diff --git a/spaces/Dragonnnext/Unicorn-proxy/greeting.md b/spaces/Dragonnnext/Unicorn-proxy/greeting.md
deleted file mode 100644
index 58a2a61fa13d548eaa7d788426b17dd313799b81..0000000000000000000000000000000000000000
--- a/spaces/Dragonnnext/Unicorn-proxy/greeting.md
+++ /dev/null
@@ -1,36 +0,0 @@
-# iF i see somEone **spoonfeed** pAssword anywheRe Proxy wiLl be disAbled from 1hour to 30days, you've beeN warnED!
-
----
-
-## Simple Rules:
-
-# 1. sHaring prOxy liNk is alrigHt
-# 2. spoOn fEediNg / shAringG passwOrd is forbIdden aNd i will not tolerAte even one roGue persOn.
-
----
-
-# password:
-
-its on the page
-
----
-
-# contact with me:
-
-## _contactdrago@proton.me_
-
-
-
-my private bots not promising any great results (feel free to share rentry):
-
-https://rentry.co/dragobots **[W.I.P]**
-
----
-
-milestones (all proxies):
-
-# `597k prompts :O (bEfore restArt)`
-
-# `total loGGed messages: 0`
-
-🦄❤️🔥❤️-❤️🦄🔥❤️-❤️🔥🔥❤️-❤️🦄🦄🔥-🔥❤️🦄🔥-🦄🦄🦄❤️-🔥❤️🦄🦄
diff --git a/spaces/Dusan/clickbaitonator/fudge/evaluate_poetry.py b/spaces/Dusan/clickbaitonator/fudge/evaluate_poetry.py
deleted file mode 100644
index 4656878c39c53dc4647ee741e953234ec3f49393..0000000000000000000000000000000000000000
--- a/spaces/Dusan/clickbaitonator/fudge/evaluate_poetry.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import os
-import random
-import time
-import pickle
-import math
-from argparse import ArgumentParser
-import string
-from collections import defaultdict
-
-from tqdm import tqdm
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline, set_seed, GPT2Tokenizer, GPT2Model
-
-from data import Dataset, load_rhyme_info
-from model import Model
-from util import save_checkpoint, ProgressMeter, AverageMeter, num_params
-from constants import *
-from poetry_util import get_rhymes, count_syllables
-from predict_poetry import predict_couplet
-
-def main(args):
- with open(args.dataset_info, 'rb') as rf:
- dataset_info = pickle.load(rf)
- gpt_tokenizer = AutoTokenizer.from_pretrained(args.model_string)
- gpt_tokenizer.add_special_tokens({'pad_token': PAD_TOKEN})
- gpt_pad_id = gpt_tokenizer.encode(PAD_TOKEN)[0]
- gpt_model = AutoModelWithLMHead.from_pretrained(args.model_string).to(args.device)
- gpt_model.eval()
-
- checkpoint = torch.load(args.iambic_ckpt, map_location=args.device)
- model_args = checkpoint['args']
- iambic_model = Model(model_args, gpt_pad_id, len(dataset_info.index2word)) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
- iambic_model.load_state_dict(checkpoint['state_dict'])
- iambic_model = iambic_model.to(args.device)
- iambic_model.eval()
- if args.verbose:
- print("=> loaded checkpoint '{}' (epoch {})"
- .format(args.iambic_ckpt, checkpoint['epoch']))
- print('iambic model num params', num_params(iambic_model))
-
- with open(args.rhyme_info, 'rb') as rf:
- rhyme_info = pickle.load(rf)
- checkpoint = torch.load(args.rhyme_ckpt, map_location=args.device)
- model_args = checkpoint['args']
- rhyme_model = Model(model_args, gpt_pad_id, len(dataset_info.index2word), rhyme_group_size=len(rhyme_info.index2rhyme_group), verbose=args.verbose) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
- rhyme_model.load_state_dict(checkpoint['state_dict'])
- rhyme_model = rhyme_model.to(args.device)
- rhyme_model.eval()
- if args.verbose:
- print("=> loaded checkpoint '{}' (epoch {})"
- .format(args.rhyme_ckpt, checkpoint['epoch']))
- print('rhyme model num params', num_params(rhyme_model))
-
- checkpoint = torch.load(args.newline_ckpt, map_location=args.device)
- model_args = checkpoint['args']
- newline_model = Model(model_args, gpt_pad_id, len(dataset_info.index2word)) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
- newline_model.load_state_dict(checkpoint['state_dict'])
- newline_model = newline_model.to(args.device)
- newline_model.eval()
- if args.verbose:
- print("=> loaded checkpoint '{}' (epoch {})"
- .format(args.newline_ckpt, checkpoint['epoch']))
- print('iambic model num params', num_params(newline_model))
-
- with open(args.prefix_file, 'r') as rf:
- lines = rf.readlines()
- for line in tqdm(lines, total=len(lines)):
- couplet = predict_couplet(gpt_model,
- gpt_tokenizer,
- iambic_model,
- rhyme_model,
- newline_model,
- [line],
- dataset_info,
- rhyme_info,
- args.precondition_topk,
- args.topk,
- condition_lambda=args.condition_lambda,
- device=args.device)
- assert len(couplet) == 2
- print(couplet[1].strip().replace('\n', ''))
-
-
-if __name__=='__main__':
- parser = ArgumentParser()
-
- # DATA
- parser.add_argument('--iambic_ckpt', type=str, required=True)
- parser.add_argument('--rhyme_ckpt', type=str, required=True)
- parser.add_argument('--newline_ckpt', type=str, required=True)
- parser.add_argument('--dataset_info', type=str, required=True, help='saved dataset info')
- parser.add_argument('--rhyme_info', type=str, required=True, help='saved rhyme info')
- parser.add_argument('--model_string', type=str, default='gpt2-medium')
-
- parser.add_argument('--prefix_file', type=str, default=None, required=True, help='file of prefix lines for couplets')
-
- parser.add_argument('--precondition_topk', type=int, default=200, help='consider top k outputs from gpt at each step before conditioning and re-pruning')
- parser.add_argument('--topk', type=int, default=10, help='consider top k outputs from gpt at each step')
- parser.add_argument('--condition_lambda', type=float, default=1.0, help='lambda weight on conditioning model')
-
- parser.add_argument('--seed', type=int, default=1, help='random seed')
- parser.add_argument('--device', type=str, default='cuda', choices=['cpu', 'cuda'])
- parser.add_argument('--debug', action='store_true', default=False)
- parser.add_argument('--verbose', action='store_true', default=False)
-
- args = parser.parse_args()
-
- random.seed(args.seed)
- np.random.seed(args.seed)
- torch.manual_seed(args.seed)
-
- main(args)
\ No newline at end of file
diff --git a/spaces/EPFL-VILAB/MultiMAE/app.py b/spaces/EPFL-VILAB/MultiMAE/app.py
deleted file mode 100644
index 94aafea722c9501dc25c54ba5d0c6516be2fa724..0000000000000000000000000000000000000000
--- a/spaces/EPFL-VILAB/MultiMAE/app.py
+++ /dev/null
@@ -1,411 +0,0 @@
-import sys, os
-import torch
-TORCH_VERSION = ".".join(torch.__version__.split(".")[:2])
-CUDA_VERSION = torch.__version__.split("+")[-1]
-print("torch: ", TORCH_VERSION, "; cuda: ", CUDA_VERSION)
-# Install detectron2 that matches the above pytorch version
-# See https://detectron2.readthedocs.io/tutorials/install.html for instructions
-os.system(f'pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/{CUDA_VERSION}/torch{TORCH_VERSION}/index.html')
-os.system("pip install jinja2")
-os.system("pip install git+https://github.com/cocodataset/panopticapi.git")
-
-# Imports
-import gradio as gr
-import detectron2
-from detectron2.utils.logger import setup_logger
-import numpy as np
-import cv2
-import torch
-import torch.nn.functional as F
-import torchvision.transforms.functional as TF
-from torchvision import datasets, transforms
-from einops import rearrange
-from PIL import Image
-import imutils
-import matplotlib.pyplot as plt
-from mpl_toolkits.axes_grid1 import ImageGrid
-from tqdm import tqdm
-import random
-from functools import partial
-import time
-
-# import some common detectron2 utilities
-from detectron2 import model_zoo
-from detectron2.engine import DefaultPredictor
-from detectron2.config import get_cfg
-from detectron2.utils.visualizer import Visualizer, ColorMode
-from detectron2.data import MetadataCatalog
-from detectron2.projects.deeplab import add_deeplab_config
-coco_metadata = MetadataCatalog.get("coco_2017_val_panoptic")
-
-# Import Mask2Former
-from mask2former import add_maskformer2_config
-
-# DPT dependencies for depth pseudo labeling
-from dpt.models import DPTDepthModel
-
-from multimae.input_adapters import PatchedInputAdapter, SemSegInputAdapter
-from multimae.output_adapters import SpatialOutputAdapter
-from multimae.multimae import pretrain_multimae_base
-from utils.data_constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-
-torch.set_grad_enabled(False)
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-print(f'device: {device}')
-
-
-# Initialize COCO Mask2Former
-cfg = get_cfg()
-cfg.MODEL.DEVICE='cpu'
-add_deeplab_config(cfg)
-add_maskformer2_config(cfg)
-cfg.merge_from_file("mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml")
-cfg.MODEL.WEIGHTS = 'https://dl.fbaipublicfiles.com/maskformer/mask2former/coco/panoptic/maskformer2_swin_small_bs16_50ep/model_final_a407fd.pkl'
-cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True
-cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = True
-cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = True
-semseg_model = DefaultPredictor(cfg)
-
-def predict_semseg(img):
- return semseg_model(255*img.permute(1,2,0).numpy())['sem_seg'].argmax(0)
-
-def plot_semseg(img, semseg, ax):
- v = Visualizer(img.permute(1,2,0), coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
- semantic_result = v.draw_sem_seg(semseg.cpu()).get_image()
- ax.imshow(semantic_result)
-
-
-# Initialize Omnidata depth model
-os.system("wget https://datasets.epfl.ch/vilab/iccv21/weights/omnidata_rgb2depth_dpt_hybrid.pth -P pretrained_models")
-omnidata_ckpt = torch.load('./pretrained_models/omnidata_rgb2depth_dpt_hybrid.pth', map_location='cpu')
-depth_model = DPTDepthModel()
-depth_model.load_state_dict(omnidata_ckpt)
-depth_model = depth_model.to(device).eval()
-
-def predict_depth(img):
- depth_model_input = (img.unsqueeze(0) - 0.5) / 0.5
- return depth_model(depth_model_input.to(device))
-
-
-# MultiMAE model setup
-DOMAIN_CONF = {
- 'rgb': {
- 'input_adapter': partial(PatchedInputAdapter, num_channels=3, stride_level=1),
- 'output_adapter': partial(SpatialOutputAdapter, num_channels=3, stride_level=1),
- },
- 'depth': {
- 'input_adapter': partial(PatchedInputAdapter, num_channels=1, stride_level=1),
- 'output_adapter': partial(SpatialOutputAdapter, num_channels=1, stride_level=1),
- },
- 'semseg': {
- 'input_adapter': partial(SemSegInputAdapter, num_classes=133,
- dim_class_emb=64, interpolate_class_emb=False, stride_level=4),
- 'output_adapter': partial(SpatialOutputAdapter, num_channels=133, stride_level=4),
- },
-}
-DOMAINS = ['rgb', 'depth', 'semseg']
-
-input_adapters = {
- domain: dinfo['input_adapter'](
- patch_size_full=16,
- )
- for domain, dinfo in DOMAIN_CONF.items()
-}
-output_adapters = {
- domain: dinfo['output_adapter'](
- patch_size_full=16,
- dim_tokens=256,
- use_task_queries=True,
- depth=2,
- context_tasks=DOMAINS,
- task=domain
- )
- for domain, dinfo in DOMAIN_CONF.items()
-}
-
-multimae = pretrain_multimae_base(
- input_adapters=input_adapters,
- output_adapters=output_adapters,
-)
-
-CKPT_URL = 'https://github.com/EPFL-VILAB/MultiMAE/releases/download/pretrained-weights/multimae-b_98_rgb+-depth-semseg_1600e_multivit-afff3f8c.pth'
-ckpt = torch.hub.load_state_dict_from_url(CKPT_URL, map_location='cpu')
-multimae.load_state_dict(ckpt['model'], strict=False)
-multimae = multimae.to(device).eval()
-
-
-# Plotting
-
-def get_masked_image(img, mask, image_size=224, patch_size=16, mask_value=0.0):
- img_token = rearrange(
- img.detach().cpu(),
- 'b c (nh ph) (nw pw) -> b (nh nw) (c ph pw)',
- ph=patch_size, pw=patch_size, nh=image_size//patch_size, nw=image_size//patch_size
- )
- img_token[mask.detach().cpu()!=0] = mask_value
- img = rearrange(
- img_token,
- 'b (nh nw) (c ph pw) -> b c (nh ph) (nw pw)',
- ph=patch_size, pw=patch_size, nh=image_size//patch_size, nw=image_size//patch_size
- )
- return img
-
-
-def denormalize(img, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD):
- return TF.normalize(
- img.clone(),
- mean= [-m/s for m, s in zip(mean, std)],
- std= [1/s for s in std]
- )
-
-def plot_semseg_gt(input_dict, ax=None, image_size=224):
- metadata = MetadataCatalog.get("coco_2017_val_panoptic")
- instance_mode = ColorMode.IMAGE
- img_viz = 255 * denormalize(input_dict['rgb'].detach().cpu())[0].permute(1,2,0)
- semseg = F.interpolate(
- input_dict['semseg'].unsqueeze(0).cpu().float(), size=image_size, mode='nearest'
- ).long()[0,0]
- visualizer = Visualizer(img_viz, metadata, instance_mode=instance_mode, scale=1)
- visualizer.draw_sem_seg(semseg)
- if ax is not None:
- ax.imshow(visualizer.get_output().get_image())
- else:
- return visualizer.get_output().get_image()
-
-
-def plot_semseg_gt_masked(input_dict, mask, ax=None, mask_value=1.0, image_size=224):
- img = plot_semseg_gt(input_dict, image_size=image_size)
- img = torch.LongTensor(img).permute(2,0,1).unsqueeze(0)
- masked_img = get_masked_image(img.float()/255.0, mask, image_size=image_size, patch_size=16, mask_value=mask_value)
- masked_img = masked_img[0].permute(1,2,0)
-
- if ax is not None:
- ax.imshow(masked_img)
- else:
- return masked_img
-
-
-def get_pred_with_input(gt, pred, mask, image_size=224, patch_size=16):
- gt_token = rearrange(
- gt.detach().cpu(),
- 'b c (nh ph) (nw pw) -> b (nh nw) (c ph pw)',
- ph=patch_size, pw=patch_size, nh=image_size//patch_size, nw=image_size//patch_size
- )
- pred_token = rearrange(
- pred.detach().cpu(),
- 'b c (nh ph) (nw pw) -> b (nh nw) (c ph pw)',
- ph=patch_size, pw=patch_size, nh=image_size//patch_size, nw=image_size//patch_size
- )
- pred_token[mask.detach().cpu()==0] = gt_token[mask.detach().cpu()==0]
- img = rearrange(
- pred_token,
- 'b (nh nw) (c ph pw) -> b c (nh ph) (nw pw)',
- ph=patch_size, pw=patch_size, nh=image_size//patch_size, nw=image_size//patch_size
- )
- return img
-
-
-def plot_semseg_pred_masked(rgb, semseg_preds, semseg_gt, mask, ax=None, image_size=224):
- metadata = MetadataCatalog.get("coco_2017_val_panoptic")
- instance_mode = ColorMode.IMAGE
- img_viz = 255 * denormalize(rgb.detach().cpu())[0].permute(1,2,0)
-
- semseg = get_pred_with_input(
- semseg_gt.unsqueeze(1),
- semseg_preds.argmax(1).unsqueeze(1),
- mask,
- image_size=image_size//4,
- patch_size=4
- )
-
- semseg = F.interpolate(semseg.float(), size=image_size, mode='nearest')[0,0].long()
-
- visualizer = Visualizer(img_viz, metadata, instance_mode=instance_mode, scale=1)
- visualizer.draw_sem_seg(semseg)
- if ax is not None:
- ax.imshow(visualizer.get_output().get_image())
- else:
- return visualizer.get_output().get_image()
-
-def plot_predictions(input_dict, preds, masks, image_size=224):
-
- masked_rgb = get_masked_image(
- denormalize(input_dict['rgb']),
- masks['rgb'],
- image_size=image_size,
- mask_value=1.0
- )[0].permute(1,2,0).detach().cpu()
- masked_depth = get_masked_image(
- input_dict['depth'],
- masks['depth'],
- image_size=image_size,
- mask_value=np.nan
- )[0,0].detach().cpu()
-
- pred_rgb = denormalize(preds['rgb'])[0].permute(1,2,0).clamp(0,1)
- pred_depth = preds['depth'][0,0].detach().cpu()
-
- pred_rgb2 = get_pred_with_input(
- denormalize(input_dict['rgb']),
- denormalize(preds['rgb']).clamp(0,1),
- masks['rgb'],
- image_size=image_size
- )[0].permute(1,2,0).detach().cpu()
- pred_depth2 = get_pred_with_input(
- input_dict['depth'],
- preds['depth'],
- masks['depth'],
- image_size=image_size
- )[0,0].detach().cpu()
-
- fig = plt.figure(figsize=(10, 10))
- grid = ImageGrid(fig, 111, nrows_ncols=(3, 3), axes_pad=0)
-
- grid[0].imshow(masked_rgb)
- grid[1].imshow(pred_rgb2)
- grid[2].imshow(denormalize(input_dict['rgb'])[0].permute(1,2,0).detach().cpu())
-
- grid[3].imshow(masked_depth)
- grid[4].imshow(pred_depth2)
- grid[5].imshow(input_dict['depth'][0,0].detach().cpu())
-
- plot_semseg_gt_masked(input_dict, masks['semseg'], grid[6], mask_value=1.0, image_size=image_size)
- plot_semseg_pred_masked(input_dict['rgb'], preds['semseg'], input_dict['semseg'], masks['semseg'], grid[7], image_size=image_size)
- plot_semseg_gt(input_dict, grid[8], image_size=image_size)
-
- for ax in grid:
- ax.set_xticks([])
- ax.set_yticks([])
-
- fontsize = 16
- grid[0].set_title('Masked inputs', fontsize=fontsize)
- grid[1].set_title('MultiMAE predictions', fontsize=fontsize)
- grid[2].set_title('Original Reference', fontsize=fontsize)
- grid[0].set_ylabel('RGB', fontsize=fontsize)
- grid[3].set_ylabel('Depth', fontsize=fontsize)
- grid[6].set_ylabel('Semantic', fontsize=fontsize)
-
- plt.savefig('./output.png', dpi=300, bbox_inches='tight')
- plt.close()
-
-
-def inference(img, num_tokens, manual_mode, num_rgb, num_depth, num_semseg, seed):
- num_tokens = int(588 * num_tokens / 100.0)
- num_rgb = int(196 * num_rgb / 100.0)
- num_depth = int(196 * num_depth / 100.0)
- num_semseg = int(196 * num_semseg / 100.0)
-
- im = Image.open(img)
-
- # Center crop and resize RGB
- image_size = 224 # Train resolution
- img = TF.center_crop(TF.to_tensor(im), min(im.size))
- img = TF.resize(img, image_size, interpolation=TF.InterpolationMode.BICUBIC)
-
- # Predict depth and semseg
- depth = predict_depth(img)
- semseg = predict_semseg(img)
-
-
- # Pre-process RGB, depth and semseg to the MultiMAE input format
- input_dict = {}
-
- # Normalize RGB
- input_dict['rgb'] = TF.normalize(img, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD).unsqueeze(0)
-
- # Normalize depth robustly
- trunc_depth = torch.sort(depth.flatten())[0]
- trunc_depth = trunc_depth[int(0.1 * trunc_depth.shape[0]): int(0.9 * trunc_depth.shape[0])]
- depth = (depth - trunc_depth.mean()[None,None,None]) / torch.sqrt(trunc_depth.var()[None,None,None] + 1e-6)
- input_dict['depth'] = depth.unsqueeze(0)
-
- # Downsample semantic segmentation
- stride = 4
- semseg = TF.resize(semseg.unsqueeze(0), (semseg.shape[0] // stride, semseg.shape[1] // stride), interpolation=TF.InterpolationMode.NEAREST)
- input_dict['semseg'] = semseg
-
- # To GPU
- input_dict = {k: v.to(device) for k,v in input_dict.items()}
-
-
- if not manual_mode:
- # Randomly sample masks
-
- torch.manual_seed(int(time.time())) # Random mode is random
-
- preds, masks = multimae.forward(
- input_dict,
- mask_inputs=True, # True if forward pass should sample random masks
- num_encoded_tokens=num_tokens,
- alphas=1.0
- )
- else:
- # Randomly sample masks using the specified number of tokens per modality
-
- torch.manual_seed(int(seed)) # change seed to resample new mask
-
- task_masks = {domain: torch.ones(1,196).long().to(device) for domain in DOMAINS}
- selected_rgb_idxs = torch.randperm(196)[:num_rgb]
- selected_depth_idxs = torch.randperm(196)[:num_depth]
- selected_semseg_idxs = torch.randperm(196)[:num_semseg]
- task_masks['rgb'][:,selected_rgb_idxs] = 0
- task_masks['depth'][:,selected_depth_idxs] = 0
- task_masks['semseg'][:,selected_semseg_idxs] = 0
-
- preds, masks = multimae.forward(
- input_dict,
- mask_inputs=True,
- task_masks=task_masks
- )
-
- preds = {domain: pred.detach().cpu() for domain, pred in preds.items()}
- masks = {domain: mask.detach().cpu() for domain, mask in masks.items()}
-
- plot_predictions(input_dict, preds, masks)
-
- return 'output.png'
-
-
-title = "MultiMAE"
-description = "Gradio demo for MultiMAE: Multi-modal Multi-task Masked Autoencoders. \
- Upload your own images or try one of the examples below to explore the multi-modal masked reconstruction of a pre-trained MultiMAE model. \
- Uploaded images are pseudo labeled using a DPT trained on Omnidata depth, and a Mask2Former trained on COCO. \
- Choose the percentage of visible tokens using the sliders below and see how MultiMAE reconstructs the modalities!"
