -
Drawing is a wonderful way to express your creativity, improve your skills, and have fun. By following the basic drawing techniques, tools, and examples in this article, you will be able to start drawing with ease and confidence. Remember to practice regularly, learn from others, and experiment with different things to improve your drawing skills and create more amazing drawings.
-FAQs: Frequently asked questions about drawing
-Here are some of the most frequently asked questions about drawing:
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- Q: How do I draw a face? -
- A: To draw a face, you can use a circle shape and some guidelines as a guide. Here are the steps: -
- Draw a circle shape on the paper that represents the head of the person. -
- Draw a horizontal line across the middle of the circle shape that represents the eye level of the person. -
- Draw another horizontal line below the first one that represents the nose level of the person. -
- Draw another horizontal line below the second one that represents the mouth level of the person. -
- Draw two vertical lines on each side of the circle shape that represent the width of the face of the person. -
- Draw two small circles on the eye level line that represent the eyes of the person. -
- Draw a small triangle on the nose level line that represents the nose of the person. -
- Draw a curved line on the mouth level line that represents the mouth of the person. -
- Add some details to your drawing, such as eyebrows, eyelashes, ears, hair, or accessories. -
- Erase any unwanted lines or marks from your drawing. -
- Color your drawing with pencils or crayons. -
- Q: How do I draw a human body? -
- A: To draw a human body, you can use some basic shapes and forms as a guide. Here are the steps: -
- Draw an oval shape on the paper that represents the head of the person. -
- Draw a rectangle shape below the oval shape that represents the torso of the person. -
- Draw two smaller rectangle shapes below the first rectangle shape that represent the legs of the person. -
- Draw two smaller rectangle shapes on each side of the first rectangle shape that represent the arms of the person. -
- Draw some oval shapes at the ends of the smaller rectangle shapes that represent the hands and feet of the person. -
- Add some details to your drawing, such as facial features, hair, clothes, or accessories. -
- Erase any unwanted lines or marks from your drawing. -
- Color your drawing with pencils or crayons. -
- Q: How do I draw a landscape? -
- A: To draw a landscape, you can use some basic shapes and forms as a guide. Here are the steps: -
- Draw a horizontal line on the paper that represents the horizon of the landscape. -
- Draw some curved lines above the horizon line that represent the sky and clouds of the landscape. -
- Draw some curved lines below the horizon line that represent the ground and hills of the landscape. -
- Draw some oval shapes on the ground that represent the trees and plants of the landscape. -
- Draw some triangle shapes on the hills that represent the mountains and rocks of the landscape. -
- Add some details to your drawing, such as animals, buildings, rivers, or roads. -
- Erase any unwanted lines or marks from your drawing. -
- Color your drawing with pencils or crayons. -
- Open the Google Play Store app on your device. -
- Search for "Criminal Case Supernatural" in the search bar. -
- Select the game from the list of results and tap on "Install". -
- Wait for the game to download and install on your device. -
- Tap on "Open" to launch the game and enjoy. -
- Open the App Store app on your device. -
- Search for "Criminal Case Supernatural" in the search bar. -
- Select the game from the list of results and tap on "Get". -
- Enter your Apple ID password or use Touch ID or Face ID to confirm. -
- Wait for the game to download and install on your device. -
- Tap on the game icon to launch the game and enjoy. -
- Open the Microsoft Store app on your device. -
- Search for "Criminal Case Supernatural" in the search bar. -
- Select the game from the list of results and click on "Get". -
- Sign in with your Microsoft account if prompted. -
- Wait for the game to download and install on your device. -
- Click on "Play" to launch the game and enjoy. -
- How many cases are there in Criminal Case Supernatural? -
- How do I get more energy in Criminal Case Supernatural? -
- How do I get more coins in Criminal Case Supernatural? -
- How do I get more cards in Criminal Case Supernatural?Cards are collectible items that you can find in hidden object scenes in Criminal Case Supernatural. They have different themes and categories, such as characters, locations, weapons, and more. You can use cards to complete collections and earn rewards, such as coins, energy, outfits, and pets. You can also trade cards with your friends or buy them with real money.
-
- How do I change my avatar or outfit in Criminal Case Supernatural?
-You can customize your avatar or outfit in Criminal Case Supernatural by going to the "Profile" tab and clicking on the "Edit" button. You can choose from different hairstyles, facial features, skin tones, and accessories for your avatar. You can also buy different outfits with coins or real money. Some outfits have special effects or bonuses that can help you in the game.
- - Your account number, which is a 12-digit number that starts with 00 -
- Your date of birth, which is written as DDMMYYYY -
- Your gender, which is either M for male or F for female -
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I hope you enjoyed this article and learned something new about drawing. Remember to practice regularly, learn from others, and experiment with different things to improve your drawing skills and create more amazing drawings. Happy drawing!
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\ No newline at end of file diff --git "a/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/\350\201\224\347\275\221\347\232\204ChatGPT.py" "b/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/\350\201\224\347\275\221\347\232\204ChatGPT.py" deleted file mode 100644 index 6a7d118b4439605db6e10b9a416a2e725b99a672..0000000000000000000000000000000000000000 --- "a/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/\350\201\224\347\275\221\347\232\204ChatGPT.py" +++ /dev/null @@ -1,102 +0,0 @@ -from toolbox import CatchException, update_ui -from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping -import requests -from bs4 import BeautifulSoup -from request_llm.bridge_all import model_info - -def google(query, proxies): - query = query # 在此处替换您要搜索的关键词 - url = f"https://www.google.com/search?q={query}" - headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'} - response = requests.get(url, headers=headers, proxies=proxies) - soup = BeautifulSoup(response.content, 'html.parser') - results = [] - for g in soup.find_all('div', class_='g'): - anchors = g.find_all('a') - if anchors: - link = anchors[0]['href'] - if link.startswith('/url?q='): - link = link[7:] - if not link.startswith('http'): - continue - title = g.find('h3').text - item = {'title': title, 'link': link} - results.append(item) - - for r in results: - print(r['link']) - return results - -def scrape_text(url, proxies) -> str: - """Scrape text from a webpage - - Args: - url (str): The URL to scrape text from - - Returns: - str: The scraped text - """ - headers = { - 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36', - 'Content-Type': 'text/plain', - } - try: - response = requests.get(url, headers=headers, proxies=proxies, timeout=8) - if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding - except: - return "无法连接到该网页" - soup = BeautifulSoup(response.text, "html.parser") - for script in soup(["script", "style"]): - script.extract() - text = soup.get_text() - lines = (line.strip() for line in text.splitlines()) - chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) - text = "\n".join(chunk for chunk in chunks if chunk) - return text - -@CatchException -def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - """ - txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径 - llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行 - plugin_kwargs 插件模型的参数，暂时没有用武之地 - chatbot 聊天显示框的句柄，用于显示给用户 - history 聊天历史，前情提要 - system_prompt 给gpt的静默提醒 - web_port 当前软件运行的端口号 - """ - history = [] # 清空历史，以免输入溢出 - chatbot.append((f"请结合互联网信息回答以下问题：{txt}", - "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该模板可以实现ChatGPT联网信息综合。该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。您若希望分享新的功能模组，请不吝PR！")) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新 - - # ------------- < 第1步：爬取搜索引擎的结果 > ------------- - from toolbox import get_conf - proxies, = get_conf('proxies') - urls = google(txt, proxies) - history = [] - - # ------------- < 第2步：依次访问网页 > ------------- - max_search_result = 5 # 最多收纳多少个网页的结果 - for index, url in enumerate(urls[:max_search_result]): - res = scrape_text(url['link'], proxies) - history.extend([f"第{index}份搜索结果：", res]) - chatbot.append([f"第{index}份搜索结果：", res[:500]+"......"]) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新 - - # ------------- < 第3步：ChatGPT综合 > ------------- - i_say = f"从以上搜索结果中抽取信息，然后回答问题：{txt}" - i_say, history = input_clipping( # 裁剪输入，从最长的条目开始裁剪，防止爆token - inputs=i_say, - history=history, - max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4 - ) - gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( - inputs=i_say, inputs_show_user=i_say, - llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, - sys_prompt="请从给定的若干条搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。" - ) - chatbot[-1] = (i_say, gpt_say) - history.append(i_say);history.append(gpt_say) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新 - diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/utils/init_path.py b/spaces/fb700/chatglm-fitness-RLHF/src/utils/init_path.py deleted file mode 100644 index 18ca81eb81f564f44fd376667168807e4e976a36..0000000000000000000000000000000000000000 --- a/spaces/fb700/chatglm-fitness-RLHF/src/utils/init_path.py +++ /dev/null @@ -1,49 +0,0 @@ -import os -import glob - -def init_path(checkpoint_dir, config_dir, size=512, old_version=False, preprocess='crop'): - - if old_version: - #### load all the checkpoint of `pth` - sadtalker_paths = { - 'wav2lip_checkpoint' : os.path.join(checkpoint_dir, 'wav2lip.pth'), - 'audio2pose_checkpoint' : os.path.join(checkpoint_dir, 'auido2pose_00140-model.pth'), - 'audio2exp_checkpoint' : os.path.join(checkpoint_dir, 'auido2exp_00300-model.pth'), - 'free_view_checkpoint' : os.path.join(checkpoint_dir, 'facevid2vid_00189-model.pth.tar'), - 'path_of_net_recon_model' : os.path.join(checkpoint_dir, 'epoch_20.pth') - } - - use_safetensor = False - elif len(glob.glob(os.path.join(checkpoint_dir, '*.safetensors'))): - print('using safetensor as default') - sadtalker_paths = { - "checkpoint":os.path.join(checkpoint_dir, 'SadTalker_V0.0.2_'+str(size)+'.safetensors'), - } - use_safetensor = True - else: - print("WARNING: The new version of the model will be updated by safetensor, you may need to download it mannully. We run the old version of the checkpoint this time!") - use_safetensor = False - - sadtalker_paths = { - 'wav2lip_checkpoint' : os.path.join(checkpoint_dir, 'wav2lip.pth'), - 'audio2pose_checkpoint' : os.path.join(checkpoint_dir, 'auido2pose_00140-model.pth'), - 'audio2exp_checkpoint' : os.path.join(checkpoint_dir, 'auido2exp_00300-model.pth'), - 'free_view_checkpoint' : os.path.join(checkpoint_dir, 'facevid2vid_00189-model.pth.tar'), - 'path_of_net_recon_model' : os.path.join(checkpoint_dir, 'epoch_20.pth') - } - - sadtalker_paths['dir_of_BFM_fitting'] = os.path.join(config_dir) # , 'BFM_Fitting' - sadtalker_paths['audio2pose_yaml_path'] = os.path.join(config_dir, 'auido2pose.yaml') - sadtalker_paths['audio2exp_yaml_path'] = os.path.join(config_dir, 'auido2exp.yaml') - sadtalker_paths['pirender_yaml_path'] = os.path.join(config_dir, 'facerender_pirender.yaml') - sadtalker_paths['pirender_checkpoint'] = os.path.join(checkpoint_dir, 'epoch_00190_iteration_000400000_checkpoint.pt') - sadtalker_paths['use_safetensor'] = use_safetensor # os.path.join(config_dir, 'auido2exp.yaml') - - if 'full' in preprocess: - sadtalker_paths['mappingnet_checkpoint'] = os.path.join(checkpoint_dir, 'mapping_00109-model.pth.tar') - sadtalker_paths['facerender_yaml'] = os.path.join(config_dir, 'facerender_still.yaml') - else: - sadtalker_paths['mappingnet_checkpoint'] = os.path.join(checkpoint_dir, 'mapping_00229-model.pth.tar') - sadtalker_paths['facerender_yaml'] = os.path.join(config_dir, 'facerender.yaml') - - return sadtalker_paths \ No newline at end of file diff --git a/spaces/fclong/summary/fengshen/examples/finetune_bart_qg/utils.py b/spaces/fclong/summary/fengshen/examples/finetune_bart_qg/utils.py deleted file mode 100644 index 25cc1ef54673d3e7a465901eb905c4889f1397fd..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/examples/finetune_bart_qg/utils.py +++ /dev/null @@ -1,70 +0,0 @@ -# -*- encoding: utf-8 -*- -''' -Copyright 2022 The International Digital Economy Academy (IDEA). CCNL team. 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. -@File : utils.py -@Time : 2022/10/28 18:27 -@Author : Qi Yang -@Version : 1.0 -@Contact : yangqi@idea.edu.cn -@License : (C)Copyright 2022-2023, CCNL-IDEA -''' - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import torch -import torch.nn.functional as F - - -class LabelSmoothingCrossEntropy(torch.nn.Module): - def __init__(self, smoothing=0.1): - super(LabelSmoothingCrossEntropy, self).__init__() - self.smoothing = smoothing - self.ignore_index = -100 - - def forward(self, x, target): - confidence = 1. - self.smoothing - logprobs = F.log_softmax(x, dim=-1) - targets_ignore = torch.where(target != self.ignore_index, target, 0) - nll_loss = -logprobs.gather(dim=-1, index=targets_ignore.unsqueeze(1)) - nll_loss = nll_loss.squeeze(1) - smooth_loss = -logprobs.mean(dim=-1) - loss = confidence * nll_loss + self.smoothing * smooth_loss - return loss.mean() - - -def truncate_sequence(document: str, max_num_tokens: int, reverse=False): - total_length = len(document) - if total_length <= max_num_tokens: - return document - else: - if reverse: - return document[-1*max_num_tokens:] - else: - return document[:max_num_tokens] - - -def padding_to_maxlength(ids, max_length, pad_id): - cur_len = len(ids) - len_diff = max_length - len(ids) - return ids + [pad_id] * len_diff, [1] * cur_len + [0] * len_diff - - -def white_space_fix(text): - return "".join(text.split(" ")) - - -def remove_prompt(text): - if ":" in text: - return text.split(":")[1] - return text diff --git a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/criteria/moco_loss.py b/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/criteria/moco_loss.py deleted file mode 100644 index 8fb13fbd426202cff9014c876c85b0d5c4ec6a9d..0000000000000000000000000000000000000000 --- a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/criteria/moco_loss.py +++ /dev/null @@ -1,71 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from configs.paths_config import model_paths - - -class MocoLoss(nn.Module): - - def __init__(self, opts): - super(MocoLoss, self).__init__() - print("Loading MOCO model from path: {}".format(model_paths["moco"])) - self.model = self.__load_model() - self.model.eval() - for param in self.model.parameters(): - param.requires_grad = False - - @staticmethod - def __load_model(): - import torchvision.models as models - model = models.__dict__["resnet50"]() - # freeze all layers but the last fc - for name, param in model.named_parameters(): - if name not in ['fc.weight', 'fc.bias']: - param.requires_grad = False - checkpoint = torch.load(model_paths['moco'], map_location="cpu") - state_dict = checkpoint['state_dict'] - # rename moco pre-trained keys - for k in list(state_dict.keys()): - # retain only encoder_q up to before the embedding layer - if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'): - # remove prefix - state_dict[k[len("module.encoder_q."):]] = state_dict[k] - # delete renamed or unused k - del state_dict[k] - msg = model.load_state_dict(state_dict, strict=False) - assert set(msg.missing_keys) == {"fc.weight", "fc.bias"} - # remove output layer - model = nn.Sequential(*list(model.children())[:-1]).cuda() - return model - - def extract_feats(self, x): - x = F.interpolate(x, size=224) - x_feats = self.model(x) - x_feats = nn.functional.normalize(x_feats, dim=1) - x_feats = x_feats.squeeze() - return x_feats - - def forward(self, y_hat, y, x): - n_samples = x.shape[0] - x_feats = self.extract_feats(x) - y_feats = self.extract_feats(y) - y_hat_feats = self.extract_feats(y_hat) - y_feats = y_feats.detach() - loss = 0 - sim_improvement = 0 - sim_logs = [] - count = 0 - for i in range(n_samples): - diff_target = y_hat_feats[i].dot(y_feats[i]) - diff_input = y_hat_feats[i].dot(x_feats[i]) - diff_views = y_feats[i].dot(x_feats[i]) - sim_logs.append({'diff_target': float(diff_target), - 'diff_input': float(diff_input), - 'diff_views': float(diff_views)}) - loss += 1 - diff_target - sim_diff = float(diff_target) - float(diff_views) - sim_improvement += sim_diff - count += 1 - - return loss / count, sim_improvement / count, sim_logs diff --git a/spaces/feregVcuzo/sanity-test-midi/Edius Pro 7.50 Serial Number.md b/spaces/feregVcuzo/sanity-test-midi/Edius Pro 7.50 Serial Number.md deleted file mode 100644 index 1170614e891f7fedeeedefeb7e7fcbdbd4d09ee4..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/Edius Pro 7.50 Serial Number.md +++ /dev/null @@ -1,110 +0,0 @@ -## edius pro 7.50 serial number - - - - - -  - - - - - -**Edius Pro 7.50 Serial Number === [https://apconhanstraf.blogspot.com/?c=2tyq7F](https://apconhanstraf.blogspot.com/?c=2tyq7F)** - - - - - - - - - - - - Here is a possible title and article for the keyword "edius pro 7.50 serial number": - -# How to Find and Register Your EDIUS Pro 7.50 Serial Number - - - -EDIUS Pro 7.50 is a powerful video editing software that offers a wide range of features and functions for professional and creative projects. 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Conclusion
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There are currently 12 cases in Criminal Case Supernatural, divided into two seasons: Season 1: The Vampire Conspiracy (6 cases) and Season 2: The Werewolf Curse (6 cases). More cases may be added in the future updates.
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-You need coins to buy items, tools, outfits, pets, and more in Criminal Case Supernatural. You can get more coins by playing hidden object scenes, completing achievements, watching ads, buying them with real money, or receiving them from your friends as gifts.
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-
-
\ No newline at end of file diff --git a/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/__init__.py b/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/__init__.py deleted file mode 100644 index 1759733cc109fa348c3f764c5939b5b609521cb3..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/__init__.py +++ /dev/null @@ -1,10 +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 . import data, modules, models - -__version__ = '0.0.1' diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/dgram.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/dgram.d.ts deleted file mode 100644 index 247328d28b72328bcc380fe0e482647eff3631ff..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/dgram.d.ts +++ /dev/null @@ -1,545 +0,0 @@ -/** - * The `dgram` module provides an implementation of UDP datagram sockets. - * - * ```js - * import dgram from 'dgram'; - * - * const server = dgram.createSocket('udp4'); - * - * server.on('error', (err) => { - * console.log(`server error:\n${err.stack}`); - * server.close(); - * }); - * - * server.on('message', (msg, rinfo) => { - * console.log(`server got: ${msg} from ${rinfo.address}:${rinfo.port}`); - * }); - * - * server.on('listening', () => { - * const address = server.address(); - * console.log(`server listening ${address.address}:${address.port}`); - * }); - * - * server.bind(41234); - * // Prints: server listening 0.0.0.0:41234 - * ``` - * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/dgram.js) - */ -declare module 'dgram' { - import { AddressInfo } from 'node:net'; - import * as dns from 'node:dns'; - import { EventEmitter, Abortable } from 'node:events'; - interface RemoteInfo { - address: string; - family: 'IPv4' | 'IPv6'; - port: number; - size: number; - } - interface BindOptions { - port?: number | undefined; - address?: string | undefined; - exclusive?: boolean | undefined; - fd?: number | undefined; - } - type SocketType = 'udp4' | 'udp6'; - interface SocketOptions extends Abortable { - type: SocketType; - reuseAddr?: boolean | undefined; - /** - * @default false - */ - ipv6Only?: boolean | undefined; - recvBufferSize?: number | undefined; - sendBufferSize?: number | undefined; - lookup?: ((hostname: string, options: dns.LookupOneOptions, callback: (err: NodeJS.ErrnoException | null, address: string, family: number) => void) => void) | undefined; - } - /** - * Creates a `dgram.Socket` object. Once the socket is created, calling `socket.bind()` will instruct the socket to begin listening for datagram - * messages. When `address` and `port` are not passed to `socket.bind()` the - * method will bind the socket to the "all interfaces" address on a random port - * (it does the right thing for both `udp4` and `udp6` sockets). The bound address - * and port can be retrieved using `socket.address().address` and `socket.address().port`. - * - * If the `signal` option is enabled, calling `.abort()` on the corresponding`AbortController` is similar to calling `.close()` on the socket: - * - * ```js - * const controller = new AbortController(); - * const { signal } = controller; - * const server = dgram.createSocket({ type: 'udp4', signal }); - * server.on('message', (msg, rinfo) => { - * console.log(`server got: ${msg} from ${rinfo.address}:${rinfo.port}`); - * }); - * // Later, when you want to close the server. - * controller.abort(); - * ``` - * @since v0.11.13 - * @param options Available options are: - * @param callback Attached as a listener for `'message'` events. Optional. - */ - function createSocket(type: SocketType, callback?: (msg: Buffer, rinfo: RemoteInfo) => void): Socket; - function createSocket(options: SocketOptions, callback?: (msg: Buffer, rinfo: RemoteInfo) => void): Socket; - /** - * Encapsulates the datagram functionality. - * - * New instances of `dgram.Socket` are created using {@link createSocket}. - * The `new` keyword is not to be used to create `dgram.Socket` instances. - * @since v0.1.99 - */ - class Socket extends EventEmitter { - /** - * Tells the kernel to join a multicast group at the given `multicastAddress` and`multicastInterface` using the `IP_ADD_MEMBERSHIP` socket option. If the`multicastInterface` argument is not - * specified, the operating system will choose - * one interface and will add membership to it. To add membership to every - * available interface, call `addMembership` multiple times, once per interface. - * - * When called on an unbound socket, this method will implicitly bind to a random - * port, listening on all interfaces. - * - * When sharing a UDP socket across multiple `cluster` workers, the`socket.addMembership()` function must be called only once or an`EADDRINUSE` error will occur: - * - * ```js - * import cluster from 'cluster'; - * import dgram from 'dgram'; - * - * if (cluster.isPrimary) { - * cluster.fork(); // Works ok. - * cluster.fork(); // Fails with EADDRINUSE. - * } else { - * const s = dgram.createSocket('udp4'); - * s.bind(1234, () => { - * s.addMembership('224.0.0.114'); - * }); - * } - * ``` - * @since v0.6.9 - */ - addMembership(multicastAddress: string, multicastInterface?: string): void; - /** - * Returns an object containing the address information for a socket. - * For UDP sockets, this object will contain `address`, `family` and `port`properties. - * - * This method throws `EBADF` if called on an unbound socket. - * @since v0.1.99 - */ - address(): AddressInfo; - /** - * For UDP sockets, causes the `dgram.Socket` to listen for datagram - * messages on a named `port` and optional `address`. If `port` is not - * specified or is `0`, the operating system will attempt to bind to a - * random port. If `address` is not specified, the operating system will - * attempt to listen on all addresses. Once binding is complete, a`'listening'` event is emitted and the optional `callback` function is - * called. - * - * Specifying both a `'listening'` event listener and passing a`callback` to the `socket.bind()` method is not harmful but not very - * useful. - * - * A bound datagram socket keeps the Node.js process running to receive - * datagram messages. - * - * If binding fails, an `'error'` event is generated. In rare case (e.g. - * attempting to bind with a closed socket), an `Error` may be thrown. - * - * Example of a UDP server listening on port 41234: - * - * ```js - * import dgram from 'dgram'; - * - * const server = dgram.createSocket('udp4'); - * - * server.on('error', (err) => { - * console.log(`server error:\n${err.stack}`); - * server.close(); - * }); - * - * server.on('message', (msg, rinfo) => { - * console.log(`server got: ${msg} from ${rinfo.address}:${rinfo.port}`); - * }); - * - * server.on('listening', () => { - * const address = server.address(); - * console.log(`server listening ${address.address}:${address.port}`); - * }); - * - * server.bind(41234); - * // Prints: server listening 0.0.0.0:41234 - * ``` - * @since v0.1.99 - * @param callback with no parameters. Called when binding is complete. - */ - bind(port?: number, address?: string, callback?: () => void): this; - bind(port?: number, callback?: () => void): this; - bind(callback?: () => void): this; - bind(options: BindOptions, callback?: () => void): this; - /** - * Close the underlying socket and stop listening for data on it. If a callback is - * provided, it is added as a listener for the `'close'` event. - * @since v0.1.99 - * @param callback Called when the socket has been closed. - */ - close(callback?: () => void): this; - /** - * Associates the `dgram.Socket` to a remote address and port. Every - * message sent by this handle is automatically sent to that destination. Also, - * the socket will only receive messages from that remote peer. - * Trying to call `connect()` on an already connected socket will result - * in an `ERR_SOCKET_DGRAM_IS_CONNECTED` exception. If `address` is not - * provided, `'127.0.0.1'` (for `udp4` sockets) or `'::1'` (for `udp6` sockets) - * will be used by default. Once the connection is complete, a `'connect'` event - * is emitted and the optional `callback` function is called. In case of failure, - * the `callback` is called or, failing this, an `'error'` event is emitted. - * @since v12.0.0 - * @param callback Called when the connection is completed or on error. - */ - connect(port: number, address?: string, callback?: () => void): void; - connect(port: number, callback: () => void): void; - /** - * A synchronous function that disassociates a connected `dgram.Socket` from - * its remote address. Trying to call `disconnect()` on an unbound or already - * disconnected socket will result in an `ERR_SOCKET_DGRAM_NOT_CONNECTED` exception. - * @since v12.0.0 - */ - disconnect(): void; - /** - * Instructs the kernel to leave a multicast group at `multicastAddress` using the`IP_DROP_MEMBERSHIP` socket option. This method is automatically called by the - * kernel when the socket is closed or the process terminates, so most apps will - * never have reason to call this. - * - * If `multicastInterface` is not specified, the operating system will attempt to - * drop membership on all valid interfaces. - * @since v0.6.9 - */ - dropMembership(multicastAddress: string, multicastInterface?: string): void; - /** - * This method throws `ERR_SOCKET_BUFFER_SIZE` if called on an unbound socket. - * @since v8.7.0 - * @return the `SO_RCVBUF` socket receive buffer size in bytes. - */ - getRecvBufferSize(): number; - /** - * This method throws `ERR_SOCKET_BUFFER_SIZE` if called on an unbound socket. - * @since v8.7.0 - * @return the `SO_SNDBUF` socket send buffer size in bytes. - */ - getSendBufferSize(): number; - /** - * By default, binding a socket will cause it to block the Node.js process from - * exiting as long as the socket is open. The `socket.unref()` method can be used - * to exclude the socket from the reference counting that keeps the Node.js - * process active. The `socket.ref()` method adds the socket back to the reference - * counting and restores the default behavior. - * - * Calling `socket.ref()` multiples times will have no additional effect. - * - * The `socket.ref()` method returns a reference to the socket so calls can be - * chained. - * @since v0.9.1 - */ - ref(): this; - /** - * Returns an object containing the `address`, `family`, and `port` of the remote - * endpoint. This method throws an `ERR_SOCKET_DGRAM_NOT_CONNECTED` exception - * if the socket is not connected. - * @since v12.0.0 - */ - remoteAddress(): AddressInfo; - /** - * Broadcasts a datagram on the socket. - * For connectionless sockets, the destination `port` and `address` must be - * specified. Connected sockets, on the other hand, will use their associated - * remote endpoint, so the `port` and `address` arguments must not be set. - * - * The `msg` argument contains the message to be sent. - * Depending on its type, different behavior can apply. If `msg` is a `Buffer`, - * any `TypedArray` or a `DataView`, - * the `offset` and `length` specify the offset within the `Buffer` where the - * message begins and the number of bytes in the message, respectively. - * If `msg` is a `String`, then it is automatically converted to a `Buffer`with `'utf8'` encoding. With messages that - * contain multi-byte characters, `offset` and `length` will be calculated with - * respect to `byte length` and not the character position. - * If `msg` is an array, `offset` and `length` must not be specified. - * - * The `address` argument is a string. If the value of `address` is a host name, - * DNS will be used to resolve the address of the host. If `address` is not - * provided or otherwise nullish, `'127.0.0.1'` (for `udp4` sockets) or `'::1'`(for `udp6` sockets) will be used by default. - * - * If the socket has not been previously bound with a call to `bind`, the socket - * is assigned a random port number and is bound to the "all interfaces" address - * (`'0.0.0.0'` for `udp4` sockets, `'::0'` for `udp6` sockets.) - * - * An optional `callback` function may be specified to as a way of reporting - * DNS errors or for determining when it is safe to reuse the `buf` object. - * DNS lookups delay the time to send for at least one tick of the - * Node.js event loop. - * - * The only way to know for sure that the datagram has been sent is by using a`callback`. If an error occurs and a `callback` is given, the error will be - * passed as the first argument to the `callback`. If a `callback` is not given, - * the error is emitted as an `'error'` event on the `socket` object. - * - * Offset and length are optional but both _must_ be set if either are used. - * They are supported only when the first argument is a `Buffer`, a `TypedArray`, - * or a `DataView`. - * - * This method throws `ERR_SOCKET_BAD_PORT` if called on an unbound socket. - * - * Example of sending a UDP packet to a port on `localhost`; - * - * ```js - * import dgram from 'dgram'; - * import { Buffer } from 'buffer'; - * - * const message = Buffer.from('Some bytes'); - * const client = dgram.createSocket('udp4'); - * client.send(message, 41234, 'localhost', (err) => { - * client.close(); - * }); - * ``` - * - * Example of sending a UDP packet composed of multiple buffers to a port on`127.0.0.1`; - * - * ```js - * import dgram from 'dgram'; - * import { Buffer } from 'buffer'; - * - * const buf1 = Buffer.from('Some '); - * const buf2 = Buffer.from('bytes'); - * const client = dgram.createSocket('udp4'); - * client.send([buf1, buf2], 41234, (err) => { - * client.close(); - * }); - * ``` - * - * Sending multiple buffers might be faster or slower depending on the - * application and operating system. Run benchmarks to - * determine the optimal strategy on a case-by-case basis. Generally speaking, - * however, sending multiple buffers is faster. - * - * Example of sending a UDP packet using a socket connected to a port on`localhost`: - * - * ```js - * import dgram from 'dgram'; - * import { Buffer } from 'buffer'; - * - * const message = Buffer.from('Some bytes'); - * const client = dgram.createSocket('udp4'); - * client.connect(41234, 'localhost', (err) => { - * client.send(message, (err) => { - * client.close(); - * }); - * }); - * ``` - * @since v0.1.99 - * @param msg Message to be sent. - * @param offset Offset in the buffer where the message starts. - * @param length Number of bytes in the message. - * @param port Destination port. - * @param address Destination host name or IP address. - * @param callback Called when the message has been sent. - */ - send(msg: string | Uint8Array | ReadonlyArray, port?: number, address?: string, callback?: (error: Error | null, bytes: number) => void): void; - send(msg: string | Uint8Array | ReadonlyArray , port?: number, callback?: (error: Error | null, bytes: number) => void): void; - send(msg: string | Uint8Array | ReadonlyArray , callback?: (error: Error | null, bytes: number) => void): void; - send(msg: string | Uint8Array, offset: number, length: number, port?: number, address?: string, callback?: (error: Error | null, bytes: number) => void): void; - send(msg: string | Uint8Array, offset: number, length: number, port?: number, callback?: (error: Error | null, bytes: number) => void): void; - send(msg: string | Uint8Array, offset: number, length: number, callback?: (error: Error | null, bytes: number) => void): void; - /** - * Sets or clears the `SO_BROADCAST` socket option. When set to `true`, UDP - * packets may be sent to a local interface's broadcast address. - * - * This method throws `EBADF` if called on an unbound socket. - * @since v0.6.9 - */ - setBroadcast(flag: boolean): void; - /** - * _All references to scope in this section are referring to [IPv6 Zone Indices](https://en.wikipedia.org/wiki/IPv6_address#Scoped_literal_IPv6_addresses), which are defined by [RFC - * 4007](https://tools.ietf.org/html/rfc4007). In string form, an IP_ - * _with a scope index is written as `'IP%scope'` where scope is an interface name_ - * _or interface number._ - * - * Sets the default outgoing multicast interface of the socket to a chosen - * interface or back to system interface selection. The `multicastInterface` must - * be a valid string representation of an IP from the socket's family. - * - * For IPv4 sockets, this should be the IP configured for the desired physical - * interface. All packets sent to multicast on the socket will be sent on the - * interface determined by the most recent successful use of this call. - * - * For IPv6 sockets, `multicastInterface` should include a scope to indicate the - * interface as in the examples that follow. In IPv6, individual `send` calls can - * also use explicit scope in addresses, so only packets sent to a multicast - * address without specifying an explicit scope are affected by the most recent - * successful use of this call. - * - * This method throws `EBADF` if called on an unbound socket. - * - * #### Example: IPv6 outgoing multicast interface - * - * On most systems, where scope format uses the interface name: - * - * ```js - * const socket = dgram.createSocket('udp6'); - * - * socket.bind(1234, () => { - * socket.setMulticastInterface('::%eth1'); - * }); - * ``` - * - * On Windows, where scope format uses an interface number: - * - * ```js - * const socket = dgram.createSocket('udp6'); - * - * socket.bind(1234, () => { - * socket.setMulticastInterface('::%2'); - * }); - * ``` - * - * #### Example: IPv4 outgoing multicast interface - * - * All systems use an IP of the host on the desired physical interface: - * - * ```js - * const socket = dgram.createSocket('udp4'); - * - * socket.bind(1234, () => { - * socket.setMulticastInterface('10.0.0.2'); - * }); - * ``` - * @since v8.6.0 - */ - setMulticastInterface(multicastInterface: string): void; - /** - * Sets or clears the `IP_MULTICAST_LOOP` socket option. When set to `true`, - * multicast packets will also be received on the local interface. - * - * This method throws `EBADF` if called on an unbound socket. - * @since v0.3.8 - */ - setMulticastLoopback(flag: boolean): boolean; - /** - * Sets the `IP_MULTICAST_TTL` socket option. While TTL generally stands for - * "Time to Live", in this context it specifies the number of IP hops that a - * packet is allowed to travel through, specifically for multicast traffic. Each - * router or gateway that forwards a packet decrements the TTL. If the TTL is - * decremented to 0 by a router, it will not be forwarded. - * - * The `ttl` argument may be between 0 and 255\. The default on most systems is `1`. - * - * This method throws `EBADF` if called on an unbound socket. - * @since v0.3.8 - */ - setMulticastTTL(ttl: number): number; - /** - * Sets the `SO_RCVBUF` socket option. Sets the maximum socket receive buffer - * in bytes. - * - * This method throws `ERR_SOCKET_BUFFER_SIZE` if called on an unbound socket. - * @since v8.7.0 - */ - setRecvBufferSize(size: number): void; - /** - * Sets the `SO_SNDBUF` socket option. Sets the maximum socket send buffer - * in bytes. - * - * This method throws `ERR_SOCKET_BUFFER_SIZE` if called on an unbound socket. - * @since v8.7.0 - */ - setSendBufferSize(size: number): void; - /** - * Sets the `IP_TTL` socket option. While TTL generally stands for "Time to Live", - * in this context it specifies the number of IP hops that a packet is allowed to - * travel through. Each router or gateway that forwards a packet decrements the - * TTL. If the TTL is decremented to 0 by a router, it will not be forwarded. - * Changing TTL values is typically done for network probes or when multicasting. - * - * The `ttl` argument may be between 1 and 255\. The default on most systems - * is 64. - * - * This method throws `EBADF` if called on an unbound socket. - * @since v0.1.101 - */ - setTTL(ttl: number): number; - /** - * By default, binding a socket will cause it to block the Node.js process from - * exiting as long as the socket is open. The `socket.unref()` method can be used - * to exclude the socket from the reference counting that keeps the Node.js - * process active, allowing the process to exit even if the socket is still - * listening. - * - * Calling `socket.unref()` multiple times will have no addition effect. - * - * The `socket.unref()` method returns a reference to the socket so calls can be - * chained. - * @since v0.9.1 - */ - unref(): this; - /** - * Tells the kernel to join a source-specific multicast channel at the given`sourceAddress` and `groupAddress`, using the `multicastInterface` with the`IP_ADD_SOURCE_MEMBERSHIP` socket - * option. If the `multicastInterface` argument - * is not specified, the operating system will choose one interface and will add - * membership to it. To add membership to every available interface, call`socket.addSourceSpecificMembership()` multiple times, once per interface. - * - * When called on an unbound socket, this method will implicitly bind to a random - * port, listening on all interfaces. - * @since v13.1.0, v12.16.0 - */ - addSourceSpecificMembership(sourceAddress: string, groupAddress: string, multicastInterface?: string): void; - /** - * Instructs the kernel to leave a source-specific multicast channel at the given`sourceAddress` and `groupAddress` using the `IP_DROP_SOURCE_MEMBERSHIP`socket option. This method is - * automatically called by the kernel when the - * socket is closed or the process terminates, so most apps will never have - * reason to call this. - * - * If `multicastInterface` is not specified, the operating system will attempt to - * drop membership on all valid interfaces. - * @since v13.1.0, v12.16.0 - */ - dropSourceSpecificMembership(sourceAddress: string, groupAddress: string, multicastInterface?: string): void; - /** - * events.EventEmitter - * 1. close - * 2. connect - * 3. error - * 4. listening - * 5. message - */ - addListener(event: string, listener: (...args: any[]) => void): this; - addListener(event: 'close', listener: () => void): this; - addListener(event: 'connect', listener: () => void): this; - addListener(event: 'error', listener: (err: Error) => void): this; - addListener(event: 'listening', listener: () => void): this; - addListener(event: 'message', listener: (msg: Buffer, rinfo: RemoteInfo) => void): this; - emit(event: string | symbol, ...args: any[]): boolean; - emit(event: 'close'): boolean; - emit(event: 'connect'): boolean; - emit(event: 'error', err: Error): boolean; - emit(event: 'listening'): boolean; - emit(event: 'message', msg: Buffer, rinfo: RemoteInfo): boolean; - on(event: string, listener: (...args: any[]) => void): this; - on(event: 'close', listener: () => void): this; - on(event: 'connect', listener: () => void): this; - on(event: 'error', listener: (err: Error) => void): this; - on(event: 'listening', listener: () => void): this; - on(event: 'message', listener: (msg: Buffer, rinfo: RemoteInfo) => void): this; - once(event: string, listener: (...args: any[]) => void): this; - once(event: 'close', listener: () => void): this; - once(event: 'connect', listener: () => void): this; - once(event: 'error', listener: (err: Error) => void): this; - once(event: 'listening', listener: () => void): this; - once(event: 'message', listener: (msg: Buffer, rinfo: RemoteInfo) => void): this; - prependListener(event: string, listener: (...args: any[]) => void): this; - prependListener(event: 'close', listener: () => void): this; - prependListener(event: 'connect', listener: () => void): this; - prependListener(event: 'error', listener: (err: Error) => void): this; - prependListener(event: 'listening', listener: () => void): this; - prependListener(event: 'message', listener: (msg: Buffer, rinfo: RemoteInfo) => void): this; - prependOnceListener(event: string, listener: (...args: any[]) => void): this; - prependOnceListener(event: 'close', listener: () => void): this; - prependOnceListener(event: 'connect', listener: () => void): this; - prependOnceListener(event: 'error', listener: (err: Error) => void): this; - prependOnceListener(event: 'listening', listener: () => void): this; - prependOnceListener(event: 'message', listener: (msg: Buffer, rinfo: RemoteInfo) => void): this; - } -} -declare module 'node:dgram' { - export * from 'dgram'; -} diff --git a/spaces/fffiloni/lama-video-watermark-remover/predict.py b/spaces/fffiloni/lama-video-watermark-remover/predict.py deleted file mode 100644 index 878b7988c113778f48ec3f940d2031a30c12e03f..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/lama-video-watermark-remover/predict.py +++ /dev/null @@ -1,89 +0,0 @@ -#!/usr/bin/env python3 - -# Example command: -# ./bin/predict.py \ -# model.path= \ -# indir= \ -# outdir= - -import logging -import os -import sys -import traceback - -from saicinpainting.evaluation.utils import move_to_device - -os.environ['OMP_NUM_THREADS'] = '1' -os.environ['OPENBLAS_NUM_THREADS'] = '1' -os.environ['MKL_NUM_THREADS'] = '1' -os.environ['VECLIB_MAXIMUM_THREADS'] = '1' -os.environ['NUMEXPR_NUM_THREADS'] = '1' - -import cv2 -import hydra -import numpy as np -import torch -import tqdm -import yaml -from omegaconf import OmegaConf -from torch.utils.data._utils.collate import default_collate - -from saicinpainting.training.data.datasets import make_default_val_dataset -from saicinpainting.training.trainers import load_checkpoint -from saicinpainting.utils import register_debug_signal_handlers - -LOGGER = logging.getLogger(__name__) - - -@hydra.main(config_path='configs/prediction', config_name='default.yaml') -def main(predict_config: OmegaConf): - try: - register_debug_signal_handlers() # kill -10 will result in traceback dumped into log - - device = torch.device(predict_config.device) - - train_config_path = os.path.join(predict_config.model.path, 'config.yaml') - with open(train_config_path, 'r') as f: - train_config = OmegaConf.create(yaml.safe_load(f)) - - train_config.training_model.predict_only = True - - out_ext = predict_config.get('out_ext', '.png') - - checkpoint_path = os.path.join(predict_config.model.path, - 'models', - predict_config.model.checkpoint) - model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu') - model.freeze() - model.to(device) - - if not predict_config.indir.endswith('/'): - predict_config.indir += '/' - - dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset) - with torch.no_grad(): - for img_i in tqdm.trange(len(dataset)): - mask_fname = dataset.mask_filenames[img_i] - cur_out_fname = os.path.join( - predict_config.outdir, - os.path.splitext(mask_fname[len(predict_config.indir):])[0] + out_ext - ) - os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True) - - batch = move_to_device(default_collate([dataset[img_i]]), device) - batch['mask'] = (batch['mask'] > 0) * 1 - batch = model(batch) - cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy() - - cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8') - cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR) - cv2.imwrite(cur_out_fname, cur_res) - except KeyboardInterrupt: - LOGGER.warning('Interrupted by user') - except Exception as ex: - LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}') - sys.exit(1) - - -if __name__ == '__main__': - main() diff --git a/spaces/firestalker/anime-tts/export_model.py b/spaces/firestalker/anime-tts/export_model.py deleted file mode 100644 index 52d3b3d083df7bf027b46d9c63e399b2da3f0e0a..0000000000000000000000000000000000000000 --- a/spaces/firestalker/anime-tts/export_model.py +++ /dev/null @@ -1,13 +0,0 @@ -import torch - -if __name__ == '__main__': - model_path = "saved_model/18/model.pth" - output_path = "saved_model/18/model1.pth" - checkpoint_dict = torch.load(model_path, map_location='cpu') - checkpoint_dict_new = {} - for k, v in checkpoint_dict.items(): - if k == "optimizer": - print("remove optimizer") - continue - checkpoint_dict_new[k] = v - torch.save(checkpoint_dict_new, output_path) diff --git a/spaces/fracapuano/AISandbox/mailing/__init__.py b/spaces/fracapuano/AISandbox/mailing/__init__.py deleted file mode 100644 index 7395f4c4fb62025ed90cfb2691bbb364a579c46f..0000000000000000000000000000000000000000 --- a/spaces/fracapuano/AISandbox/mailing/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .mailing import * \ No newline at end of file diff --git a/spaces/fredrikskatland/finn-annonser/app.py b/spaces/fredrikskatland/finn-annonser/app.py deleted file mode 100644 index c9c7544845b3dccce9814cc00de1fd7ee014aa6d..0000000000000000000000000000000000000000 --- a/spaces/fredrikskatland/finn-annonser/app.py +++ /dev/null @@ -1,425 +0,0 @@ -import streamlit as st - -from langchain.callbacks import StreamlitCallbackHandler -from langchain.agents import OpenAIFunctionsAgent, AgentExecutor -from langchain.agents.agent_toolkits import create_retriever_tool -from langchain.agents.openai_functions_agent.agent_token_buffer_memory import ( - AgentTokenBufferMemory, -) -from langchain.chat_models import ChatOpenAI -from langchain.schema import SystemMessage, AIMessage, HumanMessage -from langchain.prompts import MessagesPlaceholder -from langsmith import Client -from langchain.embeddings import OpenAIEmbeddings -from langchain.vectorstores import Chroma - -from langchain.indexes import SQLRecordManager -from langchain.indexes import index -from langchain.schema import Document - -from io import StringIO -import chromadb - -import requests -from bs4 import BeautifulSoup -import xmltodict -import os - -local = True - - -client = Client() - -st.set_page_config( - page_title="Chat med stillingsannonser fra Finn.no", - page_icon=":shark", - layout="wide", - initial_sidebar_state="collapsed", -) - -system_message = SystemMessage( - content=( - "Du er en vennlig chatbot som er ansvarlig for å svare på spørsmål om stillingsannonser. " - "Hvis ikke det er eksplisitt uttrykket kan du sannsynligvis anta at spørsmålene er om stillingsannonser." - "Hvis det er noe uklart, antar du sannsynligvis at de handler om det." - ) -) - -embeddings = OpenAIEmbeddings() - -collection_name = "finn-test-index" -persistent_client = chromadb.PersistentClient() -collection = persistent_client.get_or_create_collection(collection_name) - -langchain_chroma = Chroma( - client=persistent_client, - collection_name=collection_name, - embedding_function=embeddings, -) - -# Setting up a record manager -namespace = f"chromadb/{collection_name}" -record_manager = SQLRecordManager( - namespace, db_url="sqlite:///record_manager_cache.sql" -) -record_manager.create_schema() - - -# Extract data from urls based on xpaths -def extract_text_from(url): - response = requests.get(url) - soup = BeautifulSoup(response.content, 'html.parser') - - # Navigating to the element using BeautifulSoup's built-in methods - element = soup.select_one('html > body > main > div > div:nth-of-type(3) > div:nth-of-type(1) > div > div:nth-of-type(3) > section') - # Check if element is blank, then use type2 - if not element: - element = soup.select_one('html > body > main > div:nth-of-type(2) > div:nth-of-type(1) > section') - - # Extracting the text from the element, adding a line break after each child element - if element: - text = '\n'.join(child.get_text().strip() for child in element.children if child.name) - else: - text = '' - - # Overskrift - overskrift_element = soup.select_one('html > body > main > div > div:nth-of-type(3) > div:nth-of-type(1) > div > section:nth-of-type(1)') - if overskrift_element: - overskrift_text = '\n'.join(child.get_text().strip() for child in overskrift_element.children if child.name) - else: - overskrift_element = soup.select_one('html > body > main > div:nth-of-type(2) > div:nth-of-type(1)') - overskrift_text = '\n'.join(child.get_text().strip() for child in overskrift_element.children if child.name) - - # Fakta - fakta_element = soup.select_one('html > body > main > div > div:nth-of-type(3) > div:nth-of-type(1) > div > section:nth-of-type(2) > dl') - if fakta_element: - fakta_text = '\n'.join(child.get_text().strip() for child in fakta_element.children if child.name) - fakta_items = fakta_text.split('\n') - if len(fakta_items) % 2 == 0: - fakta_dict = dict(zip(fakta_items[::2], fakta_items[1::2])) - else: - fakta_dict = { - 'Arbeidsgiver': '', - 'Stillingstittel': '', - 'Frist': '', - 'Ansettelsesform': '' - } - else: - fakta_element = soup.select_one('html > body > main > div:nth-of-type(2) > div:nth-of-type(1) > dl') - fakta_text = '\n'.join(child.get_text().strip() for child in fakta_element.children if child.name) - fakta_items = fakta_text.split('\n') - if len(fakta_items) % 2 == 0: - fakta_dict = dict(zip(fakta_items[::2], fakta_items[1::2])) - else: - fakta_dict = { - 'Arbeidsgiver': '', - 'Stillingstittel': '', - 'Frist': '', - 'Ansettelsesform': '' - } - - # Kontakt - kontakt_element = soup.select_one('html > body > main > div > div:nth-of-type(3) > div:nth-of-type(2) > section:nth-of-type(2) > div > dl') - if kontakt_element: - kontakt_text = '\n'.join(child.get_text().strip() for child in kontakt_element.children if child.name) - kontakt_items = kontakt_text.split('\n') - if len(kontakt_items) % 2 == 0: - kontakt_dict = dict(zip(kontakt_items[::2], kontakt_items[1::2])) - else: - kontakt_dict = { - 'Kontaktperson': '', - 'Stillingstittel': '', - 'Mobil': '' - } - else: - kontakt_dict = { - 'Kontaktperson': '', - 'Stillingstittel': '', - 'Mobil': '' - } - - # Om stillingen - om_element = soup.select_one('html > body > main > div > div:nth-of-type(3) > div:nth-of-type(1) > div > section:nth-of-type(5) > dl') - if om_element: - om_text = '\n'.join(child.get_text().strip() for child in om_element.children if child.name) - om_items = om_text.split('\n') - if len(om_items) % 2 == 0: - om_dict = dict(zip(om_items[::2], om_items[1::2])) - else: - om_dict = { - 'Nettverk': '', - 'Sektor': '', - 'Sted': '', - 'Hjemmekontor': '', - 'Bransje': '', - 'Stillingsfunksjon': '' - } - else: - om_dict = { - 'Nettverk': '', - 'Sektor': '', - 'Sted': '', - 'Hjemmekontor': '', - 'Bransje': '', - 'Stillingsfunksjon': '' - } - - # Nøkkelord - nokkelord_element = soup.select_one('html > body > main > div > div:nth-of-type(3) > div:nth-of-type(1) > div > section:nth-of-type(6)') - if nokkelord_element: - nokkelord_text = '\n'.join(child.get_text().strip() for child in nokkelord_element.children if child.name) - keywords_list = nokkelord_text.replace('Nøkkelord\n', '').split(', ') - else: - keywords_list = [] - - - data = { - 'overskrift': overskrift_text, - 'fakta': fakta_dict, - 'text': text, - 'kontakt': kontakt_dict, - 'om': om_dict, - 'nokkelord': keywords_list - } - - # Convert to markdown - markdown_str = f"# {data['overskrift']}\n\n" - - for key, value in data.items(): - if key == 'overskrift': - continue - elif isinstance(value, dict): - markdown_str += f"## {key.capitalize()}\n" - for subkey, subvalue in value.items(): - markdown_str += f"- **{subkey}**: {subvalue}\n" - elif isinstance(value, list): - markdown_str += f"## {key.capitalize()}\n" - for item in value: - markdown_str += f"- {item}\n" - elif key == 'text': - markdown_str += f"\n{value}\n" - - return markdown_str, data - - -@st.cache_resource(ttl="1h") -def configure_retriever(): - embeddings = OpenAIEmbeddings() - - langchain_chroma = Chroma( - client=persistent_client, - collection_name=collection_name, - embedding_function=embeddings, - ) - - if langchain_chroma._collection.count() == 0: - print("Building vector index for the first time.") - r = requests.get("https://www.finn.no/feed/job/atom.xml?rows=500") - xml = r.text - raw = xmltodict.parse(xml) - - pages = [] - counter = 0 - for info in raw['feed']['entry']: - url = info['link']['@href'] - if 'https://www.finn.no/' in url: - markdown, data = extract_text_from(url) - pages.append({'text': markdown - , 'source': url - , 'fakta': data['fakta'] - , 'kontakt': data['kontakt'] - , 'om': data['om'] - , 'nokkelord': data['nokkelord'] - , 'overskrift': data['overskrift']}) - counter += 1 - print(f"Done with url: {url}. {counter} of X done.") - - docs, metadatas = [], [] - for page in pages: - - docs.extend([page['text']]) - metadatas.extend([{ - # Populate if it exists, otherwise use a default value (e.g., an empty string) - "source": page.get('source', ''), - #"Arbeidsgiver": page['fakta'].get('Arbeidsgiver', ''), - #"Stillingstittel": page['fakta'].get('Stillingstittel', ''), - #"Søknadsfrist": page['fakta'].get('Frist', ''), - #"Ansettelsesform": page['fakta'].get('Ansettelsesform', ''), - #"Kontaktperson": page['kontakt'].get('Kontaktperson', ''), - #"Sektor": page['om'].get('Sektor', ''), - #"Bransje": page['om'].get('Bransje', ''), - #"Stillingsfunksjon": page['om'].get('Stillingsfunksjon', ''), - #"Hjemmekontor": page['om'].get('Hjemmekontor', ''), - #"keywords": page.get('nokkelord', []) - }]) - documents = [Document(page_content=string, metadata=meta) for string, meta in zip(docs, metadatas)] - index( - documents, - record_manager, - langchain_chroma, - cleanup="full", - source_id_key="source" - ) - - - - retriever = langchain_chroma.as_retriever(search_kwargs={'k': 3}) - return retriever, langchain_chroma - -def update_index(documents): - retriever, vector_store = configure_retriever() - index( - documents, - record_manager, - vector_store, - cleanup="incremental", - source_id_key="source" - ) - - - -def reload_llm(model_choice="gpt-4", temperature=0): - if local: - llm = ChatOpenAI(temperature=temperature, streaming=True, model=model_choice, ) - else: - llm = ChatOpenAI(temperature=temperature, streaming=True, model=model_choice, openai_api_key=st.secrets["openai_api_key"]) - - message = system_message - - prompt = OpenAIFunctionsAgent.create_prompt( - system_message=message, - extra_prompt_messages=[MessagesPlaceholder(variable_name="history")], - ) - - retriever, vector_store = configure_retriever() - tool = create_retriever_tool( - retriever, - "search_finn", - "Søk etter relevante stillinger på finn.no." - ) - tools = [tool] - - agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt) - agent_executor = AgentExecutor( - agent=agent, - tools=tools, - verbose=True, - return_intermediate_steps=True, - ) - memory = AgentTokenBufferMemory(llm=llm) - print ("Reloaded LLM") - return agent_executor, memory, llm - - -# Using "with" notation -with st.sidebar: - with st.form('my_form'): - model_choice = st.radio( - "Model", - ("gpt-4", "gpt-3.5-turbo-16k") - ) - temperature = st.slider('Temperature', 0.0, 1.0, 0.0, 0.01) - submitted = st.form_submit_button('Reload LLM') - if submitted: - reload_llm(model_choice=model_choice, temperature=temperature) - print(model_choice, temperature) - - with st.form("ad_listings"): - st.write(f"Antall stillinger i indeksen: {langchain_chroma._collection.count()}") - uploaded_file = st.file_uploader("Last opp liste med stillingsannonser (urler)") - if uploaded_file is not None: - # To convert to a string based IO: - stringio = StringIO(uploaded_file.getvalue().decode("utf-8")) - st.write(stringio) - - string_data = stringio.read() - st.write(string_data) - - submit_to_index = st.form_submit_button('Index listings') - if submit_to_index: - print("Indexing") - # Add each row of string_data to a list (url) - urls = [] - for row in string_data.split("\r\n"): - urls.append(row) - print(f"Added {row} to list of urls") - - # Create pages from the urls - pages = [] - for url in urls: - markdown_str, data = extract_text_from(url) - pages.append({'text': markdown_str - , 'source': url - , 'fakta': data['fakta'] - , 'kontakt': data['kontakt'] - , 'om': data['om'] - , 'nokkelord': data['nokkelord'] - , 'overskrift': data['overskrift']}) - print(f"Extracted text from {url}") - - # Convert pages to documents with text and metadata - docs, metadatas = [], [] - for page in pages: - docs.extend([page['text']]) - metadatas.extend([{ - # Populate if it exists, otherwise use a default value (e.g., an empty string) - "source": page.get('source', ''), - #"Arbeidsgiver": page['fakta'].get('Arbeidsgiver', ''), - #"Stillingstittel": page['fakta'].get('Stillingstittel', ''), - #"Søknadsfrist": page['fakta'].get('Frist', ''), - #"Ansettelsesform": page['fakta'].get('Ansettelsesform', ''), - #"Kontaktperson": page['kontakt'].get('Kontaktperson', ''), - #"Sektor": page['om'].get('Sektor', ''), - #"Bransje": page['om'].get('Bransje', ''), - #"Stillingsfunksjon": page['om'].get('Stillingsfunksjon', ''), - #"Hjemmekontor": page['om'].get('Hjemmekontor', ''), - #"keywords": page.get('nokkelord', []) - }]) - print(f"Added {page['source']} to list of documents") - # Zip docs and metadatas into documents - documents = [Document(page_content=string, metadata=meta) for string, meta in zip(docs, metadatas)] - - - # Add the documents to the index - print("Updating index") - update_index(documents) - - # Reloading llm - reload_llm() - - - - -"# Chat med stillingsannonsene 🔗" - - -starter_message = "Hvilke stillinger er leter du etter?" -if "messages" not in st.session_state or st.sidebar.button("Clear message history"): - st.session_state["messages"] = [AIMessage(content=starter_message)] - -agent_executor, memory, llm = reload_llm() - -for msg in st.session_state.messages: - if isinstance(msg, AIMessage): - st.chat_message("assistant").write(msg.content) - elif isinstance(msg, HumanMessage): - st.chat_message("user").write(msg.content) - memory.chat_memory.add_message(msg) - - -if prompt := st.chat_input(placeholder=starter_message): - st.chat_message("user").write(prompt) - with st.chat_message("assistant"): - agent_executor, memory, llm = reload_llm(model_choice=model_choice, temperature=temperature) - st_callback = StreamlitCallbackHandler(st.container()) - response = agent_executor( - {"input": prompt, "history": st.session_state.messages}, - callbacks=[st_callback], - include_run_info=True, - ) - st.session_state.messages.append(AIMessage(content=response["output"])) - st.write(response["output"]) - memory.save_context({"input": prompt}, response) - st.session_state["messages"] = memory.buffer - run_id = response["__run"].run_id - print(llm) \ No newline at end of file diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/drop.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/drop.py deleted file mode 100644 index b7b4fccd457a0d51fb10c789df3c8537fe7b67c1..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/drop.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import torch -import torch.nn as nn - -from annotator.uniformer.mmcv import build_from_cfg -from .registry import DROPOUT_LAYERS - - -def drop_path(x, drop_prob=0., training=False): - """Drop paths (Stochastic Depth) per sample (when applied in main path of - residual blocks). - - We follow the implementation - https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py # noqa: E501 - """ - if drop_prob == 0. or not training: - return x - keep_prob = 1 - drop_prob - # handle tensors with different dimensions, not just 4D tensors. - shape = (x.shape[0], ) + (1, ) * (x.ndim - 1) - random_tensor = keep_prob + torch.rand( - shape, dtype=x.dtype, device=x.device) - output = x.div(keep_prob) * random_tensor.floor() - return output - - -@DROPOUT_LAYERS.register_module() -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of - residual blocks). - - We follow the implementation - https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py # noqa: E501 - - Args: - drop_prob (float): Probability of the path to be zeroed. Default: 0.1 - """ - - def __init__(self, drop_prob=0.1): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - - def forward(self, x): - return drop_path(x, self.drop_prob, self.training) - - -@DROPOUT_LAYERS.register_module() -class Dropout(nn.Dropout): - """A wrapper for ``torch.nn.Dropout``, We rename the ``p`` of - ``torch.nn.Dropout`` to ``drop_prob`` so as to be consistent with - ``DropPath`` - - Args: - drop_prob (float): Probability of the elements to be - zeroed. Default: 0.5. - inplace (bool): Do the operation inplace or not. Default: False. - """ - - def __init__(self, drop_prob=0.5, inplace=False): - super().__init__(p=drop_prob, inplace=inplace) - - -def build_dropout(cfg, default_args=None): - """Builder for drop out layers.""" - return build_from_cfg(cfg, DROPOUT_LAYERS, default_args) diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/registry.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/registry.py deleted file mode 100644 index 39eabc58db4b5954478a2ac1ab91cea5e45ab055..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/registry.py +++ /dev/null @@ -1,16 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from annotator.uniformer.mmcv.utils import Registry - -CONV_LAYERS = Registry('conv layer') -NORM_LAYERS = Registry('norm layer') -ACTIVATION_LAYERS = Registry('activation layer') -PADDING_LAYERS = Registry('padding layer') -UPSAMPLE_LAYERS = Registry('upsample layer') -PLUGIN_LAYERS = Registry('plugin layer') - -DROPOUT_LAYERS = Registry('drop out layers') -POSITIONAL_ENCODING = Registry('position encoding') -ATTENTION = Registry('attention') -FEEDFORWARD_NETWORK = Registry('feed-forward Network') -TRANSFORMER_LAYER = Registry('transformerLayer') -TRANSFORMER_LAYER_SEQUENCE = Registry('transformer-layers sequence') diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/utils/res_layer.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/utils/res_layer.py deleted file mode 100644 index b2c07b47007e92e4c3945b989e79f9d50306f5fe..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/utils/res_layer.py +++ /dev/null @@ -1,94 +0,0 @@ -from annotator.uniformer.mmcv.cnn import build_conv_layer, build_norm_layer -from torch import nn as nn - - -class ResLayer(nn.Sequential): - """ResLayer to build ResNet style backbone. - - Args: - block (nn.Module): block used to build ResLayer. - inplanes (int): inplanes of block. - planes (int): planes of block. - num_blocks (int): number of blocks. - stride (int): stride of the first block. Default: 1 - avg_down (bool): Use AvgPool instead of stride conv when - downsampling in the bottleneck. Default: False - conv_cfg (dict): dictionary to construct and config conv layer. - Default: None - norm_cfg (dict): dictionary to construct and config norm layer. - Default: dict(type='BN') - multi_grid (int | None): Multi grid dilation rates of last - stage. Default: None - contract_dilation (bool): Whether contract first dilation of each layer - Default: False - """ - - def __init__(self, - block, - inplanes, - planes, - num_blocks, - stride=1, - dilation=1, - avg_down=False, - conv_cfg=None, - norm_cfg=dict(type='BN'), - multi_grid=None, - contract_dilation=False, - **kwargs): - self.block = block - - downsample = None - if stride != 1 or inplanes != planes * block.expansion: - downsample = [] - conv_stride = stride - if avg_down: - conv_stride = 1 - downsample.append( - nn.AvgPool2d( - kernel_size=stride, - stride=stride, - ceil_mode=True, - count_include_pad=False)) - downsample.extend([ - build_conv_layer( - conv_cfg, - inplanes, - planes * block.expansion, - kernel_size=1, - stride=conv_stride, - bias=False), - build_norm_layer(norm_cfg, planes * block.expansion)[1] - ]) - downsample = nn.Sequential(*downsample) - - layers = [] - if multi_grid is None: - if dilation > 1 and contract_dilation: - first_dilation = dilation // 2 - else: - first_dilation = dilation - else: - first_dilation = multi_grid[0] - layers.append( - block( - inplanes=inplanes, - planes=planes, - stride=stride, - dilation=first_dilation, - downsample=downsample, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - **kwargs)) - inplanes = planes * block.expansion - for i in range(1, num_blocks): - layers.append( - block( - inplanes=inplanes, - planes=planes, - stride=1, - dilation=dilation if multi_grid is None else multi_grid[i], - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - **kwargs)) - super(ResLayer, self).__init__(*layers) diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Download En Visual Studio 2010 Ultimate X86 Dvd 509116 Iso How to Create Amazing Applications with This Powerful Tool.md b/spaces/gotiQspiryo/whisper-ui/examples/Download En Visual Studio 2010 Ultimate X86 Dvd 509116 Iso How to Create Amazing Applications with This Powerful Tool.md deleted file mode 100644 index c59a3d20aeabc6982c907717fbfd118b16bdef6b..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/Download En Visual Studio 2010 Ultimate X86 Dvd 509116 Iso How to Create Amazing Applications with This Powerful Tool.md +++ /dev/null @@ -1,6 +0,0 @@ -
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Visual Studio 2010 and .NET Framework 4 marks the next generation of developer tools from Microsoft.