-
-article = "MultiMAE: Multi-modal Multi-task Masked Autoencoders | \ - Github Repo
" - -css = '.output-image{height: 713px !important}' - -# Example images -#os.system("wget https://i.imgur.com/c9ObJdK.jpg") -#os.system("wget https://i.imgur.com/KTKgYKi.jpg") -#os.system("wget https://i.imgur.com/lWYuRI7.jpg") - -examples = [ - ['c9ObJdK.jpg', 15, False, 15, 15, 15, 0], - ['KTKgYKi.jpg', 15, False, 15, 15, 15, 0], - ['lWYuRI7.jpg', 15, False, 15, 15, 15, 0], -] - -gr.Interface( - fn=inference, - inputs=[ - gr.inputs.Image(label='RGB input image', type='filepath'), - gr.inputs.Slider(label='Percentage of input tokens', default=15, step=0.1, minimum=0, maximum=100), - gr.inputs.Checkbox(label='Manual mode: Check this to manually set the number of input tokens per modality using the sliders below', default=False), - gr.inputs.Slider(label='Percentage of RGB input tokens (for manual mode only)', default=15, step=0.1, minimum=0, maximum=100), - gr.inputs.Slider(label='Percentage of depth input tokens (for manual mode only)', default=15, step=0.1, minimum=0, maximum=100), - gr.inputs.Slider(label='Percentage of semantic input tokens (for manual mode only)', default=15, step=0.1, minimum=0, maximum=100), - gr.inputs.Number(label='Random seed: Change this to sample different masks (for manual mode only)', default=0), - ], - outputs=[ - gr.outputs.Image(label='MultiMAE predictions', type='filepath') - ], - css=css, - title=title, - description=description, - article=article, - examples=examples -).launch(enable_queue=True) diff --git a/spaces/Ekitl02/stabilityai-stable-diffusion-xl-base-1.0/README.md b/spaces/Ekitl02/stabilityai-stable-diffusion-xl-base-1.0/README.md deleted file mode 100644 index 32718b2b0f4a6f2f331cb263ad1d9a97d4d041d9..0000000000000000000000000000000000000000 --- a/spaces/Ekitl02/stabilityai-stable-diffusion-xl-base-1.0/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Stabilityai Stable Diffusion Xl Base 1.0 -emoji: 📉 -colorFrom: yellow -colorTo: pink -sdk: gradio -sdk_version: 3.40.1 -app_file: app.py -pinned: false -license: artistic-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/EleutherAI/VQGAN_CLIP/taming-transformers/taming/modules/vqvae/quantize.py b/spaces/EleutherAI/VQGAN_CLIP/taming-transformers/taming/modules/vqvae/quantize.py deleted file mode 100644 index 9c8caffad7fd4e90b2b5c627dda60d4c9fc496de..0000000000000000000000000000000000000000 --- a/spaces/EleutherAI/VQGAN_CLIP/taming-transformers/taming/modules/vqvae/quantize.py +++ /dev/null @@ -1,329 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -from torch import einsum -from einops import rearrange - - -class VectorQuantizer(nn.Module): - """ - see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py - ____________________________________________ - Discretization bottleneck part of the VQ-VAE. - Inputs: - - n_e : number of embeddings - - e_dim : dimension of embedding - - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 - _____________________________________________ - """ - - # NOTE: this class contains a bug regarding beta; see VectorQuantizer2 for - # a fix and use legacy=False to apply that fix. VectorQuantizer2 can be - # used wherever VectorQuantizer has been used before and is additionally - # more efficient. - def __init__(self, n_e, e_dim, beta): - super(VectorQuantizer, self).__init__() - self.n_e = n_e - self.e_dim = e_dim - self.beta = beta - - self.embedding = nn.Embedding(self.n_e, self.e_dim) - self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) - - def forward(self, z): - """ - Inputs the output of the encoder network z and maps it to a discrete - one-hot vector that is the index of the closest embedding vector e_j - z (continuous) -> z_q (discrete) - z.shape = (batch, channel, height, width) - quantization pipeline: - 1. get encoder input (B,C,H,W) - 2. flatten input to (B*H*W,C) - """ - # reshape z -> (batch, height, width, channel) and flatten - z = z.permute(0, 2, 3, 1).contiguous() - z_flattened = z.view(-1, self.e_dim) - # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z - - d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ - torch.sum(self.embedding.weight**2, dim=1) - 2 * \ - torch.matmul(z_flattened, self.embedding.weight.t()) - - ## could possible replace this here - # #\start... - # find closest encodings - min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) - - min_encodings = torch.zeros( - min_encoding_indices.shape[0], self.n_e).to(z) - min_encodings.scatter_(1, min_encoding_indices, 1) - - # dtype min encodings: torch.float32 - # min_encodings shape: torch.Size([2048, 512]) - # min_encoding_indices.shape: torch.Size([2048, 1]) - - # get quantized latent vectors - z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) - #.........\end - - # with: - # .........\start - #min_encoding_indices = torch.argmin(d, dim=1) - #z_q = self.embedding(min_encoding_indices) - # ......\end......... (TODO) - - # compute loss for embedding - loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ - torch.mean((z_q - z.detach()) ** 2) - - # preserve gradients - z_q = z + (z_q - z).detach() - - # perplexity - e_mean = torch.mean(min_encodings, dim=0) - perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) - - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q, loss, (perplexity, min_encodings, min_encoding_indices) - - def get_codebook_entry(self, indices, shape): - # shape specifying (batch, height, width, channel) - # TODO: check for more easy handling with nn.Embedding - min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) - min_encodings.scatter_(1, indices[:,None], 1) - - # get quantized latent vectors - z_q = torch.matmul(min_encodings.float(), self.embedding.weight) - - if shape is not None: - z_q = z_q.view(shape) - - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q - - -class GumbelQuantize(nn.Module): - """ - credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!) - Gumbel Softmax trick quantizer - Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016 - https://arxiv.org/abs/1611.01144 - """ - def __init__(self, num_hiddens, embedding_dim, n_embed, straight_through=True, - kl_weight=5e-4, temp_init=1.0, use_vqinterface=True, - remap=None, unknown_index="random"): - super().__init__() - - self.embedding_dim = embedding_dim - self.n_embed = n_embed - - self.straight_through = straight_through - self.temperature = temp_init - self.kl_weight = kl_weight - - self.proj = nn.Conv2d(num_hiddens, n_embed, 1) - self.embed = nn.Embedding(n_embed, embedding_dim) - - self.use_vqinterface = use_vqinterface - - self.remap = remap - if self.remap is not None: - self.register_buffer("used", torch.tensor(np.load(self.remap))) - self.re_embed = self.used.shape[0] - self.unknown_index = unknown_index # "random" or "extra" or integer - if self.unknown_index == "extra": - self.unknown_index = self.re_embed - self.re_embed = self.re_embed+1 - print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. " - f"Using {self.unknown_index} for unknown indices.") - else: - self.re_embed = n_embed - - def remap_to_used(self, inds): - ishape = inds.shape - assert len(ishape)>1 - inds = inds.reshape(ishape[0],-1) - used = self.used.to(inds) - match = (inds[:,:,None]==used[None,None,...]).long() - new = match.argmax(-1) - unknown = match.sum(2)<1 - if self.unknown_index == "random": - new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) - else: - new[unknown] = self.unknown_index - return new.reshape(ishape) - - def unmap_to_all(self, inds): - ishape = inds.shape - assert len(ishape)>1 - inds = inds.reshape(ishape[0],-1) - used = self.used.to(inds) - if self.re_embed > self.used.shape[0]: # extra token - inds[inds>=self.used.shape[0]] = 0 # simply set to zero - back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) - return back.reshape(ishape) - - def forward(self, z, temp=None, return_logits=False): - # force hard = True when we are in eval mode, as we must quantize. actually, always true seems to work - hard = self.straight_through if self.training else True - temp = self.temperature if temp is None else temp - - logits = self.proj(z) - if self.remap is not None: - # continue only with used logits - full_zeros = torch.zeros_like(logits) - logits = logits[:,self.used,...] - - soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard) - if self.remap is not None: - # go back to all entries but unused set to zero - full_zeros[:,self.used,...] = soft_one_hot - soft_one_hot = full_zeros - z_q = einsum('b n h w, n d -> b d h w', soft_one_hot, self.embed.weight) - - # + kl divergence to the prior loss - qy = F.softmax(logits, dim=1) - diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean() - - ind = soft_one_hot.argmax(dim=1) - if self.remap is not None: - ind = self.remap_to_used(ind) - if self.use_vqinterface: - if return_logits: - return z_q, diff, (None, None, ind), logits - return z_q, diff, (None, None, ind) - return z_q, diff, ind - - def get_codebook_entry(self, indices, shape): - b, h, w, c = shape - assert b*h*w == indices.shape[0] - indices = rearrange(indices, '(b h w) -> b h w', b=b, h=h, w=w) - if self.remap is not None: - indices = self.unmap_to_all(indices) - one_hot = F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float() - z_q = einsum('b n h w, n d -> b d h w', one_hot, self.embed.weight) - return z_q - - -class VectorQuantizer2(nn.Module): - """ - Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly - avoids costly matrix multiplications and allows for post-hoc remapping of indices. - """ - # NOTE: due to a bug the beta term was applied to the wrong term. for - # backwards compatibility we use the buggy version by default, but you can - # specify legacy=False to fix it. - def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", - sane_index_shape=False, legacy=True): - super().__init__() - self.n_e = n_e - self.e_dim = e_dim - self.beta = beta - self.legacy = legacy - - self.embedding = nn.Embedding(self.n_e, self.e_dim) - self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) - - self.remap = remap - if self.remap is not None: - self.register_buffer("used", torch.tensor(np.load(self.remap))) - self.re_embed = self.used.shape[0] - self.unknown_index = unknown_index # "random" or "extra" or integer - if self.unknown_index == "extra": - self.unknown_index = self.re_embed - self.re_embed = self.re_embed+1 - print(f"Remapping {self.n_e} indices to {self.re_embed} indices. " - f"Using {self.unknown_index} for unknown indices.") - else: - self.re_embed = n_e - - self.sane_index_shape = sane_index_shape - - def remap_to_used(self, inds): - ishape = inds.shape - assert len(ishape)>1 - inds = inds.reshape(ishape[0],-1) - used = self.used.to(inds) - match = (inds[:,:,None]==used[None,None,...]).long() - new = match.argmax(-1) - unknown = match.sum(2)<1 - if self.unknown_index == "random": - new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) - else: - new[unknown] = self.unknown_index - return new.reshape(ishape) - - def unmap_to_all(self, inds): - ishape = inds.shape - assert len(ishape)>1 - inds = inds.reshape(ishape[0],-1) - used = self.used.to(inds) - if self.re_embed > self.used.shape[0]: # extra token - inds[inds>=self.used.shape[0]] = 0 # simply set to zero - back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) - return back.reshape(ishape) - - def forward(self, z, temp=None, rescale_logits=False, return_logits=False): - assert temp is None or temp==1.0, "Only for interface compatible with Gumbel" - assert rescale_logits==False, "Only for interface compatible with Gumbel" - assert return_logits==False, "Only for interface compatible with Gumbel" - # reshape z -> (batch, height, width, channel) and flatten - z = rearrange(z, 'b c h w -> b h w c').contiguous() - z_flattened = z.view(-1, self.e_dim) - # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z - - d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ - torch.sum(self.embedding.weight**2, dim=1) - 2 * \ - torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n')) - - min_encoding_indices = torch.argmin(d, dim=1) - z_q = self.embedding(min_encoding_indices).view(z.shape) - perplexity = None - min_encodings = None - - # compute loss for embedding - if not self.legacy: - loss = self.beta * torch.mean((z_q.detach()-z)**2) + \ - torch.mean((z_q - z.detach()) ** 2) - else: - loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ - torch.mean((z_q - z.detach()) ** 2) - - # preserve gradients - z_q = z + (z_q - z).detach() - - # reshape back to match original input shape - z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous() - - if self.remap is not None: - min_encoding_indices = min_encoding_indices.reshape(z.shape[0],-1) # add batch axis - min_encoding_indices = self.remap_to_used(min_encoding_indices) - min_encoding_indices = min_encoding_indices.reshape(-1,1) # flatten - - if self.sane_index_shape: - min_encoding_indices = min_encoding_indices.reshape( - z_q.shape[0], z_q.shape[2], z_q.shape[3]) - - return z_q, loss, (perplexity, min_encodings, min_encoding_indices) - - def get_codebook_entry(self, indices, shape): - # shape specifying (batch, height, width, channel) - if self.remap is not None: - indices = indices.reshape(shape[0],-1) # add batch axis - indices = self.unmap_to_all(indices) - indices = indices.reshape(-1) # flatten again - - # get quantized latent vectors - z_q = self.embedding(indices) - - if shape is not None: - z_q = z_q.view(shape) - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q diff --git a/spaces/Epitech/Money-Recognition/app.py b/spaces/Epitech/Money-Recognition/app.py deleted file mode 100644 index a786afd70d20ca8bb9065b69fab87ef4a298a4b4..0000000000000000000000000000000000000000 --- a/spaces/Epitech/Money-Recognition/app.py +++ /dev/null @@ -1,134 +0,0 @@ -import os -import cv2 -import numpy as np -from collections import Counter -from time import time -import tkinter.filedialog -from tkinter import * -import sys -import gradio as gr - -def k_nearest_neighbors(predict, k): - distances = [] - for image in training_data: - distances.append([np.linalg.norm(image[0] - predict), image[1]]) # calcul de distance euclidienne - distances.sort() - votes = [i[1] for i in distances[:k]] - votes = ''.join(str(e) for e in votes) - votes = votes.replace(',', '') - votes = votes.replace(' ', '') - result = Counter(votes).most_common(1)[0][0] - return result - - -def test(): - start = time() - correct = 0 - total = 0 - skipped = 0 - for i in range(len(x_test)+1): - try: - prediction = k_nearest_neighbors(x_test[i], 5) - if int(prediction) == y_test[i]: - correct += 1 - total += 1 - except Exception as e: - print('An exception occured') - skipped += 1 - accuracy = correct/total - end = time() - print(end-start) - print(accuracy) - -def ia_handler(image): - pred = k_nearest_neighbors(img, 10) - if pred == 0: - return 'It\'s a coin' - return 'It\'s a banknote' - -def main(): - if len(sys.argv) > 1 and sys.argv[1] == '--cli': - root = Tk() - root.withdraw() - root.update() - filename = tkinter.filedialog.askopenfilename(title="Ouvrir fichier", filetypes=[('all files', '.*')]) # sélectionner la photo - src = cv2.imread(cv2.samples.findFile(filename), cv2.IMREAD_COLOR) # charger la photo - root.destroy() - img = resize_img(src) - pred = k_nearest_neighbors(img, 10) - if pred == '0': - print('Coin') - else: - print('Banknote') - else: - iface = gr.Interface(fn=ia_handler, inputs="image", outputs="text") - iface.launch() - - -def resize_img(img): - dim = (150, 150) - new_img = cv2.resize(img, dim) - return new_img - -if __name__=="__main__": - coin_datadir_train = '../coins-dataset/classified/train' - coin_datadir_test = '../coins-dataset/classified/test' - note_datadir_train = '../banknote-dataset/classified/train' - note_datadir_test = '../banknote-dataset/classified/test' - - categories = ['1c', '2c', '5c', '10c', '20c', '50c', '1e', '2e', '5e', '10e', '20e', '50e'] - coin_index = 8 - - training_data = [] - - for category in categories[:coin_index]: - path = os.path.join(coin_datadir_train, category) - label = 0 - for img in os.listdir(path): - img_array = cv2.imread(os.path.join(path, img)) - training_data.append([img_array, label]) - - for category in categories[coin_index:]: - path = os.path.join(note_datadir_train, category) - label = 1 - for img in os.listdir(path): - img_array = resize_img(cv2.imread(os.path.join(path, img))) - training_data.append([img_array, label]) - - - testing_data = [] - - for category in categories[:coin_index]: - path = os.path.join(coin_datadir_test, category) - label = 0 - for img in os.listdir(path): - img_array = cv2.imread(os.path.join(path, img)) - testing_data.append([img_array, label]) - - for category in categories[coin_index:]: - path = os.path.join(note_datadir_test, category) - label = 1 - for img in os.listdir(path): - img_array = resize_img(cv2.imread(os.path.join(path, img))) - testing_data.append([img_array, label]) - - - x_train = [] - y_train = [] - - for features, label in training_data: - x_train.append(features) - y_train.append(label) - - x_train = np.array(x_train) - - - x_test = [] - y_test = [] - - for features, label in testing_data: - x_test.append(features) - y_test.append(label) - - x_test = np.array(x_test) - main() diff --git a/spaces/Faridmaruf/RVCV2MODEL/config.py b/spaces/Faridmaruf/RVCV2MODEL/config.py deleted file mode 100644 index 6797dd748da4a2ddf57a97fd80ce9776d98ac82e..0000000000000000000000000000000000000000 --- a/spaces/Faridmaruf/RVCV2MODEL/config.py +++ /dev/null @@ -1,99 +0,0 @@ -import argparse -import sys -import torch -from multiprocessing import cpu_count - -class Config: - def __init__(self): - self.device = "cuda:0" - self.is_half = True - self.n_cpu = 0 - self.gpu_name = None - self.gpu_mem = None - ( - self.colab, - self.api, - self.unsupported - ) = self.arg_parse() - self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() - - @staticmethod - def arg_parse() -> tuple: - parser = argparse.ArgumentParser() - parser.add_argument("--colab", action="store_true", help="Launch in colab") - parser.add_argument("--api", action="store_true", help="Launch with api") - parser.add_argument("--unsupported", action="store_true", help="Enable unsupported feature") - cmd_opts = parser.parse_args() - - return ( - cmd_opts.colab, - cmd_opts.api, - cmd_opts.unsupported - ) - - # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+. - # check `getattr` and try it for compatibility - @staticmethod - def has_mps() -> bool: - if not torch.backends.mps.is_available(): - return False - try: - torch.zeros(1).to(torch.device("mps")) - return True - except Exception: - return False - - def device_config(self) -> tuple: - if torch.cuda.is_available(): - i_device = int(self.device.split(":")[-1]) - self.gpu_name = torch.cuda.get_device_name(i_device) - if ( - ("16" in self.gpu_name and "V100" not in self.gpu_name.upper()) - or "P40" in self.gpu_name.upper() - or "1060" in self.gpu_name - or "1070" in self.gpu_name - or "1080" in self.gpu_name - ): - print("INFO: Found GPU", self.gpu_name, ", force to fp32") - self.is_half = False - else: - print("INFO: Found GPU", self.gpu_name) - self.gpu_mem = int( - torch.cuda.get_device_properties(i_device).total_memory - / 1024 - / 1024 - / 1024 - + 0.4 - ) - elif self.has_mps(): - print("INFO: No supported Nvidia GPU found, use MPS instead") - self.device = "mps" - self.is_half = False - else: - print("INFO: No supported Nvidia GPU found, use CPU instead") - self.device = "cpu" - self.is_half = False - - if self.n_cpu == 0: - self.n_cpu = cpu_count() - - if self.is_half: - # 6G显存配置 - x_pad = 3 - x_query = 10 - x_center = 60 - x_max = 65 - else: - # 5G显存配置 - x_pad = 1 - x_query = 6 - x_center = 38 - x_max = 41 - - if self.gpu_mem != None and self.gpu_mem <= 4: - x_pad = 1 - x_query = 5 - x_center = 30 - x_max = 32 - - return x_pad, x_query, x_center, x_max diff --git a/spaces/FrankZxShen/so-vits-svc-models-ba/inference/__init__.py b/spaces/FrankZxShen/so-vits-svc-models-ba/inference/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/logger/__init__.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/logger/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Freiburg-AI-Research/dermoscopic_image_generation/glide_text2im/clip/__init__.py b/spaces/Freiburg-AI-Research/dermoscopic_image_generation/glide_text2im/clip/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/FritsLyneborg/kunstnerfrits/tools/train/train.py b/spaces/FritsLyneborg/kunstnerfrits/tools/train/train.py deleted file mode 100644 index 3e22d31d88f865d5db0d2e9fb5757d723d6f1c96..0000000000000000000000000000000000000000 --- a/spaces/FritsLyneborg/kunstnerfrits/tools/train/train.py +++ /dev/null @@ -1,1436 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 -# Copyright 2021-2022 The HuggingFace & DALL·E Mini 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. -""" -Training DALL·E Mini. -Script adapted from run_summarization_flax.py -""" - -import io -import logging -import os -import sys -import tempfile -import time -from dataclasses import asdict, dataclass, field -from pathlib import Path -from typing import Any, Callable, NamedTuple, Optional - -import datasets -import flax -import jax -import jax.numpy as jnp -import jaxlib -import numpy as np -import optax -import transformers -import wandb -from datasets import Dataset -from flax.core.frozen_dict import FrozenDict, freeze, unfreeze -from flax.serialization import from_bytes, to_bytes -from flax.training import train_state -from flax.training.common_utils import onehot -from jax.experimental import PartitionSpec, maps -from jax.experimental.compilation_cache import compilation_cache as cc -from jax.experimental.pjit import pjit, with_sharding_constraint -from scalable_shampoo.distributed_shampoo import GraftingType, distributed_shampoo -from tqdm import tqdm -from transformers import HfArgumentParser - -import dalle_mini -from dalle_mini.data import Dataset -from dalle_mini.model import ( - DalleBart, - DalleBartConfig, - DalleBartTokenizer, - set_partitions, -) - -try: - from google.cloud import storage -except: - storage = None - -cc.initialize_cache("./jax_cache", max_cache_size_bytes=10 * 2**30) - -logger = logging.getLogger(__name__) - - -@dataclass -class ModelArguments: - """ - Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. - """ - - model_name_or_path: Optional[str] = field( - default=None, - metadata={ - "help": "The model checkpoint for weights initialization. " - "Don't set if you want to train a model from scratch. " - "W&B artifact references are supported in addition to the sources supported by `PreTrainedModel`." - }, - ) - config_name: Optional[str] = field( - default=None, - metadata={ - "help": "Pretrained config name or path if not the same as model_name_or_path" - }, - ) - tokenizer_name: Optional[str] = field( - default=None, - metadata={ - "help": "Pretrained tokenizer name or path if not the same as model_name_or_path" - }, - ) - dtype: Optional[str] = field( - default="float32", - metadata={ - "help": "Floating-point format in which the computations will be performed (not the model weights). Choose one of `[float32, float16, bfloat16]`." - }, - ) - restore_state: Optional[bool] = field( - default=False, - metadata={ - "help": "Restore optimizer and training state. Can be True (will retrieve associated wandb artifact), a local directory or a Google bucket path." - }, - ) - - def __post_init__(self): - if self.tokenizer_name is None: - self.tokenizer_name = self.model_name_or_path - assert ( - self.tokenizer_name is not None - ), "Tokenizer name or model name/path needs to be specified" - if self.restore_state: - assert self.model_name_or_path is not None and ( - "/model-" in self.model_name_or_path - ), "Restoring state only available with W&B artifact reference" - - def get_metadata(self): - if self.restore_state: - if jax.process_index() == 0: - artifact = wandb.run.use_artifact(self.model_name_or_path) - else: - artifact = wandb.Api().artifact(self.model_name_or_path) - return artifact.metadata - else: - return dict() - - def get_opt_state(self): - with tempfile.TemporaryDirectory() as tmp_dir: # avoid multiple artifact copies - if self.restore_state is True: - # wandb artifact - state_artifact = self.model_name_or_path.replace( - "/model-", "/state-", 1 - ) - if jax.process_index() == 0: - artifact = wandb.run.use_artifact(state_artifact) - else: - artifact = wandb.Api().artifact(state_artifact) - if artifact.metadata.get("bucket_path"): - # we will read directly file contents - self.restore_state = artifact.metadata["bucket_path"] - else: - artifact_dir = artifact.download(tmp_dir) - self.restore_state = str(Path(artifact_dir) / "opt_state.msgpack") - - if self.restore_state.startswith("gs://"): - bucket_path = Path(self.restore_state[5:]) / "opt_state.msgpack" - bucket, blob_name = str(bucket_path).split("/", 1) - assert ( - storage is not None - ), 'Could not find google.storage. Install with "pip install google-cloud-storage"' - client = storage.Client() - bucket = client.bucket(bucket) - blob = bucket.blob(blob_name) - return blob.download_as_bytes() - - with Path(self.restore_state).open("rb") as f: - return f.read() - - -@dataclass -class DataTrainingArguments: - """ - Arguments pertaining to what data we are going to input our model for training and eval. - """ - - text_column: Optional[str] = field( - default="caption", - metadata={ - "help": "The name of the column in the datasets containing the full texts (for summarization)." - }, - ) - encoding_column: Optional[str] = field( - default="encoding", - metadata={ - "help": "The name of the column in the datasets containing the image encodings." - }, - ) - dataset_repo_or_path: str = field( - default=None, - metadata={"help": "The dataset repository containing encoded files."}, - ) - train_file: Optional[str] = field( - default=None, - metadata={ - "help": "The input training data file (glob & braceexpand acceptable)." - }, - ) - validation_file: Optional[str] = field( - default=None, - metadata={ - "help": "An optional input evaluation data file (glob & braceexpand acceptable)." - }, - ) - # data loading should not be a bottleneck so we use "streaming" mode by default - streaming: Optional[bool] = field( - default=True, - metadata={"help": "Whether to stream the dataset."}, - ) - use_auth_token: Optional[bool] = field( - default=False, - metadata={ - "help": "Whether to use the authentication token for private datasets." - }, - ) - shard_by_host: Optional[bool] = field( - default=False, - metadata={ - "help": "Whether to shard data files by host in multi-host environments." - }, - ) - blank_caption_prob: Optional[float] = field( - default=0.0, - metadata={ - "help": "Probability of removing some captions for classifier-free guidance." - }, - ) - clip_score_column: Optional[str] = field( - default="clip_score", - metadata={"help": "Column that containts clip score for filtering."}, - ) - min_clip_score: Optional[float] = field( - default=None, - metadata={"help": "Minimum clip score required."}, - ) - max_clip_score: Optional[float] = field( - default=None, - metadata={"help": "Maximum clip score required."}, - ) - filter_column: Optional[str] = field( - default=None, - metadata={"help": "Column that containts classes to be filtered."}, - ) - filter_value: Optional[str] = field( - default=None, - metadata={"help": "Class value to be kept during filtering."}, - ) - max_train_samples: Optional[int] = field( - default=None, - metadata={ - "help": "For debugging purposes or quicker training, truncate the number of training examples." - }, - ) - max_eval_samples: Optional[int] = field( - default=None, - metadata={ - "help": "For debugging purposes or quicker training, truncate the number of evaluation examples." - }, - ) - preprocessing_num_workers: Optional[int] = field( - default=None, - metadata={ - "help": "The number of processes to use for the preprocessing. Not used in streaming mode." - }, - ) - overwrite_cache: bool = field( - default=False, - metadata={ - "help": "Overwrite the cached training and evaluation sets. Not used in streaming mode." - }, - ) - # default seed of None ensures we don't repeat the same items if script was interrupted during an epoch - seed_dataset: int = field( - default=None, - metadata={ - "help": "Random seed for the dataset that will be set at the beginning of training." - }, - ) - - def __post_init__(self): - if self.dataset_repo_or_path is None: - raise ValueError("Need a dataset repository or path.") - - -@dataclass -class TrainingArguments: - """ - Arguments pertaining to training parameters. - """ - - output_dir: str = field( - metadata={ - "help": "The output directory where the model predictions and checkpoints will be written." - }, - ) - overwrite_output_dir: bool = field( - default=False, - metadata={ - "help": ( - "Overwrite the content of the output directory. " - "Use this to continue training if output_dir points to a checkpoint directory." - ) - }, - ) - - do_train: bool = field(default=False, metadata={"help": "Whether to run training."}) - do_eval: bool = field( - default=False, metadata={"help": "Whether to run eval on the validation set."} - ) - - per_device_train_batch_size: int = field( - default=8, - metadata={"help": "Batch size per data parallel device for training."}, - ) - per_device_eval_batch_size: Optional[int] = field( - default=None, - metadata={ - "help": "Batch size per data parallel device for evaluation. Same as training batch size if not set." - }, - ) - - gradient_accumulation_steps: int = field( - default=1, - metadata={ - "help": "Number of updates steps to accumulate before performing an update pass." - }, - ) - gradient_checkpointing: bool = field( - default=False, metadata={"help": "Use gradient checkpointing."} - ) - - learning_rate: float = field( - default=5e-5, metadata={"help": "The initial learning rate."} - ) - optim: str = field( - default="distributed_shampoo", - metadata={ - "help": 'The optimizer to use. Can be "distributed_shampoo" (default), "adam" or "adafactor"' - }, - ) - beta1: float = field( - default=0.9, - metadata={"help": "Beta1 for Adam & Distributed Shampoo."}, - ) - beta2: float = field( - default=0.999, - metadata={"help": "Beta2 for for Adam & Distributed Shampoo."}, - ) - adam_epsilon: float = field( - default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."} - ) - max_grad_norm: float = field( - default=1.0, metadata={"help": "Max gradient norm for Adafactor."} - ) - block_size: int = field( - default=1024, - metadata={"help": "Chunked size for large layers with Distributed Shampoo."}, - ) - preconditioning_compute_steps: int = field( - default=10, metadata={"help": "Number of steps to update preconditioner."} - ) - skip_preconditioning_dim_size_gt: int = field( - default=4096, - metadata={"help": "Max size for preconditioning with Distributed Shampoo."}, - ) - graft_type: str = field( - default="rmsprop_normalized", - metadata={ - "help": "The type of grafting to use. Can be 'rmsprop_normalized' (default), 'rmsprop', 'adagrad', 'adagrad_normalized', 'sgd' or 'sqrt_n'" - }, - ) - optim_quantized: bool = field( - default=False, - metadata={ - "help": "Whether to quantize optimizer (only supported with Distributed Shampoo)." - }, - ) - - num_train_epochs: int = field( - default=3, metadata={"help": "Total number of training epochs to perform."} - ) - - warmup_steps: int = field( - default=0, metadata={"help": "Linear warmup over warmup_steps."} - ) - lr_decay: str = field( - default=None, - metadata={ - "help": "Decay to be used in the learning rate scheduler. Can be None (default), linear or exponential." - }, - ) - lr_transition_steps: int = field( - default=None, - metadata={ - "help": "Number of transition steps associated with learning rate decay when using exponential decay." - }, - ) - lr_decay_rate: float = field( - default=None, - metadata={ - "help": "Decay rate associated with learning rate when using exponential decay." - }, - ) - lr_staircase: bool = field( - default=False, - metadata={ - "help": "Whether to use staircase or continuous learning rate when using exponential decay." - }, - ) - - logging_steps: int = field( - default=40, metadata={"help": "Log every X updates steps."} - ) - eval_steps: int = field( - default=400, metadata={"help": "Run an evaluation every X steps."} - ) - save_steps: int = field( - default=4000, metadata={"help": "Save checkpoint every X updates steps."} - ) - log_model: bool = field( - default=False, - metadata={"help": "Log model to wandb at `save_steps` frequency."}, - ) - log_norm_steps: int = field( - default=True, - metadata={"help": "Log parameters and gradients norm at this frequency."}, - ) - log_histogram_steps: int = field( - default=False, - metadata={ - "help": "Log parameters and gradients histograms at this frequency. Slows down training." - }, - ) - - seed_model: int = field( - default=42, - metadata={ - "help": "Random seed for the model that will be set at the beginning of training." - }, - ) - - wandb_entity: Optional[str] = field( - default=None, - metadata={"help": "The wandb entity to use (for teams)."}, - ) - wandb_project: str = field( - default="dalle-mini", - metadata={"help": "The name of the wandb project."}, - ) - wandb_job_type: str = field( - default="Seq2Seq", - metadata={"help": "The name of the wandb job type."}, - ) - - assert_TPU_available: bool = field( - default=False, - metadata={"help": "Verify that TPU is not in use."}, - ) - - mp_devices: Optional[int] = field( - default=1, - metadata={ - "help": "Number of devices required for model parallelism. The other dimension of available devices is used for data parallelism." - }, - ) - - dp_devices: int = field(init=False) - - def __post_init__(self): - if self.assert_TPU_available: - assert ( - jax.local_device_count() == 8 - ), "TPUs in use, please check running processes" - if self.output_dir.startswith("gs://"): - assert ( - storage is not None - ), 'Could not find google.storage. Install with "pip install google-cloud-storage"' - assert self.optim in [ - "distributed_shampoo", - "adam", - "adafactor", - ], f"Selected optimizer not supported: {self.optim}" - assert self.graft_type in [ - "rmsprop_normalized", - "rmsprop", - "adagrad", - "adagrad_normalized", - "sgd", - "sqrt_n", - ], f"Selected graft type not supported: {self.graft_type}" - assert self.lr_decay in [ - None, - "linear", - "exponential", - ], f"Selected learning rate decay not supported: {self.lr_decay}" - if self.per_device_eval_batch_size is None: - self.per_device_eval_batch_size = self.per_device_train_batch_size - if self.log_norm_steps is True: - self.log_norm_steps = self.logging_steps - if ( - os.path.exists(self.output_dir) - and os.listdir(self.output_dir) - and self.do_train - and not self.overwrite_output_dir - ): - raise ValueError( - f"Output directory ({self.output_dir}) already exists and is not empty." - "Use --overwrite_output_dir to overcome." - ) - assert ( - self.mp_devices > 0 - ), f"Number of devices for model parallelism must be > 0" - assert ( - jax.device_count() % self.mp_devices == 0 - ), f"Number of available devices ({jax.device_count()} must be divisible by number of devices used for model parallelism ({self.mp_devices})." - self.dp_devices = jax.device_count() // self.mp_devices - - -class TrainState(train_state.TrainState): - dropout_rng: jnp.ndarray = None - epoch: int = 0 - train_time: float = 0.0 # total time the model trained - train_samples: int = 0 # number of samples seen - - -def main(): - # See all possible arguments by passing the --help flag to this script. - parser = HfArgumentParser( - (ModelArguments, DataTrainingArguments, TrainingArguments) - ) - if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): - # If we pass only one argument to the script and it's the path to a json file, - # let's parse it to get our arguments. - model_args, data_args, training_args = parser.parse_json_file( - json_file=os.path.abspath(sys.argv[1]) - ) - else: - model_args, data_args, training_args = parser.parse_args_into_dataclasses() - - # Make one log on every process with the configuration for debugging. - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO, - ) - # Setup logging, we only want one process per machine to log things on the screen. - logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR) - if jax.process_index() == 0: - datasets.utils.logging.set_verbosity_warning() - transformers.utils.logging.set_verbosity_info() - else: - datasets.utils.logging.set_verbosity_error() - transformers.utils.logging.set_verbosity_error() - - # Set the verbosity to info of the Transformers logger (on main process only): - logger.info(f"Training/evaluation parameters {training_args}") - - # Load dataset - dataset = Dataset( - **asdict(data_args), - do_train=training_args.do_train, - do_eval=training_args.do_eval, - ) - - logger.info(f"Local TPUs: {jax.local_device_count()}") - logger.info(f"Global TPUs: {jax.device_count()}") - - # Set up wandb run - if jax.process_index() == 0: - wandb.init( - entity=training_args.wandb_entity, - project=training_args.wandb_project, - job_type=training_args.wandb_job_type, - config=parser.parse_args(), - ) - - # Set up our new model config - if model_args.config_name: - config = DalleBartConfig.from_pretrained(model_args.config_name) - config.gradient_checkpointing = training_args.gradient_checkpointing - else: - config = None - - # Load or create new model - if model_args.model_name_or_path: - model = DalleBart.from_pretrained( - model_args.model_name_or_path, - config=config, - seed=training_args.seed_model, - dtype=getattr(jnp, model_args.dtype), - abstract_init=True, # we overwrite them with loaded checkpoint - gradient_checkpointing=training_args.gradient_checkpointing, - ) - else: - model = DalleBart( - config, - seed=training_args.seed_model, - dtype=getattr(jnp, model_args.dtype), - abstract_init=True, - ) - - # get model metadata - model_metadata = model_args.get_metadata() - - # get PartitionSpec for model params (required to be a dict) - param_spec = set_partitions(model.params) - - # convert params to frozen dict - model._params = freeze(model.params) - - # Load tokenizer - tokenizer = DalleBartTokenizer.from_pretrained( - model_args.tokenizer_name, use_fast=True - ) - - # Preprocessing the datasets. - # We need to normalize and tokenize inputs and targets. - dataset.preprocess(tokenizer=tokenizer, config=model.config) - - # Initialize our training - dropout_rng = jax.random.PRNGKey(training_args.seed_model) - - # Store some constant - num_epochs = training_args.num_train_epochs - # batch size - batch_size_per_node_per_grad_step = ( - training_args.per_device_train_batch_size - * jax.local_device_count() - // training_args.mp_devices - ) - batch_size_per_node = ( - batch_size_per_node_per_grad_step * training_args.gradient_accumulation_steps - ) - batch_size_per_step = batch_size_per_node * jax.process_count() - eval_batch_size_per_node = ( - training_args.per_device_eval_batch_size - * jax.local_device_count() - // training_args.mp_devices - ) - eval_batch_size_per_step = eval_batch_size_per_node * jax.process_count() - len_train_dataset, len_eval_dataset = dataset.length - steps_per_epoch = ( - len_train_dataset // batch_size_per_node - if len_train_dataset is not None - else None - ) - num_train_steps = ( - steps_per_epoch * num_epochs if steps_per_epoch is not None else None - ) - num_params = model.num_params - - logger.info("***** Running training *****") - logger.info(f" Num examples = {len_train_dataset}") - logger.info(f" Num Epochs = {num_epochs}") - logger.info( - f" Batch size per dp device = {training_args.per_device_train_batch_size}" - ) - logger.info(f" Number of devices = {jax.device_count()}") - logger.info( - f" Gradient accumulation steps = {training_args.gradient_accumulation_steps}" - ) - logger.info(f" Batch size per update = {batch_size_per_step}") - logger.info(f" Model parameters = {num_params:,}") - - # set up wandb run - if jax.process_index() == 0: - # set default x-axis as 'train/step' - wandb.define_metric("*", step_metric="train/step") - - # add interesting config parameters - wandb.config.update( - { - "len_train_dataset": len_train_dataset, - "len_eval_dataset": len_eval_dataset, - "batch_size_per_step": batch_size_per_step, - "num_params": num_params, - "model_config": model.config.to_dict(), - "num_devices": jax.device_count(), - "versions": { - "jax": jax.__version__, - "jaxlib": jaxlib.__version__, - "flax": flax.__version__, - "transformers": transformers.__version__, - "datasets": datasets.__version__, - "wandb": wandb.__version__, - "dalle_mini": dalle_mini.__version__, - }, - } - ) - - # Create learning rate schedule - def create_learning_rate_fn() -> Callable[[int], jnp.array]: - """Create the learning rate function.""" - warmup_fn = optax.linear_schedule( - init_value=0.0, - end_value=training_args.learning_rate, - transition_steps=training_args.warmup_steps + 1, # ensure not 0 - ) - # offset step when resuming - if model_metadata.get("step", 0): - warmup_fn = optax.join_schedules( - schedules=[optax.constant_schedule(0.0), warmup_fn], - boundaries=[model_metadata["step"]], - ) - if training_args.lr_decay is None: - return warmup_fn - elif training_args.lr_decay == "linear": - assert ( - num_train_steps is not None - ), "linear decay requires knowing the dataset length" - decay_fn = optax.linear_schedule( - init_value=training_args.learning_rate, - end_value=0, - transition_steps=num_train_steps - training_args.warmup_steps, - ) - elif training_args.lr_decay == "exponential": - decay_fn = optax.exponential_decay( - init_value=training_args.learning_rate, - transition_steps=training_args.lr_transition_steps, - decay_rate=training_args.lr_decay_rate, - staircase=training_args.lr_staircase, - ) - schedule_fn = optax.join_schedules( - schedules=[warmup_fn, decay_fn], - boundaries=[model_metadata.get("step", 0) + training_args.warmup_steps], - ) - return schedule_fn - - learning_rate_fn = create_learning_rate_fn() - - # create adam optimizer - if training_args.optim == "distributed_shampoo": - # parameters from https://github.com/tensorflow/lingvo/blob/03ee9d7cd50764b0424c7c863733c91fc0b053ec/lingvo/jax/optimizers.py#L729 - graft_type = { - "sgd": GraftingType.SGD, - "adagrad": GraftingType.ADAGRAD, - "rmsprop": GraftingType.RMSPROP, - "rmsprop_normalized": GraftingType.RMSPROP_NORMALIZED, - "sqrt_n": GraftingType.SQRT_N, - "adagrad_normalized": GraftingType.ADAGRAD_NORMALIZED, - }[training_args.graft_type] - optimizer = distributed_shampoo( - learning_rate_fn, - block_size=training_args.block_size, - beta1=training_args.beta1, - beta2=training_args.beta2, - diagonal_epsilon=1e-10, - matrix_epsilon=1e-6, - start_preconditioning_step=max( - training_args.preconditioning_compute_steps + 1, 101 - ), - preconditioning_compute_steps=training_args.preconditioning_compute_steps, - statistics_compute_steps=1, - best_effort_shape_interpretation=True, - graft_type=graft_type, - nesterov=False, - exponent_override=0, - statistics_partition_spec=PartitionSpec(None, "dp", None), - preconditioner_partition_spec=PartitionSpec("dp", None, None), - num_devices_for_pjit=training_args.dp_devices, - shard_optimizer_states=True, - inverse_failure_threshold=0.1, - moving_average_for_momentum=True, - skip_preconditioning_dim_size_gt=training_args.skip_preconditioning_dim_size_gt, - clip_by_scaled_gradient_norm=None, - precision=jax.lax.Precision.HIGHEST, - best_effort_memory_usage_reduction=training_args.optim_quantized, - ) - # get the real optimizer and helper functions - update_fn = optimizer.update - optimizer = optimizer.init(model.params) - opt_fn = NamedTuple("opt_fn", pspec_fn=Any, shape_and_dtype_fn=Any)( - optimizer.pspec_fn, optimizer.shape_and_dtype_fn - ) - optimizer = optax.GradientTransformation(optimizer.init_fn, update_fn) - - elif training_args.optim == "adam": - optimizer = optax.adamw( - learning_rate=learning_rate_fn, - b1=training_args.beta1, - b2=training_args.beta2, - eps=training_args.adam_epsilon, - ) - elif training_args.optim == "adafactor": - # We use the default parameters here to initialize adafactor, - # For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74 - optimizer = optax.adafactor( - learning_rate=learning_rate_fn, - clipping_threshold=training_args.max_grad_norm, - ) - - # get PartitionSpec for optimizer state - def get_opt_state_spec_and_shape(param_spec): - # get opt_state shape without actual init - opt_state_shape = jax.eval_shape(optimizer.init, model.params) - - if training_args.optim == "adam": - - def _opt_state_spec_per_leaf(x): - if isinstance(x, FrozenDict): - # variables with same structure as params - return param_spec - else: - # other variables such as count - return None - - opt_state_spec = jax.tree_map( - _opt_state_spec_per_leaf, - opt_state_shape, - # return None spec for empty elements - is_leaf=lambda x: isinstance(x, (FrozenDict, optax.EmptyState)), - ) - - elif training_args.optim == "adafactor": - # factorized state must be replicated (rank different than params) - opt_state_spec = None - - elif training_args.optim == "distributed_shampoo": - opt_state_spec = opt_fn.pspec_fn( - params=model.params, - params_partition_spec=param_spec, - partition_spec_for_statistics=PartitionSpec(None, "dp", None), - ) - else: - raise NotImplementedError - return opt_state_spec, opt_state_shape - - opt_state_spec, opt_state_shape = get_opt_state_spec_and_shape(param_spec) - - # create a mesh - mesh_shape = (training_args.dp_devices, training_args.mp_devices) - devices = np.asarray(jax.devices()).reshape(*mesh_shape) - mesh = maps.Mesh(devices, ("dp", "mp")) - logger.info(f" Mesh shape: {mesh_shape}") - - # define state spec - state_spec = TrainState( - params=param_spec, - opt_state=opt_state_spec, - dropout_rng=None, - step=None, - epoch=None, - train_time=None, - train_samples=None, - apply_fn=model.__call__, - tx=optimizer, - ) - - # init params if not available yet - def maybe_init_params(params): - if model_args.model_name_or_path: - # model params are correctly loaded - return params - else: - # params have not been initialized yet - return model.init_weights() - - with mesh: - logger.info(" Creating state") - if not model_args.restore_state: - - def init_state(params): - return TrainState.create( - apply_fn=model.__call__, - tx=optimizer, - params=maybe_init_params(params), - dropout_rng=dropout_rng, - ) - - state = pjit( - init_state, - in_axis_resources=(param_spec,) - if model_args.model_name_or_path - else None, - out_axis_resources=state_spec, - donate_argnums=(0,), - )(model.params if model_args.model_name_or_path else None) - - else: - # load opt_state - opt_state = from_bytes(opt_state_shape, model_args.get_opt_state()) - - # restore other attributes - attr_state = { - k: model_metadata[k] - for k in ["step", "epoch", "train_time", "train_samples"] - } - - def restore_state(params, opt_state): - return TrainState( - apply_fn=model.__call__, - tx=optimizer, - params=params, - opt_state=opt_state, - dropout_rng=dropout_rng, - **attr_state, - ) - - state = pjit( - restore_state, - in_axis_resources=( - param_spec, - opt_state_spec, - ), - out_axis_resources=state_spec, - donate_argnums=(0, 1), - )(model.params, opt_state) - - # remove opt_state from CPU - del opt_state - - # free CPU memory - del model._params, opt_state_spec, opt_state_shape - - # define batch specs - batch_spec = PartitionSpec("dp") - grad_batch_spec = PartitionSpec(None, "dp") - - # define loss - def loss_fn(logits, labels): - loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) - loss = loss.mean() - return loss - - # "vmap trick" avoids a crash when mp_devices > 1 (not sure why it happens) - # lead to better perf: see https://wandb.ai/dalle-mini/dalle-mini/reports/JAX-pmap-vs-pjit--VmlldzoxNDg1ODA2 - use_vmap_trick = True - - # make grad_param_spec for vmap - if use_vmap_trick: - grad_param_spec = jax.tree_map( - lambda x: PartitionSpec(*("dp",) + (x if x is not None else (None,))), - param_spec, - ) - - # Define gradient update step fn - def train_step(state, batch, train_time): - - # get a minibatch (one gradient accumulation slice) - def get_minibatch(batch, grad_idx): - return jax.tree_map( - lambda x: jax.lax.dynamic_index_in_dim(x, grad_idx, keepdims=False), - batch, - ) - - def compute_loss(params, minibatch, dropout_rng): - # minibatch has dim (batch_size, ...) - minibatch, labels = minibatch.pop("labels") - logits = state.apply_fn( - **minibatch, params=params, dropout_rng=dropout_rng, train=True - )[0] - return loss_fn(logits, labels) - - grad_fn = jax.value_and_grad(compute_loss) - - def loss_and_grad(grad_idx, dropout_rng): - # minibatch at grad_idx for gradient accumulation (None otherwise) - minibatch = ( - get_minibatch(batch, grad_idx) if grad_idx is not None else batch - ) - # ensure it is sharded properly - minibatch = with_sharding_constraint(minibatch, batch_spec) - # only 1 single rng per grad step, let us handle larger batch size (not sure why) - dropout_rng, _ = jax.random.split(dropout_rng) - - if use_vmap_trick: - # "vmap trick", calculate loss and grads independently per dp_device - loss, grads = jax.vmap( - grad_fn, in_axes=(None, 0, None), out_axes=(0, 0) - )(state.params, minibatch, dropout_rng) - # ensure they are sharded correctly - loss = with_sharding_constraint(loss, batch_spec) - grads = with_sharding_constraint(grads, grad_param_spec) - # average across all devices - # Note: we could average per device only after gradient accumulation, right before params update - loss, grads = jax.tree_map(lambda x: jnp.mean(x, axis=0), (loss, grads)) - else: - # "vmap trick" does not work in multi-hosts and requires too much hbm - loss, grads = grad_fn(state.params, minibatch, dropout_rng) - # ensure grads are sharded - grads = with_sharding_constraint(grads, param_spec) - # return loss and grads - return loss, grads, dropout_rng - - if training_args.gradient_accumulation_steps == 1: - loss, grads, dropout_rng = loss_and_grad(None, state.dropout_rng) - else: - # create initial state for cumul_minibatch_step loop - init_minibatch_step = ( - 0.0, - with_sharding_constraint( - jax.tree_map(jnp.zeros_like, state.params), param_spec - ), - state.dropout_rng, - ) - - # accumulate gradients - def cumul_minibatch_step(grad_idx, cumul_loss_grad_dropout): - cumul_loss, cumul_grads, dropout_rng = cumul_loss_grad_dropout - loss, grads, dropout_rng = loss_and_grad(grad_idx, dropout_rng) - cumul_loss, cumul_grads = jax.tree_map( - jnp.add, (cumul_loss, cumul_grads), (loss, grads) - ) - cumul_grads = with_sharding_constraint(cumul_grads, param_spec) - return cumul_loss, cumul_grads, dropout_rng - - # loop over gradients - loss, grads, dropout_rng = jax.lax.fori_loop( - 0, - training_args.gradient_accumulation_steps, - cumul_minibatch_step, - init_minibatch_step, - ) - grads = with_sharding_constraint(grads, param_spec) - # sum -> mean - loss, grads = jax.tree_map( - lambda x: x / training_args.gradient_accumulation_steps, (loss, grads) - ) - - grads = with_sharding_constraint(grads, param_spec) - - # update state - state = state.apply_gradients( - grads=grads, - dropout_rng=dropout_rng, - train_time=train_time, - train_samples=state.train_samples + batch_size_per_step, - ) - - metrics = { - "loss": loss, - "learning_rate": learning_rate_fn(state.step), - } - - def maybe_fn(fn, val, zeros, freq): - """Call fn only if it is a logging step""" - return jax.lax.cond( - state.step % freq == 0, - fn, - lambda _: zeros, - val, - ) - - if training_args.log_norm_steps: - zeros_norm = jax.tree_map(lambda _: jnp.float32(0), state.params) - - def norm(val): - return jax.tree_map(lambda x: jnp.linalg.norm(x), val) - - gradients_norm = maybe_fn( - norm, grads, zeros_norm, training_args.log_norm_steps - ) - params_norm = maybe_fn( - norm, state.params, zeros_norm, training_args.log_norm_steps - ) - - metrics.update( - { - "gradients_norm": gradients_norm, - "params_norm": params_norm, - } - ) - - if training_args.log_histogram_steps: - zeros_hist = jax.tree_map( - lambda _: jnp.histogram(jnp.zeros(1), density=True), state.params - ) - - def histogram(val): - return jax.tree_map(lambda x: jnp.histogram(x, density=True), val) - - gradients_hist = maybe_fn( - histogram, grads, zeros_hist, training_args.log_histogram_steps - ) - params_hist = maybe_fn( - histogram, state.params, zeros_hist, training_args.log_histogram_steps - ) - - metrics.update( - { - "params_hist": params_hist, - "gradients_hist": gradients_hist, - } - ) - - return state, metrics - - # Define eval fn - def eval_step(state, batch): - def compute_eval_loss(batch): - batch, labels = batch.pop("labels") - logits = model(**batch, params=state.params, train=False)[0] - return loss_fn(logits, labels) - - if use_vmap_trick: - loss = jax.vmap(compute_eval_loss)(batch) - # ensure they are sharded correctly - loss = with_sharding_constraint(loss, batch_spec) - # average across all devices - loss = jnp.mean(loss) - else: - loss = compute_eval_loss(batch) - - return loss - - # Create parallel version of the train and eval step - p_train_step = pjit( - train_step, - in_axis_resources=( - state_spec, - grad_batch_spec - if training_args.gradient_accumulation_steps > 1 - else batch_spec, - None, - ), - out_axis_resources=(state_spec, None), - donate_argnums=(0,), - ) - p_eval_step = pjit( - eval_step, - in_axis_resources=(state_spec, batch_spec), - out_axis_resources=None, - ) - - # define metrics logger - class MetricsLogger: - def __init__(self, step): - # keep state - self.state_dict = {} - # estimate speed - self.step = step - self.time = time.perf_counter() - self.offset_time = 0.0 - - def update_state_metrics(self, state): - """Update internal state metrics (logged at each call to be used as x-axis)""" - self.state_dict = { - f'train/{k.split("_")[-1]}': state[k] - for k in ["step", "epoch", "train_time", "train_samples"] - } - # timing metrics - new_step = int(state["step"]) - new_time = time.perf_counter() - if new_step > self.step: - # remove time for eval & save - delta_time = new_time - self.time - self.offset_time - self.offset_time = 0 - time_per_step = delta_time / (new_step - self.step) - self.step = new_step - self.time = new_time - self.log_time("train_per_step", time_per_step, offset=False) - self.log_time("train_per_log", delta_time, offset=False) - - def log_time(self, key, duration, offset=True): - wandb.log({f"time/{key}": duration, **self.state_dict}) - if offset: - self.offset_time += duration - - def log(self, metrics, prefix=None): - if jax.process_index() == 0: - log_metrics = {} - for k, v in metrics.items(): - if "_norm" in k: - if self.step % training_args.log_norm_steps == 0: - log_metrics[f"{k}/"] = unfreeze(v) - elif "_hist" in k: - if self.step % training_args.log_histogram_steps == 0: - v = jax.tree_map(lambda x: jax.device_get(x), unfreeze(v)) - v = jax.tree_map( - lambda x: wandb.Histogram(np_histogram=x), - v, - is_leaf=lambda x: isinstance(x, tuple), - ) - log_metrics[f"{k}/"] = v - else: - if prefix is not None: - k = f"{prefix}/{k}" - log_metrics[k] = v - wandb.log({**log_metrics, **self.state_dict}) - - # keep local copy of state - local_state = { - k: jax.device_get(getattr(state, k)).item() - for k in ["step", "epoch", "train_time", "train_samples"] - } - # init variables - start_time = time.perf_counter() - local_state["train_time"] - train_metrics = None - metrics_logger = MetricsLogger(local_state["step"]) - epochs = tqdm( - range(local_state["epoch"], num_epochs), - desc=f"Epoch ... (1/{num_epochs})", - position=0, - disable=jax.process_index() > 0, - ) - - def run_evaluation(): - # ======================== Evaluating ============================== - if training_args.do_eval: - start_eval_time = time.perf_counter() - eval_loader = dataset.dataloader("eval", eval_batch_size_per_step) - eval_steps = ( - len_eval_dataset // eval_batch_size_per_step - if len_eval_dataset is not None - else None - ) - eval_loss = [] - for batch in tqdm( - eval_loader, - desc="Evaluating...", - position=2, - leave=False, - total=eval_steps, - disable=jax.process_index() > 0, - ): - # need to keep only eval_batch_size_per_node items relevant to the node - batch = jax.tree_map( - lambda x: x.reshape( - (jax.process_count(), eval_batch_size_per_node) + x.shape[1:] - ), - batch, - ) - batch = jax.tree_map(lambda x: x[jax.process_index()], batch) - - # add dp dimension when using "vmap trick" - if use_vmap_trick: - bs_shape = ( - jax.local_device_count() // training_args.mp_devices, - training_args.per_device_eval_batch_size, - ) - batch = jax.tree_map( - lambda x: x.reshape(bs_shape + x.shape[1:]), batch - ) - - # freeze batch to pass safely to jax transforms - batch = freeze(batch) - # accumulate losses async - eval_loss.append(p_eval_step(state, batch)) - - # get the mean of the loss - eval_loss = jnp.stack(eval_loss) - eval_loss = jnp.mean(eval_loss) - eval_metrics = {"loss": eval_loss} - - # log metrics - metrics_logger.log(eval_metrics, prefix="eval") - metrics_logger.log_time("eval", time.perf_counter() - start_eval_time) - - # Print metrics and update progress bar - desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']})" - epochs.write(desc) - epochs.desc = desc - - return eval_metrics - - def run_save_model(state, eval_metrics=None): - if jax.process_index() == 0: - - start_save_time = time.perf_counter() - output_dir = training_args.output_dir - use_bucket = output_dir.startswith("gs://") - if use_bucket: - bucket_path = Path(output_dir[5:]) / wandb.run.id / f"step_{state.step}" - bucket, dir_path = str(bucket_path).split("/", 1) - tmp_dir = tempfile.TemporaryDirectory() - output_dir = tmp_dir.name - - # save model - params = jax.device_get(state.params) - model.save_pretrained( - output_dir, - params=params, - ) - - # save tokenizer - tokenizer.save_pretrained(output_dir) - - # copy to bucket - if use_bucket: - client = storage.Client() - bucket = client.bucket(bucket) - for filename in Path(output_dir).glob("*"): - blob_name = str(Path(dir_path) / "model" / filename.name) - blob = bucket.blob(blob_name) - blob.upload_from_filename(str(filename)) - tmp_dir.cleanup() - - # save state - opt_state = jax.device_get(state.opt_state) - if use_bucket: - blob_name = str(Path(dir_path) / "state" / "opt_state.msgpack") - blob = bucket.blob(blob_name) - blob.upload_from_file(io.BytesIO(to_bytes(opt_state))) - else: - with (Path(output_dir) / "opt_state.msgpack").open("wb") as f: - f.write(to_bytes(opt_state)) - - # save to W&B - if training_args.log_model: - # save some space - c = wandb.wandb_sdk.wandb_artifacts.get_artifacts_cache() - c.cleanup(wandb.util.from_human_size("20GB")) - - metadata = { - k: jax.device_get(getattr(state, k)).item() - for k in ["step", "epoch", "train_time", "train_samples"] - } - metadata["num_params"] = num_params - if eval_metrics is not None: - metadata["eval"] = eval_metrics - - # create model artifact - if use_bucket: - metadata["bucket_path"] = f"gs://{bucket_path}/model" - artifact = wandb.Artifact( - name=f"model-{wandb.run.id}", - type="DalleBart_model", - metadata=metadata, - ) - if use_bucket: - artifact.add_reference(metadata["bucket_path"]) - else: - for filename in [ - "config.json", - "flax_model.msgpack", - "merges.txt", - "special_tokens_map.json", - "tokenizer.json", - "tokenizer_config.json", - "vocab.json", - ]: - artifact.add_file( - f"{Path(training_args.output_dir) / filename}" - ) - wandb.run.log_artifact(artifact) - - # create state artifact - if use_bucket: - metadata["bucket_path"] = f"gs://{bucket_path}/state" - artifact_state = wandb.Artifact( - name=f"state-{wandb.run.id}", - type="DalleBart_state", - metadata=metadata, - ) - if use_bucket: - artifact_state.add_reference(metadata["bucket_path"]) - else: - artifact_state.add_file( - f"{Path(training_args.output_dir) / 'opt_state.msgpack'}" - ) - wandb.run.log_artifact(artifact_state) - metrics_logger.log_time("save_model", time.perf_counter() - start_save_time) - - logger.info(" Ready to start training") - with mesh: - for epoch in epochs: - state.replace(epoch=epoch) - local_state["epoch"] = epoch - # ======================== Training ================================ - metrics_logger.update_state_metrics(local_state) - metrics_logger.log({}) - - # Generate an epoch by shuffling sampling indices from the train dataset - train_loader = dataset.dataloader( - "train", - batch_size_per_node, - epoch, - ) - # train - for batch in tqdm( - train_loader, - desc="Training...", - position=1, - leave=False, - total=steps_per_epoch, - disable=jax.process_index() > 0, - ): - # calculate delta time (we have a lag of one step but it's ok) - train_time = time.perf_counter() - start_time - - # set correct shape to batch - # - add grad_step dim if gradient_accumulation_steps > 1 - # - split per dp device if not multi-host for vmap trick (does not work in multi-host) - bs_shape = ( - (batch_size_per_node_per_grad_step,) - if not use_vmap_trick - else ( - jax.local_device_count() - // training_args.mp_devices, # local dp devices - training_args.per_device_train_batch_size, - ) - ) - if training_args.gradient_accumulation_steps > 1: - # reshape data into (gradient_accumulation_steps, batch_per_node, ...) - # to avoid any data redistribution when sharding - bs_shape = (training_args.gradient_accumulation_steps,) + bs_shape - - # reshape batch - batch = jax.tree_map( - lambda x: x.reshape(bs_shape + x.shape[1:]), - batch, - ) - # freeze batch to pass safely to jax transforms - batch = freeze(batch) - - # train step - state, train_metrics = p_train_step(state, batch, train_time) - local_state["step"] += 1 - local_state["train_time"] = train_time - local_state["train_samples"] += batch_size_per_step - - if ( - local_state["step"] % training_args.logging_steps == 0 - and jax.process_index() == 0 - ): - metrics_logger.update_state_metrics(local_state) - metrics_logger.log(train_metrics, prefix="train") - - eval_metrics = None - if local_state["step"] % training_args.eval_steps == 0: - eval_metrics = run_evaluation() - - if local_state["step"] % training_args.save_steps == 0: - run_save_model(state, eval_metrics) - - # log final train metrics - if train_metrics is not None: - metrics_logger.update_state_metrics(state) - metrics_logger.log(train_metrics, prefix="train") - - epochs.write( - f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metrics['loss']}, Learning Rate: {train_metrics['learning_rate']})" - ) - - # Final evaluation - eval_metrics = run_evaluation() - - # save checkpoint after each epoch - run_save_model(state, eval_metrics) - - -if __name__ == "__main__": - main() diff --git a/spaces/FrozenWolf/Neural-Style-Transfer/loss_functions.py b/spaces/FrozenWolf/Neural-Style-Transfer/loss_functions.py deleted file mode 100644 index 20ec3ea1b072b973749c2b943918dca2308860cd..0000000000000000000000000000000000000000 --- a/spaces/FrozenWolf/Neural-Style-Transfer/loss_functions.py +++ /dev/null @@ -1,40 +0,0 @@ - -import torch -import torch.nn as nn -import torch.nn.functional as F - -class ContentLoss(nn.Module): - def __init__(self, target,): - super().