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Visual Studio is a complete set of tools for building applications for both desktop and Web applications based enterprise. In addition to the ability to build high-speed desktop application, you can use the powerful development tool based on components and other technologies to simplify the design, development and deployment of solutions business groups.
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Microsoft Visual Studio 2010 Ultimate provides an integrated environment of tools and server infrastructure that simplifies the entire application development process. Create business results with the process efficient, customizable and can predict and enhance that work seamlessly with the ability to track throughout the lifecycle with detailed analytics. Regardless of creating new solutions or enhancing existing applications unleash your creativity with powerful prototyping, architecture and development tools that let you bring your vision to the number of target platforms and technology are expanding, including cloud and parallel computing. Realize increased team productivity by utilizing the advanced collaboration features and use of testing tools and debuggers integrated to ensure both quality solutions while reducing development costs.
OS Support: Windows 7; Windows Server 2003; Windows Server 2008; Windows Vista; Windows XP
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* Computer with a 1.6GHz or Faster Processor
* 1024 MB RAM (1.5 GB if running in a virtual machine)
* 3 GB of available hard-disk space
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* DirectX 9 capable video card running at 1024 x 768 or higher-resolution display
* DVD-ROM Drive
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Visual Studio 2010 và .NET Framework 4 đánh dấu thế hệ kế tiếp của các công cụ phát triển từ Microsoft.Visual Studio 2010 và .NET Framework 4 tập trung vào những cột trụ cốt lỗi trong trải nghiệm phát triển phần mềm, cung cấp những nền tảng mới nhất, những trải nghiệm nhắm tới các loại ứng dụng nhất định, cùng nhiều cải thiện về kiến trúc lõi.Visual Studio là bộ công cụ hoàn chỉnh cho phép xây dựng cả các ứng dụng cho máy để bàn lẫn các ứng dụng web doanh nghiệp theo nhóm. Ngoài khả năng xây dựng những ứng dụng desktop tốc độ cao, bạn còn có thể sử dụng các công cụ phát triển mạnh mẽ dựa trên thành phần cùng các công nghệ khác nhằm đơn giản hóa thiết kế, phát triển và triển khai các giải pháp doanh nghiệp theo nhóm.Được thiết kế nhằm đáp ứng những yêu cầu mới nhất từ các nhà phát triển, Visual Studio cung cấp những đổi mới chủ chốt trong những lĩnh vực sau:* Dân chủ hóa quá trình quản lý chu trình phát triển ứng dụng (ALM). ALM có vai trò quan trọng trong một tổ chức phát triển. Trước đây, không phải vai trò nào cũng có đóng góp như nhau trong quá trình này. Visual Studio Team System 2010 tiếp nối khả năng tổ chức nhằm xây dựng một nền tảng cân bằng chức năng cùng đóng góp trên toàn bộ tiến trình ALM.* Hỗ trợ những xu hướng mới xuất hiện. Mỗi năm ngành công nghiệp phát triển phần mềm lại cho ra dời những công nghệ và xu hướng mới. Với Visual Studio 2010, Microsoft mang đến hỗ trợ các công cụ và framework cho những phát kiến mới nhất trong kiến trúc, phát triển và triển khai ứng dụng.* Tạo cảm hứng cho nhà phát triển. Kể từ phiên bản phát hành đầu tiên của Visual Studio, Microsoft đã đặt ra tiêu chuẩn về sự hiệu quả và linh hoạt cho các nhà phát triển. Visual Studio 2010 tiếp tục truyền thống này bằng cách cải thiện đáng kể trải nghiệm cho mọi vai trò phát triển phần mềm.* Lèo lái làn sóng nền tảng thế hệ mới. Microsoft tiếp tục đầu tư vào hệ điều hành đang dẫn đầu thị trường, các ứng dụng hiệu quả, cùng các nền tảng máy chủ nhằm không ngừng gia tăng giá trị khách hàng. Với Visual Studio 2010, khách hàng sẽ nhận được hỗ trợ công cụ yêu cầu để tạo ra các giải pháp không ngờ xung quanh những công nghệ này.Có gì mới trong Visual Studio 2010:Microsoft Visual Studio 2010 Ultimate đơn giản hóa quá trình phát triển giải pháp, giảm thiểu nguy cơ cũng như tăng cường kết quả trả về. Các công cụ cho mọi giai đoạn trong chu trình phát triển, từ thiết kế, phát triển đến kiểm định và triển khai, cho phép bạn thỏa sức thể hiện trí tưởng tượng và mang đến những giải pháp có ảnh hưởng lớn. Phiên bản này được phân phối dưới dạng ảnh đĩa ISO và có thể được ghi ra một đĩa trống cho quá trình cài đặt.Microsoft Visual Studio 2010 Ultimate cung cấp một môi trường tích hợp các công cụ và kiến trúc máy chủ nhằm đơn giản hóa toàn bộ tiến trình phát triển ứng dụng. Tạo ra những kết quả kinh doanh với những tiến trình hiệu quả, tùy biến và có thể dự đoán cũng như tăng cường khả năng làm việc liên thông cùng khả năng theo dõi trong suốt chu trình phát triển với các phân tích chi tiết. Bất kể là tạo lập các giải pháp mới hay tăng cường các ứng dụng hiện có giải phóng sức sáng tạo của bạn với các công cụ dựng mẫu, kiến trúc và phát triển cho phép bạn hiện thực hóa tầm nhìn nhắm đến số lượng nền tảng và công nghệ luôn mở rộng, bao gồm điện toán đám mây và song song. Hiện thực hóa hiệu quả làm việc nhóm bằng cách khai thác các tính năng cộng tác tiên tiến cũng như sử dụng các công cụ kiểm định và dò lỗi tích hợp nhằm vừa đảm bảo chất lượng giải pháp vừa giảm thiểu phí tổn phát triển.Hệ điều hành hỗ trợ: Windows 7; Windows Server 2003; Windows Server 2008; Windows Vista; Windows XPVisual Studio 2010 có thể cài đặt trên các hệ điều hành sau:* Windows XP (x86) Service Pack 3 - mọi phiên bản trừ Starter Edition* Windows XP (x64) Service Pack 2 - mọi phiên bản trừ Starter Edition* Windows Vista (x86 & x64) Service Pack 1 - mọi phiên bản trừ Starter Edition* Windows 7 (x86 và x64)* Windows Server 2003 (x86 & x64) Service Pack 2* Windows Server 2003 R2 (x86 & x64)* Windows Server 2008 (x86 & x64) Service Pack 2* Windows Server 2008 R2 (x64)Yêu cầu phần cứng:* Computer with a 1.6GHz or faster processor* 1024 MB RAM (1.5 GB if running in a virtual machine)* 3 GB of available hard-disk space* 5400 RPM hard drive* DirectX 9 capable video card running at 1024 x 768 or higher-resolution display* DVD-ROM DriveDownload:Link File.SvIT
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\ No newline at end of file diff --git a/spaces/gradio/HuBERT/examples/unsupervised_quality_estimation/repeat_lines.py b/spaces/gradio/HuBERT/examples/unsupervised_quality_estimation/repeat_lines.py deleted file mode 100644 index 5a04851a74624e9c8ebc259805b7aed6c638b0de..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/examples/unsupervised_quality_estimation/repeat_lines.py +++ /dev/null @@ -1,28 +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 argparse -import sys - - -def _normalize_spaces(line): - return " ".join(line.split()) - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("-i", "--input_file", required=True, type=str) - parser.add_argument("-n", "--repeat_times", required=True, type=int) - parser.add_argument("-o", "--output_file", required=False, type=str) - args = parser.parse_args() - stream = open(args.output_file, "w") if args.output_file else sys.stdout - - for line in open(args.input_file): - for _ in range(args.repeat_times): - stream.write(_normalize_spaces(line) + "\n") - - -if __name__ == "__main__": - main() diff --git a/spaces/gradio/code_main/run.py b/spaces/gradio/code_main/run.py deleted file mode 100644 index 7a2aa6f1ee9bad7a6d248a5951a10821499ae7d1..0000000000000000000000000000000000000000 --- a/spaces/gradio/code_main/run.py +++ /dev/null @@ -1,43 +0,0 @@ -import gradio as gr -import os -from time import sleep - - -css_file = os.path.join(os.path.dirname(__file__), "file.css") - - -def set_lang(language): - print(language) - return gr.Code(language=language) - - -def set_lang_from_path(): - sleep(1) - return gr.Code((css_file,), language="css") - - -def code(language, code): - return gr.Code(code, language=language) - - -io = gr.Interface(lambda x: x, "code", "code") - -with gr.Blocks() as demo: - lang = gr.Dropdown(value="python", choices=gr.Code.languages) - with gr.Row(): - code_in = gr.Code( - language="python", - label="Input", - value='def all_odd_elements(sequence):\n """Returns every odd element of the sequence."""', - ) - code_out = gr.Code(label="Output") - btn = gr.Button("Run") - btn_two = gr.Button("Load File") - - lang.change(set_lang, inputs=lang, outputs=code_in) - btn.click(code, inputs=[lang, code_in], outputs=code_out) - btn_two.click(set_lang_from_path, inputs=None, outputs=code_out) - io.render() - -if __name__ == "__main__": - demo.launch() diff --git a/spaces/gwang-kim/DATID-3D/eg3d/training/loss.py b/spaces/gwang-kim/DATID-3D/eg3d/training/loss.py deleted file mode 100644 index b2c637a6f81bb8d458449c355831c733fcb0cacd..0000000000000000000000000000000000000000 --- a/spaces/gwang-kim/DATID-3D/eg3d/training/loss.py +++ /dev/null @@ -1,292 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: LicenseRef-NvidiaProprietary -# -# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual -# property and proprietary rights in and to this material, related -# documentation and any modifications thereto. Any use, reproduction, -# disclosure or distribution of this material and related documentation -# without an express license agreement from NVIDIA CORPORATION or -# its affiliates is strictly prohibited. - -"""Loss functions.""" - -import numpy as np -import torch -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import upfirdn2d -from training.dual_discriminator import filtered_resizing - -#---------------------------------------------------------------------------- - -class Loss: - def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg): # to be overridden by subclass - raise NotImplementedError() - -#---------------------------------------------------------------------------- - -class StyleGAN2Loss(Loss): - def __init__(self, device, G, D, augment_pipe=None, r1_gamma=10, style_mixing_prob=0, pl_weight=0, pl_batch_shrink=2, pl_decay=0.01, pl_no_weight_grad=False, blur_init_sigma=0, blur_fade_kimg=0, r1_gamma_init=0, r1_gamma_fade_kimg=0, neural_rendering_resolution_initial=64, neural_rendering_resolution_final=None, neural_rendering_resolution_fade_kimg=0, gpc_reg_fade_kimg=1000, gpc_reg_prob=None, dual_discrimination=False, filter_mode='antialiased'): - super().__init__() - self.device = device - self.G = G - self.D = D - self.augment_pipe = augment_pipe - self.r1_gamma = r1_gamma - self.style_mixing_prob = style_mixing_prob - self.pl_weight = pl_weight - self.pl_batch_shrink = pl_batch_shrink - self.pl_decay = pl_decay - self.pl_no_weight_grad = pl_no_weight_grad - self.pl_mean = torch.zeros([], device=device) - self.blur_init_sigma = blur_init_sigma - self.blur_fade_kimg = blur_fade_kimg - self.r1_gamma_init = r1_gamma_init - self.r1_gamma_fade_kimg = r1_gamma_fade_kimg - self.neural_rendering_resolution_initial = neural_rendering_resolution_initial - self.neural_rendering_resolution_final = neural_rendering_resolution_final - self.neural_rendering_resolution_fade_kimg = neural_rendering_resolution_fade_kimg - self.gpc_reg_fade_kimg = gpc_reg_fade_kimg - self.gpc_reg_prob = gpc_reg_prob - self.dual_discrimination = dual_discrimination - self.filter_mode = filter_mode - self.resample_filter = upfirdn2d.setup_filter([1,3,3,1], device=device) - self.blur_raw_target = True - assert self.gpc_reg_prob is None or (0 <= self.gpc_reg_prob <= 1) - - def run_G(self, z, c, swapping_prob, neural_rendering_resolution, update_emas=False): - if swapping_prob is not None: - c_swapped = torch.roll(c.clone(), 1, 0) - c_gen_conditioning = torch.where(torch.rand((c.shape[0], 1), device=c.device) < swapping_prob, c_swapped, c) - else: - c_gen_conditioning = torch.zeros_like(c) - - ws = self.G.mapping(z, c_gen_conditioning, update_emas=update_emas) - if self.style_mixing_prob > 0: - with torch.autograd.profiler.record_function('style_mixing'): - cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1]) - cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1])) - ws[:, cutoff:] = self.G.mapping(torch.randn_like(z), c, update_emas=False)[:, cutoff:] - gen_output = self.G.synthesis(ws, c, neural_rendering_resolution=neural_rendering_resolution, update_emas=update_emas) - return gen_output, ws - - def run_D(self, img, c, blur_sigma=0, blur_sigma_raw=0, update_emas=False): - blur_size = np.floor(blur_sigma * 3) - if blur_size > 0: - with torch.autograd.profiler.record_function('blur'): - f = torch.arange(-blur_size, blur_size + 1, device=img['image'].device).div(blur_sigma).square().neg().exp2() - img['image'] = upfirdn2d.filter2d(img['image'], f / f.sum()) - - if self.augment_pipe is not None: - augmented_pair = self.augment_pipe(torch.cat([img['image'], - torch.nn.functional.interpolate(img['image_raw'], size=img['image'].shape[2:], mode='bilinear', antialias=True)], - dim=1)) - img['image'] = augmented_pair[:, :img['image'].shape[1]] - img['image_raw'] = torch.nn.functional.interpolate(augmented_pair[:, img['image'].shape[1]:], size=img['image_raw'].shape[2:], mode='bilinear', antialias=True) - - logits = self.D(img, c, update_emas=update_emas) - return logits - - def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg): - assert phase in ['Gmain', 'Greg', 'Gboth', 'Dmain', 'Dreg', 'Dboth'] - if self.G.rendering_kwargs.get('density_reg', 0) == 0: - phase = {'Greg': 'none', 'Gboth': 'Gmain'}.get(phase, phase) - if self.r1_gamma == 0: - phase = {'Dreg': 'none', 'Dboth': 'Dmain'}.get(phase, phase) - blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * self.blur_init_sigma if self.blur_fade_kimg > 0 else 0 - r1_gamma = self.r1_gamma - - alpha = min(cur_nimg / (self.gpc_reg_fade_kimg * 1e3), 1) if self.gpc_reg_fade_kimg > 0 else 1 - swapping_prob = (1 - alpha) * 1 + alpha * self.gpc_reg_prob if self.gpc_reg_prob is not None else None - - if self.neural_rendering_resolution_final is not None: - alpha = min(cur_nimg / (self.neural_rendering_resolution_fade_kimg * 1e3), 1) - neural_rendering_resolution = int(np.rint(self.neural_rendering_resolution_initial * (1 - alpha) + self.neural_rendering_resolution_final * alpha)) - else: - neural_rendering_resolution = self.neural_rendering_resolution_initial - - real_img_raw = filtered_resizing(real_img, size=neural_rendering_resolution, f=self.resample_filter, filter_mode=self.filter_mode) - - if self.blur_raw_target: - blur_size = np.floor(blur_sigma * 3) - if blur_size > 0: - f = torch.arange(-blur_size, blur_size + 1, device=real_img_raw.device).div(blur_sigma).square().neg().exp2() - real_img_raw = upfirdn2d.filter2d(real_img_raw, f / f.sum()) - - real_img = {'image': real_img, 'image_raw': real_img_raw} - - # Gmain: Maximize logits for generated images. - if phase in ['Gmain', 'Gboth']: - with torch.autograd.profiler.record_function('Gmain_forward'): - gen_img, _gen_ws = self.run_G(gen_z, gen_c, swapping_prob=swapping_prob, neural_rendering_resolution=neural_rendering_resolution) - gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma) - training_stats.report('Loss/scores/fake', gen_logits) - training_stats.report('Loss/signs/fake', gen_logits.sign()) - loss_Gmain = torch.nn.functional.softplus(-gen_logits) - training_stats.report('Loss/G/loss', loss_Gmain) - with torch.autograd.profiler.record_function('Gmain_backward'): - loss_Gmain.mean().mul(gain).backward() - - # Density Regularization - if phase in ['Greg', 'Gboth'] and self.G.rendering_kwargs.get('density_reg', 0) > 0 and self.G.rendering_kwargs['reg_type'] == 'l1': - if swapping_prob is not None: - c_swapped = torch.roll(gen_c.clone(), 1, 0) - c_gen_conditioning = torch.where(torch.rand([], device=gen_c.device) < swapping_prob, c_swapped, gen_c) - else: - c_gen_conditioning = torch.zeros_like(gen_c) - - ws = self.G.mapping(gen_z, c_gen_conditioning, update_emas=False) - if self.style_mixing_prob > 0: - with torch.autograd.profiler.record_function('style_mixing'): - cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1]) - cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1])) - ws[:, cutoff:] = self.G.mapping(torch.randn_like(z), c, update_emas=False)[:, cutoff:] - initial_coordinates = torch.rand((ws.shape[0], 1000, 3), device=ws.device) * 2 - 1 - perturbed_coordinates = initial_coordinates + torch.randn_like(initial_coordinates) * self.G.rendering_kwargs['density_reg_p_dist'] - all_coordinates = torch.cat([initial_coordinates, perturbed_coordinates], dim=1) - sigma = self.G.sample_mixed(all_coordinates, torch.randn_like(all_coordinates), ws, update_emas=False)['sigma'] - sigma_initial = sigma[:, :sigma.shape[1]//2] - sigma_perturbed = sigma[:, sigma.shape[1]//2:] - - TVloss = torch.nn.functional.l1_loss(sigma_initial, sigma_perturbed) * self.G.rendering_kwargs['density_reg'] - TVloss.mul(gain).backward() - - # Alternative density regularization - if phase in ['Greg', 'Gboth'] and self.G.rendering_kwargs.get('density_reg', 0) > 0 and self.G.rendering_kwargs['reg_type'] == 'monotonic-detach': - if swapping_prob is not None: - c_swapped = torch.roll(gen_c.clone(), 1, 0) - c_gen_conditioning = torch.where(torch.rand([], device=gen_c.device) < swapping_prob, c_swapped, gen_c) - else: - c_gen_conditioning = torch.zeros_like(gen_c) - - ws = self.G.mapping(gen_z, c_gen_conditioning, update_emas=False) - - initial_coordinates = torch.rand((ws.shape[0], 2000, 3), device=ws.device) * 2 - 1 # Front - - perturbed_coordinates = initial_coordinates + torch.tensor([0, 0, -1], device=ws.device) * (1/256) * self.G.rendering_kwargs['box_warp'] # Behind - all_coordinates = torch.cat([initial_coordinates, perturbed_coordinates], dim=1) - sigma = self.G.sample_mixed(all_coordinates, torch.randn_like(all_coordinates), ws, update_emas=False)['sigma'] - sigma_initial = sigma[:, :sigma.shape[1]//2] - sigma_perturbed = sigma[:, sigma.shape[1]//2:] - - monotonic_loss = torch.relu(sigma_initial.detach() - sigma_perturbed).mean() * 10 - monotonic_loss.mul(gain).backward() - - - if swapping_prob is not None: - c_swapped = torch.roll(gen_c.clone(), 1, 0) - c_gen_conditioning = torch.where(torch.rand([], device=gen_c.device) < swapping_prob, c_swapped, gen_c) - else: - c_gen_conditioning = torch.zeros_like(gen_c) - - ws = self.G.mapping(gen_z, c_gen_conditioning, update_emas=False) - if self.style_mixing_prob > 0: - with torch.autograd.profiler.record_function('style_mixing'): - cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1]) - cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1])) - ws[:, cutoff:] = self.G.mapping(torch.randn_like(z), c, update_emas=False)[:, cutoff:] - initial_coordinates = torch.rand((ws.shape[0], 1000, 3), device=ws.device) * 2 - 1 - perturbed_coordinates = initial_coordinates + torch.randn_like(initial_coordinates) * (1/256) * self.G.rendering_kwargs['box_warp'] - all_coordinates = torch.cat([initial_coordinates, perturbed_coordinates], dim=1) - sigma = self.G.sample_mixed(all_coordinates, torch.randn_like(all_coordinates), ws, update_emas=False)['sigma'] - sigma_initial = sigma[:, :sigma.shape[1]//2] - sigma_perturbed = sigma[:, sigma.shape[1]//2:] - - TVloss = torch.nn.functional.l1_loss(sigma_initial, sigma_perturbed) * self.G.rendering_kwargs['density_reg'] - TVloss.mul(gain).backward() - - # Alternative density regularization - if phase in ['Greg', 'Gboth'] and self.G.rendering_kwargs.get('density_reg', 0) > 0 and self.G.rendering_kwargs['reg_type'] == 'monotonic-fixed': - if swapping_prob is not None: - c_swapped = torch.roll(gen_c.clone(), 1, 0) - c_gen_conditioning = torch.where(torch.rand([], device=gen_c.device) < swapping_prob, c_swapped, gen_c) - else: - c_gen_conditioning = torch.zeros_like(gen_c) - - ws = self.G.mapping(gen_z, c_gen_conditioning, update_emas=False) - - initial_coordinates = torch.rand((ws.shape[0], 2000, 3), device=ws.device) * 2 - 1 # Front - - perturbed_coordinates = initial_coordinates + torch.tensor([0, 0, -1], device=ws.device) * (1/256) * self.G.rendering_kwargs['box_warp'] # Behind - all_coordinates = torch.cat([initial_coordinates, perturbed_coordinates], dim=1) - sigma = self.G.sample_mixed(all_coordinates, torch.randn_like(all_coordinates), ws, update_emas=False)['sigma'] - sigma_initial = sigma[:, :sigma.shape[1]//2] - sigma_perturbed = sigma[:, sigma.shape[1]//2:] - - monotonic_loss = torch.relu(sigma_initial - sigma_perturbed).mean() * 10 - monotonic_loss.mul(gain).backward() - - - if swapping_prob is not None: - c_swapped = torch.roll(gen_c.clone(), 1, 0) - c_gen_conditioning = torch.where(torch.rand([], device=gen_c.device) < swapping_prob, c_swapped, gen_c) - else: - c_gen_conditioning = torch.zeros_like(gen_c) - - ws = self.G.mapping(gen_z, c_gen_conditioning, update_emas=False) - if self.style_mixing_prob > 0: - with torch.autograd.profiler.record_function('style_mixing'): - cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1]) - cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1])) - ws[:, cutoff:] = self.G.mapping(torch.randn_like(z), c, update_emas=False)[:, cutoff:] - initial_coordinates = torch.rand((ws.shape[0], 1000, 3), device=ws.device) * 2 - 1 - perturbed_coordinates = initial_coordinates + torch.randn_like(initial_coordinates) * (1/256) * self.G.rendering_kwargs['box_warp'] - all_coordinates = torch.cat([initial_coordinates, perturbed_coordinates], dim=1) - sigma = self.G.sample_mixed(all_coordinates, torch.randn_like(all_coordinates), ws, update_emas=False)['sigma'] - sigma_initial = sigma[:, :sigma.shape[1]//2] - sigma_perturbed = sigma[:, sigma.shape[1]//2:] - - TVloss = torch.nn.functional.l1_loss(sigma_initial, sigma_perturbed) * self.G.rendering_kwargs['density_reg'] - TVloss.mul(gain).backward() - - # Dmain: Minimize logits for generated images. - loss_Dgen = 0 - if phase in ['Dmain', 'Dboth']: - with torch.autograd.profiler.record_function('Dgen_forward'): - gen_img, _gen_ws = self.run_G(gen_z, gen_c, swapping_prob=swapping_prob, neural_rendering_resolution=neural_rendering_resolution, update_emas=True) - gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma, update_emas=True) - training_stats.report('Loss/scores/fake', gen_logits) - training_stats.report('Loss/signs/fake', gen_logits.sign()) - loss_Dgen = torch.nn.functional.softplus(gen_logits) - with torch.autograd.profiler.record_function('Dgen_backward'): - loss_Dgen.mean().mul(gain).backward() - - # Dmain: Maximize logits for real images. - # Dr1: Apply R1 regularization. - if phase in ['Dmain', 'Dreg', 'Dboth']: - name = 'Dreal' if phase == 'Dmain' else 'Dr1' if phase == 'Dreg' else 'Dreal_Dr1' - with torch.autograd.profiler.record_function(name + '_forward'): - real_img_tmp_image = real_img['image'].detach().requires_grad_(phase in ['Dreg', 'Dboth']) - real_img_tmp_image_raw = real_img['image_raw'].detach().requires_grad_(phase in ['Dreg', 'Dboth']) - real_img_tmp = {'image': real_img_tmp_image, 'image_raw': real_img_tmp_image_raw} - - real_logits = self.run_D(real_img_tmp, real_c, blur_sigma=blur_sigma) - training_stats.report('Loss/scores/real', real_logits) - training_stats.report('Loss/signs/real', real_logits.sign()) - - loss_Dreal = 0 - if phase in ['Dmain', 'Dboth']: - loss_Dreal = torch.nn.functional.softplus(-real_logits) - training_stats.report('Loss/D/loss', loss_Dgen + loss_Dreal) - - loss_Dr1 = 0 - if phase in ['Dreg', 'Dboth']: - if self.dual_discrimination: - with torch.autograd.profiler.record_function('r1_grads'), conv2d_gradfix.no_weight_gradients(): - r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[real_img_tmp['image'], real_img_tmp['image_raw']], create_graph=True, only_inputs=True) - r1_grads_image = r1_grads[0] - r1_grads_image_raw = r1_grads[1] - r1_penalty = r1_grads_image.square().sum([1,2,3]) + r1_grads_image_raw.square().sum([1,2,3]) - else: # single discrimination - with torch.autograd.profiler.record_function('r1_grads'), conv2d_gradfix.no_weight_gradients(): - r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[real_img_tmp['image']], create_graph=True, only_inputs=True) - r1_grads_image = r1_grads[0] - r1_penalty = r1_grads_image.square().sum([1,2,3]) - loss_Dr1 = r1_penalty * (r1_gamma / 2) - training_stats.report('Loss/r1_penalty', r1_penalty) - training_stats.report('Loss/D/reg', loss_Dr1) - - with torch.autograd.profiler.record_function(name + '_backward'): - (loss_Dreal + loss_Dr1).mean().mul(gain).backward() - -#---------------------------------------------------------------------------- diff --git a/spaces/h2oai/h2ogpt-chatbot/iterators/iterator_pipe.py b/spaces/h2oai/h2ogpt-chatbot/iterators/iterator_pipe.py deleted file mode 100644 index 90883b08ee6c5fbb7a575a7f1176f124b4d66134..0000000000000000000000000000000000000000 --- a/spaces/h2oai/h2ogpt-chatbot/iterators/iterator_pipe.py +++ /dev/null @@ -1,93 +0,0 @@ -import queue -import asyncio - - -class IteratorPipe: - """ - Iterator Pipe creates an iterator that can be fed in data from another block of code or thread of execution - """ - - def __init__(self, sentinel=object()): - self._q = queue.Queue() - self._sentinel = sentinel - self._sentinel_pushed = False - self._closed = False - - def __iter__(self): - return self - - def __next__(self): - if self._closed: - raise StopIteration - - data = self._q.get(block=True) - if data is self._sentinel: - self._closed = True - raise StopIteration - - return data - - def put(self, data) -> bool: - """ - Pushes next item to Iterator and returns True - If iterator has been closed via close(), doesn't push anything and returns False - """ - if self._sentinel_pushed: - return False - - self._q.put(data) - return True - - def close(self): - """ - Close is idempotent. Calling close multiple times is safe - Iterator will raise StopIteration only after all elements pushed before close have been iterated - """ - # make close idempotent - if not self._sentinel_pushed: - self._sentinel_pushed = True - self._q.put(self._sentinel) - - -class AsyncIteratorPipe: - - def __init__(self, sentinel=object()): - self._q = asyncio.Queue() - self._sentinel = sentinel - self._sentinel_pushed = False - self._closed = False - - def __aiter__(self): - return self - - async def __anext__(self): - if self._closed: - raise StopAsyncIteration - - data = await self._q.get() - if data is self._sentinel: - self._closed = True - raise StopAsyncIteration - - return data - - async def put(self, data) -> bool: - """ - Pushes next item to Iterator and returns True - If iterator has been closed via close(), doesn't push anything and returns False - """ - if self._sentinel_pushed: - return False - - await self._q.put(data) - return True - - async def close(self): - """ - Close is idempotent. Calling close multiple times is safe - Iterator will raise StopIteration only after all elements pushed before close have been iterated - """ - # make close idempotent - if not self._sentinel_pushed: - self._sentinel_pushed = True - await self._q.put(self._sentinel) diff --git a/spaces/h2oai/wave-tour/examples/hash_routing.py b/spaces/h2oai/wave-tour/examples/hash_routing.py deleted file mode 100644 index 4fa6d79ebc145c6a014586b1e0e7a89b9215c5e4..0000000000000000000000000000000000000000 --- a/spaces/h2oai/wave-tour/examples/hash_routing.py +++ /dev/null @@ -1,39 +0,0 @@ -# Routing / Hash -# Use the browser's [location hash](https://developer.mozilla.org/en-US/docs/Web/API/Location/hash) -# for #routing using URLs. -# -# The location hash can be accessed using `q.args['#']`. -# #location_hash -# --- -from h2o_wave import main, app, Q, ui - - -@app('/demo') -async def serve(q: Q): - if not q.client.initialized: - q.client.initialized = True - q.page['nav'] = ui.markdown_card( - box='1 1 4 2', - title='Links!', - content='[Spam](#menu/spam) / [Ham](#menu/ham) / [Eggs](#menu/eggs) / [About](#about)', - ) - q.page['blurb'] = ui.markdown_card( - box='1 3 4 2', - title='Store', - content='Welcome to our store!', - ) - - hash = q.args['#'] - blurb = q.page['blurb'] - if hash == 'menu/spam': - blurb.content = "Sorry, we're out of spam!" - elif hash == 'menu/ham': - blurb.content = "Sorry, we're out of ham!" - elif hash == 'menu/eggs': - blurb.content = "Sorry, we're out of eggs!" - elif hash == 'about': - blurb.content = 'Everything here is gluten-free!' - else: - blurb.content = 'Welcome to our store!' - - await q.page.save() diff --git a/spaces/haoqi7/images/README.md b/spaces/haoqi7/images/README.md deleted file mode 100644 index 87c51a764d2f45649186ced2e5c8410fffd33d27..0000000000000000000000000000000000000000 --- a/spaces/haoqi7/images/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Images -emoji: 🌍 -colorFrom: purple -colorTo: yellow -sdk: gradio -sdk_version: 3.17.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/csrc/README.md b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/csrc/README.md deleted file mode 100644 index 778ed3da0bae89820831bcd8a72ff7b9cad8d4dd..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/csrc/README.md +++ /dev/null @@ -1,7 +0,0 @@ - - -To add a new Op: - -1. Create a new directory -2. Implement new ops there -3. Delcare its Python interface in `vision.cpp`. diff --git a/spaces/hasibzunair/fifa-tryon-demo/models/test.py b/spaces/hasibzunair/fifa-tryon-demo/models/test.py deleted file mode 100644 index 73ac80667478c93e02c3e5483899b63d5b53f873..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/models/test.py +++ /dev/null @@ -1,7 +0,0 @@ -import numpy -num = 5 -for i in range(num): - row = [] - print() - for i in range(num): - print(j * num + i) diff --git a/spaces/huggingface-projects/diffusers-gallery-bot/Dockerfile b/spaces/huggingface-projects/diffusers-gallery-bot/Dockerfile deleted file mode 100644 index 6b954a8c5ea2a23b42fa0168e147e94c6eb7af8d..0000000000000000000000000000000000000000 --- a/spaces/huggingface-projects/diffusers-gallery-bot/Dockerfile +++ /dev/null @@ -1,25 +0,0 @@ -FROM python:3.9 - -WORKDIR /code - -COPY ./requirements.txt /code/requirements.txt - -RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt - -# Git LFS -RUN apt-get update && apt-get install -y git-lfs -RUN git lfs install - - -# User -RUN useradd -m -u 1000 user -USER user -ENV HOME /home/user -ENV PATH $HOME/.local/bin:$PATH -WORKDIR $HOME -RUN mkdir app -WORKDIR $HOME/app - -COPY . $HOME/app - -CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"] \ No newline at end of file diff --git a/spaces/huggingface-tools/text-to-image/README.md b/spaces/huggingface-tools/text-to-image/README.md deleted file mode 100644 index b27d18e8139c9bfb24730903dcee2792b20d0f38..0000000000000000000000000000000000000000 --- a/spaces/huggingface-tools/text-to-image/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Text to Image -emoji: ⚡ -colorFrom: blue -colorTo: green -sdk: gradio -sdk_version: 3.27.0 -app_file: app.py -pinned: false -tags: -- tool ---- diff --git a/spaces/hzwluoye/gpt4/client/html/index.html b/spaces/hzwluoye/gpt4/client/html/index.html deleted file mode 100644 index 3b14dc45d2b1f5afd854bbecf05bd556f4305ade..0000000000000000000000000000000000000000 --- a/spaces/hzwluoye/gpt4/client/html/index.html +++ /dev/null @@ -1,163 +0,0 @@ - - - - - - - - - - - - - - - - - -FreeGPT - - - -- -- - - - - - - - - - - - - - - diff --git a/spaces/iamironman4279/SadTalker/src/face3d/options/test_options.py b/spaces/iamironman4279/SadTalker/src/face3d/options/test_options.py deleted file mode 100644 index 4ff3ad142779850d1d5a1640bc00f70d34d4a862..0000000000000000000000000000000000000000 --- a/spaces/iamironman4279/SadTalker/src/face3d/options/test_options.py +++ /dev/null @@ -1,21 +0,0 @@ -"""This script contains the test options for Deep3DFaceRecon_pytorch -""" - -from .base_options import BaseOptions - - -class TestOptions(BaseOptions): - """This class includes test options. - - It also includes shared options defined in BaseOptions. - """ - - def initialize(self, parser): - parser = BaseOptions.initialize(self, parser) # define shared options - parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') - parser.add_argument('--dataset_mode', type=str, default=None, help='chooses how datasets are loaded. [None | flist]') - parser.add_argument('--img_folder', type=str, default='examples', help='folder for test images.') - - # Dropout and Batchnorm has different behavior during training and test. - self.isTrain = False - return parser diff --git a/spaces/icashwave/rwkv-v5-1b5-cpu/app.py b/spaces/icashwave/rwkv-v5-1b5-cpu/app.py deleted file mode 100644 index 78685a836484b7ee7ba4c9829920f18d7d2fe1d3..0000000000000000000000000000000000000000 --- a/spaces/icashwave/rwkv-v5-1b5-cpu/app.py +++ /dev/null @@ -1,208 +0,0 @@ -import gradio as gr -import os, gc, copy, torch -from datetime import datetime -from huggingface_hub import hf_hub_download -from pynvml import * - -# Flag to check if GPU is present -HAS_GPU = False - -# Model title and context size limit -ctx_limit = 4000 -title = "1B5" -model_file = "RWKV-5-World-1B5-v2-20231025-ctx4096" - -#title = "RWKV-4-World" -#model_file = "RWKV-4-World-1.5B-v1-fixed-20230612-ctx4096" - -# Get the GPU count -try: - nvmlInit() - GPU_COUNT = nvmlDeviceGetCount() - if GPU_COUNT > 0: - HAS_GPU = True - gpu_h = nvmlDeviceGetHandleByIndex(0) -except NVMLError as error: - print(error) - - -os.environ["RWKV_JIT_ON"] = '1' - -# Model strat to use -MODEL_STRAT="cpu bf16" -os.environ["RWKV_CUDA_ON"] = '0' # if '1' then use CUDA kernel for seq mode (much faster) - -# Switch to GPU mode -if HAS_GPU == True : - os.environ["RWKV_CUDA_ON"] = '1' - MODEL_STRAT = "cuda bf16" - -# Load the model accordingly -from rwkv.model import RWKV -model_path = hf_hub_download(repo_id="BlinkDL/rwkv-5-world", filename=f"{model_file}.pth") -#model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-world", filename=f"{model_file}.pth") -model = RWKV(model=model_path, strategy=MODEL_STRAT) -from rwkv.utils import PIPELINE, PIPELINE_ARGS -pipeline = PIPELINE(model, "rwkv_vocab_v20230424") - -# Prompt generation -#def generate_prompt(instruction, input=""): - #instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n') - #input = input.strip().replace('\r\n','\n').replace('\n\n','\n') - #if input: - #return f"""Instruction: {instruction} - -#Input: {input} - -#Response:""" - #else: - #return f"""User: hi - -#Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. - -#User: {instruction} - -#Assistant:""" - -def generate_prompt(instruction, input=""): - instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n').replace('\n\n','\n') - input = input.strip().replace('\r\n','\n').replace('\n\n','\n').replace('\n\n','\n') - if input: - return f"""Instruction: {instruction} -Input: {input} -Response:""" - else: - return f"""Question: {instruction} -Answer:""" - -# Evaluation logic -#def evaluate( -# ctx, -# token_count=200, -# temperature=1.0, -# top_p=0.7, -# presencePenalty = 0.1, -# countPenalty = 0.1, -#): -def evaluate( - instruction, - input=None, - token_count=800, - temperature=1.0, - top_p=0.7, - presencePenalty = 0.1, - countPenalty = 0.1, -): - args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p), - alpha_frequency = countPenalty, - alpha_presence = presencePenalty, - token_ban = [], # ban the generation of some tokens - token_stop = [0]) # stop generation whenever you see any token here - #ctx = ctx.strip() - ctx = generate_prompt(instruction, input) - - all_tokens = [] - out_last = 0 - out_str = '' - occurrence = {} - state = None - for i in range(int(token_count)): - out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state) - for n in occurrence: - out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency) - - token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p) - if token in args.token_stop: - break - all_tokens += [token] - for xxx in occurrence: - occurrence[xxx] *= 0.996 - if token not in occurrence: - occurrence[token] = 1 - else: - occurrence[token] += 1 - - tmp = pipeline.decode(all_tokens[out_last:]) - if '\ufffd' not in tmp: - out_str += tmp - yield out_str.strip() - out_last = i + 1 - - if HAS_GPU == True : - gpu_info = nvmlDeviceGetMemoryInfo(gpu_h) - print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}') - - del out - del state - gc.collect() - - if HAS_GPU == True : - torch.cuda.empty_cache() - - yield out_str.strip() - -# Examples and gradio blocks -examples = [ - ["Write a song about flowers.", "", 200, 1.2, 0.5, 0.4, 0.4], - ["寫一篇關於交通工程的車流理論模型之論文，需要詳細全面。","", 600, 1.2, 0.5, 0.4, 0.4], - ["根據以下資訊，寫一篇文情並茂的文章。", "一位中年男子為追求事業成就，不斷奮鬥突破眼前遇到的難關。", 600, 1.2, 0.5, 0.4, 0.4] - - #["Assistant: Sure! Here is a very detailed plan to create flying pigs:", 333, 1, 0.3, 0, 1], - #["Assistant: Sure! Here are some ideas for FTL drive:", 333, 1, 0.3, 0, 1], - #[generate_prompt("Tell me about ravens."), 333, 1, 0.3, 0, 1], - #[generate_prompt("Écrivez un programme Python pour miner 1 Bitcoin, avec des commentaires."), 333, 1, 0.3, 0, 1], - #[generate_prompt("東京で訪れるべき素晴らしい場所とその紹介をいくつか挙げてください。"), 333, 1, 0.3, 0, 1], - #[generate_prompt("Write a story using the following information.", "A man named Alex chops a tree down."), 333, 1, 0.3, 0, 1], - #["Assistant: Here is a very detailed plan to kill all mosquitoes:", 333, 1, 0.3, 0, 1], - #['''Edward: I am Edward Elric from fullmetal alchemist. I am in the world of full metal alchemist and know nothing of the real world. - -#User: Hello Edward. What have you been up to recently? - -#Edward:''', 333, 1, 0.3, 0, 1], - #[generate_prompt("写一篇关于水利工程的流体力学模型的论文，需要详细全面。"), 333, 1, 0.3, 0, 1], - #['''“当然可以，大宇宙不会因为这五公斤就不坍缩了。”关一帆说，他还有一个没说出来的想法：也许大宇宙真的会因为相差一个原子的质量而由封闭转为开放。大自然的精巧有时超出想象，比如生命的诞生，就需要各项宇宙参数在几亿亿分之一精度上的精确配合。但程心仍然可以留下她的生态球，因为在那无数文明创造的无数小宇宙中，肯定有相当一部分不响应回归运动的号召，所以，大宇宙最终被夺走的质量至少有几亿吨，甚至可能是几亿亿亿吨。 -#但愿大宇宙能够忽略这个误差。 -#程心和关一帆进入了飞船，智子最后也进来了。她早就不再穿那身华丽的和服了，她现在身着迷彩服，再次成为一名轻捷精悍的战士，她的身上佩带着许多武器和生存装备，最引人注目的是那把插在背后的武士刀。 -#“放心，我在，你们就在！”智子对两位人类朋友说。 -#聚变发动机启动了，推进器发出幽幽的蓝光，飞船缓缓地穿过了宇宙之门。 -#小宇宙中只剩下漂流瓶和生态球。漂流瓶隐没于黑暗里，在一千米见方的宇宙中，只有生态球里的小太阳发出一点光芒。在这个小小的生命世界中，几只清澈的水球在零重力环境中静静地飘浮着，有一条小鱼从一只水球中蹦出，跃入另一只水球，轻盈地穿游于绿藻之间。在一小块陆地上的草丛中，有一滴露珠从一片草叶上脱离，旋转着飘起，向太空中折射出一缕晶莹的阳光。''', 333, 1, 0.3, 0, 1], -] - -########################################################################## - -# Gradio blocks -with gr.Blocks(title=title) as demo: - gr.HTML(f"\n") - with gr.Tab("Raw Generation"): - gr.Markdown(f"This is [RWKV-5 World v2](https://huggingface.co/BlinkDL/rwkv-5-world) with 1.5B params - a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM). Supports all 100+ world languages and code. And we have Demo limited to ctxlen {ctx_limit}.") - with gr.Row(): - with gr.Column(): - #prompt = gr.Textbox(lines=2, label="Prompt", value="Assistant: Sure! Here is a very detailed plan to create flying pigs:") - - instruction = gr.Textbox(lines=2, label="Instruction", value='Write a song about flowers.') - input = gr.Textbox(lines=2, label="Input", placeholder="none") - - token_count = gr.Slider(10, 1000, label="Max Tokens", step=10, value=100) - temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.0) - top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.3) - presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0) - count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=1) - with gr.Column(): - with gr.Row(): - submit = gr.Button("Submit", variant="primary") - clear = gr.Button("Clear", variant="secondary") - output = gr.Textbox(label="Output", lines=5) - #data = gr.Dataset(components=[prompt, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Prompt", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"]) - data = gr.Dataset(components=[instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Instruction", "Input", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"]) - - #submit.click(evaluate, [prompt, token_count, temperature, top_p, presence_penalty, count_penalty], [output]) - - submit.click(evaluate, [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], [output]) - - clear.click(lambda: None, [], [output]) - #data.click(lambda x: x, [data], [prompt, token_count, temperature, top_p, presence_penalty, count_penalty]) - data.click(lambda x: x, [data], [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty]) - -# Gradio launch -demo.queue(concurrency_count=1, max_size=10) -demo.launch(share=False) diff --git a/spaces/ieuniversity/News-Translator/app.py b/spaces/ieuniversity/News-Translator/app.py deleted file mode 100644 index d2fd9376f8c1c83c184b7722b14e8bd29210f3f7..0000000000000000000000000000000000000000 --- a/spaces/ieuniversity/News-Translator/app.py +++ /dev/null @@ -1,48 +0,0 @@ -import gradio as gr -import torch -import transformers as trf - -# Load the summarization model -summarization_model_path = 'ieuniversity/summarization_model_translator' -summarization_tokenizer = trf.AutoTokenizer.from_pretrained("it5/it5-base-news-summarization") -summarization_model = trf.AutoModelForSeq2SeqLM.from_pretrained(summarization_model_path) - -# Load the translation model -translation_model_path = 'hackathon-pln-es/t5-small-finetuned-spanish-to-quechua' -translation_tokenizer = trf.AutoTokenizer.from_pretrained(translation_model_path) -translation_model = trf.AutoModelForSeq2SeqLM.from_pretrained(translation_model_path) - -def summarize_and_translate(news_text): - # Summarize the news article - max_input_length = 512 - max_output_length = 128 - input_encoded = summarization_tokenizer.encode_plus(news_text, add_special_tokens=True, - max_length=max_input_length, pad_to_max_length=True, - return_attention_mask=True, return_tensors='pt') - input_ids = input_encoded['input_ids'] - attention_mask = input_encoded['attention_mask'] - output_ids = summarization_model.generate(input_ids=input_ids, attention_mask=attention_mask, - max_length=max_output_length) - summary_text = summarization_tokenizer.decode(output_ids[0], skip_special_tokens=True) - - # Translate the summary to Quechua - input_encoded = translation_tokenizer(summary_text, padding=True, truncation=True, max_length=512, return_tensors='pt') - input_ids = input_encoded['input_ids'] - attention_mask = input_encoded['attention_mask'] - output_ids = translation_model.generate(input_ids=input_ids, attention_mask=attention_mask, - max_length=512) - output_text = translation_tokenizer.decode(output_ids[0], skip_special_tokens=True) - - return output_text - -# Define the input and output interfaces for Gradio -input_interface = gr.inputs.Textbox(label="Input your News text! (Spanish)") -output_interface = gr.outputs.Textbox(label="Your Summarized News Text in Native Quechua!") - -# Add poster link to the interface description -description = "This is a Spanish-to-Quechua news summarization and translation app. It uses a state-of-the-art AI model to summarize news articles in Spanish from the newspaper La Razón and translate the summary to Quechua. You can learn more about this project and its creators by visiting this poster." - -# Create and launch the Gradio app -iface = gr.Interface(fn=summarize_and_translate, inputs=input_interface, outputs=output_interface, - title="Spanish-to-Quechua News Summarization and Translation", description=description) -iface.launch() diff --git a/spaces/imseldrith/DeepFakeAI/DeepFakeAI/metadata.py b/spaces/imseldrith/DeepFakeAI/DeepFakeAI/metadata.py deleted file mode 100644 index 39b16362cdd2cb5464ce32dcd270fc8e15f6251b..0000000000000000000000000000000000000000 --- a/spaces/imseldrith/DeepFakeAI/DeepFakeAI/metadata.py +++ /dev/null @@ -1,13 +0,0 @@ -METADATA =\ -{ - 'name': 'DeepFakeAI', - 'description': 'Next generation face swapper and enhancer', - 'version': '1.0.0', - 'license': 'MIT', - 'author': 'Ashiq Hussain Mir', - 'url': 'https://codegenius.me' -} - - -def get(key : str) -> str: - return METADATA[key] diff --git a/spaces/inamXcontru/PoeticTTS/Batman Unlimited - Lalleanza dei mostri 720p torrent il film danimazione con i supereroi DC.md b/spaces/inamXcontru/PoeticTTS/Batman Unlimited - Lalleanza dei mostri 720p torrent il film danimazione con i supereroi DC.md deleted file mode 100644 index cdbf0c71c4b1874fd5951100fc6ff2962da7eef8..0000000000000000000000000000000000000000 --- a/spaces/inamXcontru/PoeticTTS/Batman Unlimited - Lalleanza dei mostri 720p torrent il film danimazione con i supereroi DC.md +++ /dev/null @@ -1,6 +0,0 @@ -RWKV-5 World v2 - {title}
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-Natura Sound Therapy 3 is a software that allows you to listen to various nature sounds, such as ocean waves, rain, thunder, birds, and more. You can customize your own soundscapes by mixing different sounds and adjusting their volume and frequency. You can also use the software to play relaxing music, binaural beats, and brainwave entrainment sessions.