__init__() - self.target = target.detach() - - def forward(self, input): - self.loss = F.mse_loss(input, self.target) - return input - - -class StyleLoss(nn.Module): - def __init__(self, target_feature): - super().__init__() - self.target = self.gram_matrix(target_feature).detach() - - def gram_matrix(self,input): - a, b, c, d = input.size() - features = input.view(a * b, c * d) - G = torch.mm(features, features.t()) - return G.div(a * b * c * d) - - def forward(self, input): - G = self.gram_matrix(input) - self.loss = F.mse_loss(G, self.target) - return input - - -class Normalization(nn.Module): - def __init__(self, mean, std): - super().__init__() - self.mean = torch.tensor(mean).view(-1, 1, 1) - self.std = torch.tensor(std).view(-1, 1, 1) - - def forward(self, img): - return (img - self.mean) / self.std \ No newline at end of file diff --git a/spaces/Fu-chiang/Bit-50-Glaucoma/README.md b/spaces/Fu-chiang/Bit-50-Glaucoma/README.md deleted file mode 100644 index 7d848c8a3bf25bbc549b8aab96aecd4c9ff6545d..0000000000000000000000000000000000000000 --- a/spaces/Fu-chiang/Bit-50-Glaucoma/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Bit 50 Glaucoma -emoji: 🐨 -colorFrom: blue -colorTo: green -sdk: gradio -sdk_version: 3.47.1 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Gradio-Blocks/StyleGAN-NADA/op/fused_act.py b/spaces/Gradio-Blocks/StyleGAN-NADA/op/fused_act.py deleted file mode 100644 index 8459d510d7b79684779dfe47f5b46d81c94b4a4d..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/StyleGAN-NADA/op/fused_act.py +++ /dev/null @@ -1,86 +0,0 @@ -import os - -import torch -from torch import nn -from torch.autograd import Function -from torch.utils.cpp_extension import load - - -module_path = os.path.dirname(__file__) -fused = load( - 'fused', - sources=[ - os.path.join(module_path, 'fused_bias_act.cpp'), - os.path.join(module_path, 'fused_bias_act_kernel.cu'), - ], -) - - -class FusedLeakyReLUFunctionBackward(Function): - @staticmethod - def forward(ctx, grad_output, out, negative_slope, scale): - ctx.save_for_backward(out) - ctx.negative_slope = negative_slope - ctx.scale = scale - - empty = grad_output.new_empty(0) - - grad_input = fused.fused_bias_act( - grad_output, empty, out, 3, 1, negative_slope, scale - ) - - dim = [0] - - if grad_input.ndim > 2: - dim += list(range(2, grad_input.ndim)) - - grad_bias = grad_input.sum(dim).detach() - - return grad_input, grad_bias - - @staticmethod - def backward(ctx, gradgrad_input, gradgrad_bias): - out, = ctx.saved_tensors - gradgrad_out = fused.fused_bias_act( - gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale - ) - - return gradgrad_out, None, None, None - - -class FusedLeakyReLUFunction(Function): - @staticmethod - def forward(ctx, input, bias, negative_slope, scale): - empty = input.new_empty(0) - out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) - ctx.save_for_backward(out) - ctx.negative_slope = negative_slope - ctx.scale = scale - - return out - - @staticmethod - def backward(ctx, grad_output): - out, = ctx.saved_tensors - - grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply( - grad_output, out, ctx.negative_slope, ctx.scale - ) - - return grad_input, grad_bias, None, None - - -class FusedLeakyReLU(nn.Module): - def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): - super().__init__() - - self.bias = nn.Parameter(torch.zeros(channel)) - self.negative_slope = negative_slope - self.scale = scale - - def forward(self, input): - return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) - - -def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): - return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/dense_heads/yolact_head.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/dense_heads/yolact_head.py deleted file mode 100644 index 10d311f94ee99e1bf65ee3e5827f1699c28a23e3..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/dense_heads/yolact_head.py +++ /dev/null @@ -1,943 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import ConvModule, xavier_init -from mmcv.runner import force_fp32 - -from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply -from ..builder import HEADS, build_loss -from .anchor_head import AnchorHead - - -@HEADS.register_module() -class YOLACTHead(AnchorHead): - """YOLACT box head used in https://arxiv.org/abs/1904.02689. - - Note that YOLACT head is a light version of RetinaNet head. - Four differences are described as follows: - - 1. YOLACT box head has three-times fewer anchors. - 2. YOLACT box head shares the convs for box and cls branches. - 3. YOLACT box head uses OHEM instead of Focal loss. - 4. YOLACT box head predicts a set of mask coefficients for each box. - - Args: - num_classes (int): Number of categories excluding the background - category. - in_channels (int): Number of channels in the input feature map. - anchor_generator (dict): Config dict for anchor generator - loss_cls (dict): Config of classification loss. - loss_bbox (dict): Config of localization loss. - num_head_convs (int): Number of the conv layers shared by - box and cls branches. - num_protos (int): Number of the mask coefficients. - use_ohem (bool): If true, ``loss_single_OHEM`` will be used for - cls loss calculation. If false, ``loss_single`` will be used. - conv_cfg (dict): Dictionary to construct and config conv layer. - norm_cfg (dict): Dictionary to construct and config norm layer. - """ - - def __init__(self, - num_classes, - in_channels, - anchor_generator=dict( - type='AnchorGenerator', - octave_base_scale=3, - scales_per_octave=1, - ratios=[0.5, 1.0, 2.0], - strides=[8, 16, 32, 64, 128]), - loss_cls=dict( - type='CrossEntropyLoss', - use_sigmoid=False, - reduction='none', - loss_weight=1.0), - loss_bbox=dict( - type='SmoothL1Loss', beta=1.0, loss_weight=1.5), - num_head_convs=1, - num_protos=32, - use_ohem=True, - conv_cfg=None, - norm_cfg=None, - **kwargs): - self.num_head_convs = num_head_convs - self.num_protos = num_protos - self.use_ohem = use_ohem - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - super(YOLACTHead, self).__init__( - num_classes, - in_channels, - loss_cls=loss_cls, - loss_bbox=loss_bbox, - anchor_generator=anchor_generator, - **kwargs) - if self.use_ohem: - sampler_cfg = dict(type='PseudoSampler') - self.sampler = build_sampler(sampler_cfg, context=self) - self.sampling = False - - def _init_layers(self): - """Initialize layers of the head.""" - self.relu = nn.ReLU(inplace=True) - self.head_convs = nn.ModuleList() - for i in range(self.num_head_convs): - chn = self.in_channels if i == 0 else self.feat_channels - self.head_convs.append( - ConvModule( - chn, - self.feat_channels, - 3, - stride=1, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg)) - self.conv_cls = nn.Conv2d( - self.feat_channels, - self.num_anchors * self.cls_out_channels, - 3, - padding=1) - self.conv_reg = nn.Conv2d( - self.feat_channels, self.num_anchors * 4, 3, padding=1) - self.conv_coeff = nn.Conv2d( - self.feat_channels, - self.num_anchors * self.num_protos, - 3, - padding=1) - - def init_weights(self): - """Initialize weights of the head.""" - for m in self.head_convs: - xavier_init(m.conv, distribution='uniform', bias=0) - xavier_init(self.conv_cls, distribution='uniform', bias=0) - xavier_init(self.conv_reg, distribution='uniform', bias=0) - xavier_init(self.conv_coeff, distribution='uniform', bias=0) - - def forward_single(self, x): - """Forward feature of a single scale level. - - Args: - x (Tensor): Features of a single scale level. - - Returns: - tuple: - cls_score (Tensor): Cls scores for a single scale level \ - the channels number is num_anchors * num_classes. - bbox_pred (Tensor): Box energies / deltas for a single scale \ - level, the channels number is num_anchors * 4. - coeff_pred (Tensor): Mask coefficients for a single scale \ - level, the channels number is num_anchors * num_protos. - """ - for head_conv in self.head_convs: - x = head_conv(x) - cls_score = self.conv_cls(x) - bbox_pred = self.conv_reg(x) - coeff_pred = self.conv_coeff(x).tanh() - return cls_score, bbox_pred, coeff_pred - - @force_fp32(apply_to=('cls_scores', 'bbox_preds')) - def loss(self, - cls_scores, - bbox_preds, - gt_bboxes, - gt_labels, - img_metas, - gt_bboxes_ignore=None): - """A combination of the func:``AnchorHead.loss`` and - func:``SSDHead.loss``. - - When ``self.use_ohem == True``, it functions like ``SSDHead.loss``, - otherwise, it follows ``AnchorHead.loss``. Besides, it additionally - returns ``sampling_results``. - - Args: - cls_scores (list[Tensor]): Box scores for each scale level - Has shape (N, num_anchors * num_classes, H, W) - bbox_preds (list[Tensor]): Box energies / deltas for each scale - level with shape (N, num_anchors * 4, H, W) - gt_bboxes (list[Tensor]): Ground truth bboxes for each image with - shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. - gt_labels (list[Tensor]): Class indices corresponding to each box - img_metas (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - gt_bboxes_ignore (None | list[Tensor]): Specify which bounding - boxes can be ignored when computing the loss. Default: None - - Returns: - tuple: - dict[str, Tensor]: A dictionary of loss components. - List[:obj:``SamplingResult``]: Sampler results for each image. - """ - featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] - assert len(featmap_sizes) == self.anchor_generator.num_levels - - device = cls_scores[0].device - - anchor_list, valid_flag_list = self.get_anchors( - featmap_sizes, img_metas, device=device) - label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 - cls_reg_targets = self.get_targets( - anchor_list, - valid_flag_list, - gt_bboxes, - img_metas, - gt_bboxes_ignore_list=gt_bboxes_ignore, - gt_labels_list=gt_labels, - label_channels=label_channels, - unmap_outputs=not self.use_ohem, - return_sampling_results=True) - if cls_reg_targets is None: - return None - (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, - num_total_pos, num_total_neg, sampling_results) = cls_reg_targets - - if self.use_ohem: - num_images = len(img_metas) - all_cls_scores = torch.cat([ - s.permute(0, 2, 3, 1).reshape( - num_images, -1, self.cls_out_channels) for s in cls_scores - ], 1) - all_labels = torch.cat(labels_list, -1).view(num_images, -1) - all_label_weights = torch.cat(label_weights_list, - -1).view(num_images, -1) - all_bbox_preds = torch.cat([ - b.permute(0, 2, 3, 1).reshape(num_images, -1, 4) - for b in bbox_preds - ], -2) - all_bbox_targets = torch.cat(bbox_targets_list, - -2).view(num_images, -1, 4) - all_bbox_weights = torch.cat(bbox_weights_list, - -2).view(num_images, -1, 4) - - # concat all level anchors to a single tensor - all_anchors = [] - for i in range(num_images): - all_anchors.append(torch.cat(anchor_list[i])) - - # check NaN and Inf - assert torch.isfinite(all_cls_scores).all().item(), \ - 'classification scores become infinite or NaN!' - assert torch.isfinite(all_bbox_preds).all().item(), \ - 'bbox predications become infinite or NaN!' - - losses_cls, losses_bbox = multi_apply( - self.loss_single_OHEM, - all_cls_scores, - all_bbox_preds, - all_anchors, - all_labels, - all_label_weights, - all_bbox_targets, - all_bbox_weights, - num_total_samples=num_total_pos) - else: - num_total_samples = ( - num_total_pos + - num_total_neg if self.sampling else num_total_pos) - - # anchor number of multi levels - num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] - # concat all level anchors and flags to a single tensor - concat_anchor_list = [] - for i in range(len(anchor_list)): - concat_anchor_list.append(torch.cat(anchor_list[i])) - all_anchor_list = images_to_levels(concat_anchor_list, - num_level_anchors) - losses_cls, losses_bbox = multi_apply( - self.loss_single, - cls_scores, - bbox_preds, - all_anchor_list, - labels_list, - label_weights_list, - bbox_targets_list, - bbox_weights_list, - num_total_samples=num_total_samples) - - return dict( - loss_cls=losses_cls, loss_bbox=losses_bbox), sampling_results - - def loss_single_OHEM(self, cls_score, bbox_pred, anchors, labels, - label_weights, bbox_targets, bbox_weights, - num_total_samples): - """"See func:``SSDHead.loss``.""" - loss_cls_all = self.loss_cls(cls_score, labels, label_weights) - - # FG cat_id: [0, num_classes -1], BG cat_id: num_classes - pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero( - as_tuple=False).reshape(-1) - neg_inds = (labels == self.num_classes).nonzero( - as_tuple=False).view(-1) - - num_pos_samples = pos_inds.size(0) - if num_pos_samples == 0: - num_neg_samples = neg_inds.size(0) - else: - num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples - if num_neg_samples > neg_inds.size(0): - num_neg_samples = neg_inds.size(0) - topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples) - loss_cls_pos = loss_cls_all[pos_inds].sum() - loss_cls_neg = topk_loss_cls_neg.sum() - loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples - if self.reg_decoded_bbox: - # When the regression loss (e.g. `IouLoss`, `GIouLoss`) - # is applied directly on the decoded bounding boxes, it - # decodes the already encoded coordinates to absolute format. - bbox_pred = self.bbox_coder.decode(anchors, bbox_pred) - loss_bbox = self.loss_bbox( - bbox_pred, - bbox_targets, - bbox_weights, - avg_factor=num_total_samples) - return loss_cls[None], loss_bbox - - @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'coeff_preds')) - def get_bboxes(self, - cls_scores, - bbox_preds, - coeff_preds, - img_metas, - cfg=None, - rescale=False): - """"Similiar to func:``AnchorHead.get_bboxes``, but additionally - processes coeff_preds. - - Args: - cls_scores (list[Tensor]): Box scores for each scale level - with shape (N, num_anchors * num_classes, H, W) - bbox_preds (list[Tensor]): Box energies / deltas for each scale - level with shape (N, num_anchors * 4, H, W) - coeff_preds (list[Tensor]): Mask coefficients for each scale - level with shape (N, num_anchors * num_protos, H, W) - img_metas (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - cfg (mmcv.Config | None): Test / postprocessing configuration, - if None, test_cfg would be used - rescale (bool): If True, return boxes in original image space. - Default: False. - - Returns: - list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is - a 3-tuple. The first item is an (n, 5) tensor, where the - first 4 columns are bounding box positions - (tl_x, tl_y, br_x, br_y) and the 5-th column is a score - between 0 and 1. The second item is an (n,) tensor where each - item is the predicted class label of the corresponding box. - The third item is an (n, num_protos) tensor where each item - is the predicted mask coefficients of instance inside the - corresponding box. - """ - assert len(cls_scores) == len(bbox_preds) - num_levels = len(cls_scores) - - device = cls_scores[0].device - featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)] - mlvl_anchors = self.anchor_generator.grid_anchors( - featmap_sizes, device=device) - - det_bboxes = [] - det_labels = [] - det_coeffs = [] - for img_id in range(len(img_metas)): - cls_score_list = [ - cls_scores[i][img_id].detach() for i in range(num_levels) - ] - bbox_pred_list = [ - bbox_preds[i][img_id].detach() for i in range(num_levels) - ] - coeff_pred_list = [ - coeff_preds[i][img_id].detach() for i in range(num_levels) - ] - img_shape = img_metas[img_id]['img_shape'] - scale_factor = img_metas[img_id]['scale_factor'] - bbox_res = self._get_bboxes_single(cls_score_list, bbox_pred_list, - coeff_pred_list, mlvl_anchors, - img_shape, scale_factor, cfg, - rescale) - det_bboxes.append(bbox_res[0]) - det_labels.append(bbox_res[1]) - det_coeffs.append(bbox_res[2]) - return det_bboxes, det_labels, det_coeffs - - def _get_bboxes_single(self, - cls_score_list, - bbox_pred_list, - coeff_preds_list, - mlvl_anchors, - img_shape, - scale_factor, - cfg, - rescale=False): - """"Similiar to func:``AnchorHead._get_bboxes_single``, but - additionally processes coeff_preds_list and uses fast NMS instead of - traditional NMS. - - Args: - cls_score_list (list[Tensor]): Box scores for a single scale level - Has shape (num_anchors * num_classes, H, W). - bbox_pred_list (list[Tensor]): Box energies / deltas for a single - scale level with shape (num_anchors * 4, H, W). - coeff_preds_list (list[Tensor]): Mask coefficients for a single - scale level with shape (num_anchors * num_protos, H, W). - mlvl_anchors (list[Tensor]): Box reference for a single scale level - with shape (num_total_anchors, 4). - img_shape (tuple[int]): Shape of the input image, - (height, width, 3). - scale_factor (ndarray): Scale factor of the image arange as - (w_scale, h_scale, w_scale, h_scale). - cfg (mmcv.Config): Test / postprocessing configuration, - if None, test_cfg would be used. - rescale (bool): If True, return boxes in original image space. - - Returns: - tuple[Tensor, Tensor, Tensor]: The first item is an (n, 5) tensor, - where the first 4 columns are bounding box positions - (tl_x, tl_y, br_x, br_y) and the 5-th column is a score between - 0 and 1. The second item is an (n,) tensor where each item is - the predicted class label of the corresponding box. The third - item is an (n, num_protos) tensor where each item is the - predicted mask coefficients of instance inside the - corresponding box. - """ - cfg = self.test_cfg if cfg is None else cfg - assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors) - mlvl_bboxes = [] - mlvl_scores = [] - mlvl_coeffs = [] - for cls_score, bbox_pred, coeff_pred, anchors in \ - zip(cls_score_list, bbox_pred_list, - coeff_preds_list, mlvl_anchors): - assert cls_score.size()[-2:] == bbox_pred.size()[-2:] - cls_score = cls_score.permute(1, 2, - 0).reshape(-1, self.cls_out_channels) - if self.use_sigmoid_cls: - scores = cls_score.sigmoid() - else: - scores = cls_score.softmax(-1) - bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) - coeff_pred = coeff_pred.permute(1, 2, - 0).reshape(-1, self.num_protos) - nms_pre = cfg.get('nms_pre', -1) - if nms_pre > 0 and scores.shape[0] > nms_pre: - # Get maximum scores for foreground classes. - if self.use_sigmoid_cls: - max_scores, _ = scores.max(dim=1) - else: - # remind that we set FG labels to [0, num_class-1] - # since mmdet v2.0 - # BG cat_id: num_class - max_scores, _ = scores[:, :-1].max(dim=1) - _, topk_inds = max_scores.topk(nms_pre) - anchors = anchors[topk_inds, :] - bbox_pred = bbox_pred[topk_inds, :] - scores = scores[topk_inds, :] - coeff_pred = coeff_pred[topk_inds, :] - bboxes = self.bbox_coder.decode( - anchors, bbox_pred, max_shape=img_shape) - mlvl_bboxes.append(bboxes) - mlvl_scores.append(scores) - mlvl_coeffs.append(coeff_pred) - mlvl_bboxes = torch.cat(mlvl_bboxes) - if rescale: - mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) - mlvl_scores = torch.cat(mlvl_scores) - mlvl_coeffs = torch.cat(mlvl_coeffs) - if self.use_sigmoid_cls: - # Add a dummy background class to the backend when using sigmoid - # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 - # BG cat_id: num_class - padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) - mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) - det_bboxes, det_labels, det_coeffs = fast_nms(mlvl_bboxes, mlvl_scores, - mlvl_coeffs, - cfg.score_thr, - cfg.iou_thr, cfg.top_k, - cfg.max_per_img) - return det_bboxes, det_labels, det_coeffs - - -@HEADS.register_module() -class YOLACTSegmHead(nn.Module): - """YOLACT segmentation head used in https://arxiv.org/abs/1904.02689. - - Apply a semantic segmentation loss on feature space using layers that are - only evaluated during training to increase performance with no speed - penalty. - - Args: - in_channels (int): Number of channels in the input feature map. - num_classes (int): Number of categories excluding the background - category. - loss_segm (dict): Config of semantic segmentation loss. - """ - - def __init__(self, - num_classes, - in_channels=256, - loss_segm=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0)): - super(YOLACTSegmHead, self).__init__() - self.in_channels = in_channels - self.num_classes = num_classes - self.loss_segm = build_loss(loss_segm) - self._init_layers() - self.fp16_enabled = False - - def _init_layers(self): - """Initialize layers of the head.""" - self.segm_conv = nn.Conv2d( - self.in_channels, self.num_classes, kernel_size=1) - - def init_weights(self): - """Initialize weights of the head.""" - xavier_init(self.segm_conv, distribution='uniform') - - def forward(self, x): - """Forward feature from the upstream network. - - Args: - x (Tensor): Feature from the upstream network, which is - a 4D-tensor. - - Returns: - Tensor: Predicted semantic segmentation map with shape - (N, num_classes, H, W). - """ - return self.segm_conv(x) - - @force_fp32(apply_to=('segm_pred', )) - def loss(self, segm_pred, gt_masks, gt_labels): - """Compute loss of the head. - - Args: - segm_pred (list[Tensor]): Predicted semantic segmentation map - with shape (N, num_classes, H, W). - gt_masks (list[Tensor]): Ground truth masks for each image with - the same shape of the input image. - gt_labels (list[Tensor]): Class indices corresponding to each box. - - Returns: - dict[str, Tensor]: A dictionary of loss components. - """ - loss_segm = [] - num_imgs, num_classes, mask_h, mask_w = segm_pred.size() - for idx in range(num_imgs): - cur_segm_pred = segm_pred[idx] - cur_gt_masks = gt_masks[idx].float() - cur_gt_labels = gt_labels[idx] - segm_targets = self.get_targets(cur_segm_pred, cur_gt_masks, - cur_gt_labels) - if segm_targets is None: - loss = self.loss_segm(cur_segm_pred, - torch.zeros_like(cur_segm_pred), - torch.zeros_like(cur_segm_pred)) - else: - loss = self.loss_segm( - cur_segm_pred, - segm_targets, - avg_factor=num_imgs * mask_h * mask_w) - loss_segm.append(loss) - return dict(loss_segm=loss_segm) - - def get_targets(self, segm_pred, gt_masks, gt_labels): - """Compute semantic segmentation targets for each image. - - Args: - segm_pred (Tensor): Predicted semantic segmentation map - with shape (num_classes, H, W). - gt_masks (Tensor): Ground truth masks for each image with - the same shape of the input image. - gt_labels (Tensor): Class indices corresponding to each box. - - Returns: - Tensor: Semantic segmentation targets with shape - (num_classes, H, W). - """ - if gt_masks.size(0) == 0: - return None - num_classes, mask_h, mask_w = segm_pred.size() - with torch.no_grad(): - downsampled_masks = F.interpolate( - gt_masks.unsqueeze(0), (mask_h, mask_w), - mode='bilinear', - align_corners=False).squeeze(0) - downsampled_masks = downsampled_masks.gt(0.5).float() - segm_targets = torch.zeros_like(segm_pred, requires_grad=False) - for obj_idx in range(downsampled_masks.size(0)): - segm_targets[gt_labels[obj_idx] - 1] = torch.max( - segm_targets[gt_labels[obj_idx] - 1], - downsampled_masks[obj_idx]) - return segm_targets - - -@HEADS.register_module() -class YOLACTProtonet(nn.Module): - """YOLACT mask head used in https://arxiv.org/abs/1904.02689. - - This head outputs the mask prototypes for YOLACT. - - Args: - in_channels (int): Number of channels in the input feature map. - proto_channels (tuple[int]): Output channels of protonet convs. - proto_kernel_sizes (tuple[int]): Kernel sizes of protonet convs. - include_last_relu (Bool): If keep the last relu of protonet. - num_protos (int): Number of prototypes. - num_classes (int): Number of categories excluding the background - category. - loss_mask_weight (float): Reweight the mask loss by this factor. - max_masks_to_train (int): Maximum number of masks to train for - each image. - """ - - def __init__(self, - num_classes, - in_channels=256, - proto_channels=(256, 256, 256, None, 256, 32), - proto_kernel_sizes=(3, 3, 3, -2, 3, 1), - include_last_relu=True, - num_protos=32, - loss_mask_weight=1.0, - max_masks_to_train=100): - super(YOLACTProtonet, self).