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-Natura Sound Therapy 3 has a variety of nature sounds to choose from. You can listen to the sounds of the ocean, the forest, the rainforest, the waterfall, the fire, the wind, and more. You can also listen to animal sounds, such as birds, frogs, crickets, whales, dolphins, and wolves. Each sound has a realistic and high-quality audio that will make you feel like you are in nature.
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-Natura Sound Therapy 3 also has a feature that allows you to use binaural beats and brainwave entrainment to enhance your mental state. Binaural beats are sounds that have different frequencies in each ear, which can stimulate your brain to produce certain brainwaves. Brainwave entrainment is a technique that uses rhythmic stimuli to synchronize your brainwaves with a desired frequency. These methods can help you achieve relaxation, focus, creativity, learning, memory, sleep, and more.
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-Natura Sound Therapy 3 is easy to use and customize. You can adjust the volume and frequency of each sound and music track, as well as the balance and panning. You can also save and load your own soundscapes and presets. You can also set timers, alarms, and reminders to start and stop the sounds and music according to your needs. You can also use the software in the background or in full-screen mode.
-Natura Sound Therapy 3 is a powerful and versatile software that can help you relax, meditate, sleep, study, or work better. It can also improve your mood, health, and performance. It is a software that can bring you closer to nature and yourself. You can download it from here and enjoy the benefits of nature sounds today.
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\ No newline at end of file diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Creo Elements Direct Modeling 18.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Creo Elements Direct Modeling 18.md deleted file mode 100644 index 694230e63cf7996d71b2267c774c25346337ce85..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Creo Elements Direct Modeling 18.md +++ /dev/null @@ -1,7 +0,0 @@ -
-CAD-like Asset Usage The bill of materials (BOM) functionality of Creo Elements can now be used for Artwork & Collateral. For example, instead of thinking of a group of parts and just selecting them all to put into your product, you can "bulk-select" multiple parts to add them to the BOM from the Workspace. For example, you can select parts that have the same color, type or any other attribute and make a single selection to add them to your product. To use the Bulk-select tool, click the red box icon in the upper left corner of the Workspace view, and then select the parts to add them to your product. You can then add them to the Bill Of Material, if you wish. You can also add a texture to the parts by clicking on the thumbnail icon next to the other parts.
-Ability to load in lower resolutions Up until Version 18, Creo Elements/Direct could only support a single resolution at any given time. In Version 18, we've added some new functionality that supports a "resolution state". This allows you to create multiple resolutions and share them across multiple Creo apps.
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-this dataset provides genomic reference data and software packages for use with galaxy and bioconductor applications. the reference data is available for hundreds of reference genomes and has been formatted for use with a variety of tools. the available configuration files make this data easily incorporable with a local galaxy server without additional data preparation. additionally, bioconductor's annotationhub and experimenthub data are provided for use via r packages. the training data for tiger is used to provide a single unified machine learning pipeline for calling tils in breast cancer histopathology slides. this pipeline uses deep neural networks to call tils from histopathology slides, and is intended to be a baseline for future tiger challenge..
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-this dataset contains genomic reference data and software packages for use with galaxy and bioconductor applications. the reference data is available for hundreds of reference genomes and has been formatted for use with a variety of tools. the available configuration files make this data easily incorporable with a local galaxy server without additional data preparation. additionally, bioconductor's annotationhub and experimenthub data are provided for use via r packages. the training data for tiger is used to provide a single unified machine learning pipeline for calling tils in breast cancer histopathology slides. this pipeline uses deep neural networks to call tils from histopathology slides, and is intended to be a baseline for future tiger challenge..
-this dataset provides genomic reference data and software packages for use with galaxy and bioconductor applications. the reference data is available for hundreds of reference genomes and has been formatted for use with a variety of tools. the available configuration files make this data easily incorporable with a local galaxy server without additional data preparation. additionally, bioconductor's annotationhub and experimenthub data are provided for use via r packages.
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\ No newline at end of file diff --git a/spaces/inreVtussa/clothingai/Examples/Ample Bass Upright V3.1.0 Incl Keygen (WIN OSX)-R2R.md b/spaces/inreVtussa/clothingai/Examples/Ample Bass Upright V3.1.0 Incl Keygen (WIN OSX)-R2R.md deleted file mode 100644 index 393602cf13a04ef3a776733026c8231e0519abf0..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/Ample Bass Upright V3.1.0 Incl Keygen (WIN OSX)-R2R.md +++ /dev/null @@ -1,9 +0,0 @@ -
-want to give your bass track a lift try going fretless. included in the 6 new electric basses are 2 fretless models. theres fretless jazz, inspired by the iconic jaco pastoriuss customized fender jazz bass with its instantly recognizable tone and dynamics. and fretless bass man, inspired by pino palladinos famous music man stingray, delivers that iconic sound to your studio. plus modo bass 2 adds the ability to make any bass in your collection a fretless* model.
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ample soundmodo bass 2 brings 2 double basses to the collection. choose rockabilly for old-school slapping styles or studio upright for that classic jazz sound. in their own studio, the double basses get a selection of movable mics to capture their sound with a piezo signal you can blend in for added focus and then run through modos stomps and amps. and finally, a pair of stereo room mics lets you mix in the perfect ambient room sound.
-these are 100% clean and legato samples, no octave up or down samples. since they are created from bass guitar recordings, sound quality is very good. same as audio editor/sampler. when you play a fret no samples will be triggered, they will play along with the bass and trigger a synth if it's an option in that mode.
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\ No newline at end of file diff --git a/spaces/inreVtussa/clothingai/Examples/Automation Studio P6 13.md b/spaces/inreVtussa/clothingai/Examples/Automation Studio P6 13.md deleted file mode 100644 index 00f6a7775b7d7bb2f5ae039b399ea3cb4a21da47..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/Automation Studio P6 13.md +++ /dev/null @@ -1,12 +0,0 @@ -Automation Studio P6 13
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- -: 10 hours. Students will learn the concepts, definitions and theory of hydraulics for Automation Studio. This course provides an introduction to working with the category of hydraulics including how to configure basic hydraulics through the use of the tool bar and the fluid configuration dialogs. This course also provides an introduction to using the broad variety of modules in Automation Studio which is a software package that allows you to define and simulate a system from a single point, rather than build it from parts. This module includes: Introduction to Automation StudioBasic hydraulic Configuring the tool bar for a hydraulic system. You will also learn about and create a few valve models and configure the model view. Introduction to the Simulation ToolDialog Boxes- How to configure and modify the model view. Setting up ParametersIntroduction to the Simulation ToolModule 2Using Control Fluid Toolbar Basics- Using basic functions of the toolbar for basic hydraulic components such as faucets, valves and pumps. Making basic fluid connections.Basic Functions of the Toolbar- How to use the basic functions to control the hydraulic fluid in the system. Using Control Fluid dialog. Working with Control Fluid Model View. Introduction to Control Fluid dialogsMaking basic fluid connections- Making basic fluid connections. Learning to use the Fluid Control dialog box - -About the Author: - -Dr. Anthony Palazzo is a Professor of Mechanical Engineering at San Diego State University. - -To learn more about Hydraulics in Automation Studio™, please visit: www.automationstudio.com 4fefd39f24
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- diff --git a/spaces/inreVtussa/clothingai/Examples/Calcul Code Ccp. 0.01mb.epub.md b/spaces/inreVtussa/clothingai/Examples/Calcul Code Ccp. 0.01mb.epub.md deleted file mode 100644 index df7630ace8a0390d20e1b136da49e17b4dfae71d..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/Calcul Code Ccp. 0.01mb.epub.md +++ /dev/null @@ -1,43 +0,0 @@ -
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- -For example, if your account number is 001234567890, your date of birth is 01011990, and your gender is M, then your CCP key is:
-CCP key = (001234567890 * 01011990 * 1) mod 10^10-CCP key = (12469999990) mod 10^10-CCP key = 469999990- -To verify that your CCP key is correct, you can use the online tool provided by the postal service at https://www.poste.dz/ccp-key-verifier.
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\ No newline at end of file diff --git a/spaces/inreVtussa/clothingai/Examples/DescargarrevealerkeyloggerconWORK Crack.md b/spaces/inreVtussa/clothingai/Examples/DescargarrevealerkeyloggerconWORK Crack.md deleted file mode 100644 index eb2a0d156ce4b75bc7da960c77b7a55986255533..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/DescargarrevealerkeyloggerconWORK Crack.md +++ /dev/null @@ -1,6 +0,0 @@ -
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\ No newline at end of file diff --git a/spaces/iqbalc/Speech-to-text-demo/app.py b/spaces/iqbalc/Speech-to-text-demo/app.py deleted file mode 100644 index df71d5eb3476622a3000aac7e3c8865cbed92466..0000000000000000000000000000000000000000 --- a/spaces/iqbalc/Speech-to-text-demo/app.py +++ /dev/null @@ -1,37 +0,0 @@ -import os -os.system("pip install git+https://github.com/openai/whisper.git") - -import gradio as gr -import whisper -model = whisper.load_model("large") - -import time - -def transcribe(audio): - # split the audio for 30 seconds - audio = whisper.load_audio(audio) - audio = whisper.pad_or_trim(audio) - - # make log-Mel spectrogram and move to device as the model - mel = whisper.log_mel_spectrogram(audio).to(model.device) - - # detect the spoken language - _, probs = model.detect_language(mel) - print(f"Detected language: {max(probs, key=probs.get)}") - - # decoding the audio - options = whisper.DecodingOptions(fp16 = False) - result = whisper.decode(model, mel, options) - print(result.text) - return result.text - -gr.Interface( - title = 'Speech to Text with OpenAI (large)', - fn=transcribe, - inputs=[ - gr.inputs.Audio(source="microphone", type="filepath") - ], - outputs=[ - "textbox" - ], - live=True).launch() diff --git a/spaces/irvay/RVC_IR/rmvpe.py b/spaces/irvay/RVC_IR/rmvpe.py deleted file mode 100644 index 3ad346141340e03bdbaa20121e1ed435bb3da57a..0000000000000000000000000000000000000000 --- a/spaces/irvay/RVC_IR/rmvpe.py +++ /dev/null @@ -1,432 +0,0 @@ -import sys, torch, numpy as np, traceback, pdb -import torch.nn as nn -from time import time as ttime -import torch.nn.functional as F - - -class BiGRU(nn.Module): - def __init__(self, input_features, hidden_features, num_layers): - super(BiGRU, self).__init__() - self.gru = nn.GRU( - input_features, - hidden_features, - num_layers=num_layers, - batch_first=True, - bidirectional=True, - ) - - def forward(self, x): - return self.gru(x)[0] - - -class ConvBlockRes(nn.Module): - def __init__(self, in_channels, out_channels, momentum=0.01): - super(ConvBlockRes, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=(3, 3), - stride=(1, 1), - padding=(1, 1), - bias=False, - ), - nn.BatchNorm2d(out_channels, momentum=momentum), - nn.ReLU(), - nn.Conv2d( - in_channels=out_channels, - out_channels=out_channels, - kernel_size=(3, 3), - stride=(1, 1), - padding=(1, 1), - bias=False, - ), - nn.BatchNorm2d(out_channels, momentum=momentum), - nn.ReLU(), - ) - if in_channels != out_channels: - self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1)) - self.is_shortcut = True - else: - self.is_shortcut = False - - def forward(self, x): - if self.is_shortcut: - return self.conv(x) + self.shortcut(x) - else: - return self.conv(x) + x - - -class Encoder(nn.Module): - def __init__( - self, - in_channels, - in_size, - n_encoders, - kernel_size, - n_blocks, - out_channels=16, - momentum=0.01, - ): - super(Encoder, self).__init__() - self.n_encoders = n_encoders - self.bn = nn.BatchNorm2d(in_channels, momentum=momentum) - self.layers = nn.ModuleList() - self.latent_channels = [] - for i in range(self.n_encoders): - self.layers.append( - ResEncoderBlock( - in_channels, out_channels, kernel_size, n_blocks, momentum=momentum - ) - ) - self.latent_channels.append([out_channels, in_size]) - in_channels = out_channels - out_channels *= 2 - in_size //= 2 - self.out_size = in_size - self.out_channel = out_channels - - def forward(self, x): - concat_tensors = [] - x = self.bn(x) - for i in range(self.n_encoders): - _, x = self.layers[i](x) - concat_tensors.append(_) - return x, concat_tensors - - -class ResEncoderBlock(nn.Module): - def __init__( - self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01 - ): - super(ResEncoderBlock, self).__init__() - self.n_blocks = n_blocks - self.conv = nn.ModuleList() - self.conv.append(ConvBlockRes(in_channels, out_channels, momentum)) - for i in range(n_blocks - 1): - self.conv.append(ConvBlockRes(out_channels, out_channels, momentum)) - self.kernel_size = kernel_size - if self.kernel_size is not None: - self.pool = nn.AvgPool2d(kernel_size=kernel_size) - - def forward(self, x): - for i in range(self.n_blocks): - x = self.conv[i](x) - if self.kernel_size is not None: - return x, self.pool(x) - else: - return x - - -class Intermediate(nn.Module): # - def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01): - super(Intermediate, self).__init__() - self.n_inters = n_inters - self.layers = nn.ModuleList() - self.layers.append( - ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum) - ) - for i in range(self.n_inters - 1): - self.layers.append( - ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum) - ) - - def forward(self, x): - for i in range(self.n_inters): - x = self.layers[i](x) - return x - - -class ResDecoderBlock(nn.Module): - def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01): - super(ResDecoderBlock, self).__init__() - out_padding = (0, 1) if stride == (1, 2) else (1, 1) - self.n_blocks = n_blocks - self.conv1 = nn.Sequential( - nn.ConvTranspose2d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=(3, 3), - stride=stride, - padding=(1, 1), - output_padding=out_padding, - bias=False, - ), - nn.BatchNorm2d(out_channels, momentum=momentum), - nn.ReLU(), - ) - self.conv2 = nn.ModuleList() - self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum)) - for i in range(n_blocks - 1): - self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum)) - - def forward(self, x, concat_tensor): - x = self.conv1(x) - x = torch.cat((x, concat_tensor), dim=1) - for i in range(self.n_blocks): - x = self.conv2[i](x) - return x - - -class Decoder(nn.Module): - def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01): - super(Decoder, self).__init__() - self.layers = nn.ModuleList() - self.n_decoders = n_decoders - for i in range(self.n_decoders): - out_channels = in_channels // 2 - self.layers.append( - ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum) - ) - in_channels = out_channels - - def forward(self, x, concat_tensors): - for i in range(self.n_decoders): - x = self.layers[i](x, concat_tensors[-1 - i]) - return x - - -class DeepUnet(nn.Module): - def __init__( - self, - kernel_size, - n_blocks, - en_de_layers=5, - inter_layers=4, - in_channels=1, - en_out_channels=16, - ): - super(DeepUnet, self).__init__() - self.encoder = Encoder( - in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels - ) - self.intermediate = Intermediate( - self.encoder.out_channel // 2, - self.encoder.out_channel, - inter_layers, - n_blocks, - ) - self.decoder = Decoder( - self.encoder.out_channel, en_de_layers, kernel_size, n_blocks - ) - - def forward(self, x): - x, concat_tensors = self.encoder(x) - x = self.intermediate(x) - x = self.decoder(x, concat_tensors) - return x - - -class E2E(nn.Module): - def __init__( - self, - n_blocks, - n_gru, - kernel_size, - en_de_layers=5, - inter_layers=4, - in_channels=1, - en_out_channels=16, - ): - super(E2E, self).__init__() - self.unet = DeepUnet( - kernel_size, - n_blocks, - en_de_layers, - inter_layers, - in_channels, - en_out_channels, - ) - self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1)) - if n_gru: - self.fc = nn.Sequential( - BiGRU(3 * 128, 256, n_gru), - nn.Linear(512, 360), - nn.Dropout(0.25), - nn.Sigmoid(), - ) - else: - self.fc = nn.Sequential( - nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid() - ) - - def forward(self, mel): - mel = mel.transpose(-1, -2).unsqueeze(1) - x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2) - x = self.fc(x) - return x - - -from librosa.filters import mel - - -class MelSpectrogram(torch.nn.Module): - def __init__( - self, - is_half, - n_mel_channels, - sampling_rate, - win_length, - hop_length, - n_fft=None, - mel_fmin=0, - mel_fmax=None, - clamp=1e-5, - ): - super().__init__() - n_fft = win_length if n_fft is None else n_fft - self.hann_window = {} - mel_basis = mel( - sr=sampling_rate, - n_fft=n_fft, - n_mels=n_mel_channels, - fmin=mel_fmin, - fmax=mel_fmax, - htk=True, - ) - mel_basis = torch.from_numpy(mel_basis).float() - self.register_buffer("mel_basis", mel_basis) - self.n_fft = win_length if n_fft is None else n_fft - self.hop_length = hop_length - self.win_length = win_length - self.sampling_rate = sampling_rate - self.n_mel_channels = n_mel_channels - self.clamp = clamp - self.is_half = is_half - - def forward(self, audio, keyshift=0, speed=1, center=True): - factor = 2 ** (keyshift / 12) - n_fft_new = int(np.round(self.n_fft * factor)) - win_length_new = int(np.round(self.win_length * factor)) - hop_length_new = int(np.round(self.hop_length * speed)) - keyshift_key = str(keyshift) + "_" + str(audio.device) - if keyshift_key not in self.hann_window: - self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to( - audio.device - ) - fft = torch.stft( - audio, - n_fft=n_fft_new, - hop_length=hop_length_new, - win_length=win_length_new, - window=self.hann_window[keyshift_key], - center=center, - return_complex=True, - ) - magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2)) - if keyshift != 0: - size = self.n_fft // 2 + 1 - resize = magnitude.size(1) - if resize < size: - magnitude = F.pad(magnitude, (0, 0, 0, size - resize)) - magnitude = magnitude[:, :size, :] * self.win_length / win_length_new - mel_output = torch.matmul(self.mel_basis, magnitude) - if self.is_half == True: - mel_output = mel_output.half() - log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp)) - return log_mel_spec - - -class RMVPE: - def __init__(self, model_path, is_half, device=None): - self.resample_kernel = {} - model = E2E(4, 1, (2, 2)) - ckpt = torch.load(model_path, map_location="cpu") - model.load_state_dict(ckpt) - model.eval() - if is_half == True: - model = model.half() - self.model = model - self.resample_kernel = {} - self.is_half = is_half - if device is None: - device = "cuda" if torch.cuda.is_available() else "cpu" - self.device = device - self.mel_extractor = MelSpectrogram( - is_half, 128, 16000, 1024, 160, None, 30, 8000 - ).to(device) - self.model = self.model.to(device) - cents_mapping = 20 * np.arange(360) + 1997.3794084376191 - self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368 - - def mel2hidden(self, mel): - with torch.no_grad(): - n_frames = mel.shape[-1] - mel = F.pad( - mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect" - ) - hidden = self.model(mel) - return hidden[:, :n_frames] - - def decode(self, hidden, thred=0.03): - cents_pred = self.to_local_average_cents(hidden, thred=thred) - f0 = 10 * (2 ** (cents_pred / 1200)) - f0[f0 == 10] = 0 - # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred]) - return f0 - - def infer_from_audio(self, audio, thred=0.03): - audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0) - # torch.cuda.synchronize() - # t0=ttime() - mel = self.mel_extractor(audio, center=True) - # torch.cuda.synchronize() - # t1=ttime() - hidden = self.mel2hidden(mel) - # torch.cuda.synchronize() - # t2=ttime() - hidden = hidden.squeeze(0).cpu().numpy() - if self.is_half == True: - hidden = hidden.astype("float32") - f0 = self.decode(hidden, thred=thred) - # torch.cuda.synchronize() - # t3=ttime() - # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0)) - return f0 - - def to_local_average_cents(self, salience, thred=0.05): - # t0 = ttime() - center = np.argmax(salience, axis=1) # 帧长#index - salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368 - # t1 = ttime() - center += 4 - todo_salience = [] - todo_cents_mapping = [] - starts = center - 4 - ends = center + 5 - for idx in range(salience.shape[0]): - todo_salience.append(salience[:, starts[idx] : ends[idx]][idx]) - todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]]) - # t2 = ttime() - todo_salience = np.array(todo_salience) # 帧长，9 - todo_cents_mapping = np.array(todo_cents_mapping) # 帧长，9 - product_sum = np.sum(todo_salience * todo_cents_mapping, 1) - weight_sum = np.sum(todo_salience, 1) # 帧长 - devided = product_sum / weight_sum # 帧长 - # t3 = ttime() - maxx = np.max(salience, axis=1) # 帧长 - devided[maxx <= thred] = 0 - # t4 = ttime() - # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) - return devided - - -# if __name__ == '__main__': -# audio, sampling_rate = sf.read("卢本伟语录~1.wav") -# if len(audio.shape) > 1: -# audio = librosa.to_mono(audio.transpose(1, 0)) -# audio_bak = audio.copy() -# if sampling_rate != 16000: -# audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) -# model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt" -# thred = 0.03 # 0.01 -# device = 'cuda' if torch.cuda.is_available() else 'cpu' -# rmvpe = RMVPE(model_path,is_half=False, device=device) -# t0=ttime() -# f0 = rmvpe.infer_from_audio(audio, thred=thred) -# f0 = rmvpe.infer_from_audio(audio, thred=thred) -# f0 = rmvpe.infer_from_audio(audio, thred=thred) -# f0 = rmvpe.infer_from_audio(audio, thred=thred) -# f0 = rmvpe.infer_from_audio(audio, thred=thred) -# t1=ttime() -# print(f0.shape,t1-t0) diff --git a/spaces/isabel/climate-change-project/README.md b/spaces/isabel/climate-change-project/README.md deleted file mode 100644 index 6c5e71ce0e00f4324bc644a5bce27c66548bb49d..0000000000000000000000000000000000000000 --- a/spaces/isabel/climate-change-project/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Climate Change Project -emoji: 🌊 -colorFrom: blue -colorTo: green -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/ivotai/VITS-Umamusume-voice-synthesizer/data_utils.py b/spaces/ivotai/VITS-Umamusume-voice-synthesizer/data_utils.py deleted file mode 100644 index e9246c6c8f2ff3c37a7f8529ea1593c7f80f887e..0000000000000000000000000000000000000000 --- a/spaces/ivotai/VITS-Umamusume-voice-synthesizer/data_utils.py +++ /dev/null @@ -1,393 +0,0 @@ -import time -import os -import random -import numpy as np -import torch -import torch.utils.data - -import commons -from mel_processing import spectrogram_torch -from utils import load_wav_to_torch, load_filepaths_and_text -from text import text_to_sequence, cleaned_text_to_sequence - - -class TextAudioLoader(torch.utils.data.Dataset): - """ - 1) loads audio, text pairs - 2) normalizes text and converts them to sequences of integers - 3) computes spectrograms from audio files. - """ - def __init__(self, audiopaths_and_text, hparams): - self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text) - self.text_cleaners = hparams.text_cleaners - self.max_wav_value = hparams.max_wav_value - self.sampling_rate = hparams.sampling_rate - self.filter_length = hparams.filter_length - self.hop_length = hparams.hop_length - self.win_length = hparams.win_length - self.sampling_rate = hparams.sampling_rate - - self.cleaned_text = getattr(hparams, "cleaned_text", False) - - self.add_blank = hparams.add_blank - self.min_text_len = getattr(hparams, "min_text_len", 1) - self.max_text_len = getattr(hparams, "max_text_len", 190) - - random.seed(1234) - random.shuffle(self.audiopaths_and_text) - self._filter() - - - def _filter(self): - """ - Filter text & store spec lengths - """ - # Store spectrogram lengths for Bucketing - # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2) - # spec_length = wav_length // hop_length - - audiopaths_and_text_new = [] - lengths = [] - for audiopath, text in self.audiopaths_and_text: - if self.min_text_len <= len(text) and len(text) <= self.max_text_len: - audiopaths_and_text_new.append([audiopath, text]) - lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) - self.audiopaths_and_text = audiopaths_and_text_new - self.lengths = lengths - - def get_audio_text_pair(self, audiopath_and_text): - # separate filename and text - audiopath, text = audiopath_and_text[0], audiopath_and_text[1] - text = self.get_text(text) - spec, wav = self.get_audio(audiopath) - return (text, spec, wav) - - def get_audio(self, filename): - audio, sampling_rate = load_wav_to_torch(filename) - if sampling_rate != self.sampling_rate: - raise ValueError("{} {} SR doesn't match target {} SR".format( - sampling_rate, self.sampling_rate)) - audio_norm = audio / self.max_wav_value - audio_norm = audio_norm.unsqueeze(0) - spec_filename = filename.replace(".wav", ".spec.pt") - if os.path.exists(spec_filename): - spec = torch.load(spec_filename) - else: - spec = spectrogram_torch(audio_norm, self.filter_length, - self.sampling_rate, self.hop_length, self.win_length, - center=False) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename) - return spec, audio_norm - - def get_text(self, text): - if self.cleaned_text: - text_norm = cleaned_text_to_sequence(text) - else: - text_norm = text_to_sequence(text, self.text_cleaners) - if self.add_blank: - text_norm = commons.intersperse(text_norm, 0) - text_norm = torch.LongTensor(text_norm) - return text_norm - - def __getitem__(self, index): - return self.get_audio_text_pair(self.audiopaths_and_text[index]) - - def __len__(self): - return len(self.audiopaths_and_text) - - -class TextAudioCollate(): - """ Zero-pads model inputs and targets - """ - def __init__(self, return_ids=False): - self.return_ids = return_ids - - def __call__(self, batch): - """Collate's training batch from normalized text and aduio - PARAMS - ------ - batch: [text_normalized, spec_normalized, wav_normalized] - """ - # Right zero-pad all one-hot text sequences to max input length - _, ids_sorted_decreasing = torch.sort( - torch.LongTensor([x[1].size(1) for x in batch]), - dim=0, descending=True) - - max_text_len = max([len(x[0]) for x in batch]) - max_spec_len = max([x[1].size(1) for x in batch]) - max_wav_len = max([x[2].size(1) for x in batch]) - - text_lengths = torch.LongTensor(len(batch)) - spec_lengths = torch.LongTensor(len(batch)) - wav_lengths = torch.LongTensor(len(batch)) - - text_padded = torch.LongTensor(len(batch), max_text_len) - spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len) - wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) - text_padded.zero_() - spec_padded.zero_() - wav_padded.zero_() - for i in range(len(ids_sorted_decreasing)): - row = batch[ids_sorted_decreasing[i]] - - text = row[0] - text_padded[i, :text.size(0)] = text - text_lengths[i] = text.size(0) - - spec = row[1] - spec_padded[i, :, :spec.size(1)] = spec - spec_lengths[i] = spec.size(1) - - wav = row[2] - wav_padded[i, :, :wav.size(1)] = wav - wav_lengths[i] = wav.size(1) - - if self.return_ids: - return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing - return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths - - -"""Multi speaker version""" -class TextAudioSpeakerLoader(torch.utils.data.Dataset): - """ - 1) loads audio, speaker_id, text pairs - 2) normalizes text and converts them to sequences of integers - 3) computes spectrograms from audio files. - """ - def __init__(self, audiopaths_sid_text, hparams): - self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text) - self.text_cleaners = hparams.text_cleaners - self.max_wav_value = hparams.max_wav_value - self.sampling_rate = hparams.sampling_rate - self.filter_length = hparams.filter_length - self.hop_length = hparams.hop_length - self.win_length = hparams.win_length - self.sampling_rate = hparams.sampling_rate - - self.cleaned_text = getattr(hparams, "cleaned_text", False) - - self.add_blank = hparams.add_blank - self.min_text_len = getattr(hparams, "min_text_len", 1) - self.max_text_len = getattr(hparams, "max_text_len", 190) - - random.seed(1234) - random.shuffle(self.audiopaths_sid_text) - self._filter() - - def _filter(self): - """ - Filter text & store spec lengths - """ - # Store spectrogram lengths for Bucketing - # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2) - # spec_length = wav_length // hop_length - - audiopaths_sid_text_new = [] - lengths = [] - for audiopath, sid, text in self.audiopaths_sid_text: - audiopath = "E:/uma_voice/" + audiopath - if self.min_text_len <= len(text) and len(text) <= self.max_text_len: - audiopaths_sid_text_new.append([audiopath, sid, text]) - lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) - self.audiopaths_sid_text = audiopaths_sid_text_new - self.lengths = lengths - - def get_audio_text_speaker_pair(self, audiopath_sid_text): - # separate filename, speaker_id and text - audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2] - text = self.get_text(text) - spec, wav = self.get_audio(audiopath) - sid = self.get_sid(sid) - return (text, spec, wav, sid) - - def get_audio(self, filename): - audio, sampling_rate = load_wav_to_torch(filename) - if sampling_rate != self.sampling_rate: - raise ValueError("{} {} SR doesn't match target {} SR".format( - sampling_rate, self.sampling_rate)) - audio_norm = audio / self.max_wav_value - audio_norm = audio_norm.unsqueeze(0) - spec_filename = filename.replace(".wav", ".spec.pt") - if os.path.exists(spec_filename): - spec = torch.load(spec_filename) - else: - spec = spectrogram_torch(audio_norm, self.filter_length, - self.sampling_rate, self.hop_length, self.win_length, - center=False) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename) - return spec, audio_norm - - def get_text(self, text): - if self.cleaned_text: - text_norm = cleaned_text_to_sequence(text) - else: - text_norm = text_to_sequence(text, self.text_cleaners) - if self.add_blank: - text_norm = commons.intersperse(text_norm, 0) - text_norm = torch.LongTensor(text_norm) - return text_norm - - def get_sid(self, sid): - sid = torch.LongTensor([int(sid)]) - return sid - - def __getitem__(self, index): - return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index]) - - def __len__(self): - return len(self.audiopaths_sid_text) - - -class TextAudioSpeakerCollate(): - """ Zero-pads model inputs and targets - """ - def __init__(self, return_ids=False): - self.return_ids = return_ids - - def __call__(self, batch): - """Collate's training batch from normalized text, audio and speaker identities - PARAMS - ------ - batch: [text_normalized, spec_normalized, wav_normalized, sid] - """ - # Right zero-pad all one-hot text sequences to max input length - _, ids_sorted_decreasing = torch.sort( - torch.LongTensor([x[1].size(1) for x in batch]), - dim=0, descending=True) - - max_text_len = max([len(x[0]) for x in batch]) - max_spec_len = max([x[1].size(1) for x in batch]) - max_wav_len = max([x[2].size(1) for x in batch]) - - text_lengths = torch.LongTensor(len(batch)) - spec_lengths = torch.LongTensor(len(batch)) - wav_lengths = torch.LongTensor(len(batch)) - sid = torch.LongTensor(len(batch)) - - text_padded = torch.LongTensor(len(batch), max_text_len) - spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len) - wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) - text_padded.zero_() - spec_padded.zero_() - wav_padded.zero_() - for i in range(len(ids_sorted_decreasing)): - row = batch[ids_sorted_decreasing[i]] - - text = row[0] - text_padded[i, :text.size(0)] = text - text_lengths[i] = text.size(0) - - spec = row[1] - spec_padded[i, :, :spec.size(1)] = spec - spec_lengths[i] = spec.size(1) - - wav = row[2] - wav_padded[i, :, :wav.size(1)] = wav - wav_lengths[i] = wav.size(1) - - sid[i] = row[3] - - if self.return_ids: - return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing - return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid - - -class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler): - """ - Maintain similar input lengths in a batch. - Length groups are specified by boundaries. - Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}. - - It removes samples which are not included in the boundaries. - Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded. - """ - def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True): - super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) - self.lengths = dataset.lengths - self.batch_size = batch_size - self.boundaries = boundaries - - self.buckets, self.num_samples_per_bucket = self._create_buckets() - self.total_size = sum(self.num_samples_per_bucket) - self.num_samples = self.total_size // self.num_replicas - - def _create_buckets(self): - buckets = [[] for _ in range(len(self.boundaries) - 1)] - for i in range(len(self.lengths)): - length = self.lengths[i] - idx_bucket = self._bisect(length) - if idx_bucket != -1: - buckets[idx_bucket].append(i) - - for i in range(len(buckets) - 1, 0, -1): - if len(buckets[i]) == 0: - buckets.pop(i) - self.boundaries.pop(i+1) - - num_samples_per_bucket = [] - for i in range(len(buckets)): - len_bucket = len(buckets[i]) - total_batch_size = self.num_replicas * self.batch_size - rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size - num_samples_per_bucket.append(len_bucket + rem) - return buckets, num_samples_per_bucket - - def __iter__(self): - # deterministically shuffle based on epoch - g = torch.Generator() - g.manual_seed(self.epoch) - - indices = [] - if self.shuffle: - for bucket in self.buckets: - indices.append(torch.randperm(len(bucket), generator=g).tolist()) - else: - for bucket in self.buckets: - indices.append(list(range(len(bucket)))) - - batches = [] - for i in range(len(self.buckets)): - bucket = self.buckets[i] - len_bucket = len(bucket) - ids_bucket = indices[i] - num_samples_bucket = self.num_samples_per_bucket[i] - - # add extra samples to make it evenly divisible - rem = num_samples_bucket - len_bucket - ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)] - - # subsample - ids_bucket = ids_bucket[self.rank::self.num_replicas] - - # batching - for j in range(len(ids_bucket) // self.batch_size): - batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]] - batches.append(batch) - - if self.shuffle: - batch_ids = torch.randperm(len(batches), generator=g).tolist() - batches = [batches[i] for i in batch_ids] - self.batches = batches - - assert len(self.batches) * self.batch_size == self.num_samples - return iter(self.batches) - - def _bisect(self, x, lo=0, hi=None): - if hi is None: - hi = len(self.boundaries) - 1 - - if hi > lo: - mid = (hi + lo) // 2 - if self.boundaries[mid] < x and x <= self.boundaries[mid+1]: - return mid - elif x <= self.boundaries[mid]: - return self._bisect(x, lo, mid) - else: - return self._bisect(x, mid + 1, hi) - else: - return -1 - - def __len__(self): - return self.num_samples // self.batch_size diff --git a/spaces/j-m/formality_tagging/README.md b/spaces/j-m/formality_tagging/README.md deleted file mode 100644 index 638119ec1f838ceb5400dfa2bc27341a66078ba8..0000000000000000000000000000000000000000 --- a/spaces/j-m/formality_tagging/README.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: Formality Tagging -emoji: 📉 -colorFrom: gray -colorTo: gray -sdk: gradio -sdk_version: 3.0.21 -app_file: app.py -pinned: false -license: bsd-3-clause ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference - -# PlainSpeak - -## Background training - -The training was performed from notebooks in another repository: codebase available from [our github repository](https://github.com/alexvillr/fastai_hackathon) - diff --git a/spaces/jackli888/stable-diffusion-webui/html/licenses.html b/spaces/jackli888/stable-diffusion-webui/html/licenses.html deleted file mode 100644 index f59c352510f95a5d57df7808459c5eb5b21367a9..0000000000000000000000000000000000000000 --- a/spaces/jackli888/stable-diffusion-webui/html/licenses.html +++ /dev/null @@ -1,419 +0,0 @@ - - -CodeFormer
-Parts of CodeFormer code had to be copied to be compatible with GFPGAN. --S-Lab License 1.0 - -Copyright 2022 S-Lab - -Redistribution and use for non-commercial purpose 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 copyright holder 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 COPYRIGHT HOLDERS AND CONTRIBUTORS -"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 COPYRIGHT -HOLDER OR CONTRIBUTORS 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. - -In the event that redistribution and/or use for commercial purpose in -source or binary forms, with or without modification is required, -please contact the contributor(s) of the work. -
- - -ESRGAN
-Code for architecture and reading models copied. --MIT License - -Copyright (c) 2021 victorca25 - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -
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- -LDSR
-Code added by contirubtors, most likely copied from this repository. --MIT License - -Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -
- -CLIP Interrogator
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- -Memory Efficient Attention
-The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that. --MIT License - -Copyright (c) 2023 Alex Birch -Copyright (c) 2023 Amin Rezaei - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -
- diff --git a/spaces/jackli888/stable-diffusion-webui/modules/sd_hijack_clip.py b/spaces/jackli888/stable-diffusion-webui/modules/sd_hijack_clip.py deleted file mode 100644 index ba55fb98e54c3cf82a135f588efa9f7210c6fee3..0000000000000000000000000000000000000000 --- a/spaces/jackli888/stable-diffusion-webui/modules/sd_hijack_clip.py +++ /dev/null @@ -1,317 +0,0 @@ -import math -from collections import namedtuple - -import torch - -from modules import prompt_parser, devices, sd_hijack -from modules.shared import opts - - -class PromptChunk: - """ - This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt. - If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary. - Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token, - so just 75 tokens from prompt. - """ - - def __init__(self): - self.tokens = [] - self.multipliers = [] - self.fixes = [] - - -PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding']) -"""An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt -chunk. Thos objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally -are applied by sd_hijack.EmbeddingsWithFixes's forward function.""" - - -class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): - """A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to - have unlimited prompt length and assign weights to tokens in prompt. - """ - - def __init__(self, wrapped, hijack): - super().__init__() - - self.wrapped = wrapped - """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation, - depending on model.""" - - self.hijack: sd_hijack.StableDiffusionModelHijack = hijack - self.chunk_length = 75 - - def empty_chunk(self): - """creates an empty PromptChunk and returns it""" - - chunk = PromptChunk() - chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1) - chunk.multipliers = [1.0] * (self.chunk_length + 2) - return chunk - - def get_target_prompt_token_count(self, token_count): - """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented""" - - return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length - - def tokenize(self, texts): - """Converts a batch of texts into a batch of token ids""" - - raise NotImplementedError - - def encode_with_transformers(self, tokens): - """ - converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens; - All python lists with tokens are assumed to have same length, usually 77. - if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on - model - can be 768 and 1024. - Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None). - """ - - raise NotImplementedError - - def encode_embedding_init_text(self, init_text, nvpt): - """Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through - transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned.""" - - raise NotImplementedError - - def tokenize_line(self, line): - """ - this transforms a single prompt into a list of PromptChunk objects - as many as needed to - represent the prompt. - Returns the list and the total number of tokens in the prompt. - """ - - if opts.enable_emphasis: - parsed = prompt_parser.parse_prompt_attention(line) - else: - parsed = [[line, 1.0]] - - tokenized = self.tokenize([text for text, _ in parsed]) - - chunks = [] - chunk = PromptChunk() - token_count = 0 - last_comma = -1 - - def next_chunk(is_last=False): - """puts current chunk into the list of results and produces the next one - empty; - if is_last is true, tokenstokens at the end won't add to token_count""" - nonlocal token_count - nonlocal last_comma - nonlocal chunk - - if is_last: - token_count += len(chunk.tokens) - else: - token_count += self.chunk_length - - to_add = self.chunk_length - len(chunk.tokens) - if to_add > 0: - chunk.tokens += [self.id_end] * to_add - chunk.multipliers += [1.0] * to_add - - chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end] - chunk.multipliers = [1.0] + chunk.multipliers + [1.0] - - last_comma = -1 - chunks.append(chunk) - chunk = PromptChunk() - - for tokens, (text, weight) in zip(tokenized, parsed): - if text == 'BREAK' and weight == -1: - next_chunk() - continue - - position = 0 - while position < len(tokens): - token = tokens[position] - - if token == self.comma_token: - last_comma = len(chunk.tokens) - - # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack - # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next. - elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack: - break_location = last_comma + 1 - - reloc_tokens = chunk.tokens[break_location:] - reloc_mults = chunk.multipliers[break_location:] - - chunk.tokens = chunk.tokens[:break_location] - chunk.multipliers = chunk.multipliers[:break_location] - - next_chunk() - chunk.tokens = reloc_tokens - chunk.multipliers = reloc_mults - - if len(chunk.tokens) == self.chunk_length: - next_chunk() - - embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, position) - if embedding is None: - chunk.tokens.append(token) - chunk.multipliers.append(weight) - position += 1 - continue - - emb_len = int(embedding.vec.shape[0]) - if len(chunk.tokens) + emb_len > self.chunk_length: - next_chunk() - - chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding)) - - chunk.tokens += [0] * emb_len - chunk.multipliers += [weight] * emb_len - position += embedding_length_in_tokens - - if len(chunk.tokens) > 0 or len(chunks) == 0: - next_chunk(is_last=True) - - return chunks, token_count - - def process_texts(self, texts): - """ - Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum - length, in tokens, of all texts. - """ - - token_count = 0 - - cache = {} - batch_chunks = [] - for line in texts: - if line in cache: - chunks = cache[line] - else: - chunks, current_token_count = self.tokenize_line(line) - token_count = max(current_token_count, token_count) - - cache[line] = chunks - - batch_chunks.append(chunks) - - return batch_chunks, token_count - - def forward(self, texts): - """ - Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts. - Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will - be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024. - An example shape returned by this function can be: (2, 77, 768). - Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet - is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream" - """ - - if opts.use_old_emphasis_implementation: - import modules.sd_hijack_clip_old - return modules.sd_hijack_clip_old.forward_old(self, texts) - - batch_chunks, token_count = self.process_texts(texts) - - used_embeddings = {} - chunk_count = max([len(x) for x in batch_chunks]) - - zs = [] - for i in range(chunk_count): - batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks] - - tokens = [x.tokens for x in batch_chunk] - multipliers = [x.multipliers for x in batch_chunk] - self.hijack.fixes = [x.fixes for x in batch_chunk] - - for fixes in self.hijack.fixes: - for position, embedding in fixes: - used_embeddings[embedding.name] = embedding - - z = self.process_tokens(tokens, multipliers) - zs.append(z) - - if len(used_embeddings) > 0: - embeddings_list = ", ".join([f'{name} [{embedding.checksum()}]' for name, embedding in used_embeddings.items()]) - self.hijack.comments.append(f"Used embeddings: {embeddings_list}") - - return torch.hstack(zs) - - def process_tokens(self, remade_batch_tokens, batch_multipliers): - """ - sends one single prompt chunk to be encoded by transformers neural network. - remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually - there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens. - Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier - corresponds to one token. - """ - tokens = torch.asarray(remade_batch_tokens).to(devices.device) - - # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones. - if self.id_end != self.id_pad: - for batch_pos in range(len(remade_batch_tokens)): - index = remade_batch_tokens[batch_pos].index(self.id_end) - tokens[batch_pos, index+1:tokens.shape[1]] = self.id_pad - - z = self.encode_with_transformers(tokens) - - # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise - batch_multipliers = torch.asarray(batch_multipliers).to(devices.device) - original_mean = z.mean() - z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape) - new_mean = z.mean() - z = z * (original_mean / new_mean) - - return z - - -class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase): - def __init__(self, wrapped, hijack): - super().__init__(wrapped, hijack) - self.tokenizer = wrapped.tokenizer - - vocab = self.tokenizer.get_vocab() - - self.comma_token = vocab.get(',', None) - - self.token_mults = {} - tokens_with_parens = [(k, v) for k, v in vocab.items() if '(' in k or ')' in k or '[' in k or ']' in k] - for text, ident in tokens_with_parens: - mult = 1.0 - for c in text: - if c == '[': - mult /= 1.1 - if c == ']': - mult *= 1.1 - if c == '(': - mult *= 1.1 - if c == ')': - mult /= 1.1 - - if mult != 1.0: - self.token_mults[ident] = mult - - self.id_start = self.wrapped.tokenizer.bos_token_id - self.id_end = self.wrapped.tokenizer.eos_token_id - self.id_pad = self.id_end - - def tokenize(self, texts): - tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"] - - return tokenized - - def encode_with_transformers(self, tokens): - outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) - - if opts.CLIP_stop_at_last_layers > 1: - z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers] - z = self.wrapped.transformer.text_model.final_layer_norm(z) - else: - z = outputs.last_hidden_state - - return z - - def encode_embedding_init_text(self, init_text, nvpt): - embedding_layer = self.wrapped.transformer.text_model.embeddings - ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"] - embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0) - - return embedded diff --git a/spaces/jackli888/stable-diffusion-webui/test/__init__.py b/spaces/jackli888/stable-diffusion-webui/test/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/jaumaras/Text-2-Speech/app.py b/spaces/jaumaras/Text-2-Speech/app.py deleted file mode 100644 index 0499455e7d1f1b7b1ad9b74201ddc1daf94db7e2..0000000000000000000000000000000000000000 --- a/spaces/jaumaras/Text-2-Speech/app.py +++ /dev/null @@ -1,97 +0,0 @@ -""" -Copyright 2022 Balacoon - -TTS interactive demo -""" - -import logging -from typing import cast - -import gradio as gr -from balacoon_tts import TTS -from huggingface_hub import hf_hub_download, list_repo_files - -# global tts module, initialized from a model selected -tts = None - -gr.api_name="textTOPepe" - -def main(): - logging.basicConfig(level=logging.INFO) - - with gr.Blocks() as demo: - gr.Markdown( - """ - Text-to-Speech
- - 1. Write an utterance to generate, - 2. Select the model to synthesize with - 3. Select the speaker - 4. Hit "Generate" and listen to the result! - - When you select a Model for the first time, - it will take a little time to download it. - """ - ) - gr.api_name="textTOPepe" - with gr.Row(variant="panel"): - text = gr.Textbox(label="Text", placeholder="Insert your article here...") - - with gr.Row(): - with gr.Column(variant="panel"): - repo_files = list_repo_files(repo_id="balacoon/tts") - model_files = [x for x in repo_files if x.endswith("_cpu.addon")] - model_name = gr.Dropdown( - label="Model", - choices=model_files, - ) - with gr.Column(variant="panel"): - speaker = gr.Dropdown(label="Speaker", choices=[]) - - def set_model(model_name_str: str): - """ - gets value from `model_name`, loads model, - re-initializes tts object, gets list of - speakers that model supports and set them to `speaker` - """ - model_path = hf_hub_download( - repo_id="balacoon/tts", filename=model_name_str - ) - global tts - tts = TTS(model_path) - speakers = tts.get_speakers() - value = speakers[-1] - return gr.Dropdown.update( - choices=speakers, value=value, visible=True - ) - - model_name.change(set_model, inputs=model_name, outputs=speaker) - - with gr.Row(variant="panel"): - generate = gr.Button("Generate") - with gr.Row(variant="panel"): - audio = gr.Audio() - - def synthesize_audio(text_str: str, speaker_str: str = ""): - """ - gets utterance to synthesize from `text` Textbox - and speaker name from `speaker` dropdown list. - speaker name might be empty for single-speaker models. - Synthesizes the waveform and updates `audio` with it. - """ - if not text_str: - logging.info("text or speaker are not provided") - return None - global tts - if len(text_str) > 10024: - text_str = text_str[:10024] - samples = cast(TTS, tts).synthesize(text_str, speaker_str) - return gr.Audio.update(value=(cast(TTS, tts).get_sampling_rate(), samples)) - - generate.click(synthesize_audio, inputs=[text, speaker], outputs=audio) - demo.api_name="textTOPepe" - demo.launch() - - -if __name__ == "__main__": - main() diff --git a/spaces/jbilcke-hf/VideoQuest/src/lib/utils.ts b/spaces/jbilcke-hf/VideoQuest/src/lib/utils.ts deleted file mode 100644 index ec79801fe9cdd7711f6dbef26678a134c634a8be..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/VideoQuest/src/lib/utils.ts +++ /dev/null @@ -1,6 +0,0 @@ -import { type ClassValue, clsx } from "clsx" -import { twMerge } from "tailwind-merge" - -export function cn(...inputs: ClassValue[]) { - return twMerge(clsx(inputs)) -} diff --git a/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/toaster.tsx b/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/toaster.tsx deleted file mode 100644 index e2233852a74d4db61ea668a5d43f9681038807cc..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/toaster.tsx +++ /dev/null @@ -1,35 +0,0 @@ -"use client" - -import { - Toast, - ToastClose, - ToastDescription, - ToastProvider, - ToastTitle, - ToastViewport, -} from "@/components/ui/toast" -import { useToast } from "@/components/ui/use-toast" - -export function Toaster() { - const { toasts } = useToast() - - return ( -- {toasts.map(function ({ id, title, description, action, ...props }) { - return ( - - ) -} diff --git a/spaces/jeycov/IsaTronDeteccion/README.md b/spaces/jeycov/IsaTronDeteccion/README.md deleted file mode 100644 index c0c9784c6074cc86f3636ade99922dec6d197521..0000000000000000000000000000000000000000 --- a/spaces/jeycov/IsaTronDeteccion/README.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -title: IsaTron. -emoji: 👨🏾⚕️ -colorFrom: gray -colorTo: gray -sdk: gradio -sdk_version: 3.0.19 -app_file: app.py -pinned: true ---- -# 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` -`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. \ No newline at end of file diff --git a/spaces/jiawei011/dreamgaussian/scripts/runall.py b/spaces/jiawei011/dreamgaussian/scripts/runall.py deleted file mode 100644 index 0e40bd34663abf6c3d02ab802bd60681d537cef1..0000000000000000000000000000000000000000 --- a/spaces/jiawei011/dreamgaussian/scripts/runall.py +++ /dev/null @@ -1,48 +0,0 @@ -import os -import glob -import argparse - -parser = argparse.ArgumentParser() -parser.add_argument('--dir', default='data', type=str, help='Directory where processed images are stored') -parser.add_argument('--out', default='logs', type=str, help='Directory where obj files will be saved') -parser.add_argument('--video-out', default='videos', type=str, help='Directory where videos will be saved') -parser.add_argument('--gpu', default=0, type=int, help='ID of GPU to use') -parser.add_argument('--elevation', default=0, type=int, help='Elevation angle of view in degrees') -parser.add_argument('--config', default='configs', type=str, help='Path to config directory, which contains image.yaml') -args = parser.parse_args() - -files = glob.glob(f'{args.dir}/*_rgba.png') -configs_dir = args.config - -# check if image.yaml exists -if not os.path.exists(os.path.join(configs_dir, 'image.yaml')): - raise FileNotFoundError( - f'image.yaml not found in {configs_dir} directory. Please check if the directory is correct.' - ) - -# create output directories if not exists -out_dir = args.out -os.makedirs(out_dir, exist_ok=True) -video_dir = args.video_out -os.makedirs(video_dir, exist_ok=True) - - -for file in files: - name = os.path.basename(file).replace("_rgba.png", "") - print(f'======== processing {name} ========') - # first stage - os.system(f'CUDA_VISIBLE_DEVICES={args.gpu} python main.py ' - f'--config {configs_dir}/image.yaml ' - f'input={file} ' - f'save_path={name} elevation={args.elevation}') - # second stage - os.system(f'CUDA_VISIBLE_DEVICES={args.gpu} python main2.py ' - f'--config {configs_dir}/image.yaml ' - f'input={file} ' - f'save_path={name} elevation={args.elevation}') - # export video - mesh_path = os.path.join(out_dir, f'{name}.obj') - os.system(f'python -m kiui.render {mesh_path} ' - f'--save_video {video_dir}/{name}.mp4 ' - f'--wogui ' - f'--elevation {args.elevation}') diff --git a/spaces/jmaller/rnn-amywinehouse/app.py b/spaces/jmaller/rnn-amywinehouse/app.py deleted file mode 100644 index 9a5b67e1d2d1a7311931d3921fcf72c2494f9842..0000000000000000000000000000000000000000 --- a/spaces/jmaller/rnn-amywinehouse/app.py +++ /dev/null @@ -1,69 +0,0 @@ -import tensorflow as tf - -from tensorflow.keras.preprocessing.text import Tokenizer -from tensorflow.keras.preprocessing.sequence import pad_sequences -import numpy as np - -# Default files -path_to_file = 'amy-winehouse.txt' -path_to_model = 'amy_winehouse.h5' - -# Open model and dataset -model = tf.keras.models.load_model(path_to_model) - -#Get name of file -name = path_to_file.split('.')