__init__() - self.in_channels = in_channels - self.proto_channels = proto_channels - self.proto_kernel_sizes = proto_kernel_sizes - self.include_last_relu = include_last_relu - self.protonet = self._init_layers() - - self.loss_mask_weight = loss_mask_weight - self.num_protos = num_protos - self.num_classes = num_classes - self.max_masks_to_train = max_masks_to_train - self.fp16_enabled = False - - def _init_layers(self): - """A helper function to take a config setting and turn it into a - network.""" - # Possible patterns: - # ( 256, 3) -> conv - # ( 256,-2) -> deconv - # (None,-2) -> bilinear interpolate - in_channels = self.in_channels - protonets = nn.ModuleList() - for num_channels, kernel_size in zip(self.proto_channels, - self.proto_kernel_sizes): - if kernel_size > 0: - layer = nn.Conv2d( - in_channels, - num_channels, - kernel_size, - padding=kernel_size // 2) - else: - if num_channels is None: - layer = InterpolateModule( - scale_factor=-kernel_size, - mode='bilinear', - align_corners=False) - else: - layer = nn.ConvTranspose2d( - in_channels, - num_channels, - -kernel_size, - padding=kernel_size // 2) - protonets.append(layer) - protonets.append(nn.ReLU(inplace=True)) - in_channels = num_channels if num_channels is not None \ - else in_channels - if not self.include_last_relu: - protonets = protonets[:-1] - return nn.Sequential(*protonets) - - def init_weights(self): - """Initialize weights of the head.""" - for m in self.protonet: - if isinstance(m, nn.Conv2d): - xavier_init(m, distribution='uniform') - - def forward(self, x, coeff_pred, bboxes, img_meta, sampling_results=None): - """Forward feature from the upstream network to get prototypes and - linearly combine the prototypes, using masks coefficients, into - instance masks. Finally, crop the instance masks with given bboxes. - - Args: - x (Tensor): Feature from the upstream network, which is - a 4D-tensor. - coeff_pred (list[Tensor]): Mask coefficients for each scale - level with shape (N, num_anchors * num_protos, H, W). - bboxes (list[Tensor]): Box used for cropping with shape - (N, num_anchors * 4, H, W). During training, they are - ground truth boxes. During testing, they are predicted - boxes. - img_meta (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - sampling_results (List[:obj:``SamplingResult``]): Sampler results - for each image. - - Returns: - list[Tensor]: Predicted instance segmentation masks. - """ - prototypes = self.protonet(x) - prototypes = prototypes.permute(0, 2, 3, 1).contiguous() - - num_imgs = x.size(0) - # Training state - if self.training: - coeff_pred_list = [] - for coeff_pred_per_level in coeff_pred: - coeff_pred_per_level = \ - coeff_pred_per_level.permute(0, 2, 3, 1)\ - .reshape(num_imgs, -1, self.num_protos) - coeff_pred_list.append(coeff_pred_per_level) - coeff_pred = torch.cat(coeff_pred_list, dim=1) - - mask_pred_list = [] - for idx in range(num_imgs): - cur_prototypes = prototypes[idx] - cur_coeff_pred = coeff_pred[idx] - cur_bboxes = bboxes[idx] - cur_img_meta = img_meta[idx] - - # Testing state - if not self.training: - bboxes_for_cropping = cur_bboxes - else: - cur_sampling_results = sampling_results[idx] - pos_assigned_gt_inds = \ - cur_sampling_results.pos_assigned_gt_inds - bboxes_for_cropping = cur_bboxes[pos_assigned_gt_inds].clone() - pos_inds = cur_sampling_results.pos_inds - cur_coeff_pred = cur_coeff_pred[pos_inds] - - # Linearly combine the prototypes with the mask coefficients - mask_pred = cur_prototypes @ cur_coeff_pred.t() - mask_pred = torch.sigmoid(mask_pred) - - h, w = cur_img_meta['img_shape'][:2] - bboxes_for_cropping[:, 0] /= w - bboxes_for_cropping[:, 1] /= h - bboxes_for_cropping[:, 2] /= w - bboxes_for_cropping[:, 3] /= h - - mask_pred = self.crop(mask_pred, bboxes_for_cropping) - mask_pred = mask_pred.permute(2, 0, 1).contiguous() - mask_pred_list.append(mask_pred) - return mask_pred_list - - @force_fp32(apply_to=('mask_pred', )) - def loss(self, mask_pred, gt_masks, gt_bboxes, img_meta, sampling_results): - """Compute loss of the head. - - Args: - mask_pred (list[Tensor]): Predicted prototypes with shape - (num_classes, H, W). - gt_masks (list[Tensor]): Ground truth masks for each image with - the same shape of the input image. - gt_bboxes (list[Tensor]): Ground truth bboxes for each image with - shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. - img_meta (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - sampling_results (List[:obj:``SamplingResult``]): Sampler results - for each image. - - Returns: - dict[str, Tensor]: A dictionary of loss components. - """ - loss_mask = [] - num_imgs = len(mask_pred) - total_pos = 0 - for idx in range(num_imgs): - cur_mask_pred = mask_pred[idx] - cur_gt_masks = gt_masks[idx].float() - cur_gt_bboxes = gt_bboxes[idx] - cur_img_meta = img_meta[idx] - cur_sampling_results = sampling_results[idx] - - pos_assigned_gt_inds = cur_sampling_results.pos_assigned_gt_inds - num_pos = pos_assigned_gt_inds.size(0) - # Since we're producing (near) full image masks, - # it'd take too much vram to backprop on every single mask. - # Thus we select only a subset. - if num_pos > self.max_masks_to_train: - perm = torch.randperm(num_pos) - select = perm[:self.max_masks_to_train] - cur_mask_pred = cur_mask_pred[select] - pos_assigned_gt_inds = pos_assigned_gt_inds[select] - num_pos = self.max_masks_to_train - total_pos += num_pos - - gt_bboxes_for_reweight = cur_gt_bboxes[pos_assigned_gt_inds] - - mask_targets = self.get_targets(cur_mask_pred, cur_gt_masks, - pos_assigned_gt_inds) - if num_pos == 0: - loss = cur_mask_pred.sum() * 0. - elif mask_targets is None: - loss = F.binary_cross_entropy(cur_mask_pred, - torch.zeros_like(cur_mask_pred), - torch.zeros_like(cur_mask_pred)) - else: - cur_mask_pred = torch.clamp(cur_mask_pred, 0, 1) - loss = F.binary_cross_entropy( - cur_mask_pred, mask_targets, - reduction='none') * self.loss_mask_weight - - h, w = cur_img_meta['img_shape'][:2] - gt_bboxes_width = (gt_bboxes_for_reweight[:, 2] - - gt_bboxes_for_reweight[:, 0]) / w - gt_bboxes_height = (gt_bboxes_for_reweight[:, 3] - - gt_bboxes_for_reweight[:, 1]) / h - loss = loss.mean(dim=(1, - 2)) / gt_bboxes_width / gt_bboxes_height - loss = torch.sum(loss) - loss_mask.append(loss) - - if total_pos == 0: - total_pos += 1 # avoid nan - loss_mask = [x / total_pos for x in loss_mask] - - return dict(loss_mask=loss_mask) - - def get_targets(self, mask_pred, gt_masks, pos_assigned_gt_inds): - """Compute instance segmentation targets for each image. - - Args: - mask_pred (Tensor): Predicted prototypes with shape - (num_classes, H, W). - gt_masks (Tensor): Ground truth masks for each image with - the same shape of the input image. - pos_assigned_gt_inds (Tensor): GT indices of the corresponding - positive samples. - Returns: - Tensor: Instance segmentation targets with shape - (num_instances, H, W). - """ - if gt_masks.size(0) == 0: - return None - mask_h, mask_w = mask_pred.shape[-2:] - gt_masks = F.interpolate( - gt_masks.unsqueeze(0), (mask_h, mask_w), - mode='bilinear', - align_corners=False).squeeze(0) - gt_masks = gt_masks.gt(0.5).float() - mask_targets = gt_masks[pos_assigned_gt_inds] - return mask_targets - - def get_seg_masks(self, mask_pred, label_pred, img_meta, rescale): - """Resize, binarize, and format the instance mask predictions. - - Args: - mask_pred (Tensor): shape (N, H, W). - label_pred (Tensor): shape (N, ). - img_meta (dict): Meta information of each image, e.g., - image size, scaling factor, etc. - rescale (bool): If rescale is False, then returned masks will - fit the scale of imgs[0]. - Returns: - list[ndarray]: Mask predictions grouped by their predicted classes. - """ - ori_shape = img_meta['ori_shape'] - scale_factor = img_meta['scale_factor'] - if rescale: - img_h, img_w = ori_shape[:2] - else: - img_h = np.round(ori_shape[0] * scale_factor[1]).astype(np.int32) - img_w = np.round(ori_shape[1] * scale_factor[0]).astype(np.int32) - - cls_segms = [[] for _ in range(self.num_classes)] - if mask_pred.size(0) == 0: - return cls_segms - - mask_pred = F.interpolate( - mask_pred.unsqueeze(0), (img_h, img_w), - mode='bilinear', - align_corners=False).squeeze(0) > 0.5 - mask_pred = mask_pred.cpu().numpy().astype(np.uint8) - - for m, l in zip(mask_pred, label_pred): - cls_segms[l].append(m) - return cls_segms - - def crop(self, masks, boxes, padding=1): - """Crop predicted masks by zeroing out everything not in the predicted - bbox. - - Args: - masks (Tensor): shape [H, W, N]. - boxes (Tensor): bbox coords in relative point form with - shape [N, 4]. - - Return: - Tensor: The cropped masks. - """ - h, w, n = masks.size() - x1, x2 = self.sanitize_coordinates( - boxes[:, 0], boxes[:, 2], w, padding, cast=False) - y1, y2 = self.sanitize_coordinates( - boxes[:, 1], boxes[:, 3], h, padding, cast=False) - - rows = torch.arange( - w, device=masks.device, dtype=x1.dtype).view(1, -1, - 1).expand(h, w, n) - cols = torch.arange( - h, device=masks.device, dtype=x1.dtype).view(-1, 1, - 1).expand(h, w, n) - - masks_left = rows >= x1.view(1, 1, -1) - masks_right = rows < x2.view(1, 1, -1) - masks_up = cols >= y1.view(1, 1, -1) - masks_down = cols < y2.view(1, 1, -1) - - crop_mask = masks_left * masks_right * masks_up * masks_down - - return masks * crop_mask.float() - - def sanitize_coordinates(self, x1, x2, img_size, padding=0, cast=True): - """Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0, - and x2 <= image_size. Also converts from relative to absolute - coordinates and casts the results to long tensors. - - Warning: this does things in-place behind the scenes so - copy if necessary. - - Args: - _x1 (Tensor): shape (N, ). - _x2 (Tensor): shape (N, ). - img_size (int): Size of the input image. - padding (int): x1 >= padding, x2 <= image_size-padding. - cast (bool): If cast is false, the result won't be cast to longs. - - Returns: - tuple: - x1 (Tensor): Sanitized _x1. - x2 (Tensor): Sanitized _x2. - """ - x1 = x1 * img_size - x2 = x2 * img_size - if cast: - x1 = x1.long() - x2 = x2.long() - x1 = torch.min(x1, x2) - x2 = torch.max(x1, x2) - x1 = torch.clamp(x1 - padding, min=0) - x2 = torch.clamp(x2 + padding, max=img_size) - return x1, x2 - - -class InterpolateModule(nn.Module): - """This is a module version of F.interpolate. - - Any arguments you give it just get passed along for the ride. - """ - - def __init__(self, *args, **kwargs): - super().__init__() - - self.args = args - self.kwargs = kwargs - - def forward(self, x): - """Forward features from the upstream network.""" - return F.interpolate(x, *self.args, **self.kwargs) diff --git a/spaces/GrandaddyShmax/MusicGen_Plus_hfv2/audiocraft/__init__.py b/spaces/GrandaddyShmax/MusicGen_Plus_hfv2/audiocraft/__init__.py deleted file mode 100644 index 6b8594f470200ff5c000542ef115375ed69b749c..0000000000000000000000000000000000000000 --- a/spaces/GrandaddyShmax/MusicGen_Plus_hfv2/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__ = '0.0.2a2' diff --git a/spaces/GroveStreet/GTA_SOVITS/modules/mel_processing.py b/spaces/GroveStreet/GTA_SOVITS/modules/mel_processing.py deleted file mode 100644 index 99c5b35beb83f3b288af0fac5b49ebf2c69f062c..0000000000000000000000000000000000000000 --- a/spaces/GroveStreet/GTA_SOVITS/modules/mel_processing.py +++ /dev/null @@ -1,112 +0,0 @@ -import math -import os -import random -import torch -from torch import nn -import torch.nn.functional as F -import torch.utils.data -import numpy as np -import librosa -import librosa.util as librosa_util -from librosa.util import normalize, pad_center, tiny -from scipy.signal import get_window -from scipy.io.wavfile import read -from librosa.filters import mel as librosa_mel_fn - -MAX_WAV_VALUE = 32768.0 - - -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - """ - PARAMS - ------ - C: compression factor - """ - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def dynamic_range_decompression_torch(x, C=1): - """ - PARAMS - ------ - C: compression factor used to compress - """ - return torch.exp(x) / C - - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def spectral_de_normalize_torch(magnitudes): - output = dynamic_range_decompression_torch(magnitudes) - return output - - -mel_basis = {} -hann_window = {} - - -def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - return spec - - -def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): - global mel_basis - dtype_device = str(spec.dtype) + '_' + str(spec.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device) - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - return spec - - -def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global mel_basis, hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device) - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - - return spec diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/pos_embed.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/pos_embed.py deleted file mode 100644 index 836bd43d0bfe699b0b37bfec81509e06a2a28f27..0000000000000000000000000000000000000000 --- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/pos_embed.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright (c) EPFL VILAB. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -------------------------------------------------------- -# Based on BEiT, timm, DINO DeiT and MAE-priv code bases -# https://github.com/microsoft/unilm/tree/master/beit -# https://github.com/rwightman/pytorch-image-models/tree/master/timm -# https://github.com/facebookresearch/deit -# https://github.com/facebookresearch/dino -# https://github.com/BUPT-PRIV/MAE-priv -# -------------------------------------------------------- - -import re - -import torch - - -def interpolate_pos_embed_vit(model, checkpoint_model): - if 'pos_embed' in checkpoint_model: - pos_embed_checkpoint = checkpoint_model['pos_embed'] - embedding_size = pos_embed_checkpoint.shape[-1] - num_patches = model.patch_embed.num_patches - num_extra_tokens = model.pos_embed.shape[-2] - num_patches - # height (== width) for the checkpoint position embedding - orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) - # height (== width) for the new position embedding - new_size = int(num_patches ** 0.5) - # class_token and dist_token are kept unchanged - if orig_size != new_size: - print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) - extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] - # only the position tokens are interpolated - pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] - pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) - pos_tokens = torch.nn.functional.interpolate( - pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) - pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) - new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) - checkpoint_model['pos_embed'] = new_pos_embed - - -def interpolate_pos_embed_multimae(model, checkpoint_model): - pattern = "input_adapters\.(.*)\.pos_emb" - matched_keys = [k for k in checkpoint_model if bool(re.match(pattern, k))] - - for key in matched_keys: - domain = re.match(pattern, key).group(1) # group(0) is entire matched regex - if getattr(model.input_adapters, domain, None) is not None: - pos_embed_checkpoint = checkpoint_model[key] - _, _, orig_H, orig_W = pos_embed_checkpoint.shape - _, _, new_H, new_W = getattr(model.input_adapters, domain).pos_emb.shape - if (orig_H != new_H) or (orig_W != new_W): - print(f"Key {key}: Position interpolate from {orig_H}x{orig_W} to {new_H}x{new_W}") - pos_embed_checkpoint = torch.nn.functional.interpolate( - pos_embed_checkpoint, size=(new_H, new_W), mode='bicubic', align_corners=False) - checkpoint_model[key] = pos_embed_checkpoint diff --git a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/tool/makesample.py b/spaces/HaHaBill/LandShapes-Antarctica/netdissect/tool/makesample.py deleted file mode 100644 index 36276267677360d8238a8dbf71e9753dcc327681..0000000000000000000000000000000000000000 --- a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/tool/makesample.py +++ /dev/null @@ -1,169 +0,0 @@ -''' -A simple tool to generate sample of output of a GAN, -subject to filtering, sorting, or intervention. -''' - -import torch, numpy, os, argparse, numbers, sys, shutil -from PIL import Image -from torch.utils.data import TensorDataset -from netdissect.zdataset import standard_z_sample -from netdissect.progress import default_progress, verbose_progress -from netdissect.autoeval import autoimport_eval -from netdissect.workerpool import WorkerBase, WorkerPool -from netdissect.nethook import edit_layers, retain_layers - -def main(): - parser = argparse.ArgumentParser(description='GAN sample making utility') - parser.add_argument('--model', type=str, default=None, - help='constructor for the model to test') - parser.add_argument('--pthfile', type=str, default=None, - help='filename of .pth file for the model') - parser.add_argument('--outdir', type=str, default='images', - help='directory for image output') - parser.add_argument('--size', type=int, default=100, - help='number of images to output') - parser.add_argument('--test_size', type=int, default=None, - help='number of images to test') - parser.add_argument('--layer', type=str, default=None, - help='layer to inspect') - parser.add_argument('--seed', type=int, default=1, - help='seed') - parser.add_argument('--maximize_units', type=int, nargs='+', default=None, - help='units to maximize') - parser.add_argument('--ablate_units', type=int, nargs='+', default=None, - help='units to ablate') - parser.add_argument('--quiet', action='store_true', default=False, - help='silences console output') - if len(sys.argv) == 1: - parser.print_usage(sys.stderr) - sys.exit(1) - args = parser.parse_args() - verbose_progress(not args.quiet) - - # Instantiate the model - model = autoimport_eval(args.model) - if args.pthfile is not None: - data = torch.load(args.pthfile) - if 'state_dict' in data: - meta = {} - for key in data: - if isinstance(data[key], numbers.Number): - meta[key] = data[key] - data = data['state_dict'] - model.load_state_dict(data) - # Unwrap any DataParallel-wrapped model - if isinstance(model, torch.nn.DataParallel): - model = next(model.children()) - # Examine first conv in model to determine input feature size. - first_layer = [c for c in model.modules() - if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d, - torch.nn.Linear))][0] - # 4d input if convolutional, 2d input if first layer is linear. - if isinstance(first_layer, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)): - z_channels = first_layer.in_channels - spatialdims = (1, 1) - else: - z_channels = first_layer.in_features - spatialdims = () - # Instrument the model if needed - if args.maximize_units is not None: - retain_layers(model, [args.layer]) - model.cuda() - - # Get the sample of z vectors - if args.maximize_units is None: - indexes = torch.arange(args.size) - z_sample = standard_z_sample(args.size, z_channels, seed=args.seed) - z_sample = z_sample.view(tuple(z_sample.shape) + spatialdims) - else: - # By default, if maximizing units, get a 'top 5%' sample. - if args.test_size is None: - args.test_size = args.size * 20 - z_universe = standard_z_sample(args.test_size, z_channels, - seed=args.seed) - z_universe = z_universe.view(tuple(z_universe.shape) + spatialdims) - indexes = get_highest_znums(model, z_universe, args.maximize_units, - args.size, seed=args.seed) - z_sample = z_universe[indexes] - - if args.ablate_units: - edit_layers(model, [args.layer]) - dims = max(2, max(args.ablate_units) + 1) # >=2 to avoid broadcast - model.ablation[args.layer] = torch.zeros(dims) - model.ablation[args.layer][args.ablate_units] = 1 - - save_znum_images(args.outdir, model, z_sample, indexes, - args.layer, args.ablate_units) - copy_lightbox_to(args.outdir) - - -def get_highest_znums(model, z_universe, max_units, size, - batch_size=100, seed=1): - # The model should have been instrumented already - retained_items = list(model.retained.items()) - assert len(retained_items) == 1 - layer = retained_items[0][0] - # By default, a 10% sample - progress = default_progress() - num_units = None - with torch.no_grad(): - # Pass 1: collect max activation stats - z_loader = torch.utils.data.DataLoader(TensorDataset(z_universe), - batch_size=batch_size, num_workers=2, - pin_memory=True) - scores = [] - for [z] in progress(z_loader, desc='Finding max activations'): - z = z.cuda() - model(z) - feature = model.retained[layer] - num_units = feature.shape[1] - max_feature = feature[:, max_units, ...].view( - feature.shape[0], len(max_units), -1).max(2)[0] - total_feature = max_feature.sum(1) - scores.append(total_feature.cpu()) - scores = torch.cat(scores, 0) - highest = (-scores).sort(0)[1][:size].sort(0)[0] - return highest - - -def save_znum_images(dirname, model, z_sample, indexes, layer, ablated_units, - name_template="image_{}.png", lightbox=False, batch_size=100, seed=1): - progress = default_progress() - os.makedirs(dirname, exist_ok=True) - with torch.no_grad(): - # Pass 2: now generate images - z_loader = torch.utils.data.DataLoader(TensorDataset(z_sample), - batch_size=batch_size, num_workers=2, - pin_memory=True) - saver = WorkerPool(SaveImageWorker) - if ablated_units is not None: - dims = max(2, max(ablated_units) + 1) # >=2 to avoid broadcast - mask = torch.zeros(dims) - mask[ablated_units] = 1 - model.ablation[layer] = mask[None,:,None,None].cuda() - for batch_num, [z] in enumerate(progress(z_loader, - desc='Saving images')): - z = z.cuda() - start_index = batch_num * batch_size - im = ((model(z) + 1) / 2 * 255).clamp(0, 255).byte().permute( - 0, 2, 3, 1).cpu() - for i in range(len(im)): - index = i + start_index - if indexes is not None: - index = indexes[index].item() - filename = os.path.join(dirname, name_template.format(index)) - saver.add(im[i].numpy(), filename) - saver.join() - -def copy_lightbox_to(dirname): - srcdir = os.path.realpath( - os.path.join(os.getcwd(), os.path.dirname(__file__))) - shutil.copy(os.path.join(srcdir, 'lightbox.html'), - os.path.join(dirname, '+lightbox.html')) - -class SaveImageWorker(WorkerBase): - def work(self, data, filename): - Image.fromarray(data).save(filename, optimize=True, quality=100) - -if __name__ == '__main__': - main() diff --git a/spaces/Harshitthaa/Harshitthaamyfirstai/README.md b/spaces/Harshitthaa/Harshitthaamyfirstai/README.md deleted file mode 100644 index 8069b2a90ff774731151e2c4f68c3312b50249a0..0000000000000000000000000000000000000000 --- a/spaces/Harshitthaa/Harshitthaamyfirstai/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Harshitthaamyfirstai -emoji: 📊 -colorFrom: gray -colorTo: gray -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.b43d8183.js b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.b43d8183.js deleted file mode 100644 index 4fb8a721b8135fcfe849a9cd9b544874c8fdf442..0000000000000000000000000000000000000000 --- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.b43d8183.js +++ /dev/null @@ -1,2 +0,0 @@ -import{C as p}from"./Column.06c172ac.js";import"./index.396f4a72.js";const t=["static"];export{p as Component,t as modes}; -//# sourceMappingURL=index.b43d8183.js.map diff --git a/spaces/Hyperion1970/JosefJilek-loliDiffusion/app.py b/spaces/Hyperion1970/JosefJilek-loliDiffusion/app.py deleted file mode 100644 index c6fe0376ea04cdfb76e4a1e5e544b6015b99c517..0000000000000000000000000000000000000000 --- a/spaces/Hyperion1970/JosefJilek-loliDiffusion/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/JosefJilek/loliDiffusion").launch() \ No newline at end of file diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/characters.py b/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/characters.py deleted file mode 100644 index 494ea219392716dc75d2c1e19d71cd55b9b2f4ba..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/characters.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -from fairseq.data.encoders import register_bpe - - -SPACE = chr(32) -SPACE_ESCAPE = chr(9601) - - -@register_bpe("characters") -class Characters(object): - def __init__(self, *unused): - pass - - @staticmethod - def add_args(parser): - pass - - @staticmethod - def encode(x: str) -> str: - escaped = x.replace(SPACE, SPACE_ESCAPE) - return SPACE.join(list(escaped)) - - @staticmethod - def decode(x: str) -> str: - return x.replace(SPACE, "").replace(SPACE_ESCAPE, SPACE) diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/models/bart/__init__.py b/spaces/ICML2022/OFA/fairseq/fairseq/models/bart/__init__.py deleted file mode 100644 index a701923f7e5a2a8aa9b75e5580ddea22907f53ee..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/fairseq/models/bart/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .hub_interface import * # noqa -from .model import * # noqa diff --git a/spaces/Intel/ldm3d/static/three6dof.html b/spaces/Intel/ldm3d/static/three6dof.html deleted file mode 100644 index 9927c447a39bfeb32e0bcf15afd96e645be23828..0000000000000000000000000000000000000000 --- a/spaces/Intel/ldm3d/static/three6dof.html +++ /dev/null @@ -1,180 +0,0 @@ - - - -${rounded_usage}${usage_limit}
- """
- except requests.exceptions.ConnectTimeout:
- status_text = (
- STANDARD_ERROR_MSG + CONNECTION_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
- )
- return status_text
- except requests.exceptions.ReadTimeout:
- status_text = STANDARD_ERROR_MSG + READ_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
- return status_text
- except Exception as e:
- import traceback
- traceback.print_exc()
- logging.error(i18n("获取API使用情况失败:") + str(e))
- return STANDARD_ERROR_MSG + ERROR_RETRIEVE_MSG
-
- def set_token_upper_limit(self, new_upper_limit):
- pass
-
- @shared.state.switching_api_key # 在不开启多账号模式的时候，这个装饰器不会起作用
- def _get_response(self, stream=False):
- openai_api_key = self.api_key
- system_prompt = self.system_prompt
- history = self.history
- logging.debug(colorama.Fore.YELLOW +
- f"{history}" + colorama.Fore.RESET)
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {openai_api_key}",
- }
-
- if system_prompt is not None:
- history = [construct_system(system_prompt), *history]
-
- payload = {
- "model": self.model_name,
- "messages": history,
- "temperature": self.temperature,
- "top_p": self.top_p,
- "n": self.n_choices,
- "stream": stream,
- "presence_penalty": self.presence_penalty,
- "frequency_penalty": self.frequency_penalty,
- }
-
- if self.max_generation_token is not None:
- payload["max_tokens"] = self.max_generation_token
- if self.stop_sequence is not None:
- payload["stop"] = self.stop_sequence
- if self.logit_bias is not None:
- payload["logit_bias"] = self.logit_bias
- if self.user_identifier:
- payload["user"] = self.user_identifier
-
- if stream:
- timeout = TIMEOUT_STREAMING
- else:
- timeout = TIMEOUT_ALL
-
- # 如果有自定义的api-host，使用自定义host发送请求，否则使用默认设置发送请求
- if shared.state.completion_url != COMPLETION_URL:
- logging.info(f"使用自定义API URL: {shared.state.completion_url}")
-
- with retrieve_proxy():
- try:
- response = requests.post(
- shared.state.completion_url,
- headers=headers,
- json=payload,
- stream=stream,
- timeout=timeout,
- )
- except:
- return None
- return response
-
- def _refresh_header(self):
- self.headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {self.api_key}",
- }
-
- def _get_billing_data(self, billing_url):
- with retrieve_proxy():
- response = requests.get(
- billing_url,
- headers=self.headers,
- timeout=TIMEOUT_ALL,
- )
-
- if response.status_code == 200:
- data = response.json()
- return data
- else:
- raise Exception(
- f"API request failed with status code {response.status_code}: {response.text}"
- )
-
- def _decode_chat_response(self, response):
- error_msg = ""
- for chunk in response.iter_lines():
- if chunk:
- chunk = chunk.decode()
- chunk_length = len(chunk)
- try:
- chunk = json.loads(chunk[6:])
- except json.JSONDecodeError:
- print(i18n("JSON解析错误,收到的内容: ") + f"{chunk}")
- error_msg += chunk
- continue
- if chunk_length > 6 and "delta" in chunk["choices"][0]:
- if chunk["choices"][0]["finish_reason"] == "stop":
- break
- try:
- yield chunk["choices"][0]["delta"]["content"]
- except Exception as e:
- # logging.error(f"Error: {e}")
- continue
- if error_msg:
- raise Exception(error_msg)
-
- def set_key(self, new_access_key):
- ret = super().set_key(new_access_key)
- self._refresh_header()
- return ret
-
-
-class ChatGLM_Client(BaseLLMModel):
- def __init__(self, model_name, user_name="") -> None:
- super().__init__(model_name=model_name, user=user_name)
- from transformers import AutoTokenizer, AutoModel
- import torch
- global CHATGLM_TOKENIZER, CHATGLM_MODEL
- if CHATGLM_TOKENIZER is None or CHATGLM_MODEL is None:
- system_name = platform.system()
- model_path = None
- if os.path.exists("models"):
- model_dirs = os.listdir("models")
- if model_name in model_dirs:
- model_path = f"models/{model_name}"
- if model_path is not None:
- model_source = model_path
- else:
- model_source = f"THUDM/{model_name}"
- CHATGLM_TOKENIZER = AutoTokenizer.from_pretrained(
- model_source, trust_remote_code=True
- )
- quantified = False
- if "int4" in model_name:
- quantified = True
- model = AutoModel.from_pretrained(
- model_source, trust_remote_code=True
- )
- if torch.cuda.is_available():
- # run on CUDA
- logging.info("CUDA is available, using CUDA")
- model = model.half().cuda()
- # mps加速还存在一些问题，暂时不使用
- elif system_name == "Darwin" and model_path is not None and not quantified:
- logging.info("Running on macOS, using MPS")
- # running on macOS and model already downloaded
- model = model.half().to("mps")
- else:
- logging.info("GPU is not available, using CPU")
- model = model.float()
- model = model.eval()
- CHATGLM_MODEL = model
-
- def _get_glm_style_input(self):
- history = [x["content"] for x in self.history]
- query = history.pop()
- logging.debug(colorama.Fore.YELLOW +
- f"{history}" + colorama.Fore.RESET)
- assert (
- len(history) % 2 == 0
- ), f"History should be even length. current history is: {history}"
- history = [[history[i], history[i + 1]]
- for i in range(0, len(history), 2)]
- return history, query
-
- def get_answer_at_once(self):
- history, query = self._get_glm_style_input()
- response, _ = CHATGLM_MODEL.chat(
- CHATGLM_TOKENIZER, query, history=history)
- return response, len(response)
-
- def get_answer_stream_iter(self):
- history, query = self._get_glm_style_input()
- for response, history in CHATGLM_MODEL.stream_chat(
- CHATGLM_TOKENIZER,
- query,
- history,
- max_length=self.token_upper_limit,
- top_p=self.top_p,
- temperature=self.temperature,
- ):
- yield response
-
-
-class LLaMA_Client(BaseLLMModel):
- def __init__(
- self,
- model_name,
- lora_path=None,
- user_name=""
- ) -> None:
- super().__init__(model_name=model_name, user=user_name)
- from lmflow.datasets.dataset import Dataset
- from lmflow.pipeline.auto_pipeline import AutoPipeline
- from lmflow.models.auto_model import AutoModel
- from lmflow.args import ModelArguments, DatasetArguments, InferencerArguments
-
- self.max_generation_token = 1000
- self.end_string = "\n\n"
- # We don't need input data
- data_args = DatasetArguments(dataset_path=None)
- self.dataset = Dataset(data_args)
- self.system_prompt = ""
-
- global LLAMA_MODEL, LLAMA_INFERENCER
- if LLAMA_MODEL is None or LLAMA_INFERENCER is None:
- model_path = None
- if os.path.exists("models"):
- model_dirs = os.listdir("models")
- if model_name in model_dirs:
- model_path = f"models/{model_name}"
- if model_path is not None:
- model_source = model_path
- else:
- model_source = f"decapoda-research/{model_name}"
- # raise Exception(f"models目录下没有这个模型: {model_name}")
- if lora_path is not None:
- lora_path = f"lora/{lora_path}"
- model_args = ModelArguments(model_name_or_path=model_source, lora_model_path=lora_path, model_type=None, config_overrides=None, config_name=None, tokenizer_name=None, cache_dir=None,
- use_fast_tokenizer=True, model_revision='main', use_auth_token=False, torch_dtype=None, use_lora=False, lora_r=8, lora_alpha=32, lora_dropout=0.1, use_ram_optimized_load=True)
- pipeline_args = InferencerArguments(
- local_rank=0, random_seed=1, deepspeed='configs/ds_config_chatbot.json', mixed_precision='bf16')
-
- with open(pipeline_args.deepspeed, "r") as f:
- ds_config = json.load(f)
- LLAMA_MODEL = AutoModel.get_model(
- model_args,
- tune_strategy="none",
- ds_config=ds_config,
- )
- LLAMA_INFERENCER = AutoPipeline.get_pipeline(
- pipeline_name="inferencer",
- model_args=model_args,
- data_args=data_args,
- pipeline_args=pipeline_args,
- )
-
- def _get_llama_style_input(self):
- history = []
- instruction = ""
- if self.system_prompt:
- instruction = (f"Instruction: {self.system_prompt}\n")
- for x in self.history:
- if x["role"] == "user":
- history.append(f"{instruction}Input: {x['content']}")
- else:
- history.append(f"Output: {x['content']}")
- context = "\n\n".join(history)
- context += "\n\nOutput: "
- return context
-
- def get_answer_at_once(self):
- context = self._get_llama_style_input()
-
- input_dataset = self.dataset.from_dict(
- {"type": "text_only", "instances": [{"text": context}]}
- )
-
- output_dataset = LLAMA_INFERENCER.inference(
- model=LLAMA_MODEL,
- dataset=input_dataset,
- max_new_tokens=self.max_generation_token,
- temperature=self.temperature,
- )
-
- response = output_dataset.to_dict()["instances"][0]["text"]
- return response, len(response)
-
- def get_answer_stream_iter(self):
- context = self._get_llama_style_input()
- partial_text = ""
- step = 1
- for _ in range(0, self.max_generation_token, step):
- input_dataset = self.dataset.from_dict(
- {"type": "text_only", "instances": [
- {"text": context + partial_text}]}
- )
- output_dataset = LLAMA_INFERENCER.inference(
- model=LLAMA_MODEL,
- dataset=input_dataset,
- max_new_tokens=step,
- temperature=self.temperature,
- )
- response = output_dataset.to_dict()["instances"][0]["text"]
- if response == "" or response == self.end_string:
- break
- partial_text += response
- yield partial_text
-
-
-class XMChat(BaseLLMModel):