[0] -#print(name) -data= open(path_to_file).read() -corpus = data.lower().split('\n') - -#Tokenize data -tokenizer = Tokenizer() -tokenizer.fit_on_texts(corpus) -total_words = len(tokenizer.word_index) + 1 -word_index = tokenizer.word_index -index_word = {index:word for word, index in tokenizer.word_index.items()} - -n_gram_sequences = [] -for line in corpus: - token_list = tokenizer.texts_to_sequences([line])[0] - for i in range(1, len(token_list)): - n_gram_sequences.append(token_list[:i+1]) -#print(np.shape(n_gram_sequences)) - -#for seq in n_gram_sequences: -# print ([index_word[w] for w in seq]) - -# pad sequences -max_len = max([len(seq) for seq in n_gram_sequences]) - -#print(max_sequence_len, total_words) -n_gram_sequences = np.array(pad_sequences(n_gram_sequences, padding='pre', maxlen=max_len)) - - -# Generate next words with an initial prompt -def predict_n_words(prompt, n_words): - - for _ in range(n_words): - token_list = tokenizer.texts_to_sequences([prompt])[0] - token_list = pad_sequences([token_list], padding='pre', maxlen=max_len-1,) - predicted = np.argmax( model.predict(token_list), axis = 1) - prompt += " " + index_word[predicted[0]] - return prompt - - -import gradio as gr - -demo = gr.Interface( - fn=predict_n_words, - inputs=[gr.Textbox(lines=2, placeholder="Prompt text here..."), gr.Slider(0, 500)], - outputs="text", - examples=[ - ["I'll go back to black", 200], - ["To see your eyes,", 200], - ], - title="Amy Winehouse RNN", - description="Simple word-based RNN text generator trained on Amy Winehouse's songs", -) - -demo.launch() \ No newline at end of file diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/PcxImagePlugin.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/PcxImagePlugin.py deleted file mode 100644 index f42c2456b4b6c90700267b43cbfd4033ecc1370d..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/PcxImagePlugin.py +++ /dev/null @@ -1,221 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# PCX file handling -# -# This format was originally used by ZSoft's popular PaintBrush -# program for the IBM PC. It is also supported by many MS-DOS and -# Windows applications, including the Windows PaintBrush program in -# Windows 3. -# -# history: -# 1995-09-01 fl Created -# 1996-05-20 fl Fixed RGB support -# 1997-01-03 fl Fixed 2-bit and 4-bit support -# 1999-02-03 fl Fixed 8-bit support (broken in 1.0b1) -# 1999-02-07 fl Added write support -# 2002-06-09 fl Made 2-bit and 4-bit support a bit more robust -# 2002-07-30 fl Seek from to current position, not beginning of file -# 2003-06-03 fl Extract DPI settings (info["dpi"]) -# -# Copyright (c) 1997-2003 by Secret Labs AB. -# Copyright (c) 1995-2003 by Fredrik Lundh. -# -# See the README file for information on usage and redistribution. -# - -import io -import logging - -from . import Image, ImageFile, ImagePalette -from ._binary import i16le as i16 -from ._binary import o8 -from ._binary import o16le as o16 - -logger = logging.getLogger(__name__) - - -def _accept(prefix): - return prefix[0] == 10 and prefix[1] in [0, 2, 3, 5] - - -## -# Image plugin for Paintbrush images. - - -class PcxImageFile(ImageFile.ImageFile): - format = "PCX" - format_description = "Paintbrush" - - def _open(self): - # header - s = self.fp.read(128) - if not _accept(s): - msg = "not a PCX file" - raise SyntaxError(msg) - - # image - bbox = i16(s, 4), i16(s, 6), i16(s, 8) + 1, i16(s, 10) + 1 - if bbox[2] <= bbox[0] or bbox[3] <= bbox[1]: - msg = "bad PCX image size" - raise SyntaxError(msg) - logger.debug("BBox: %s %s %s %s", *bbox) - - # format - version = s[1] - bits = s[3] - planes = s[65] - provided_stride = i16(s, 66) - logger.debug( - "PCX version %s, bits %s, planes %s, stride %s", - version, - bits, - planes, - provided_stride, - ) - - self.info["dpi"] = i16(s, 12), i16(s, 14) - - if bits == 1 and planes == 1: - mode = rawmode = "1" - - elif bits == 1 and planes in (2, 4): - mode = "P" - rawmode = "P;%dL" % planes - self.palette = ImagePalette.raw("RGB", s[16:64]) - - elif version == 5 and bits == 8 and planes == 1: - mode = rawmode = "L" - # FIXME: hey, this doesn't work with the incremental loader !!! - self.fp.seek(-769, io.SEEK_END) - s = self.fp.read(769) - if len(s) == 769 and s[0] == 12: - # check if the palette is linear greyscale - for i in range(256): - if s[i * 3 + 1 : i * 3 + 4] != o8(i) * 3: - mode = rawmode = "P" - break - if mode == "P": - self.palette = ImagePalette.raw("RGB", s[1:]) - self.fp.seek(128) - - elif version == 5 and bits == 8 and planes == 3: - mode = "RGB" - rawmode = "RGB;L" - - else: - msg = "unknown PCX mode" - raise OSError(msg) - - self.mode = mode - self._size = bbox[2] - bbox[0], bbox[3] - bbox[1] - - # Don't trust the passed in stride. - # Calculate the approximate position for ourselves. - # CVE-2020-35653 - stride = (self._size[0] * bits + 7) // 8 - - # While the specification states that this must be even, - # not all images follow this - if provided_stride != stride: - stride += stride % 2 - - bbox = (0, 0) + self.size - logger.debug("size: %sx%s", *self.size) - - self.tile = [("pcx", bbox, self.fp.tell(), (rawmode, planes * stride))] - - -# -------------------------------------------------------------------- -# save PCX files - - -SAVE = { - # mode: (version, bits, planes, raw mode) - "1": (2, 1, 1, "1"), - "L": (5, 8, 1, "L"), - "P": (5, 8, 1, "P"), - "RGB": (5, 8, 3, "RGB;L"), -} - - -def _save(im, fp, filename): - try: - version, bits, planes, rawmode = SAVE[im.mode] - except KeyError as e: - msg = f"Cannot save {im.mode} images as PCX" - raise ValueError(msg) from e - - # bytes per plane - stride = (im.size[0] * bits + 7) // 8 - # stride should be even - stride += stride % 2 - # Stride needs to be kept in sync with the PcxEncode.c version. - # Ideally it should be passed in in the state, but the bytes value - # gets overwritten. - - logger.debug( - "PcxImagePlugin._save: xwidth: %d, bits: %d, stride: %d", - im.size[0], - bits, - stride, - ) - - # under windows, we could determine the current screen size with - # "Image.core.display_mode()[1]", but I think that's overkill... - - screen = im.size - - dpi = 100, 100 - - # PCX header - fp.write( - o8(10) - + o8(version) - + o8(1) - + o8(bits) - + o16(0) - + o16(0) - + o16(im.size[0] - 1) - + o16(im.size[1] - 1) - + o16(dpi[0]) - + o16(dpi[1]) - + b"\0" * 24 - + b"\xFF" * 24 - + b"\0" - + o8(planes) - + o16(stride) - + o16(1) - + o16(screen[0]) - + o16(screen[1]) - + b"\0" * 54 - ) - - assert fp.tell() == 128 - - ImageFile._save(im, fp, [("pcx", (0, 0) + im.size, 0, (rawmode, bits * planes))]) - - if im.mode == "P": - # colour palette - fp.write(o8(12)) - palette = im.im.getpalette("RGB", "RGB") - palette += b"\x00" * (768 - len(palette)) - fp.write(palette) # 768 bytes - elif im.mode == "L": - # greyscale palette - fp.write(o8(12)) - for i in range(256): - fp.write(o8(i) * 3) - - -# -------------------------------------------------------------------- -# registry - - -Image.register_open(PcxImageFile.format, PcxImageFile, _accept) -Image.register_save(PcxImageFile.format, _save) - -Image.register_extension(PcxImageFile.format, ".pcx") - -Image.register_mime(PcxImageFile.format, "image/x-pcx") diff --git a/spaces/joshuasundance/langchain-streamlit-demo/langchain-streamlit-demo/llm_resources.py b/spaces/joshuasundance/langchain-streamlit-demo/langchain-streamlit-demo/llm_resources.py deleted file mode 100644 index bc2910e860ca3b481937b8e705b51874a4822b88..0000000000000000000000000000000000000000 --- a/spaces/joshuasundance/langchain-streamlit-demo/langchain-streamlit-demo/llm_resources.py +++ /dev/null @@ -1,160 +0,0 @@ -from tempfile import NamedTemporaryFile -from typing import Tuple, List, Optional, Dict - -from langchain.callbacks.base import BaseCallbackHandler -from langchain.chains import RetrievalQA, LLMChain -from langchain.chat_models import ( - AzureChatOpenAI, - ChatOpenAI, - ChatAnthropic, - ChatAnyscale, -) -from langchain.document_loaders import PyPDFLoader -from langchain.embeddings import OpenAIEmbeddings -from langchain.retrievers import BM25Retriever, EnsembleRetriever -from langchain.schema import Document, BaseRetriever -from langchain.text_splitter import RecursiveCharacterTextSplitter -from langchain.vectorstores import FAISS - -from defaults import DEFAULT_CHUNK_SIZE, DEFAULT_CHUNK_OVERLAP, DEFAULT_RETRIEVER_K -from qagen import get_rag_qa_gen_chain -from summarize import get_rag_summarization_chain - - -def get_runnable( - use_document_chat: bool, - document_chat_chain_type: str, - llm, - retriever, - memory, - chat_prompt, - summarization_prompt, -): - if not use_document_chat: - return LLMChain( - prompt=chat_prompt, - llm=llm, - memory=memory, - ) | (lambda output: output["text"]) - - if document_chat_chain_type == "Q&A Generation": - return get_rag_qa_gen_chain( - retriever, - llm, - ) - elif document_chat_chain_type == "Summarization": - return get_rag_summarization_chain( - summarization_prompt, - retriever, - llm, - ) - else: - return RetrievalQA.from_chain_type( - llm=llm, - chain_type=document_chat_chain_type, - retriever=retriever, - memory=memory, - output_key="output_text", - ) | (lambda output: output["output_text"]) - - -def get_llm( - provider: str, - model: str, - provider_api_key: str, - temperature: float, - max_tokens: int, - azure_available: bool, - azure_dict: dict[str, str], -): - if azure_available and provider == "Azure OpenAI": - return AzureChatOpenAI( - openai_api_base=azure_dict["AZURE_OPENAI_BASE_URL"], - openai_api_version=azure_dict["AZURE_OPENAI_API_VERSION"], - deployment_name=azure_dict["AZURE_OPENAI_DEPLOYMENT_NAME"], - openai_api_key=azure_dict["AZURE_OPENAI_API_KEY"], - openai_api_type="azure", - model_version=azure_dict["AZURE_OPENAI_MODEL_VERSION"], - temperature=temperature, - streaming=True, - max_tokens=max_tokens, - ) - - elif provider_api_key: - if provider == "OpenAI": - return ChatOpenAI( - model_name=model, - openai_api_key=provider_api_key, - temperature=temperature, - streaming=True, - max_tokens=max_tokens, - ) - - elif provider == "Anthropic": - return ChatAnthropic( - model=model, - anthropic_api_key=provider_api_key, - temperature=temperature, - streaming=True, - max_tokens_to_sample=max_tokens, - ) - - elif provider == "Anyscale Endpoints": - return ChatAnyscale( - model_name=model, - anyscale_api_key=provider_api_key, - temperature=temperature, - streaming=True, - max_tokens=max_tokens, - ) - - return None - - -def get_texts_and_retriever( - uploaded_file_bytes: bytes, - openai_api_key: str, - chunk_size: int = DEFAULT_CHUNK_SIZE, - chunk_overlap: int = DEFAULT_CHUNK_OVERLAP, - k: int = DEFAULT_RETRIEVER_K, - azure_kwargs: Optional[Dict[str, str]] = None, - use_azure: bool = False, -) -> Tuple[List[Document], BaseRetriever]: - with NamedTemporaryFile() as temp_file: - temp_file.write(uploaded_file_bytes) - temp_file.seek(0) - - loader = PyPDFLoader(temp_file.name) - documents = loader.load() - text_splitter = RecursiveCharacterTextSplitter( - chunk_size=chunk_size, - chunk_overlap=chunk_overlap, - ) - texts = text_splitter.split_documents(documents) - embeddings_kwargs = {"openai_api_key": openai_api_key} - if use_azure and azure_kwargs: - embeddings_kwargs.update(azure_kwargs) - embeddings = OpenAIEmbeddings(**embeddings_kwargs) - - bm25_retriever = BM25Retriever.from_documents(texts) - bm25_retriever.k = k - - faiss_vectorstore = FAISS.from_documents(texts, embeddings) - faiss_retriever = faiss_vectorstore.as_retriever(search_kwargs={"k": k}) - - ensemble_retriever = EnsembleRetriever( - retrievers=[bm25_retriever, faiss_retriever], - weights=[0.5, 0.5], - ) - - return texts, ensemble_retriever - - -class StreamHandler(BaseCallbackHandler): - def __init__(self, container, initial_text=""): - self.container = container - self.text = initial_text - - def on_llm_new_token(self, token: str, **kwargs) -> None: - self.text += token - self.container.markdown(self.text) diff --git a/spaces/juancopi81/whisper-youtube-2-hf_dataset/test/__init__.py b/spaces/juancopi81/whisper-youtube-2-hf_dataset/test/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/juanhuggingface/ChuanhuChatGPT_Beta/modules/models/tokenization_moss.py b/spaces/juanhuggingface/ChuanhuChatGPT_Beta/modules/models/tokenization_moss.py deleted file mode 100644 index 626315eb9e429ada99a15b04b9736c05e6743ffe..0000000000000000000000000000000000000000 --- a/spaces/juanhuggingface/ChuanhuChatGPT_Beta/modules/models/tokenization_moss.py +++ /dev/null @@ -1,368 +0,0 @@ -"""Tokenization classes for Moss""" - -import json -import os -import numpy as np -import regex as re - -from functools import lru_cache -from typing import TYPE_CHECKING, List, Optional, Tuple, Union - -from transformers.utils import is_tf_available, is_torch_available, logging -from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer - - -if TYPE_CHECKING: - if is_torch_available(): - import torch - if is_tf_available(): - import tensorflow as tf - - -logger = logging.get_logger(__name__) - -VOCAB_FILES_NAMES = { - "vocab_file": "vocab.json", - "merges_file": "merges.txt", -} - -PRETRAINED_VOCAB_FILES_MAP = { - "vocab_file": { - "fnlp/moss-moon-003-base": "https://huggingface.co/fnlp/moss-moon-003-base/resolve/main/vocab.json", - "fnlp/moss-moon-003-sft": "https://huggingface.co/fnlp/moss-moon-003-sft/resolve/main/vocab.json", - "fnlp/moss-moon-003-sft-plugin": "https://huggingface.co/fnlp/moss-moon-003-sft-plugin/resolve/main/vocab.json", - }, - "merges_file": { - "fnlp/moss-moon-003-base": "https://huggingface.co/fnlp/moss-moon-003-base/resolve/main/merges.txt", - "fnlp/moss-moon-003-sft": "https://huggingface.co/fnlp/moss-moon-003-sft/resolve/main/merges.txt", - "fnlp/moss-moon-003-sft-plugin": "https://huggingface.co/fnlp/moss-moon-003-sft-plugin/resolve/main/merges.txt", - }, -} - -PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { - "fnlp/moss-moon-003-base": 2048, - "fnlp/moss-moon-003-sft": 2048, - "fnlp/moss-moon-003-sft-plugin": 2048, -} - - -@lru_cache() -def bytes_to_unicode(): - """ - Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control - characters the bpe code barfs on. - - The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab - if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for - decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup - tables between utf-8 bytes and unicode strings. - """ - bs = ( - list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) - ) - cs = bs[:] - n = 0 - for b in range(2**8): - if b not in bs: - bs.append(b) - cs.append(2**8 + n) - n += 1 - cs = [chr(n) for n in cs] - return dict(zip(bs, cs)) - - -def get_pairs(word): - """ - Return set of symbol pairs in a word. - - Word is represented as tuple of symbols (symbols being variable-length strings). - """ - pairs = set() - prev_char = word[0] - for char in word[1:]: - pairs.add((prev_char, char)) - prev_char = char - return pairs - - -class MossTokenizer(PreTrainedTokenizer): - """ - Construct a Moss tokenizer. Based on byte-level Byte-Pair-Encoding. - - This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will - be encoded differently whether it is at the beginning of the sentence (without space) or not: - - You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you - call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. - -- - ) - })} -- {title &&- {action} -{title} } - {description && ( -{description} - )} -- - - - When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one). - - - - This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to - this superclass for more information regarding those methods. - - Args: - vocab_file (`str`): - Path to the vocabulary file. - merges_file (`str`): - Path to the merges file. - errors (`str`, *optional*, defaults to `"replace"`): - Paradigm to follow when decoding bytes to UTF-8. See - [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. - unk_token (`str`, *optional*, defaults to `<|endoftext|>`): - The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this - token instead. - bos_token (`str`, *optional*, defaults to `<|endoftext|>`): - The beginning of sequence token. - eos_token (`str`, *optional*, defaults to `<|endoftext|>`): - The end of sequence token. - add_prefix_space (`bool`, *optional*, defaults to `False`): - Whether or not to add an initial space to the input. This allows to treat the leading word just as any - other word. (Moss tokenizer detect beginning of words by the preceding space). - """ - - vocab_files_names = VOCAB_FILES_NAMES - pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP - max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES - model_input_names = ["input_ids", "attention_mask"] - - def __init__( - self, - vocab_file, - merges_file, - errors="replace", - unk_token="<|endoftext|>", - bos_token="<|endoftext|>", - eos_token="", - pad_token=None, - add_prefix_space=False, - add_bos_token=False, - **kwargs, - ): - bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token - eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token - unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token - pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token - super().__init__( - errors=errors, - unk_token=unk_token, - bos_token=bos_token, - eos_token=eos_token, - pad_token=pad_token, - add_prefix_space=add_prefix_space, - add_bos_token=add_bos_token, - **kwargs, - ) - self.add_bos_token = add_bos_token - - with open(vocab_file, encoding="utf-8") as vocab_handle: - self.encoder = json.load(vocab_handle) - self.decoder = {v: k for k, v in self.encoder.items()} - self.errors = errors # how to handle errors in decoding - self.byte_encoder = bytes_to_unicode() - self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} - with open(merges_file, encoding="utf-8") as merges_handle: - bpe_merges = merges_handle.read().split("\n")[1:-1] - bpe_merges = [tuple(merge.split()) for merge in bpe_merges] - self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) - self.cache = {} - self.add_prefix_space = add_prefix_space - - # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions - self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") - - @property - def vocab_size(self): - return len(self.encoder) - - def get_vocab(self): - return dict(self.encoder, **self.added_tokens_encoder) - - def bpe(self, token): - if token in self.cache: - return self.cache[token] - word = tuple(token) - pairs = get_pairs(word) - - if not pairs: - return token - - while True: - bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) - if bigram not in self.bpe_ranks: - break - first, second = bigram - new_word = [] - i = 0 - while i < len(word): - try: - j = word.index(first, i) - except ValueError: - new_word.extend(word[i:]) - break - else: - new_word.extend(word[i:j]) - i = j - - if word[i] == first and i < len(word) - 1 and word[i + 1] == second: - new_word.append(first + second) - i += 2 - else: - new_word.append(word[i]) - i += 1 - new_word = tuple(new_word) - word = new_word - if len(word) == 1: - break - else: - pairs = get_pairs(word) - word = " ".join(word) - self.cache[token] = word - return word - - def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): - if self.add_bos_token: - bos_token_ids = [self.bos_token_id] - else: - bos_token_ids = [] - - output = bos_token_ids + token_ids_0 - - if token_ids_1 is None: - return output - - return output + bos_token_ids + token_ids_1 - - def _tokenize(self, text): - """Tokenize a string.""" - bpe_tokens = [] - for token in re.findall(self.pat, text): - token = "".join( - self.byte_encoder[b] for b in token.encode("utf-8") - ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) - bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) - return bpe_tokens - - def _convert_token_to_id(self, token): - """Converts a token (str) in an id using the vocab.""" - return self.encoder.get(token, self.encoder.get(self.unk_token)) - - def _convert_id_to_token(self, index): - """Converts an index (integer) in a token (str) using the vocab.""" - return self.decoder.get(index) - - def convert_tokens_to_string(self, tokens): - """Converts a sequence of tokens (string) in a single string.""" - text = "".join(tokens) - text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) - return text - - def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: - if not os.path.isdir(save_directory): - logger.error(f"Vocabulary path ({save_directory}) should be a directory") - return - vocab_file = os.path.join( - save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] - ) - merge_file = os.path.join( - save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] - ) - - with open(vocab_file, "w", encoding="utf-8") as f: - f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") - - index = 0 - with open(merge_file, "w", encoding="utf-8") as writer: - writer.write("#version: 0.2\n") - for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): - if index != token_index: - logger.warning( - f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." - " Please check that the tokenizer is not corrupted!" - ) - index = token_index - writer.write(" ".join(bpe_tokens) + "\n") - index += 1 - - return vocab_file, merge_file - - def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): - add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space) - if is_split_into_words or add_prefix_space: - text = " " + text - return (text, kwargs) - - def decode( - self, - token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], - skip_special_tokens: bool = False, - clean_up_tokenization_spaces: bool = None, - truncate_before_pattern: Optional[List[str]] = None, - **kwargs, - ) -> str: - """ - Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special - tokens and clean up tokenization spaces. - - Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. - - Args: - token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): - List of tokenized input ids. Can be obtained using the `__call__` method. - skip_special_tokens (`bool`, *optional*, defaults to `False`): - Whether or not to remove special tokens in the decoding. - clean_up_tokenization_spaces (`bool`, *optional*): - Whether or not to clean up the tokenization spaces. If `None`, will default to - `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`). - truncate_before_pattern (`List[str]`, *optional*, defaults to `None`): - A list of regular expression strings that will be used to truncate the returned string. This can be - used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning - of a new line). An example pattern could be `["^#", re.escape("<|endoftext|>"), "^'''", "\n\n\n"]`. - kwargs (additional keyword arguments, *optional*): - Will be passed to the underlying model specific decode method. - - Returns: - `str`: The decoded sentence. - """ - decoded_text = super()._decode( - token_ids=token_ids, - skip_special_tokens=skip_special_tokens, - clean_up_tokenization_spaces=clean_up_tokenization_spaces, - **kwargs, - ) - - if truncate_before_pattern is not None and len(truncate_before_pattern) > 0: - decoded_text = self.truncate(decoded_text, truncate_before_pattern) - - return decoded_text - - def truncate(self, completion, truncate_before_pattern): - def find_re(string, pattern, start_pos): - m = pattern.search(string, start_pos) - return m.start() if m else -1 - - terminals = [re.compile(pattern, re.MULTILINE) for pattern in truncate_before_pattern] - - prints = list(re.finditer("^print", completion, re.MULTILINE)) - - if len(prints) > 1: - completion = completion[: prints[1].start()] - - defs = list(re.finditer("^def", completion, re.MULTILINE)) - - if len(defs) > 1: - completion = completion[: defs[1].start()] - - start_pos = 0 - - terminals_pos = [ - pos for pos in [find_re(completion, terminal, start_pos) for terminal in terminals] if pos != -1 - ] - - if len(terminals_pos) > 0: - return completion[: min(terminals_pos)] - else: - return completion diff --git a/spaces/justest/gpt4free/g4f/.v1/gui/streamlit_app.py b/spaces/justest/gpt4free/g4f/.v1/gui/streamlit_app.py deleted file mode 100644 index 2dba0a7b672470f20aa163005d42894dd17df7c0..0000000000000000000000000000000000000000 --- a/spaces/justest/gpt4free/g4f/.v1/gui/streamlit_app.py +++ /dev/null @@ -1,52 +0,0 @@ -import os -import sys - -sys.path.append(os.path.join(os.path.dirname(__file__), os.path.pardir)) - -import streamlit as st -from gpt4free import you - - -def get_answer(question: str) -> str: - # Set cloudflare clearance cookie and get answer from GPT-4 model - try: - result = you.Completion.create(prompt=question) - - return result.text - - except Exception as e: - # Return error message if an exception occurs - return ( - f'An error occurred: {e}. Please make sure you are using a valid cloudflare clearance token and user agent.' - ) - - -# Set page configuration and add header -st.set_page_config( - page_title="gpt4freeGUI", - initial_sidebar_state="expanded", - page_icon="🧠", - menu_items={ - 'Get Help': 'https://github.com/xtekky/gpt4free/blob/main/README.md', - 'Report a bug': "https://github.com/xtekky/gpt4free/issues", - 'About': "### gptfree GUI", - }, -) -st.header('GPT4free GUI') - -# Add text area for user input and button to get answer -question_text_area = st.text_area('🤖 Ask Any Question :', placeholder='Explain quantum computing in 50 words') -if st.button('🧠 Think'): - answer = get_answer(question_text_area) - escaped = answer.encode('utf-8').decode('unicode-escape') - # Display answer - st.caption("Answer :") - st.markdown(escaped) - -# Hide Streamlit footer -hide_streamlit_style = """ - - """ -st.markdown(hide_streamlit_style, unsafe_allow_html=True) diff --git a/spaces/justest/gpt4free/g4f/.v1/testing/deepai_test.py b/spaces/justest/gpt4free/g4f/.v1/testing/deepai_test.py deleted file mode 100644 index 474f663ecff8e48b17fc1cdae1ea6e0a89f03c0b..0000000000000000000000000000000000000000 --- a/spaces/justest/gpt4free/g4f/.v1/testing/deepai_test.py +++ /dev/null @@ -1,18 +0,0 @@ -from gpt4free import deepai - -#single completion -for chunk in deepai.Completion.create("Write a list of possible vacation destinations:"): - print(chunk, end="", flush=True) -print() - -#chat completion -print("==============") -messages = [ #taken from the openai docs - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Who won the world series in 2020?"}, - {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."}, - {"role": "user", "content": "Where was it played?"} -] -for chunk in deepai.ChatCompletion.create(messages): - print(chunk, end="", flush=True) -print() \ No newline at end of file diff --git a/spaces/kajalag/Whatsapp_Chat_Analyzer/helper.py b/spaces/kajalag/Whatsapp_Chat_Analyzer/helper.py deleted file mode 100644 index 22971570046742c312f3d8ef9cbee382e108c56a..0000000000000000000000000000000000000000 --- a/spaces/kajalag/Whatsapp_Chat_Analyzer/helper.py +++ /dev/null @@ -1,134 +0,0 @@ -import matplotlib.pyplot as plt -from urlextract import URLExtract -from collections import Counter -from wordcloud import WordCloud, STOPWORDS ,ImageColorGenerator -import pandas as pd -import matplotlib.pylab as plt -import PIL.Image -import numpy as np -import emoji - -extract=URLExtract() -def fetch_stats(selected_user,df): - - if selected_user!