- def __init__(self, api_key, user_name=""):
- super().__init__(model_name="xmchat", user=user_name)
- self.api_key = api_key
- self.session_id = None
- self.reset()
- self.image_bytes = None
- self.image_path = None
- self.xm_history = []
- self.url = "https://xmbot.net/web"
- self.last_conv_id = None
-
- def reset(self):
- self.session_id = str(uuid.uuid4())
- self.last_conv_id = None
- return [], "已重置"
-
- def image_to_base64(self, image_path):
- # 打开并加载图片
- img = Image.open(image_path)
-
- # 获取图片的宽度和高度
- width, height = img.size
-
- # 计算压缩比例，以确保最长边小于4096像素
- max_dimension = 2048
- scale_ratio = min(max_dimension / width, max_dimension / height)
-
- if scale_ratio < 1:
- # 按压缩比例调整图片大小
- new_width = int(width * scale_ratio)
- new_height = int(height * scale_ratio)
- img = img.resize((new_width, new_height), Image.ANTIALIAS)
-
- # 将图片转换为jpg格式的二进制数据
- buffer = BytesIO()
- if img.mode == "RGBA":
- img = img.convert("RGB")
- img.save(buffer, format='JPEG')
- binary_image = buffer.getvalue()
-
- # 对二进制数据进行Base64编码
- base64_image = base64.b64encode(binary_image).decode('utf-8')
-
- return base64_image
-
- def try_read_image(self, filepath):
- def is_image_file(filepath):
- # 判断文件是否为图片
- valid_image_extensions = [
- ".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff"]
- file_extension = os.path.splitext(filepath)[1].lower()
- return file_extension in valid_image_extensions
-
- if is_image_file(filepath):
- logging.info(f"读取图片文件: {filepath}")
- self.image_bytes = self.image_to_base64(filepath)
- self.image_path = filepath
- else:
- self.image_bytes = None
- self.image_path = None
-
- def like(self):
- if self.last_conv_id is None:
- return "点赞失败，你还没发送过消息"
- data = {
- "uuid": self.last_conv_id,
- "appraise": "good"
- }
- requests.post(self.url, json=data)
- return "👍点赞成功，感谢反馈～"
-
- def dislike(self):
- if self.last_conv_id is None:
- return "点踩失败，你还没发送过消息"
- data = {
- "uuid": self.last_conv_id,
- "appraise": "bad"
- }
- requests.post(self.url, json=data)
- return "👎点踩成功，感谢反馈～"
-
- def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot):
- fake_inputs = real_inputs
- display_append = ""
- limited_context = False
- return limited_context, fake_inputs, display_append, real_inputs, chatbot
-
- def handle_file_upload(self, files, chatbot):
- """if the model accepts multi modal input, implement this function"""
- if files:
- for file in files:
- if file.name:
- logging.info(f"尝试读取图像: {file.name}")
- self.try_read_image(file.name)
- if self.image_path is not None:
- chatbot = chatbot + [((self.image_path,), None)]
- if self.image_bytes is not None:
- logging.info("使用图片作为输入")
- # XMChat的一轮对话中实际上只能处理一张图片
- self.reset()
- conv_id = str(uuid.uuid4())
- data = {
- "user_id": self.api_key,
- "session_id": self.session_id,
- "uuid": conv_id,
- "data_type": "imgbase64",
- "data": self.image_bytes
- }
- response = requests.post(self.url, json=data)
- response = json.loads(response.text)
- logging.info(f"图片回复: {response['data']}")
- return None, chatbot, None
-
- def get_answer_at_once(self):
- question = self.history[-1]["content"]
- conv_id = str(uuid.uuid4())
- self.last_conv_id = conv_id
- data = {
- "user_id": self.api_key,
- "session_id": self.session_id,
- "uuid": conv_id,
- "data_type": "text",
- "data": question
- }
- response = requests.post(self.url, json=data)
- try:
- response = json.loads(response.text)
- return response["data"], len(response["data"])
- except Exception as e:
- return response.text, len(response.text)
-
-
-def get_model(
- model_name,
- lora_model_path=None,
- access_key=None,
- temperature=None,
- top_p=None,
- system_prompt=None,
- user_name=""
-) -> BaseLLMModel:
- msg = i18n("模型设置为了：") + f" {model_name}"
- model_type = ModelType.get_type(model_name)
- lora_selector_visibility = False
- lora_choices = []
- dont_change_lora_selector = False
- if model_type != ModelType.OpenAI:
- config.local_embedding = True
- # del current_model.model
- model = None
- try:
- if model_type == ModelType.OpenAI:
- logging.info(f"正在加载OpenAI模型: {model_name}")
- model = OpenAIClient(
- model_name=model_name,
- api_key=access_key,
- system_prompt=system_prompt,
- temperature=temperature,
- top_p=top_p,
- user_name=user_name,
- )
- elif model_type == ModelType.ChatGLM:
- logging.info(f"正在加载ChatGLM模型: {model_name}")
- model = ChatGLM_Client(model_name, user_name=user_name)
- elif model_type == ModelType.LLaMA and lora_model_path == "":
- msg = f"现在请为 {model_name} 选择LoRA模型"
- logging.info(msg)
- lora_selector_visibility = True
- if os.path.isdir("lora"):
- lora_choices = get_file_names(
- "lora", plain=True, filetypes=[""])
- lora_choices = ["No LoRA"] + lora_choices
- elif model_type == ModelType.LLaMA and lora_model_path != "":
- logging.info(f"正在加载LLaMA模型: {model_name} + {lora_model_path}")
- dont_change_lora_selector = True
- if lora_model_path == "No LoRA":
- lora_model_path = None
- msg += " + No LoRA"
- else:
- msg += f" + {lora_model_path}"
- model = LLaMA_Client(
- model_name, lora_model_path, user_name=user_name)
- elif model_type == ModelType.XMChat:
- if os.environ.get("XMCHAT_API_KEY") != "":
- access_key = os.environ.get("XMCHAT_API_KEY")
- model = XMChat(api_key=access_key, user_name=user_name)
- elif model_type == ModelType.StableLM:
- from .StableLM import StableLM_Client
- model = StableLM_Client(model_name, user_name=user_name)
- elif model_type == ModelType.MOSS:
- from .MOSS import MOSS_Client
- model = MOSS_Client(model_name, user_name=user_name)
- elif model_type == ModelType.YuanAI:
- from .inspurai import Yuan_Client
- model = Yuan_Client(model_name, api_key=access_key, user_name=user_name, system_prompt=system_prompt)
- elif model_type == ModelType.Unknown:
- raise ValueError(f"未知模型: {model_name}")
- logging.info(msg)
- chatbot = gr.Chatbot.update(label=model_name)
- except Exception as e:
- logging.error(e)
- msg = f"{STANDARD_ERROR_MSG}: {e}"
- if dont_change_lora_selector:
- return model, msg, chatbot
- else:
- return model, msg, chatbot, gr.Dropdown.update(choices=lora_choices, visible=lora_selector_visibility)
-
-
-if __name__ == "__main__":
- with open("config.json", "r") as f:
- openai_api_key = cjson.load(f)["openai_api_key"]
- # set logging level to debug
- logging.basicConfig(level=logging.DEBUG)
- # client = ModelManager(model_name="gpt-3.5-turbo", access_key=openai_api_key)
- client = get_model(model_name="chatglm-6b-int4")
- chatbot = []
- stream = False
- # 测试账单功能
- logging.info(colorama.Back.GREEN + "测试账单功能" + colorama.Back.RESET)
- logging.info(client.billing_info())
- # 测试问答
- logging.info(colorama.Back.GREEN + "测试问答" + colorama.Back.RESET)
- question = "巴黎是中国的首都吗？"
- for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
- logging.info(i)
- logging.info(f"测试问答后history : {client.history}")
- # 测试记忆力
- logging.info(colorama.Back.GREEN + "测试记忆力" + colorama.Back.RESET)
- question = "我刚刚问了你什么问题？"
- for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
- logging.info(i)
- logging.info(f"测试记忆力后history : {client.history}")
- # 测试重试功能
- logging.info(colorama.Back.GREEN + "测试重试功能" + colorama.Back.RESET)
- for i in client.retry(chatbot=chatbot, stream=stream):
- logging.info(i)
- logging.info(f"重试后history : {client.history}")
- # # 测试总结功能
- # print(colorama.Back.GREEN + "测试总结功能" + colorama.Back.RESET)
- # chatbot, msg = client.reduce_token_size(chatbot=chatbot)
- # print(chatbot, msg)
- # print(f"总结后history: {client.history}")
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/syncbn/modules/functional/csrc/bn.h b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/syncbn/modules/functional/csrc/bn.h
deleted file mode 100644
index 52567a478633aa043ad86624253763e594121bd1..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/syncbn/modules/functional/csrc/bn.h
+++ /dev/null
@@ -1,70 +0,0 @@
-/*****************************************************************************
-
-SyncBN
-
-*****************************************************************************/
-#pragma once
-
-#ifdef WITH_CUDA
-#include "cuda/ext_lib.h"
-#endif
-
-/// SyncBN
-
-std::vector([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2) | newstest2014:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2)
newstest2012/2013:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.ntst1213.tar.bz2) -Convolutional
([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2) | newstest2014:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-de.newstest2014.tar.bz2) -Convolutional
([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT17 English-German](http://statmt.org/wmt17/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2) | newstest2014:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.v2.en-de.newstest2014.tar.bz2) - -## Example usage - -See the [translation README](../translation/README.md) for instructions on reproducing results for WMT'14 En-De and -WMT'14 En-Fr using the `fconv_wmt_en_de` and `fconv_wmt_en_fr` model architectures. - -## Citation - -```bibtex -@inproceedings{gehring2017convs2s, - title = {Convolutional Sequence to Sequence Learning}, - author = {Gehring, Jonas, and Auli, Michael and Grangier, David and Yarats, Denis and Dauphin, Yann N}, - booktitle = {Proc. of ICML}, - year = 2017, -} -``` diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py deleted file mode 100644 index 49e2ca498bf67ad226af5de796b9f44afa65198d..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py +++ /dev/null @@ -1,107 +0,0 @@ -""" from https://github.com/keithito/tacotron """ -import numpy as np -import re -from . import cleaners -from .symbols import symbols - - -# Mappings from symbol to numeric ID and vice versa: -_symbol_to_id = {s: i for i, s in enumerate(symbols)} -_id_to_symbol = {i: s for i, s in enumerate(symbols)} - -# Regular expression matching text enclosed in curly braces: -_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)') - -# Special symbols -SOS_TOK = '
-
-
-
-
-
-
- """,
- theme=gr.themes.Soft(),
- live=False,
-)
-
-iface.launch()
diff --git a/spaces/ParisNeo/FaceRecognition/app.py b/spaces/ParisNeo/FaceRecognition/app.py
deleted file mode 100644
index ead3e5e966357a12fa4621fec7dce321c4ddd147..0000000000000000000000000000000000000000
--- a/spaces/ParisNeo/FaceRecognition/app.py
+++ /dev/null
@@ -1,396 +0,0 @@
-"""=============
- Example : extract_record.py
- Author : Saifeddine ALOUI (ParisNeo)
- Description :
- Make sure you install deepface
- pip install deepface
-
-<================"""
-
-import numpy as np
-from pathlib import Path
-import cv2
-
-from numpy.lib.type_check import imag
-from FaceAnalyzer import FaceAnalyzer
-
-from pathlib import Path
-import pickle
-from deepface import DeepFace
-
-# Number of images to use to build the embedding
-nb_images=50
-
-
-
-
-
-# Build face analyzer while specifying that we want to extract just a single face
-fa = FaceAnalyzer(max_nb_faces=3)
-
-
-box_colors=[
- (255,0,0),
- (0,255,0),
- (0,0,255),
- (255,255,0),
- (255,0,255),
-
-]
-
-
-import gradio as gr
-import numpy as np
-class UI():
- def __init__(self) -> None:
- # If faces path is empty then make it
- self.faces_path = Path(__file__).parent/"faces"
- if not self.faces_path.exists():
- self.faces_path.mkdir(parents=True, exist_ok=True)
- self.i=0
- self.embeddings_cloud = []
- self.is_recording=False
- self.face_name=None
- self.nb_images = 20
- self.nb_faces = 3
-
- # Important to set. If higher than this distance, the face is considered unknown
- self.threshold = 3e-1
- self.faces_db_preprocessed_path = Path(__file__).parent/"faces_db_preprocessed"
- self.current_name = None
- self.current_face_files = []
- self.draw_landmarks = True
- self.webcam_process = False
- self.upgrade_faces()
- try:
- DeepFace.represent(np.zeros((100,100,3)), enforce_detection=False)
- except Exception as ex:
- pass
-
- with gr.Blocks() as demo:
- self.faces = gr.State([])
- self.distance_type = gr.State("cosine")
- gr.Markdown("## FaceAnalyzer face recognition test")
- with gr.Tabs():
- with gr.TabItem('Image Recognize'):
- with gr.Blocks():
- with gr.Row():
- with gr.Column():
- self.rt_inp_img = gr.Image(label="Input Image")
- with gr.Column():
- self.rt_rec_img = gr.Image(label="Output Image")
- self.rt_inp_img.change(self.process_image, inputs=self.rt_inp_img, outputs=self.rt_rec_img, show_progress=True)
- with gr.TabItem('Add face from files'):
- with gr.Blocks():
- with gr.Row():
- with gr.Column():
- self.add_file = gr.Files(label="Files",file_types=["image"])
- with gr.Row():
- self.txtFace_name2 = gr.Textbox(label="face_name")
- self.btn_start = gr.Button("Build face embeddings")
- self.status = gr.Label(label="Status")
-
- self.gallery = gr.Gallery(
- label="Uploaded Images", show_label=True, height=300, elem_id="gallery", visible=False
- ).style(grid=[8], height="auto")
- self.btn_clear = gr.Button("Clear Gallery")
- self.btn_start.click(self.record_from_files, inputs=[self.gallery, self.txtFace_name2], outputs=self.status, show_progress=True)
- self.btn_clear.click(self.clear_galery,[],[self.gallery, self.add_file])
- self.add_file.change(self.add_files, self.add_file, [self.gallery, self.faces])
- with gr.TabItem('Known Faces List'):
- with gr.Blocks():
- with gr.Row():
- with gr.Column():
- self.btn_reset_faces = gr.Button("clear faces list")
- self.btn_get_known_faces_list = gr.Button("get known faces list")
- self.faces_list_status = gr.Label(label="Status")
- self.btn_reset_faces.click(self.clear_faces,[],[self.faces_list_status])
- self.btn_get_known_faces_list.click(self.get_known_faces_list,[],[self.faces_list_status])
- with gr.Row():
- with gr.Accordion(label="Options", open=False):
- self.sld_threshold = gr.Slider(1e-2,10,4e-1,step=1e-2,label="Recognition threshold")
- self.sld_threshold.change(self.set_th,inputs=self.sld_threshold)
- self.sld_nb_images = gr.Slider(2,50,20,label="Number of images")
- self.sld_nb_images.change(self.set_nb_images, self.sld_nb_images)
- self.cb_draw_landmarks = gr.Checkbox(label="Draw landmarks", value=True)
- self.cb_draw_landmarks.change(self.set_draw_landmarks, self.cb_draw_landmarks)
- self.sld_nb_faces = gr.Slider(1,50,3,label="Maximum number of faces")
- self.sld_nb_faces.change(self.set_nb_faces, self.sld_nb_faces)
- self.rd_distance_type = gr.Radio(
- ["cosine", "L1", "L2"], label="Distance", value="cosine"
- )
- self.rd_distance_type.change(self.change_distance, self.rd_distance_type, self.distance_type)
-
-
- demo.queue().launch()
-
- def get_known_faces_list(self):
- return ", ".join(self.known_faces_names)
-
- def clear_directory(self, directory_path):
- """
- Recursively removes all files and subdirectories within the specified directory.
-
- Args:
- directory_path (str): The path to the directory to clear.
-
- Returns:
- None
- """
- directory = Path(directory_path)
- for item in directory.iterdir():
- if item.is_file():
- item.unlink()
- elif item.is_dir():
- self.clear_directory(item)
- item.rmdir()
-
- def clear_faces(self):
- """
- clears faces
- """
- self.clear_directory(self.faces_path)
- self.upgrade_faces()
- return "Faces removed"
-
- def change_distance(self, type):
- return self.distance_type.update(value=type)
-
- def clear_galery(self):
- return self.gallery.update(value=[]), self.add_file.update(value=[])
-
- def start_webcam(self):
- self.webcam_process=not self.webcam_process
- return self.start_streaming.update(value="Stop webcam") if self.webcam_process else self.start_streaming.update(value="Start webcam")
-
-
- def add_files(self, files):
- current_face_files = []
- if files is not None:
- for file in files:
- img = cv2.cvtColor(cv2.imread(file.name), cv2.COLOR_BGR2RGB)
- current_face_files.append(img)
- return current_face_files, current_face_files
- else:
- return []
-
- def set_th(self, value):
- self.threshold=value
-
- def set_nb_images(self, value):
- self.nb_images=value
-
- def set_draw_landmarks(self, value):
- self.draw_landmarks=value
-
- def set_nb_faces(self,nb_faces):
- self.nb_faces = nb_faces
- fa.nb_faces = nb_faces
-
- def cosine_distance(self, u, v):
- """
- Computes the cosine distance between two vectors.
-
- Parameters:
- u (numpy array): A 1-dimensional numpy array representing the first vector.
- v (numpy array): A 1-dimensional numpy array representing the second vector.
-
- Returns:
- float: The cosine distance between the two vectors.
- """
- dot_product = np.dot(u, v)
- norm_u = np.linalg.norm(u)
- norm_v = np.linalg.norm(v)
- return 1 - (dot_product / (norm_u * norm_v))
-
- def upgrade_faces(self):
- # Load faces
- print("Reloading faces")
- self.known_faces=[]
- self.known_faces_names=[]
- face_files = [f for f in self.faces_path.iterdir() if f.name.endswith("pkl")]
- for file in face_files:
- with open(str(file),"rb") as f:
- finger_print = pickle.load(f)
- self.known_faces.append(finger_print)
- self.known_faces_names.append(file.stem)
-
- # if hasattr(self, "faces_list"):
- # self.faces_list.update(value=[[n] for n in self.known_faces_names])
-
- def set_face_name(self, face_name):
- self.face_name=face_name
- return f"face name set to {self.face_name}"
-
- def start_stop(self):
- self.is_recording=True
-
-
- def process_db(self, images):
- for i,image in enumerate(images):
- # Opencv uses BGR format while mediapipe uses RGB format. So we need to convert it to RGB before processing the image
- image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
- image = cv2.resize(image, (640, 480))
- # Process the image to extract faces and draw the masks on the face in the image
- fa.process(image)
-
- if fa.nb_faces>0:
- if fa.nb_faces>1:
- print("Found too many faces!!")
- face = fa.faces[0]
- try:
- # Get a realigned version of the landmarksx
- vertices = face.get_face_outer_vertices()
- image = face.getFaceBox(image, vertices,margins=(30,30,30,30))
- embedding = DeepFace.represent(image, enforce_detection=False)[0]["embedding"]
- embeddings_cloud.append(embedding)
- cv2.imwrite(str(self.faces_db_preprocessed_path/f"im_{i}.png"), cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
- except Exception as ex:
- print(ex)
- embeddings_cloud = np.array(embeddings_cloud)
- embeddings_cloud_mean = embeddings_cloud.mean(axis=0)
- embeddings_cloud_inv_cov = np.linalg.inv(np.cov(embeddings_cloud.T))
- # Now we save it.
- # create a dialog box to ask for the subject name
- name = self.face_name
- with open(str(self.faces_path/f"{name}.pkl"),"wb") as f:
- pickle.dump({"mean":embeddings_cloud_mean, "inv_cov":embeddings_cloud_inv_cov},f)
- print(f"Saved {name}")
-
-
- def record_from_files(self, images, face_name):
- if face_name is None or face_name=="":
- self.embeddings_cloud=[]
- self.is_recording=False
- return "Please input a face name"
-
- if images is not None:
- for entry in images:
- print(f"Processing image {entry['name']}")
- image = cv2.cvtColor(cv2.imread(entry["name"]), cv2.COLOR_BGR2RGB)
- if image is None:
- return None
- # Process the image to extract faces and draw the masks on the face in the image
- if image.shape[1]>640:
- image = cv2.resize(image,(int(640*(image.shape[1]/image.shape[0])),640))
- fa.image_size=(image.shape[1],image.shape[0],3)
- # Process the image to extract faces and draw the masks on the face in the image
- fa.process(image)
- if fa.nb_faces>0:
- print(f"Found {fa.nb_faces} faces")
- try:
- face = fa.faces[0]
- vertices = face.get_face_outer_vertices()
- image = face.getFaceBox(image, vertices, margins=(40,40,40,40))
- embedding = DeepFace.represent(image, enforce_detection=False)[0]["embedding"]
- self.embeddings_cloud.append(embedding)
- self.i+=1
- except Exception as ex:
- print(ex)
- # Now let's find out where the face lives inside the latent space (128 dimensions space)
-
- embeddings_cloud = np.array(self.embeddings_cloud)
- embeddings_cloud_mean = embeddings_cloud.mean(axis=0)
- embeddings_cloud_inv_cov = embeddings_cloud.std(axis=0)
- # Now we save it.
- # create a dialog box to ask for the subject name
- with open(str(self.faces_path/f"{face_name}.pkl"),"wb") as f:
- pickle.dump({"mean":embeddings_cloud_mean, "inv_cov":embeddings_cloud_inv_cov},f)
- print(f"Saved {face_name} embeddings")
- self.i=0
- self.embeddings_cloud=[]
- self.is_recording=False
- self.upgrade_faces()
-
- return f"Saved {face_name} embeddings"
- else:
- return "Waiting"
-
- def process_webcam(self, image):
- if not self.webcam_process:
- return None
-
- fa.image_size=(640, 480, 3)
- # Process the image to extract faces and draw the masks on the face in the image
- fa.process(image)
-
- if fa.nb_faces>0:
- bboxes_and_names=[]
- for j in range(fa.nb_faces):
- try:
- face = fa.faces[j]
- vertices = face.get_face_outer_vertices()
- face_image = face.getFaceBox(image, vertices, margins=(40,40,40,40))
- embedding = DeepFace.represent(face_image, enforce_detection=False)[0]["embedding"]
- if self.draw_landmarks:
- face.draw_landmarks(image, color=(0,0,0))
- nearest_distance = 1e100
- nearest = 0
- for i, known_face in enumerate(self.known_faces):
- if self.distance_type.value == "cosine":
- # Cosine distance
- distance = self.cosine_distance(known_face["mean"], embedding)
- elif self.distance_type.value =="L1":
- # absolute distance
- distance = np.abs(known_face["mean"]-embedding).sum()
- elif self.distance_type.value == "L2":
- # absolute distance
- distance = np.sqrt(np.square(known_face["mean"]-embedding).sum())
- if distanceTurn your selfie into insights! Discover age and gender predictions, symmetry evaluations, and detailed proportions and texture analyses with our app.
- -
Instructions: Upload up to 2 photos. For optimal results, upload a clear front-facing image (see example). To do so, either drag and drop your photo or click Upload Face, then press Submit.
-Other information:
--
-
- The output computation requires approximately 5 to 30 seconds. -
- No uploaded photo is stored. -
- If an error occurs try again or try a different photo or angle. -
- Once submitted, a section detailing the results and associated images will be displayed. -
GitHub: https://github.com/xuebinqin/DIS
Telegram bot: https://t.me/restoration_photo_bot
[](https://twitter.com/DoEvent)" -# article = "
-
-"""
-
-article = """
-
-"""
-
-css = '''
- #col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
- a {text-decoration-line: underline; font-weight: 600;}
- .footer {
- margin-bottom: 45px;
- margin-top: 35px;
- text-align: center;
- border-bottom: 1px solid #e5e5e5;
- }
- .footer>p {
- font-size: .8rem;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(10px);
- background: white;
- }
- .dark .footer {
- border-color: #303030;
- }
- .dark .footer>p {
- background: #0b0f19;
- }
-'''
-
-import gradio as gr
-
-with gr.Blocks(css=css) as demo:
-
- with gr.Column(elem_id="col-container"):
-
- gr.HTML(title)
-
- with gr.Row():
- record_input = gr.Audio(source="microphone",type="filepath", show_label=False)
- send_btn = gr.Button("Send my message !")
- custom_token = gr.Textbox(label='If it fails, use your own session token', placeholder="your own session token")
- with gr.Column():
- audio_translation = gr.Textbox(type="text",label="Whisper translation")
- gpt_response = gr.Textbox(type="text",label="chatGPT response")
-
- gr.HTML(article)
-
- send_btn.click(chat_hf, inputs=[record_input, custom_token], outputs=[audio_translation, gpt_response])
-
-demo.queue(max_size=32, concurrency_count=20).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/XzJosh/Diana-Bert-VITS2/text/__init__.py b/spaces/XzJosh/Diana-Bert-VITS2/text/__init__.py
deleted file mode 100644
index 7566bf351ca9b95af9cdc6d729557a9da083800f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Diana-Bert-VITS2/text/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from text.symbols import *
-
-
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-def cleaned_text_to_sequence(cleaned_text, tones, language):
- '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
- Args:
- text: string to convert to a sequence
- Returns:
- List of integers corresponding to the symbols in the text
- '''
- phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
- tone_start = language_tone_start_map[language]
- tones = [i + tone_start for i in tones]
- lang_id = language_id_map[language]
- lang_ids = [lang_id for i in phones]
- return phones, tones, lang_ids
-
-def get_bert(norm_text, word2ph, language):
- from .chinese_bert import get_bert_feature as zh_bert
- from .english_bert_mock import get_bert_feature as en_bert
- lang_bert_func_map = {
- 'ZH': zh_bert,
- 'EN': en_bert
- }
- bert = lang_bert_func_map[language](norm_text, word2ph)
- return bert
diff --git a/spaces/XzJosh/Eileen-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md b/spaces/XzJosh/Eileen-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
deleted file mode 100644
index 7bce039b7f81ee328fdf8efe3f14409200aacbef..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Eileen-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
+++ /dev/null
@@ -1,57 +0,0 @@
----
-language:
-- zh
-tags:
-- bert
-license: "apache-2.0"
----
-
-# Please use 'Bert' related functions to load this model!
-
-## Chinese BERT with Whole Word Masking
-For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
-
-**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
-Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
-
-This repository is developed based on：https://github.com/google-research/bert
-
-You may also interested in,
-- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
-- Chinese MacBERT: https://github.com/ymcui/MacBERT
-- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
-- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
-- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
-
-More resources by HFL: https://github.com/ymcui/HFL-Anthology
-
-## Citation
-If you find the technical report or resource is useful, please cite the following technical report in your paper.
-- Primary: https://arxiv.org/abs/2004.13922
-```
-@inproceedings{cui-etal-2020-revisiting,
- title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
- author = "Cui, Yiming and
- Che, Wanxiang and
- Liu, Ting and
- Qin, Bing and
- Wang, Shijin and
- Hu, Guoping",
- booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
- month = nov,
- year = "2020",
- address = "Online",
- publisher = "Association for Computational Linguistics",
- url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
- pages = "657--668",
-}
-```
-- Secondary: https://arxiv.org/abs/1906.08101
-```
-@article{chinese-bert-wwm,
- title={Pre-Training with Whole Word Masking for Chinese BERT},
- author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
- journal={arXiv preprint arXiv:1906.08101},
- year={2019}
- }
-```
\ No newline at end of file
diff --git a/spaces/YONG627/456123/yolov5-code-main/utils/loggers/__init__.py b/spaces/YONG627/456123/yolov5-code-main/utils/loggers/__init__.py
deleted file mode 100644
index 9de1f226233ce1c15ec30b853f39092c8524df3a..0000000000000000000000000000000000000000
--- a/spaces/YONG627/456123/yolov5-code-main/utils/loggers/__init__.py
+++ /dev/null
@@ -1,401 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Logging utils
-"""
-
-import os
-import warnings
-from pathlib import Path
-
-import pkg_resources as pkg
-import torch
-from torch.utils.tensorboard import SummaryWriter
-
-from utils.general import LOGGER, colorstr, cv2
-from utils.loggers.clearml.clearml_utils import ClearmlLogger
-from utils.loggers.wandb.wandb_utils import WandbLogger
-from utils.plots import plot_images, plot_labels, plot_results
-from utils.torch_utils import de_parallel
-
-LOGGERS = ('csv', 'tb', 'wandb', 'clearml', 'comet') # *.csv, TensorBoard, Weights & Biases, ClearML
-RANK = int(os.getenv('RANK', -1))
-
-try:
- import wandb
-
- assert hasattr(wandb, '__version__') # verify package import not local dir
- if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in {0, -1}:
- try:
- wandb_login_success = wandb.login(timeout=30)
- except wandb.errors.UsageError: # known non-TTY terminal issue
- wandb_login_success = False
- if not wandb_login_success:
- wandb = None
-except (ImportError, AssertionError):
- wandb = None
-
-try:
- import clearml
-
- assert hasattr(clearml, '__version__') # verify package import not local dir
-except (ImportError, AssertionError):
- clearml = None
-
-try:
- if RANK not in [0, -1]:
- comet_ml = None
- else:
- import comet_ml
-
- assert hasattr(comet_ml, '__version__') # verify package import not local dir
- from utils.loggers.comet import CometLogger
-
-except (ModuleNotFoundError, ImportError, AssertionError):
- comet_ml = None
-
-
-class Loggers():
- # YOLOv5 Loggers class
- def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
- self.save_dir = save_dir
- self.weights = weights
- self.opt = opt
- self.hyp = hyp
- self.plots = not opt.noplots # plot results
- self.logger = logger # for printing results to console
- self.include = include
- self.keys = [
- 'train/box_loss',
- 'train/obj_loss',
- 'train/cls_loss', # train loss
- 'metrics/precision',
- 'metrics/recall',
- 'metrics/mAP_0.5',
- 'metrics/mAP_0.5:0.95', # metrics
- 'val/box_loss',
- 'val/obj_loss',
- 'val/cls_loss', # val loss
- 'x/lr0',
- 'x/lr1',
- 'x/lr2'] # params
- self.best_keys = ['best/epoch', 'best/precision', 'best/recall', 'best/mAP_0.5', 'best/mAP_0.5:0.95']
- for k in LOGGERS:
- setattr(self, k, None) # init empty logger dictionary
- self.csv = True # always log to csv
-
- # Messages
- if not clearml:
- prefix = colorstr('ClearML: ')
- s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML"
- self.logger.info(s)
- if not comet_ml:
- prefix = colorstr('Comet: ')
- s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet"
- self.logger.info(s)
- # TensorBoard
- s = self.save_dir
- if 'tb' in self.include and not self.opt.evolve:
- prefix = colorstr('TensorBoard: ')
- self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/")
- self.tb = SummaryWriter(str(s))
-
- # W&B
- if wandb and 'wandb' in self.include:
- self.opt.hyp = self.hyp # add hyperparameters
- self.wandb = WandbLogger(self.opt)
- else:
- self.wandb = None
-
- # ClearML
- if clearml and 'clearml' in self.include:
- try:
- self.clearml = ClearmlLogger(self.opt, self.hyp)
- except Exception:
- self.clearml = None
- prefix = colorstr('ClearML: ')
- LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.'
- f' See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme')
-
- else:
- self.clearml = None
-
- # Comet
- if comet_ml and 'comet' in self.include:
- if isinstance(self.opt.resume, str) and self.opt.resume.startswith('comet://'):
- run_id = self.opt.resume.split('/')[-1]
- self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id)
-
- else:
- self.comet_logger = CometLogger(self.opt, self.hyp)
-
- else:
- self.comet_logger = None
-
- @property
- def remote_dataset(self):
- # Get data_dict if custom dataset artifact link is provided
- data_dict = None
- if self.clearml:
- data_dict = self.clearml.data_dict
- if self.wandb:
- data_dict = self.wandb.data_dict
- if self.comet_logger:
- data_dict = self.comet_logger.data_dict
-
- return data_dict
-
- def on_train_start(self):
- if self.comet_logger:
- self.comet_logger.on_train_start()
-
- def on_pretrain_routine_start(self):
- if self.comet_logger:
- self.comet_logger.on_pretrain_routine_start()
-
- def on_pretrain_routine_end(self, labels, names):
- # Callback runs on pre-train routine end
- if self.plots:
- plot_labels(labels, names, self.save_dir)
- paths = self.save_dir.glob('*labels*.jpg') # training labels
- if self.wandb:
- self.wandb.log({'Labels': [wandb.Image(str(x), caption=x.name) for x in paths]})
- # if self.clearml:
- # pass # ClearML saves these images automatically using hooks
- if self.comet_logger:
- self.comet_logger.on_pretrain_routine_end(paths)
-
- def on_train_batch_end(self, model, ni, imgs, targets, paths, vals):
- log_dict = dict(zip(self.keys[:3], vals))
- # Callback runs on train batch end
- # ni: number integrated batches (since train start)
- if self.plots:
- if ni < 3:
- f = self.save_dir / f'train_batch{ni}.jpg' # filename
- plot_images(imgs, targets, paths, f)
- if ni == 0 and self.tb and not self.opt.sync_bn:
- log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz))
- if ni == 10 and (self.wandb or self.clearml):
- files = sorted(self.save_dir.glob('train*.jpg'))
- if self.wandb:
- self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
- if self.clearml:
- self.clearml.log_debug_samples(files, title='Mosaics')
-
- if self.comet_logger:
- self.comet_logger.on_train_batch_end(log_dict, step=ni)
-
- def on_train_epoch_end(self, epoch):
- # Callback runs on train epoch end
- if self.wandb:
- self.wandb.current_epoch = epoch + 1
-
- if self.comet_logger:
- self.comet_logger.on_train_epoch_end(epoch)
-
- def on_val_start(self):
- if self.comet_logger:
- self.comet_logger.on_val_start()
-
- def on_val_image_end(self, pred, predn, path, names, im):
- # Callback runs on val image end
- if self.wandb:
- self.wandb.val_one_image(pred, predn, path, names, im)
- if self.clearml:
- self.clearml.log_image_with_boxes(path, pred, names, im)
-
- def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out):
- if self.comet_logger:
- self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out)
-
- def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
- # Callback runs on val end
- if self.wandb or self.clearml:
- files = sorted(self.save_dir.glob('val*.jpg'))
- if self.wandb:
- self.wandb.log({'Validation': [wandb.Image(str(f), caption=f.name) for f in files]})
- if self.clearml:
- self.clearml.log_debug_samples(files, title='Validation')
-
- if self.comet_logger:
- self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
-
- def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
- # Callback runs at the end of each fit (train+val) epoch
- x = dict(zip(self.keys, vals))
- if self.csv:
- file = self.save_dir / 'results.csv'
- n = len(x) + 1 # number of cols
- s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n') # add header
- with open(file, 'a') as f:
- f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
-
- if self.tb:
- for k, v in x.items():
- self.tb.add_scalar(k, v, epoch)
- elif self.clearml: # log to ClearML if TensorBoard not used
- for k, v in x.items():
- title, series = k.split('/')
- self.clearml.task.get_logger().report_scalar(title, series, v, epoch)
-
- if self.wandb:
- if best_fitness == fi:
- best_results = [epoch] + vals[3:7]
- for i, name in enumerate(self.best_keys):
- self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary
- self.wandb.log(x)
- self.wandb.end_epoch()
-
- if self.clearml:
- self.clearml.current_epoch_logged_images = set() # reset epoch image limit
- self.clearml.current_epoch += 1
-
- if self.comet_logger:
- self.comet_logger.on_fit_epoch_end(x, epoch=epoch)
-
- def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
- # Callback runs on model save event
- if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1:
- if self.wandb:
- self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
- if self.clearml:
- self.clearml.task.update_output_model(model_path=str(last),
- model_name='Latest Model',
- auto_delete_file=False)
-
- if self.comet_logger:
- self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi)
-
- def on_train_end(self, last, best, epoch, results):
- # Callback runs on training end, i.e. saving best model
- if self.plots:
- plot_results(file=self.save_dir / 'results.csv') # save results.png
- files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
- files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter
- self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}")
-
- if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles
- for f in files:
- self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
-
- if self.wandb:
- self.wandb.log(dict(zip(self.keys[3:10], results)))
- self.wandb.log({'Results': [wandb.Image(str(f), caption=f.name) for f in files]})
- # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
- if not self.opt.evolve:
- wandb.log_artifact(str(best if best.exists() else last),
- type='model',
- name=f'run_{self.wandb.wandb_run.id}_model',
- aliases=['latest', 'best', 'stripped'])
- self.wandb.finish_run()
-
- if self.clearml and not self.opt.evolve:
- self.clearml.task.update_output_model(model_path=str(best if best.exists() else last),
- name='Best Model',
- auto_delete_file=False)
-
- if self.comet_logger:
- final_results = dict(zip(self.keys[3:10], results))
- self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results)
-
- def on_params_update(self, params: dict):
- # Update hyperparams or configs of the experiment
- if self.wandb:
- self.wandb.wandb_run.config.update(params, allow_val_change=True)
- if self.comet_logger:
- self.comet_logger.on_params_update(params)
-
-
-class GenericLogger:
- """
- YOLOv5 General purpose logger for non-task specific logging
- Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...)
- Arguments
- opt: Run arguments
- console_logger: Console logger
- include: loggers to include
- """
-
- def __init__(self, opt, console_logger, include=('tb', 'wandb')):
- # init default loggers
- self.save_dir = Path(opt.save_dir)
- self.include = include
- self.console_logger = console_logger
- self.csv = self.save_dir / 'results.csv' # CSV logger
- if 'tb' in self.include:
- prefix = colorstr('TensorBoard: ')
- self.console_logger.info(
- f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/")
- self.tb = SummaryWriter(str(self.save_dir))
-
- if wandb and 'wandb' in self.include:
- self.wandb = wandb.init(project=web_project_name(str(opt.project)),
- name=None if opt.name == 'exp' else opt.name,
- config=opt)
- else:
- self.wandb = None
-
- def log_metrics(self, metrics, epoch):
- # Log metrics dictionary to all loggers
- if self.csv:
- keys, vals = list(metrics.keys()), list(metrics.values())
- n = len(metrics) + 1 # number of cols
- s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n') # header
- with open(self.csv, 'a') as f:
- f.write(s + ('%23.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
-
- if self.tb:
- for k, v in metrics.items():
- self.tb.add_scalar(k, v, epoch)
-
- if self.wandb:
- self.wandb.log(metrics, step=epoch)
-
- def log_images(self, files, name='Images', epoch=0):
- # Log images to all loggers
- files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path
- files = [f for f in files if f.exists()] # filter by exists
-
- if self.tb:
- for f in files:
- self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
-
- if self.wandb:
- self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch)
-
- def log_graph(self, model, imgsz=(640, 640)):
- # Log model graph to all loggers
- if self.tb:
- log_tensorboard_graph(self.tb, model, imgsz)
-
- def log_model(self, model_path, epoch=0, metadata={}):
- # Log model to all loggers
- if self.wandb:
- art = wandb.Artifact(name=f'run_{wandb.run.id}_model', type='model', metadata=metadata)
- art.add_file(str(model_path))
- wandb.log_artifact(art)
-
- def update_params(self, params):
- # Update the parameters logged
- if self.wandb:
- wandb.run.config.update(params, allow_val_change=True)
-
-
-def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
- # Log model graph to TensorBoard
- try:
- p = next(model.parameters()) # for device, type
- imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand
- im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty)
- with warnings.catch_warnings():
- warnings.simplefilter('ignore') # suppress jit trace warning
- tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])
- except Exception as e:
- LOGGER.warning(f'WARNING ⚠️ TensorBoard graph visualization failure {e}')
-
-
-def web_project_name(project):
- # Convert local project name to web project name
- if not project.startswith('runs/train'):
- return project
- suffix = '-Classify' if project.endswith('-cls') else '-Segment' if project.endswith('-seg') else ''
- return f'YOLOv5{suffix}'
diff --git a/spaces/Yilin98/Stock_Prediction/sentiment_analysis.py b/spaces/Yilin98/Stock_Prediction/sentiment_analysis.py
deleted file mode 100644
index 681692a6de9c8bc9bfa594134c7098566d3b9209..0000000000000000000000000000000000000000
--- a/spaces/Yilin98/Stock_Prediction/sentiment_analysis.py
+++ /dev/null
@@ -1,210 +0,0 @@
-# Linear Algebra and DataFrames
-import numpy as np
-import pandas as pd
-
-# Visualization libraries
-import seaborn as sns
-sns.set_style("whitegrid")
-
-# NLP Preprocessing and Basic tools
-import nltk
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
-import re
-import string
-from emoji import demojize
-
-from data_loader_functions import *
-
-
-## Crawl down the news from investing.com
-def news_scraping(company):
- # scrape news
- urls = get_articles_urls('apple-computer', 1, 3)
- if company == 'Amazon':
- urls = get_articles_urls('amazon-com', 1, 3)
- elif company == 'Meta':
- urls = get_articles_urls('facebook', 1, 3)
- articles_df = pd.DataFrame({'ticker':[],
- 'publish_date':[],
- 'title': [],
- 'body_text': [],
- 'url':[]})
- articles_df=scrape_news(urls, articles_df, company)
-
- # Checking the data for duplicates
- articles_df[articles_df.duplicated('body_text',keep=False)].sort_values('body_text')
-
- # Dropping all duplicates
- articles_df.drop_duplicates(('body_text'), inplace=True)
- return articles_df
-
-
-## Fetch news from hopsworks
-def fetching_news(company):
- articles_df = get_news_from_hopsworks()
- articles_df.loc[articles_df['ticker'] == company]
- articles_df['publish_date'] = articles_df['publish_date'].apply(time_2_datetime)
- return articles_df
-
-
-## NLP Processes
-# Remove mentions
-def remove_urls(text):
- return re.sub(r'https?://\S+|www\.\S_+', '', text)
-
-def remove_usernames_ressource(text):
- text_split = text.split("-",1)
- if len(text_split)>1:
- text=text_split[1]
- text = re.sub(r'@[A-Za-z0-9_]+', ' ', text)
- return text
-
-# Remove hashtags
-def remove_hashtags(text):
- return re.sub("#[A-Za-z0-9_]+"," ", text)
-
-# Remove punctuations
-def remove_punctuation(text, punc_list):
- return text.translate(str.maketrans('', '', punc_list))
-
-# Convert emojis to texts
-def convert_emojis(text):
- return demojize(text).replace(":","")
-
-# Apply the previous functions
-def full_preprocessing(text):
- """
- @param text (str): a string to be processed.
- @return text (Str): the processed string.
- """
- punc_list = string.punctuation
-
- # Remove non-ascii words
- text = re.sub(r'[^\x00-\x7F]+', ' ', text)
-
- # Replace '&' with 'and'
- text = re.sub(r'&', 'and', text)
-
- # Remove trailing whitespace
- text = re.sub(r'\s+', ' ', text).strip()
-
- text = remove_urls(text)
- text = remove_usernames_ressource(text)
- text = remove_hashtags(text)
- text = remove_punctuation(text, punc_list)
- text = convert_emojis(text)
-
- return text.lower()
-
-# Trasform text to tokens (separated words)
-def tokenize(text) :
- text = text.split()
- return text
-
-# Remove stopwords
-def remove_stopwords(text, stop_words):
- words_to_keep = ["not","no","nor"]
- stopword = [elem for elem in stop_words if not elem in words_to_keep]
- text = [w.lower() for w in text if not w.lower() in stopword]
- return text
-
-# Lemmatization
-def lemmatize(text, wn):
- text = [wn.lemmatize(word) for word in text]
- return text
-
-# Stemming
-def stemming(text, ps, ls):
- text = [ps.stem(word) for word in text]
- text = [ls.stem(word) for word in text]
- return text
-
-def full_processing(df):
- stop_words = stopwords.words('english')
- wn = nltk.WordNetLemmatizer()
- ps = nltk.PorterStemmer()
- ls = nltk.LancasterStemmer()
-
- df["text_W_puncts"] =df["body_text"].apply(lambda x: full_preprocessing(x))
- df["text_tokenized"] = df["text_W_puncts"].apply(lambda x: tokenize(x))
- df["text_W_stopwords"] = df["text_tokenized"].apply(lambda x: remove_stopwords(x, stop_words))
- df["text_lemmatized"] = df["text_W_stopwords"].apply(lambda x: lemmatize(x, wn))
- df["text_stemmed"] = df["text_lemmatized"].apply(lambda x: stemming(x, ps, ls))
- df["text_processed"] = df["text_stemmed"].apply(lambda x: ' '.join(str(e) for e in x))
-
- return df
-
-
-def nlp_processing(articles_df):
- news=articles_df[['body_text','publish_date','title']]
- # Number of mentions, hashtags, urls
- cnt_1, cnt_2, cnt_3 = 0, 0, 0
- max_len, min_len, mean_len = -float("inf"), float("inf"), 0
- for row in news.values:
- text = row[0] # 0 for text content
- if "@" in text:
- cnt_1 += 1
- if "#" in text:
- cnt_2 += 1
- if 'http' or 'www' in text:
- cnt_3 += 1
- if len(text) < min_len:
- min_len = len(text)
- if len(text) > max_len:
- max_len = len(text)
-
- mean_len += len(text)
-
- mean_len /= len(articles_df)
-
- nltk.download('stopwords')
- nltk.download('wordnet')
- nltk.download('omw-1.4')
-
- articles_processed = full_processing(articles_df)
-
- return articles_processed
-
-
-## Vader Sentiment
-def predicted_label(x):
- if x<=-0.5:
- return 0
- elif x>=0.5:
- return 2
- else:
- return 1
-
-def score_Vader(df,analyzer):
- df['neg'] = df['text_processed'].apply(lambda x:analyzer.polarity_scores(x)['neg'])
- df['neu'] = df['text_processed'].apply(lambda x:analyzer.polarity_scores(x)['neu'])
- df['pos'] = df['text_processed'].apply(lambda x:analyzer.polarity_scores(x)['pos'])
- df['compound'] = df['text_processed'].apply(lambda x:analyzer.polarity_scores(x)['compound'])
-
- df['predicted_class'] = df['compound'].map(predicted_label)
- return df
-
-def vader_sentiment(articles_processed):
- from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
- analyzer = SentimentIntensityAnalyzer()
- articles_processed=score_Vader(articles_processed, analyzer)
- return articles_processed
-
-def sentiment_analysis(company, day):
- articles_df = fetching_news(company)
- articles_df = select_oneday_news(articles_df, day)
- articles_df = articles_df.loc[articles_df['ticker'] == company.upper()]
- # articles_processed = nlp_processing(articles_df)
- # articles_sentimentalized = vader_sentiment(articles_processed)
- return articles_df
-
-## Aggregate News Sentiments Each Day
-def aggregate_by_date(articles_sentiments):
- articles_sentiments = change_date_format(articles_sentiments)
- keep_columns = ['ticker', 'publish_date', 'neg', 'neu', 'pos', 'compound']
- sentiment_df = articles_sentiments[keep_columns]
- daily_sentiment = sentiment_df.groupby([sentiment_df['publish_date'].dt.date, 'ticker']).agg({'neg': 'mean', 'neu': 'mean', 'pos': 'mean', 'compound': 'mean'}).reset_index()
- return daily_sentiment
-
-
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/data/transforms/augmentation_impl.py b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/data/transforms/augmentation_impl.py
deleted file mode 100644
index 652a34a9aef2d4004f46ad7814befe6d1c230bc4..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/data/transforms/augmentation_impl.py
+++ /dev/null
@@ -1,614 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-Implement many useful :class:`Augmentation`.
-"""
-import numpy as np
-import sys
-from typing import Tuple
-import torch
-from fvcore.transforms.transform import (
- BlendTransform,
- CropTransform,
- HFlipTransform,
- NoOpTransform,
- PadTransform,
- Transform,
- TransformList,
- VFlipTransform,
-)
-from PIL import Image
-
-from .augmentation import Augmentation, _transform_to_aug
-from .transform import ExtentTransform, ResizeTransform, RotationTransform
-
-__all__ = [
- "FixedSizeCrop",
- "RandomApply",
- "RandomBrightness",
- "RandomContrast",
- "RandomCrop",
- "RandomExtent",
- "RandomFlip",
- "RandomSaturation",
- "RandomLighting",
- "RandomRotation",
- "Resize",
- "ResizeScale",
- "ResizeShortestEdge",
- "RandomCrop_CategoryAreaConstraint",
-]
-
-
-class RandomApply(Augmentation):
- """
- Randomly apply an augmentation with a given probability.
- """
-
- def __init__(self, tfm_or_aug, prob=0.5):
- """
- Args:
- tfm_or_aug (Transform, Augmentation): the transform or augmentation
- to be applied. It can either be a `Transform` or `Augmentation`
- instance.
- prob (float): probability between 0.0 and 1.0 that
- the wrapper transformation is applied
- """
- super().__init__()
- self.aug = _transform_to_aug(tfm_or_aug)
- assert 0.0 <= prob <= 1.0, f"Probablity must be between 0.0 and 1.0 (given: {prob})"
- self.prob = prob
-
- def get_transform(self, *args):
- do = self._rand_range() < self.prob
- if do:
- return self.aug.get_transform(*args)
- else:
- return NoOpTransform()
-
- def __call__(self, aug_input):
- do = self._rand_range() < self.prob
- if do:
- return self.aug(aug_input)
- else:
- return NoOpTransform()
-
-
-class RandomFlip(Augmentation):
- """
- Flip the image horizontally or vertically with the given probability.
- """
-
- def __init__(self, prob=0.5, *, horizontal=True, vertical=False):
- """
- Args:
- prob (float): probability of flip.
- horizontal (boolean): whether to apply horizontal flipping
- vertical (boolean): whether to apply vertical flipping
- """
- super().__init__()
-
- if horizontal and vertical:
- raise ValueError("Cannot do both horiz and vert. Please use two Flip instead.")
- if not horizontal and not vertical:
- raise ValueError("At least one of horiz or vert has to be True!")
- self._init(locals())
-
- def get_transform(self, image):
- h, w = image.shape[:2]
- do = self._rand_range() < self.prob
- if do:
- if self.horizontal:
- return HFlipTransform(w)
- elif self.vertical:
- return VFlipTransform(h)
- else:
- return NoOpTransform()
-
-
-class Resize(Augmentation):
- """Resize image to a fixed target size"""
-
- def __init__(self, shape, interp=Image.BILINEAR):
- """
- Args:
- shape: (h, w) tuple or a int
- interp: PIL interpolation method
- """
- if isinstance(shape, int):
- shape = (shape, shape)
- shape = tuple(shape)
- self._init(locals())
-
- def get_transform(self, image):
- return ResizeTransform(
- image.shape[0], image.shape[1], self.shape[0], self.shape[1], self.interp
- )
-
-
-class ResizeShortestEdge(Augmentation):
- """
- Resize the image while keeping the aspect ratio unchanged.
- It attempts to scale the shorter edge to the given `short_edge_length`,
- as long as the longer edge does not exceed `max_size`.
- If `max_size` is reached, then downscale so that the longer edge does not exceed max_size.
- """
-
- @torch.jit.unused
- def __init__(
- self, short_edge_length, max_size=sys.maxsize, sample_style="range", interp=Image.BILINEAR
- ):
- """
- Args:
- short_edge_length (list[int]): If ``sample_style=="range"``,
- a [min, max] interval from which to sample the shortest edge length.
- If ``sample_style=="choice"``, a list of shortest edge lengths to sample from.
- max_size (int): maximum allowed longest edge length.
- sample_style (str): either "range" or "choice".
- """
- super().__init__()
- assert sample_style in ["range", "choice"], sample_style
-
- self.is_range = sample_style == "range"
- if isinstance(short_edge_length, int):
- short_edge_length = (short_edge_length, short_edge_length)
- if self.is_range:
- assert len(short_edge_length) == 2, (
- "short_edge_length must be two values using 'range' sample style."
- f" Got {short_edge_length}!"
- )
- self._init(locals())
-
- @torch.jit.unused
- def get_transform(self, image):
- h, w = image.shape[:2]
- if self.is_range:
- size = np.random.randint(self.short_edge_length[0], self.short_edge_length[1] + 1)
- else:
- size = np.random.choice(self.short_edge_length)
- if size == 0:
- return NoOpTransform()
-
- newh, neww = ResizeShortestEdge.get_output_shape(h, w, size, self.max_size)
- return ResizeTransform(h, w, newh, neww, self.interp)
-
- @staticmethod
- def get_output_shape(
- oldh: int, oldw: int, short_edge_length: int, max_size: int
- ) -> Tuple[int, int]:
- """
- Compute the output size given input size and target short edge length.
- """
- h, w = oldh, oldw
- size = short_edge_length * 1.0
- scale = size / min(h, w)
- if h < w:
- newh, neww = size, scale * w
- else:
- newh, neww = scale * h, size
- if max(newh, neww) > max_size:
- scale = max_size * 1.0 / max(newh, neww)
- newh = newh * scale
- neww = neww * scale
- neww = int(neww + 0.5)
- newh = int(newh + 0.5)
- return (newh, neww)
-
-
-class ResizeScale(Augmentation):
- """
- Takes target size as input and randomly scales the given target size between `min_scale`
- and `max_scale`. It then scales the input image such that it fits inside the scaled target
- box, keeping the aspect ratio constant.
- This implements the resize part of the Google's 'resize_and_crop' data augmentation:
- https://github.com/tensorflow/tpu/blob/master/models/official/detection/utils/input_utils.py#L127
- """
-
- def __init__(
- self,
- min_scale: float,
- max_scale: float,
- target_height: int,
- target_width: int,
- interp: int = Image.BILINEAR,
- ):
- """
- Args:
- min_scale: minimum image scale range.
- max_scale: maximum image scale range.
- target_height: target image height.
- target_width: target image width.
- interp: image interpolation method.
- """
- super().__init__()
- self._init(locals())
-
- def _get_resize(self, image: np.ndarray, scale: float) -> Transform:
- input_size = image.shape[:2]
-
- # Compute new target size given a scale.
- target_size = (self.target_height, self.target_width)
- target_scale_size = np.multiply(target_size, scale)
-
- # Compute actual rescaling applied to input image and output size.
- output_scale = np.minimum(
- target_scale_size[0] / input_size[0], target_scale_size[1] / input_size[1]
- )
- output_size = np.round(np.multiply(input_size, output_scale)).astype(int)
-
- return ResizeTransform(
- input_size[0], input_size[1], output_size[0], output_size[1], self.interp
- )
-
- def get_transform(self, image: np.ndarray) -> Transform:
- random_scale = np.random.uniform(self.min_scale, self.max_scale)
- return self._get_resize(image, random_scale)
-
-
-class RandomRotation(Augmentation):
- """
- This method returns a copy of this image, rotated the given
- number of degrees counter clockwise around the given center.
- """
-
- def __init__(self, angle, expand=True, center=None, sample_style="range", interp=None):
- """
- Args:
- angle (list[float]): If ``sample_style=="range"``,
- a [min, max] interval from which to sample the angle (in degrees).
- If ``sample_style=="choice"``, a list of angles to sample from
- expand (bool): choose if the image should be resized to fit the whole
- rotated image (default), or simply cropped
- center (list[[float, float]]): If ``sample_style=="range"``,
- a [[minx, miny], [maxx, maxy]] relative interval from which to sample the center,
- [0, 0] being the top left of the image and [1, 1] the bottom right.
- If ``sample_style=="choice"``, a list of centers to sample from
- Default: None, which means that the center of rotation is the center of the image
- center has no effect if expand=True because it only affects shifting
- """
- super().__init__()
- assert sample_style in ["range", "choice"], sample_style
- self.is_range = sample_style == "range"
- if isinstance(angle, (float, int)):
- angle = (angle, angle)
- if center is not None and isinstance(center[0], (float, int)):
- center = (center, center)
- self._init(locals())
-
- def get_transform(self, image):
- h, w = image.shape[:2]
- center = None
- if self.is_range:
- angle = np.random.uniform(self.angle[0], self.angle[1])
- if self.center is not None:
- center = (
- np.random.uniform(self.center[0][0], self.center[1][0]),
- np.random.uniform(self.center[0][1], self.center[1][1]),
- )
- else:
- angle = np.random.choice(self.angle)
- if self.center is not None:
- center = np.random.choice(self.center)
-
- if center is not None:
- center = (w * center[0], h * center[1]) # Convert to absolute coordinates
-
- if angle % 360 == 0:
- return NoOpTransform()
-
- return RotationTransform(h, w, angle, expand=self.expand, center=center, interp=self.interp)
-
-
-class FixedSizeCrop(Augmentation):
- """
- If `crop_size` is smaller than the input image size, then it uses a random crop of
- the crop size. If `crop_size` is larger than the input image size, then it pads
- the right and the bottom of the image to the crop size if `pad` is True, otherwise
- it returns the smaller image.
- """
-
- def __init__(self, crop_size: Tuple[int], pad: bool = True, pad_value: float = 128.0):
- """
- Args:
- crop_size: target image (height, width).
- pad: if True, will pad images smaller than `crop_size` up to `crop_size`
- pad_value: the padding value.
- """
- super().__init__()
- self._init(locals())
-
- def _get_crop(self, image: np.ndarray) -> Transform:
- # Compute the image scale and scaled size.
- input_size = image.shape[:2]
- output_size = self.crop_size
-
- # Add random crop if the image is scaled up.
- max_offset = np.subtract(input_size, output_size)
- max_offset = np.maximum(max_offset, 0)
- offset = np.multiply(max_offset, np.random.uniform(0.0, 1.0))
- offset = np.round(offset).astype(int)
- return CropTransform(
- offset[1], offset[0], output_size[1], output_size[0], input_size[1], input_size[0]
- )
-
- def _get_pad(self, image: np.ndarray) -> Transform:
- # Compute the image scale and scaled size.
- input_size = image.shape[:2]
- output_size = self.crop_size
-
- # Add padding if the image is scaled down.
- pad_size = np.subtract(output_size, input_size)
- pad_size = np.maximum(pad_size, 0)
- original_size = np.minimum(input_size, output_size)
- return PadTransform(
- 0, 0, pad_size[1], pad_size[0], original_size[1], original_size[0], self.pad_value
- )
-
- def get_transform(self, image: np.ndarray) -> TransformList:
- transforms = [self._get_crop(image)]
- if self.pad:
- transforms.append(self._get_pad(image))
- return TransformList(transforms)
-
-
-class RandomCrop(Augmentation):
- """
- Randomly crop a rectangle region out of an image.
- """
-
- def __init__(self, crop_type: str, crop_size):
- """
- Args:
- crop_type (str): one of "relative_range", "relative", "absolute", "absolute_range".
- crop_size (tuple[float, float]): two floats, explained below.
-
- - "relative": crop a (H * crop_size[0], W * crop_size[1]) region from an input image of
- size (H, W). crop size should be in (0, 1]
- - "relative_range": uniformly sample two values from [crop_size[0], 1]
- and [crop_size[1]], 1], and use them as in "relative" crop type.
- - "absolute" crop a (crop_size[0], crop_size[1]) region from input image.