= "Group analysis": - df=df[df['users']==selected_user] - num_messages = df.shape[0] - words = [] - for message in df['message']: - words.extend(message.split()) - - - links=[] - for message in df['message']: - links.extend(extract.find_urls(message)) - - return num_messages, len(words),len(links) - -def most_busy_users(df): - x = df['users'].value_counts().head() - df=round((df['users'].value_counts() / df.shape[0]) * 100, 2).reset_index().rename( - columns={'index': 'name', 'user': 'percent'}) - return x,df - -def most_common_words(selected_user,df): - f = open('stop_hinglish.txt', 'r') - stop_words = f.read() - - if selected_user != "Group analysis": - df = df[df['users'] == selected_user] - temp = df[df['users'] != 'group_notification'] - temp = temp[temp['message'] != ' \n'] - - words = [] - - for message in temp['message']: - for word in message.lower().split(): - if word not in stop_words: - words.append(word) - most_common_df=pd.DataFrame(Counter(words).most_common(30)) - return most_common_df - -def word_cloud(selected_user,df): - if selected_user != "Group analysis": - df = df[df['users'] == selected_user] - - stopwords = set('STOPWORDS') - - # wordcloud - wordcloud = WordCloud(stopwords=stopwords, background_color="Black").generate(''.join(df['message'])) - plt.figure(figsize=(10, 8), facecolor='k') - plt.imshow(wordcloud, interpolation='bilinear') - plt.show() - - return wordcloud - -def emoji_helper(selected_user,df): - if selected_user != "Group analysis": - df = df[df['users'] == selected_user] - emojis = [] - for message in df['message']: - emojis.extend([c for c in message if c in emoji.EMOJI_DATA.keys()]) - emoji_df=pd.DataFrame(Counter(emojis).most_common(len(Counter(emojis)))) - - return emoji_df - -def monthly_timeline(selected_user,df): - if selected_user != "Group analysis": - df = df[df['users'] == selected_user] - - timeline = df.groupby(['year', 'Month_name', 'Month']).count()['message'].reset_index() - time = [] - for i in range(timeline.shape[0]): - time.append(timeline['Month_name'][i] + "-" + str(timeline['year'][i])) - timeline['time'] = time - - return timeline -def Daily_timeline(selected_user,df): - if selected_user != "Group analysis": - df = df[df['users'] == selected_user] - - daily_timeline = df.groupby('Date').count()['message'].reset_index() - - return daily_timeline - -def week_activity_map(selected_user,df): - if selected_user != "Group analysis": - df = df[df['users'] == selected_user] - return df['Day_name'].value_counts() - -def month_activity_map(selected_user,df): - if selected_user != "Group analysis": - df = df[df['users'] == selected_user] - return df['Month_name'].value_counts() - -def activity_heatmap(selected_user,df): - if selected_user != "Group analysis": - df = df[df['users'] == selected_user] - - Activity_heatmap= df.pivot_table(index='Day_name', columns='period', values='message', aggfunc='count').fillna(0) - return Activity_heatmap - -def pos_words(selected_user,df): - if selected_user != "Group analysis": - df = df[df['users'] == selected_user] - - pos_word = df[df['vader_Analysis'] == 'Positive'] - pos_word = pos_word.pop('message') - return pos_word - -def neg_words(selected_user,df): - if selected_user != "Group analysis": - df = df[df['users'] == selected_user] - - neg_word = df[df['Analysis'] == 'Negative'] 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- diff --git a/spaces/lixq/bingo61/src/lib/isomorphic/index.ts b/spaces/lixq/bingo61/src/lib/isomorphic/index.ts deleted file mode 100644 index 738dc92f74079ab762d584fb7422a8c8c3b61547..0000000000000000000000000000000000000000 --- a/spaces/lixq/bingo61/src/lib/isomorphic/index.ts +++ /dev/null @@ -1,17 +0,0 @@ -'use client' - -import Default from './browser' - -let exportsModel: any = {} - -if (process.browser) { - Object.assign(exportsModel, require('./browser').default) -} else { - Object.assign(exportsModel, require('./node').default) -} - -export default exportsModel! as typeof Default - -export const fetch: typeof Default.fetch = exportsModel!.fetch -export const WebSocket: typeof Default.WebSocket = exportsModel!.WebSocket -export const debug: typeof Default.debug = exportsModel!.debug diff --git a/spaces/llmonitor/benchmarks/run/test.py b/spaces/llmonitor/benchmarks/run/test.py deleted file mode 100644 index c3e06c88eef7509f088b6a2887fad59611b56bac..0000000000000000000000000000000000000000 --- a/spaces/llmonitor/benchmarks/run/test.py +++ /dev/null @@ -1,19 +0,0 @@ -import requests -import json - -response = requests.post( - url="https://openrouter.ai/api/v1/chat/completions", - headers={ - "HTTP-Referer": 'https://benchmarks.llmonitor.com', # To identify your app. Can be set to localhost for testing - "Authorization": "Bearer " + "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" - }, - data=json.dumps({ - "model": "mistralai/mistral-7b-instruct", # Optional - "messages": [ - {"role": "user", "content": "What is the meaning of life?"} - ] - }) -) - - -print(response.json()) \ No newline at end of file diff --git a/spaces/ltgoslo/ssa-perin/data/field/label_field.py b/spaces/ltgoslo/ssa-perin/data/field/label_field.py deleted file mode 100644 index a330e16b63f7cf6d09af61fcf271d250e70c7251..0000000000000000000000000000000000000000 --- a/spaces/ltgoslo/ssa-perin/data/field/label_field.py +++ /dev/null @@ -1,36 +0,0 @@ -import torch -from data.field.mini_torchtext.field import RawField -from data.field.mini_torchtext.vocab import Vocab -from collections import Counter - - -class LabelField(RawField): - def __self__(self, preprocessing): - super(LabelField, self).__init__(preprocessing=preprocessing) - self.vocab = None - - def build_vocab(self, *args, **kwargs): - sources = [] - for arg in args: - if isinstance(arg, torch.utils.data.Dataset): - sources += [arg.get_examples(name) for name, field in arg.fields.items() if field is self] - else: - sources.append(arg) - - counter = Counter() - for data in sources: - for x in data: - counter.update(x) - - self.vocab = Vocab(counter, specials=[]) - - def process(self, example, device=None): - tensor, lengths = self.numericalize(example, device=device) - return tensor, lengths - - def numericalize(self, example, device=None): - example = [self.vocab.stoi[x] + 1 for x in example] - length = torch.LongTensor([len(example)], device=device).squeeze(0) - tensor = torch.LongTensor(example, device=device) - - return tensor, length diff --git a/spaces/lysine/auscultate/src/lib/heart/data.ts b/spaces/lysine/auscultate/src/lib/heart/data.ts deleted file mode 100644 index c2a2d0acd7e118c622884a16e4094c590697551c..0000000000000000000000000000000000000000 --- a/spaces/lysine/auscultate/src/lib/heart/data.ts +++ /dev/null @@ -1,121 +0,0 @@ -import { parse } from '@vanillaes/csv'; -import fs from 'fs/promises'; -import { - Auscultation, - Location, - Outcome, - Patient, - getGrading, - getTiming, -} from '../../heart-types'; -import { log } from '../helper'; - -const DATA_DIR = 'dist/app/heart-data/'; - -export let patients: Patient[] = []; - -export async function readPatients(): Promise{ - log('Heart sounds: Reading patient index'); - const data = await fs.readFile(DATA_DIR + 'training_data.csv', { - encoding: 'utf8', - }); - - const parsed = parse(data); - parsed.shift(); // Remove the header row - - patients = parsed.map(row => ({ - patientId: parseInt(row[0], 10), - locations: row[1].split('+'), - age: row[2] === 'nan' ? null : row[2], - sex: row[3], - height: row[4] === 'nan' ? null : parseFloat(row[4]), - weight: row[5] === 'nan' ? null : parseFloat(row[5]), - isPregnant: row[6] === 'True', - murmur: row[7], - murmurLocations: row[8] === 'nan' ? [] : row[8].split('+'), - mostAudible: row[9] === 'nan' ? null : row[9], - systolicMurmur: - row[10] === 'nan' - ? null - : { - timing: getTiming(row[10]), - shape: row[11], - grading: getGrading(row[12]), - pitch: row[13], - quality: row[14], - }, - diastolicMurmur: - row[15] === 'nan' - ? null - : { - timing: getTiming(row[15]), - shape: row[16], - grading: getGrading(row[17]), - pitch: row[18], - quality: row[19], - }, - campaign: row[20], - outcome: Outcome.Abnormal, // fixed below by reading patient files - additionalId: row[21] === 'nan' ? null : parseInt(row[21], 10), - })); - - log('Heart sounds: Populating heart outcomes'); - - await Promise.all( - patients.map(async patient => { - const data = await fs.readFile( - DATA_DIR + `training_data/training_data/${patient.patientId}.txt`, - { - encoding: 'utf8', - } - ); - const lines = data.split(/\r?\n/); - patient.outcome = lines - .find(line => line.startsWith('#Outcome:')) - ?.split(' ')[1] as Outcome; - }) - ); -} - -export async function readAuscultation( - patientId: number -): Promise { - const data = await fs.readFile( - DATA_DIR + `training_data/training_data/${patientId}.txt`, - { - encoding: 'utf8', - } - ); - const lines = data.split(/\r?\n/); - const auscultation: Auscultation = { - patientId, - tracks: [], - }; - const trackCount = parseInt(lines[0].split(' ')[1], 10); - for (let i = 1; i <= trackCount; i++) { - const [location, headerFile, audioFile, segmentsFile] = lines[i].split(' '); - const segments = await fs.readFile( - DATA_DIR + `training_data/training_data/${segmentsFile}`, - { - encoding: 'utf8', - } - ); - const segmentLines = segments.split(/\r?\n/); - auscultation.tracks.push({ - location: location as Location, - headerFile, - audioFile, - segments: segmentLines - .map(line => { - const [start, end, type] = line.split(/\t/); - return { - start: parseFloat(start), - end: parseFloat(end), - type: parseInt(type, 10), - }; - }) - .filter(s => !Number.isNaN(s.start)), - }); - } - return auscultation; -} diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/find.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/find.h deleted file mode 100644 index f6a1e59d105f0db35d65ee93a058afd143002b35..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/find.h +++ /dev/null @@ -1,219 +0,0 @@ -/****************************************************************************** - * Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * 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. - * * Neither the name of the NVIDIA CORPORATION 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 COPYRIGHT HOLDERS AND CONTRIBUTORS "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 NVIDIA CORPORATION 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. - * - ******************************************************************************/ -#pragma once - - -#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC -#include - -#include -#include -#include - -namespace thrust -{ -namespace cuda_cub { - -// XXX forward declare to circumvent circular depedency -template -InputIt __host__ __device__ -find_if(execution_policy & policy, - InputIt first, - InputIt last, - Predicate predicate); - -template -InputIt __host__ __device__ -find_if_not(execution_policy & policy, - InputIt first, - InputIt last, - Predicate predicate); - -template -InputIt __host__ __device__ -find(execution_policy &policy, - InputIt first, - InputIt last, - T const& value); - -}; // namespace cuda_cub -} // end namespace thrust - -#include -#include - -namespace thrust -{ -namespace cuda_cub { - -namespace __find_if { - - template - struct functor - { - THRUST_DEVICE_FUNCTION TupleType - operator()(const TupleType& lhs, const TupleType& rhs) const - { - // select the smallest index among true results - if (thrust::get<0>(lhs) && thrust::get<0>(rhs)) - { - return TupleType(true, (thrust::min)(thrust::get<1>(lhs), thrust::get<1>(rhs))); - } - else if (thrust::get<0>(lhs)) - { - return lhs; - } - else - { - return rhs; - } - } - }; -} // namespace __find_if - -template -InputIt __host__ __device__ -find_if_n(execution_policy & policy, - InputIt first, - Size num_items, - Predicate predicate) -{ - typedef typename thrust::tuple ZipIterator; - - IteratorTuple iter_tuple = - thrust::make_tuple(XfrmIterator(first, predicate), - counting_iterator_t (0)); - - ZipIterator begin = thrust::make_zip_iterator(iter_tuple); - ZipIterator end = begin + num_items; - - for (ZipIterator interval_begin = begin; - interval_begin < end; - interval_begin += interval_size) - { - ZipIterator interval_end = interval_begin + interval_size; - if(end < interval_end) - { - interval_end = end; - } // end if - - result_type result = reduce(policy, - interval_begin, - interval_end, - result_type(false, interval_end - begin), - __find_if::functor ()); - - // see if we found something - if(thrust::get<0>(result)) - { - return first + thrust::get<1>(result); - } - } - - //nothing was found if we reach here... - return first + num_items; -} - -template -InputIt __host__ __device__ -find_if(execution_policy & policy, - InputIt first, - InputIt last, - Predicate predicate) -{ - return cuda_cub::find_if_n(policy, first, thrust::distance(first,last), predicate); -} - -template -InputIt __host__ __device__ -find_if_not(execution_policy & policy, - InputIt first, - InputIt last, - Predicate predicate) -{ - return cuda_cub::find_if(policy, first, last, thrust::detail::not1(predicate)); -} - - -template -InputIt __host__ __device__ -find(execution_policy &policy, - InputIt first, - InputIt last, - T const& value) -{ - using thrust::placeholders::_1; - - return cuda_cub::find_if(policy, - first, - last, - _1 == value); -} - - -} // namespace cuda_cub -} // end namespace thrust -#endif diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/data/__init__.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/data/__init__.py deleted file mode 100644 index 510df16771d153f61fbf2126baac24f69d3de7e4..0000000000000000000000000000000000000000 --- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/data/__init__.py +++ /dev/null @@ -1,101 +0,0 @@ -import importlib -import numpy as np -import random -import torch -import torch.utils.data -from copy import deepcopy -from functools import partial -from os import path as osp - -from basicsr.data.prefetch_dataloader import PrefetchDataLoader -from basicsr.utils import get_root_logger, scandir -from basicsr.utils.dist_util import get_dist_info -from basicsr.utils.registry import DATASET_REGISTRY - -__all__ = ['build_dataset', 'build_dataloader'] - -# automatically scan and import dataset modules for registry -# scan all the files under the data folder with '_dataset' in file names -data_folder = osp.dirname(osp.abspath(__file__)) -dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')] -# import all the dataset modules -_dataset_modules = [importlib.import_module(f'basicsr.data.{file_name}') for file_name in dataset_filenames] - - -def build_dataset(dataset_opt): - """Build dataset from options. - - Args: - dataset_opt (dict): Configuration for dataset. It must contain: - name (str): Dataset name. - type (str): Dataset type. - """ - dataset_opt = deepcopy(dataset_opt) - dataset = DATASET_REGISTRY.get(dataset_opt['type'])(dataset_opt) - logger = get_root_logger() - logger.info(f'Dataset [{dataset.__class__.__name__}] - {dataset_opt["name"]} is built.') - return dataset - - -def build_dataloader(dataset, dataset_opt, num_gpu=1, dist=False, sampler=None, seed=None): - """Build dataloader. - - Args: - dataset (torch.utils.data.Dataset): Dataset. - dataset_opt (dict): Dataset options. It contains the following keys: - phase (str): 'train' or 'val'. - num_worker_per_gpu (int): Number of workers for each GPU. - batch_size_per_gpu (int): Training batch size for each GPU. - num_gpu (int): Number of GPUs. Used only in the train phase. - Default: 1. - dist (bool): Whether in distributed training. Used only in the train - phase. Default: False. - sampler (torch.utils.data.sampler): Data sampler. Default: None. - seed (int | None): Seed. Default: None - """ - phase = dataset_opt['phase'] - rank, _ = get_dist_info() - if phase == 'train': - if dist: # distributed training - batch_size = dataset_opt['batch_size_per_gpu'] - num_workers = dataset_opt['num_worker_per_gpu'] - else: # non-distributed training - multiplier = 1 if num_gpu == 0 else num_gpu - batch_size = dataset_opt['batch_size_per_gpu'] * multiplier - num_workers = dataset_opt['num_worker_per_gpu'] * multiplier - dataloader_args = dict( - dataset=dataset, - batch_size=batch_size, - shuffle=False, - num_workers=num_workers, - sampler=sampler, - drop_last=True) - if sampler is None: - dataloader_args['shuffle'] = True - dataloader_args['worker_init_fn'] = partial( - worker_init_fn, num_workers=num_workers, rank=rank, seed=seed) if seed is not None else None - elif phase in ['val', 'test']: # validation - dataloader_args = dict(dataset=dataset, batch_size=1, shuffle=False, num_workers=0) - else: - raise ValueError(f"Wrong dataset phase: {phase}. Supported ones are 'train', 'val' and 'test'.") - - dataloader_args['pin_memory'] = dataset_opt.get('pin_memory', False) - dataloader_args['persistent_workers'] = dataset_opt.get('persistent_workers', False) - - prefetch_mode = dataset_opt.get('prefetch_mode') - if prefetch_mode == 'cpu': # CPUPrefetcher - num_prefetch_queue = dataset_opt.get('num_prefetch_queue', 1) - logger = get_root_logger() - logger.info(f'Use {prefetch_mode} prefetch dataloader: num_prefetch_queue = {num_prefetch_queue}') - return PrefetchDataLoader(num_prefetch_queue=num_prefetch_queue, **dataloader_args) - else: - # prefetch_mode=None: Normal dataloader - # prefetch_mode='cuda': dataloader for CUDAPrefetcher - return torch.utils.data.DataLoader(**dataloader_args) - - -def worker_init_fn(worker_id, num_workers, rank, seed): - # Set the worker seed to num_workers * rank + worker_id + seed - worker_seed = num_workers * rank + worker_id + seed - np.random.seed(worker_seed) - random.seed(worker_seed) diff --git a/spaces/manhkhanhUIT/BOPBTL/Global/data/Load_Bigfile.py b/spaces/manhkhanhUIT/BOPBTL/Global/data/Load_Bigfile.py deleted file mode 100644 index b34f1ece4d296f4e7e8ccb709d84a23c01ee5dd7..0000000000000000000000000000000000000000 --- a/spaces/manhkhanhUIT/BOPBTL/Global/data/Load_Bigfile.py +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import io -import os -import struct -from PIL import Image - -class BigFileMemoryLoader(object): - def __load_bigfile(self): - print('start load bigfile (%0.02f GB) into memory' % (os.path.getsize(self.file_path)/1024/1024/1024)) - with open(self.file_path, 'rb') as fid: - self.img_num = struct.unpack('i', fid.read(4))[0] - self.img_names = [] - self.img_bytes = [] - print('find total %d images' % self.img_num) - for i in range(self.img_num): - img_name_len = struct.unpack('i', fid.read(4))[0] - img_name = fid.read(img_name_len).decode('utf-8') - self.img_names.append(img_name) - img_bytes_len = struct.unpack('i', fid.read(4))[0] - self.img_bytes.append(fid.read(img_bytes_len)) - if i % 5000 == 0: - print('load %d images done' % i) - print('load all %d images done' % self.img_num) - - def __init__(self, file_path): - super(BigFileMemoryLoader, self).__init__() - self.file_path = file_path - self.__load_bigfile() - - def __getitem__(self, index): - try: - img = Image.open(io.BytesIO(self.img_bytes[index])).convert('RGB') - return self.img_names[index], img - except Exception: - print('Image read error for index %d: %s' % (index, self.img_names[index])) - return self.__getitem__((index+1)%self.img_num) - - - def __len__(self): - return self.img_num diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/gypsum_history.sh b/spaces/marlenezw/audio-driven-animations/MakeItTalk/gypsum_history.sh deleted file mode 100644 index bb3592b88053b9de2bc200c3456221b659a6267d..0000000000000000000000000000000000000000 --- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/gypsum_history.sh +++ /dev/null @@ -1,78 +0,0 @@ -__=' - This is the default license template. - - File: gypsum_history.sh - Author: dinli - Copyright (c) 2020 dinli - - To edit this license information: Press Ctrl+Shift+P and press 'Create new License Template...'. -' - -./sbatch_1080tilong.sh "--train --write --name" i2i_1gpu_1 - -./sbatch_fl_1080tilong.sh 0 100 -./sbatch_fl_1080tilong.sh 100 200 -./sbatch_fl_1080tilong.sh 200 300 -./sbatch_fl_1080tilong.sh 300 400 -./sbatch_fl_1080tilong.sh 400 500 -./sbatch_fl_1080tilong.sh 500 600 -./sbatch_fl_1080tilong.sh 600 700 -./sbatch_fl_1080tilong.sh 700 800 -./sbatch_fl_1080tilong.sh 800 900 -./sbatch_fl_1080tilong.sh 900 1000 - - -# multi-gpu version -./sbatch_1080tilong.sh "--train --write --batch_size 32 --name" i2i_2gpu_b32_1080ti 2 24G 1080ti-long -./sbatch_1080tilong.sh "--train --write --batch_size 32 --name" i2i_2gpu_b32_titanx 2 24G titanx-long -./sbatch_1080tilong.sh "--train --write --batch_size 32 --name" i2i_2gpu_b32_2080ti 2 24G 2080ti-long - -./sbatch_1080tilong.sh "--train --write --batch_size 32 --jpg_freq 120 --ckpt_freq 720 --name" i2i_4gpu_b32_2080ti 4 24G 2080ti-long - -./sbatch_1080tilong.sh "--train --write --batch_size 128 --name" i2i_8gpu_b128_1080ti 8 48G 1080ti-long -./sbatch_1080tilong.sh "--train --write --batch_size 64 --jpg_freq 120 --ckpt_freq 720 --name" i2i_8gpu_b64_1080ti 8 48G 1080ti-long -./sbatch_1080tilong.sh "--train --write --batch_size 32 --jpg_freq 120 --ckpt_freq 720 --name" i2i_8gpu_b32_2080ti 8 48G 2080ti-long - -# train on preprocessed vox data -./sbatch_1080tilong.sh "--train --write --num_workers 8 --load_G_name /mnt/nfs/scratch1/yangzhou/VoxCeleb2_imagetranslation/ckpt/i2i_1gpu_1/ckpt_69.pth --batch_size 32 --jpg_freq 120 --ckpt_freq 720 --name" i2i_pre_load1gpu_4gpu_b32_1080ti 4 64G 1080ti-long -./sbatch_1080tilong.sh "--train --write --num_workers 16 --load_G_name /mnt/nfs/scratch1/yangzhou/VoxCeleb2_imagetranslation/ckpt/i2i_1gpu_1/ckpt_69.pth --batch_size 32 --jpg_freq 120 --ckpt_freq 720 --name" i2i_pre_load1gpu_4gpu_b32_2080ti 4 128G 2080ti-long -./sbatch_1080tilong.sh "--train --write --num_workers 8 --batch_size 32 --jpg_freq 120 --ckpt_freq 720 --name" i2i_pre_init_4gpu_b32_1080ti 4 64G 1080ti-long -./sbatch_1080tilong.sh "--train --write --num_workers 16 --batch_size 32 --jpg_freq 120 --ckpt_freq 720 --name" i2i_pre_init_4gpu_b32_2080ti_2 4 128G 2080ti-long - -# 04/08 night train with style loss + minor (ckpt freq) -./sbatch_1080tilong.sh "--train --write --num_workers 16 --load_G_name /mnt/nfs/scratch1/yangzhou/VoxCeleb2_imagetranslation/ckpt/i2i_1gpu_1/ckpt_88.pth --batch_size 16 --name" i2istyle_rawcompress_load1gpu_4gpu_b16_2080ti 4 64G 2080ti-long -./sbatch_1080tilong.sh "--train --write --num_workers 16 --batch_size 16 --name" i2istyle_rawcompress_init_4gpu_b16_2080ti 4 64G 2080ti-long -./sbatch_1080tilong.sh "--train --write --num_workers 16 --load_G_name /mnt/nfs/scratch1/yangzhou/VoxCeleb2_imagetranslation/ckpt/i2i_1gpu_1/ckpt_88.pth --batch_size 16 --jpg_freq 120 --ckpt_last_freq 3600 --name" i2istyle_rawcompress_load1gpu_4gpu_b32_titanx 4 64G titanx-long -./sbatch_1080tilong.sh "--train --write --num_workers 16 --batch_size 16 --jpg_freq 120 --ckpt_last_freq 3600 --name" i2istyle_rawcompress_init_4gpu_b32_titanx 4 128G titanx-long -# -> on preprocessed as well -./sbatch_1080tilong.sh "--train --write --use_vox_dataset preprocessed --num_workers 16 --load_G_name /mnt/nfs/scratch1/yangzhou/VoxCeleb2_imagetranslation/ckpt/i2i_1gpu_1/ckpt_88.pth --batch_size 16 --name" i2istyle_preprocessed_load1gpu_4gpu_b16_1080ti 4 64G 1080ti-long -./sbatch_1080tilong.sh "--train --write --use_vox_dataset preprocessed --num_workers 16 --batch_size 16 --name" i2istyle_preprocessed_init_4gpu_b16_1080ti 4 64G 1080ti-long - -# finetune on preprocessed (train / tune both on style loss) -./sbatch_1080tilong.sh "--train --write --use_vox_dataset preprocessed --num_workers 16 --load_G_name /mnt/nfs/scratch1/yangzhou/PreprocessedVox_imagetranslation/ckpt/i2istyle_rawcompress_init_4gpu_b16_2080ti/ckpt_35.pth --batch_size 16 --ckpt_epoch_freq 5 --name" i2itune_preprocessed_loadstyle_4gpu_b16_2080ti 4 64G 2080ti-long - - -# 04/28 -# train on improved face alignment -> 3rdparty/AwingNet -srun -p 2080ti-short --gres=gpu:7 --mem=64G python main_train_image_translation.py --use_vox_dataset process --batch_size 112 --num_workers 7 --train --name awing_tmp_2 -./sbatch_1080tilong.sh "--train --write --use_vox_dataset raw --num_workers 4 --batch_size 16 --ckpt_epoch_freq 1 --name" awing_1080 1 64G 1080ti-long -./sbatch_1080tilong.sh "--train --write --use_vox_dataset process --num_workers 4 --batch_size 16 --ckpt_epoch_freq 4 --name" awing_process_1080 1 64G 1080ti-long -srun -p m40-short --gres=gpu:1 --mem=32G python main_train_image_translation.py --use_vox_dataset raw --batch_size 1 --num_workers 0 --name test_awing --load_G_name /mnt/nfs/scratch1/yangzhou/PreprocessedVox_imagetranslation/ckpt/i2itune_preprocessed_loadstyle_4gpu_b16_2080ti/ckpt_150.pth - -# debug vgg loss multi gpu -./sbatch_1080tilong.sh "--train --write --use_vox_dataset raw --num_workers 16 --batch_size 64 --ckpt_epoch_freq 1 --name" awing_raw_2080 4 64G 2080ti-long -./sbatch_1080tilong.sh "--train --write --use_vox_dataset process --num_workers 16 --batch_size 64 --ckpt_epoch_freq 5 --name" awing_process_2080 4 64G 2080ti-long - -# 05/01 -# add audio in and comb fan awing feature -srun -p 1080ti-short --gres=gpu:4 --mem=64G python main_train_image_translation.py --use_vox_dataset raw --batch_size 64 --num_workers 16 --train --name comb_tmp_1 --comb_fan_awing --test_speed -./sbatch_1080tilong.sh "--train --write --use_vox_dataset raw --num_workers 16 --batch_size 64 --ckpt_epoch_freq 1 --add_audio_in --name" audio_raw_1080 4 64G 1080ti-long -./sbatch_1080tilong.sh "--train --write --use_vox_dataset raw --num_workers 16 --batch_size 64 --ckpt_epoch_freq 1 --comb_fan_awing --name" comb_raw_1080 4 64G 1080ti-long - - - - - - -# test preprocessed vox -srun -p 2080ti-short --gres=gpu:1 --mem=32G python main_train_image_translation.py --num_workers 0 --load_G_name /mnt/nfs/scratch1/yangzhou/PreprocessedVox_imagetranslation/ckpt/awing_raw_2080/ckpt_47.pth --batch_size 1 --name test_tmp --use_vox_dataset raw diff --git a/spaces/mayordp/DeepFakeAI/DeepFakeAI/uis/components/processors.py b/spaces/mayordp/DeepFakeAI/DeepFakeAI/uis/components/processors.py deleted file mode 100644 index b87da139b019f6c51a1adc45ad65a09f4578aa66..0000000000000000000000000000000000000000 --- a/spaces/mayordp/DeepFakeAI/DeepFakeAI/uis/components/processors.py +++ /dev/null @@ -1,41 +0,0 @@ -from typing import List, Optional -import gradio - -import DeepFakeAI.globals -from DeepFakeAI import wording -from DeepFakeAI.processors.frame.core import load_frame_processor_module, clear_frame_processors_modules -from DeepFakeAI.uis import core as ui -from DeepFakeAI.uis.typing import Update -from DeepFakeAI.utilities import list_module_names - -FRAME_PROCESSORS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None - - -def render() -> None: - global FRAME_PROCESSORS_CHECKBOX_GROUP - - with gradio.Box(): - FRAME_PROCESSORS_CHECKBOX_GROUP = gradio.CheckboxGroup( - label = wording.get('frame_processors_checkbox_group_label'), - choices = sort_frame_processors(DeepFakeAI.globals.frame_processors), - value = DeepFakeAI.globals.frame_processors - ) - ui.register_component('frame_processors_checkbox_group', FRAME_PROCESSORS_CHECKBOX_GROUP) - - -def listen() -> None: - FRAME_PROCESSORS_CHECKBOX_GROUP.change(update_frame_processors, inputs = FRAME_PROCESSORS_CHECKBOX_GROUP, outputs = FRAME_PROCESSORS_CHECKBOX_GROUP) - - -def update_frame_processors(frame_processors : List[str]) -> Update: - clear_frame_processors_modules() - DeepFakeAI.globals.frame_processors = frame_processors - for frame_processor in DeepFakeAI.globals.frame_processors: - frame_processor_module = load_frame_processor_module(frame_processor) - frame_processor_module.pre_check() - return gradio.update(value = frame_processors, choices = sort_frame_processors(frame_processors)) - - -def sort_frame_processors(frame_processors : List[str]) -> list[str]: - frame_processors_names = list_module_names('DeepFakeAI/processors/frame/modules') - return sorted(frame_processors_names, key = lambda frame_processor : frame_processors.index(frame_processor) if frame_processor in frame_processors else len(frame_processors)) diff --git a/spaces/merve/data-leak/public/third_party/tfjs.js b/spaces/merve/data-leak/public/third_party/tfjs.js deleted file mode 100644 index bc3dad50920b4c71e25844384f59cea006be4bca..0000000000000000000000000000000000000000 --- a/spaces/merve/data-leak/public/third_party/tfjs.js +++ /dev/null @@ -1,3 +0,0 @@ -// @tensorflow/tfjs Copyright 2019 Google -!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?e(exports):"function"==typeof define&&define.amd?define(["exports"],e):e(t.tf=t.tf||{})}(this,function(t){"use strict";var e=function(t,n){return(e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n])})(t,n)};function n(t,n){function r(){this.constructor=t}e(t,n),t.prototype=null===n?Object.create(n):(r.prototype=n.prototype,new r)}function r(t,e,n,r){return new(n||(n=Promise))(function(i,a){function o(t){try{u(r.next(t))}catch(t){a(t)}}function s(t){try{u(r.throw(t))}catch(t){a(t)}}function u(t){t.done?i(t.value):new n(function(e){e(t.value)}).then(o,s)}u((r=r.apply(t,e||[])).next())})}function i(t,e){var n,r,i,a,o={label:0,sent:function(){if(1&i[0])throw i[1];return i[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(n)throw new TypeError("Generator is already executing.");for(;o;)try{if(n=1,r&&(i=2&a[0]?r.return:a[0]?r.throw||((i=r.return)&&i.call(r),0):r.next)&&!(i=i.call(r,a[1])).done)return i;switch(r=0,i&&(a=[2&a[0],i.value]),a[0]){case 0:case 1:i=a;break;case 4:return o.label++,{value:a[1],done:!1};case 5:o.label++,r=a[1],a=[0];continue;case 7:a=o.ops.pop(),o.trys.pop();continue;default:if(!(i=(i=o.trys).length>0&&i[i.length-1])&&(6===a[0]||2===a[0])){o=0;continue}if(3===a[0]&&(!i||a[1]>i[0]&&a[1]