- crop_size must be smaller than the input image size.
- - "absolute_range", for an input of size (H, W), uniformly sample H_crop in
- [crop_size[0], min(H, crop_size[1])] and W_crop in [crop_size[0], min(W, crop_size[1])].
- Then crop a region (H_crop, W_crop).
- """
- # TODO style of relative_range and absolute_range are not consistent:
- # one takes (h, w) but another takes (min, max)
- super().__init__()
- assert crop_type in ["relative_range", "relative", "absolute", "absolute_range"]
- self._init(locals())
-
- def get_transform(self, image):
- h, w = image.shape[:2]
- croph, cropw = self.get_crop_size((h, w))
- assert h >= croph and w >= cropw, "Shape computation in {} has bugs.".format(self)
- h0 = np.random.randint(h - croph + 1)
- w0 = np.random.randint(w - cropw + 1)
- return CropTransform(w0, h0, cropw, croph)
-
- def get_crop_size(self, image_size):
- """
- Args:
- image_size (tuple): height, width
-
- Returns:
- crop_size (tuple): height, width in absolute pixels
- """
- h, w = image_size
- if self.crop_type == "relative":
- ch, cw = self.crop_size
- return int(h * ch + 0.5), int(w * cw + 0.5)
- elif self.crop_type == "relative_range":
- crop_size = np.asarray(self.crop_size, dtype=np.float32)
- ch, cw = crop_size + np.random.rand(2) * (1 - crop_size)
- return int(h * ch + 0.5), int(w * cw + 0.5)
- elif self.crop_type == "absolute":
- return (min(self.crop_size[0], h), min(self.crop_size[1], w))
- elif self.crop_type == "absolute_range":
- assert self.crop_size[0] <= self.crop_size[1]
- ch = np.random.randint(min(h, self.crop_size[0]), min(h, self.crop_size[1]) + 1)
- cw = np.random.randint(min(w, self.crop_size[0]), min(w, self.crop_size[1]) + 1)
- return ch, cw
- else:
- raise NotImplementedError("Unknown crop type {}".format(self.crop_type))
-
-
-class RandomCrop_CategoryAreaConstraint(Augmentation):
- """
- Similar to :class:`RandomCrop`, but find a cropping window such that no single category
- occupies a ratio of more than `single_category_max_area` in semantic segmentation ground
- truth, which can cause unstability in training. The function attempts to find such a valid
- cropping window for at most 10 times.
- """
-
- def __init__(
- self,
- crop_type: str,
- crop_size,
- single_category_max_area: float = 1.0,
- ignored_category: int = None,
- ):
- """
- Args:
- crop_type, crop_size: same as in :class:`RandomCrop`
- single_category_max_area: the maximum allowed area ratio of a
- category. Set to 1.0 to disable
- ignored_category: allow this category in the semantic segmentation
- ground truth to exceed the area ratio. Usually set to the category
- that's ignored in training.
- """
- self.crop_aug = RandomCrop(crop_type, crop_size)
- self._init(locals())
-
- def get_transform(self, image, sem_seg):
- if self.single_category_max_area >= 1.0:
- return self.crop_aug.get_transform(image)
- else:
- h, w = sem_seg.shape
- for _ in range(10):
- crop_size = self.crop_aug.get_crop_size((h, w))
- y0 = np.random.randint(h - crop_size[0] + 1)
- x0 = np.random.randint(w - crop_size[1] + 1)
- sem_seg_temp = sem_seg[y0 : y0 + crop_size[0], x0 : x0 + crop_size[1]]
- labels, cnt = np.unique(sem_seg_temp, return_counts=True)
- if self.ignored_category is not None:
- cnt = cnt[labels != self.ignored_category]
- if len(cnt) > 1 and np.max(cnt) < np.sum(cnt) * self.single_category_max_area:
- break
- crop_tfm = CropTransform(x0, y0, crop_size[1], crop_size[0])
- return crop_tfm
-
-
-class RandomExtent(Augmentation):
- """
- Outputs an image by cropping a random "subrect" of the source image.
-
- The subrect can be parameterized to include pixels outside the source image,
- in which case they will be set to zeros (i.e. black). The size of the output
- image will vary with the size of the random subrect.
- """
-
- def __init__(self, scale_range, shift_range):
- """
- Args:
- output_size (h, w): Dimensions of output image
- scale_range (l, h): Range of input-to-output size scaling factor
- shift_range (x, y): Range of shifts of the cropped subrect. The rect
- is shifted by [w / 2 * Uniform(-x, x), h / 2 * Uniform(-y, y)],
- where (w, h) is the (width, height) of the input image. Set each
- component to zero to crop at the image's center.
- """
- super().__init__()
- self._init(locals())
-
- def get_transform(self, image):
- img_h, img_w = image.shape[:2]
-
- # Initialize src_rect to fit the input image.
- src_rect = np.array([-0.5 * img_w, -0.5 * img_h, 0.5 * img_w, 0.5 * img_h])
-
- # Apply a random scaling to the src_rect.
- src_rect *= np.random.uniform(self.scale_range[0], self.scale_range[1])
-
- # Apply a random shift to the coordinates origin.
- src_rect[0::2] += self.shift_range[0] * img_w * (np.random.rand() - 0.5)
- src_rect[1::2] += self.shift_range[1] * img_h * (np.random.rand() - 0.5)
-
- # Map src_rect coordinates into image coordinates (center at corner).
- src_rect[0::2] += 0.5 * img_w
- src_rect[1::2] += 0.5 * img_h
-
- return ExtentTransform(
- src_rect=(src_rect[0], src_rect[1], src_rect[2], src_rect[3]),
- output_size=(int(src_rect[3] - src_rect[1]), int(src_rect[2] - src_rect[0])),
- )
-
-
-class RandomContrast(Augmentation):
- """
- Randomly transforms image contrast.
-
- Contrast intensity is uniformly sampled in (intensity_min, intensity_max).
- - intensity < 1 will reduce contrast
- - intensity = 1 will preserve the input image
- - intensity > 1 will increase contrast
-
- See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html
- """
-
- def __init__(self, intensity_min, intensity_max):
- """
- Args:
- intensity_min (float): Minimum augmentation
- intensity_max (float): Maximum augmentation
- """
- super().__init__()
- self._init(locals())
-
- def get_transform(self, image):
- w = np.random.uniform(self.intensity_min, self.intensity_max)
- return BlendTransform(src_image=image.mean(), src_weight=1 - w, dst_weight=w)
-
-
-class RandomBrightness(Augmentation):
- """
- Randomly transforms image brightness.
-
- Brightness intensity is uniformly sampled in (intensity_min, intensity_max).
- - intensity < 1 will reduce brightness
- - intensity = 1 will preserve the input image
- - intensity > 1 will increase brightness
-
- See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html
- """
-
- def __init__(self, intensity_min, intensity_max):
- """
- Args:
- intensity_min (float): Minimum augmentation
- intensity_max (float): Maximum augmentation
- """
- super().__init__()
- self._init(locals())
-
- def get_transform(self, image):
- w = np.random.uniform(self.intensity_min, self.intensity_max)
- return BlendTransform(src_image=0, src_weight=1 - w, dst_weight=w)
-
-
-class RandomSaturation(Augmentation):
- """
- Randomly transforms saturation of an RGB image.
- Input images are assumed to have 'RGB' channel order.
-
- Saturation intensity is uniformly sampled in (intensity_min, intensity_max).
- - intensity < 1 will reduce saturation (make the image more grayscale)
- - intensity = 1 will preserve the input image
- - intensity > 1 will increase saturation
-
- See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html
- """
-
- def __init__(self, intensity_min, intensity_max):
- """
- Args:
- intensity_min (float): Minimum augmentation (1 preserves input).
- intensity_max (float): Maximum augmentation (1 preserves input).
- """
- super().__init__()
- self._init(locals())
-
- def get_transform(self, image):
- assert image.shape[-1] == 3, "RandomSaturation only works on RGB images"
- w = np.random.uniform(self.intensity_min, self.intensity_max)
- grayscale = image.dot([0.299, 0.587, 0.114])[:, :, np.newaxis]
- return BlendTransform(src_image=grayscale, src_weight=1 - w, dst_weight=w)
-
-
-class RandomLighting(Augmentation):
- """
- The "lighting" augmentation described in AlexNet, using fixed PCA over ImageNet.
- Input images are assumed to have 'RGB' channel order.
-
- The degree of color jittering is randomly sampled via a normal distribution,
- with standard deviation given by the scale parameter.
- """
-
- def __init__(self, scale):
- """
- Args:
- scale (float): Standard deviation of principal component weighting.
- """
- super().__init__()
- self._init(locals())
- self.eigen_vecs = np.array(
- [[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], [-0.5836, -0.6948, 0.4203]]
- )
- self.eigen_vals = np.array([0.2175, 0.0188, 0.0045])
-
- def get_transform(self, image):
- assert image.shape[-1] == 3, "RandomLighting only works on RGB images"
- weights = np.random.normal(scale=self.scale, size=3)
- return BlendTransform(
- src_image=self.eigen_vecs.dot(weights * self.eigen_vals), src_weight=1.0, dst_weight=1.0
- )
diff --git a/spaces/YuFuji/CalqTalk/auth_config.py b/spaces/YuFuji/CalqTalk/auth_config.py
deleted file mode 100644
index a0cf42dde3cf2ace8fb0783da08afabc88b520a0..0000000000000000000000000000000000000000
--- a/spaces/YuFuji/CalqTalk/auth_config.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import os
-
-def auth(user:str, psw:str):
- if (user == os.getenv("User")) and (psw == os.getenv("Password")):
- return True
- else:
- return False
\ No newline at end of file
diff --git a/spaces/Yuelili/RealNagrse/tests/test_dataset.py b/spaces/Yuelili/RealNagrse/tests/test_dataset.py
deleted file mode 100644
index 715b4082645c131d43d728ae8f65bcc2430aa8c9..0000000000000000000000000000000000000000
--- a/spaces/Yuelili/RealNagrse/tests/test_dataset.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import pytest
-import yaml
-
-from realesrgan.data.realesrgan_dataset import RealESRGANDataset
-from realesrgan.data.realesrgan_paired_dataset import RealESRGANPairedDataset
-
-
-def test_realesrgan_dataset():
-
- with open('tests/data/test_realesrgan_dataset.yml', mode='r') as f:
- opt = yaml.load(f, Loader=yaml.FullLoader)
-
- dataset = RealESRGANDataset(opt)
- assert dataset.io_backend_opt['type'] == 'disk' # io backend
- assert len(dataset) == 2 # whether to read correct meta info
- assert dataset.kernel_list == [
- 'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
- ] # correct initialization the degradation configurations
- assert dataset.betag_range2 == [0.5, 4]
-
- # test __getitem__
- result = dataset.__getitem__(0)
- # check returned keys
- expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
- assert set(expected_keys).issubset(set(result.keys()))
- # check shape and contents
- assert result['gt'].shape == (3, 400, 400)
- assert result['kernel1'].shape == (21, 21)
- assert result['kernel2'].shape == (21, 21)
- assert result['sinc_kernel'].shape == (21, 21)
- assert result['gt_path'] == 'tests/data/gt/baboon.png'
-
- # ------------------ test lmdb backend -------------------- #
- opt['dataroot_gt'] = 'tests/data/gt.lmdb'
- opt['io_backend']['type'] = 'lmdb'
-
- dataset = RealESRGANDataset(opt)
- assert dataset.io_backend_opt['type'] == 'lmdb' # io backend
- assert len(dataset.paths) == 2 # whether to read correct meta info
- assert dataset.kernel_list == [
- 'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
- ] # correct initialization the degradation configurations
- assert dataset.betag_range2 == [0.5, 4]
-
- # test __getitem__
- result = dataset.__getitem__(1)
- # check returned keys
- expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
- assert set(expected_keys).issubset(set(result.keys()))
- # check shape and contents
- assert result['gt'].shape == (3, 400, 400)
- assert result['kernel1'].shape == (21, 21)
- assert result['kernel2'].shape == (21, 21)
- assert result['sinc_kernel'].shape == (21, 21)
- assert result['gt_path'] == 'comic'
-
- # ------------------ test with sinc_prob = 0 -------------------- #
- opt['dataroot_gt'] = 'tests/data/gt.lmdb'
- opt['io_backend']['type'] = 'lmdb'
- opt['sinc_prob'] = 0
- opt['sinc_prob2'] = 0
- opt['final_sinc_prob'] = 0
- dataset = RealESRGANDataset(opt)
- result = dataset.__getitem__(0)
- # check returned keys
- expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
- assert set(expected_keys).issubset(set(result.keys()))
- # check shape and contents
- assert result['gt'].shape == (3, 400, 400)
- assert result['kernel1'].shape == (21, 21)
- assert result['kernel2'].shape == (21, 21)
- assert result['sinc_kernel'].shape == (21, 21)
- assert result['gt_path'] == 'baboon'
-
- # ------------------ lmdb backend should have paths ends with lmdb -------------------- #
- with pytest.raises(ValueError):
- opt['dataroot_gt'] = 'tests/data/gt'
- opt['io_backend']['type'] = 'lmdb'
- dataset = RealESRGANDataset(opt)
-
-
-def test_realesrgan_paired_dataset():
-
- with open('tests/data/test_realesrgan_paired_dataset.yml', mode='r') as f:
- opt = yaml.load(f, Loader=yaml.FullLoader)
-
- dataset = RealESRGANPairedDataset(opt)
- assert dataset.io_backend_opt['type'] == 'disk' # io backend
- assert len(dataset) == 2 # whether to read correct meta info
-
- # test __getitem__
- result = dataset.__getitem__(0)
- # check returned keys
- expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
- assert set(expected_keys).issubset(set(result.keys()))
- # check shape and contents
- assert result['gt'].shape == (3, 128, 128)
- assert result['lq'].shape == (3, 32, 32)
- assert result['gt_path'] == 'tests/data/gt/baboon.png'
- assert result['lq_path'] == 'tests/data/lq/baboon.png'
-
- # ------------------ test lmdb backend -------------------- #
- opt['dataroot_gt'] = 'tests/data/gt.lmdb'
- opt['dataroot_lq'] = 'tests/data/lq.lmdb'
- opt['io_backend']['type'] = 'lmdb'
-
- dataset = RealESRGANPairedDataset(opt)
- assert dataset.io_backend_opt['type'] == 'lmdb' # io backend
- assert len(dataset) == 2 # whether to read correct meta info
-
- # test __getitem__
- result = dataset.__getitem__(1)
- # check returned keys
- expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
- assert set(expected_keys).issubset(set(result.keys()))
- # check shape and contents
- assert result['gt'].shape == (3, 128, 128)
- assert result['lq'].shape == (3, 32, 32)
- assert result['gt_path'] == 'comic'
- assert result['lq_path'] == 'comic'
-
- # ------------------ test paired_paths_from_folder -------------------- #
- opt['dataroot_gt'] = 'tests/data/gt'
- opt['dataroot_lq'] = 'tests/data/lq'
- opt['io_backend'] = dict(type='disk')
- opt['meta_info'] = None
-
- dataset = RealESRGANPairedDataset(opt)
- assert dataset.io_backend_opt['type'] == 'disk' # io backend
- assert len(dataset) == 2 # whether to read correct meta info
-
- # test __getitem__
- result = dataset.__getitem__(0)
- # check returned keys
- expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
- assert set(expected_keys).issubset(set(result.keys()))
- # check shape and contents
- assert result['gt'].shape == (3, 128, 128)
- assert result['lq'].shape == (3, 32, 32)
-
- # ------------------ test normalization -------------------- #
- dataset.mean = [0.5, 0.5, 0.5]
- dataset.std = [0.5, 0.5, 0.5]
- # test __getitem__
- result = dataset.__getitem__(0)
- # check returned keys
- expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
- assert set(expected_keys).issubset(set(result.keys()))
- # check shape and contents
- assert result['gt'].shape == (3, 128, 128)
- assert result['lq'].shape == (3, 32, 32)
diff --git a/spaces/Yuliang/ICON/lib/renderer/gl/init_gl.py b/spaces/Yuliang/ICON/lib/renderer/gl/init_gl.py
deleted file mode 100644
index 613c1d034733cab395617e61c80fc3cc716e3759..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ICON/lib/renderer/gl/init_gl.py
+++ /dev/null
@@ -1,24 +0,0 @@
-_glut_window = None
-_context_inited = None
-
-
-def initialize_GL_context(width=512, height=512, egl=False):
- '''
- default context uses GLUT
- '''
- if not egl:
- import OpenGL.GLUT as GLUT
- display_mode = GLUT.GLUT_DOUBLE | GLUT.GLUT_RGB | GLUT.GLUT_DEPTH
- global _glut_window
- if _glut_window is None:
- GLUT.glutInit()
- GLUT.glutInitDisplayMode(display_mode)
- GLUT.glutInitWindowSize(width, height)
- GLUT.glutInitWindowPosition(0, 0)
- _glut_window = GLUT.glutCreateWindow("My Render.")
- else:
- from .glcontext import create_opengl_context
- global _context_inited
- if _context_inited is None:
- create_opengl_context((width, height))
- _context_inited = True
diff --git a/spaces/a-v-bely/spanish-task-generator/utilities_cookies/build/index.html b/spaces/a-v-bely/spanish-task-generator/utilities_cookies/build/index.html
deleted file mode 100644
index 39c8dcfa6c4939d89305768faa02d92bf85bb99d..0000000000000000000000000000000000000000
--- a/spaces/a-v-bely/spanish-task-generator/utilities_cookies/build/index.html
+++ /dev/null
@@ -1 +0,0 @@
-
-
- - Whisper to chatGPT -
-
- Chat with GPT with your voice in your native language !
-
If it fails enter custom session key see video for reference refer
- Bhavesh Baht video
-
- You can skip the queue by duplicating this space:
-
- 
Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation | Github Repo
" - -examples = [['example.wav']] -gr.Interface( - inference, - gr.inputs.Audio(type="file", label="Input"), - [gr.outputs.Audio(type="file", label="Vocals"),gr.outputs.Audio(type="file", label="Accompaniment")], - title=title, - description=description, - article=article, - enable_queue=True, - examples=examples - ).launch(debug=True) \ No newline at end of file diff --git a/spaces/akhaliq/Music_Source_Separation/bytesep/data/__init__.py b/spaces/akhaliq/Music_Source_Separation/bytesep/data/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/akhaliq/multi-modal_chinese_stable_diffusion_v1.0/app.py b/spaces/akhaliq/multi-modal_chinese_stable_diffusion_v1.0/app.py deleted file mode 100644 index f818aab7add38f534726b81ef9339c29b65f31b3..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/multi-modal_chinese_stable_diffusion_v1.0/app.py +++ /dev/null @@ -1,21 +0,0 @@ -import os -# os.system("""pip install "modelscope[multi-modal]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html""") -import gradio as gr -import cv2 -from modelscope.pipelines import pipeline -from modelscope.utils.constant import Tasks - -text2image = pipeline(Tasks.text_to_image_synthesis,"damo/multi-modal_chinese_stable_diffusion_v1.0") - -def inference(text): - - result = text2image({"text":text}) - cv2.imwrite("result.png", result["output_imgs"][0]) - - return "result.png" - - - -title = "chinese stable diffusion" - -gr.Interface(inference, "text","image", title=title).launch() \ No newline at end of file diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/_inspect.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/_inspect.py deleted file mode 100644 index 262695b1c4723bfb57569f3badd6f81f1cccd3df..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/_inspect.py +++ /dev/null @@ -1,210 +0,0 @@ -from __future__ import absolute_import - -from inspect import cleandoc, getdoc, getfile, isclass, ismodule, signature -from typing import Any, Iterable, Optional, Tuple - -from .console import RenderableType, Group -from .highlighter import ReprHighlighter -from .jupyter import JupyterMixin -from .panel import Panel -from .pretty import Pretty -from .table import Table -from .text import Text, TextType - - -def _first_paragraph(doc: str) -> str: - """Get the first paragraph from a docstring.""" - paragraph, _, _ = doc.partition("\n\n") - return paragraph - - -def _reformat_doc(doc: str) -> str: - """Reformat docstring.""" - doc = cleandoc(doc).strip() - return doc - - -class Inspect(JupyterMixin): - """A renderable to inspect any Python Object. - - Args: - obj (Any): An object to inspect. - title (str, optional): Title to display over inspect result, or None use type. Defaults to None. - help (bool, optional): Show full help text rather than just first paragraph. Defaults to False. - methods (bool, optional): Enable inspection of callables. Defaults to False. - docs (bool, optional): Also render doc strings. Defaults to True. - private (bool, optional): Show private attributes (beginning with underscore). Defaults to False. - dunder (bool, optional): Show attributes starting with double underscore. Defaults to False. - sort (bool, optional): Sort attributes alphabetically. Defaults to True. - all (bool, optional): Show all attributes. Defaults to False. - value (bool, optional): Pretty print value of object. Defaults to True. - """ - - def __init__( - self, - obj: Any, - *, - title: Optional[TextType] = None, - help: bool = False, - methods: bool = False, - docs: bool = True, - private: bool = False, - dunder: bool = False, - sort: bool = True, - all: bool = True, - value: bool = True, - ) -> None: - self.highlighter = ReprHighlighter() - self.obj = obj - self.title = title or self._make_title(obj) - if all: - methods = private = dunder = True - self.help = help - self.methods = methods - self.docs = docs or help - self.private = private or dunder - self.dunder = dunder - self.sort = sort - self.value = value - - def _make_title(self, obj: Any) -> Text: - """Make a default title.""" - title_str = ( - str(obj) - if (isclass(obj) or callable(obj) or ismodule(obj)) - else str(type(obj)) - ) - title_text = self.highlighter(title_str) - return title_text - - def __rich__(self) -> Panel: - return Panel.fit( - Group(*self._render()), - title=self.title, - border_style="scope.border", - padding=(0, 1), - ) - - def _get_signature(self, name: str, obj: Any) -> Optional[Text]: - """Get a signature for a callable.""" - try: - _signature = str(signature(obj)) + ":" - except ValueError: - _signature = "(...)" - except TypeError: - return None - - source_filename: Optional[str] = None - try: - source_filename = getfile(obj) - except TypeError: - pass - - callable_name = Text(name, style="inspect.callable") - if source_filename: - callable_name.stylize(f"link file://{source_filename}") - signature_text = self.highlighter(_signature) - - qualname = name or getattr(obj, "__qualname__", name) - qual_signature = Text.assemble( - ("def ", "inspect.def"), (qualname, "inspect.callable"), signature_text - ) - - return qual_signature - - def _render(self) -> Iterable[RenderableType]: - """Render object.""" - - def sort_items(item: Tuple[str, Any]) -> Tuple[bool, str]: - key, (_error, value) = item - return (callable(value), key.strip("_").lower()) - - def safe_getattr(attr_name: str) -> Tuple[Any, Any]: - """Get attribute or any exception.""" - try: - return (None, getattr(obj, attr_name)) - except Exception as error: - return (error, None) - - obj = self.obj - keys = dir(obj) - total_items = len(keys) - if not self.dunder: - keys = [key for key in keys if not key.startswith("__")] - if not self.private: - keys = [key for key in keys if not key.startswith("_")] - not_shown_count = total_items - len(keys) - items = [(key, safe_getattr(key)) for key in keys] - if self.sort: - items.sort(key=sort_items) - - items_table = Table.grid(padding=(0, 1), expand=False) - items_table.add_column(justify="right") - add_row = items_table.add_row - highlighter = self.highlighter - - if callable(obj): - signature = self._get_signature("", obj) - if signature is not None: - yield signature - yield "" - - if self.docs: - _doc = getdoc(obj) - if _doc is not None: - if not self.help: - _doc = _first_paragraph(_doc) - doc_text = Text(_reformat_doc(_doc), style="inspect.help") - doc_text = highlighter(doc_text) - yield doc_text - yield "" - - if self.value and not (isclass(obj) or callable(obj) or ismodule(obj)): - yield Panel( - Pretty(obj, indent_guides=True, max_length=10, max_string=60), - border_style="inspect.value.border", - ) - yield "" - - for key, (error, value) in items: - key_text = Text.assemble( - ( - key, - "inspect.attr.dunder" if key.startswith("__") else "inspect.attr", - ), - (" =", "inspect.equals"), - ) - if error is not None: - warning = key_text.copy() - warning.stylize("inspect.error") - add_row(warning, highlighter(repr(error))) - continue - - if callable(value): - if not self.methods: - continue - - _signature_text = self._get_signature(key, value) - if _signature_text is None: - add_row(key_text, Pretty(value, highlighter=highlighter)) - else: - if self.docs: - docs = getdoc(value) - if docs is not None: - _doc = _reformat_doc(str(docs)) - if not self.help: - _doc = _first_paragraph(_doc) - _signature_text.append("\n" if "\n" in _doc else " ") - doc = highlighter(_doc) - doc.stylize("inspect.doc") - _signature_text.append(doc) - - add_row(key_text, _signature_text) - else: - add_row(key_text, Pretty(value, highlighter=highlighter)) - if items_table.row_count: - yield items_table - else: - yield Text.from_markup( - f"[b cyan]{not_shown_count}[/][i] attribute(s) not shown.[/i] Run [b][magenta]inspect[/]([not b]inspect[/])[/b] for options." - ) diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/markup.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/markup.py deleted file mode 100644 index 619540202cb554f93112b7779c235cf5309d499f..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/markup.py +++ /dev/null @@ -1,244 +0,0 @@ -from ast import literal_eval -from operator import attrgetter -import re -from typing import Callable, Iterable, List, Match, NamedTuple, Optional, Tuple, Union - -from .errors import MarkupError -from .style import Style -from .text import Span, Text -from .emoji import EmojiVariant -from ._emoji_replace import _emoji_replace - - -RE_TAGS = re.compile( - r"""((\\*)\[([a-z#\/@].*?)\])""", - re.VERBOSE, -) - -RE_HANDLER = re.compile(r"^([\w\.]*?)(\(.*?\))?$") - - -class Tag(NamedTuple): - """A tag in console markup.""" - - name: str - """The tag name. e.g. 'bold'.""" - parameters: Optional[str] - """Any additional parameters after the name.""" - - def __str__(self) -> str: - return ( - self.name if self.parameters is None else f"{self.name} {self.parameters}" - ) - - @property - def markup(self) -> str: - """Get the string representation of this tag.""" - return ( - f"[{self.name}]" - if self.parameters is None - else f"[{self.name}={self.parameters}]" - ) - - -_ReStringMatch = Match[str] # regex match object -_ReSubCallable = Callable[[_ReStringMatch], str] # Callable invoked by re.sub -_EscapeSubMethod = Callable[[_ReSubCallable, str], str] # Sub method of a compiled re - - -def escape( - markup: str, _escape: _EscapeSubMethod = re.compile(r"(\\*)(\[[a-z#\/@].*?\])").sub -) -> str: - """Escapes text so that it won't be interpreted as markup. - - Args: - markup (str): Content to be inserted in to markup. - - Returns: - str: Markup with square brackets escaped. - """ - - def escape_backslashes(match: Match[str]) -> str: - """Called by re.sub replace matches.""" - backslashes, text = match.groups() - return f"{backslashes}{backslashes}\\{text}" - - markup = _escape(escape_backslashes, markup) - return markup - - -def _parse(markup: str) -> Iterable[Tuple[int, Optional[str], Optional[Tag]]]: - """Parse markup in to an iterable of tuples of (position, text, tag). - - Args: - markup (str): A string containing console markup - - """ - position = 0 - _divmod = divmod - _Tag = Tag - for match in RE_TAGS.finditer(markup): - full_text, escapes, tag_text = match.groups() - start, end = match.span() - if start > position: - yield start, markup[position:start], None - if escapes: - backslashes, escaped = _divmod(len(escapes), 2) - if backslashes: - # Literal backslashes - yield start, "\\" * backslashes, None - start += backslashes * 2 - if escaped: - # Escape of tag - yield start, full_text[len(escapes) :], None - position = end - continue - text, equals, parameters = tag_text.partition("=") - yield start, None, _Tag(text, parameters if equals else None) - position = end - if position < len(markup): - yield position, markup[position:], None - - -def render( - markup: str, - style: Union[str, Style] = "", - emoji: bool = True, - emoji_variant: Optional[EmojiVariant] = None, -) -> Text: - """Render console markup in to a Text instance. - - Args: - markup (str): A string containing console markup. - emoji (bool, optional): Also render emoji code. Defaults to True. - - Raises: - MarkupError: If there is a syntax error in the markup. - - Returns: - Text: A test instance. - """ - emoji_replace = _emoji_replace - if "[" not in markup: - return Text( - emoji_replace(markup, default_variant=emoji_variant) if emoji else markup, - style=style, - ) - text = Text(style=style) - append = text.append - normalize = Style.normalize - - style_stack: List[Tuple[int, Tag]] = [] - pop = style_stack.pop - - spans: List[Span] = [] - append_span = spans.append - - _Span = Span - _Tag = Tag - - def pop_style(style_name: str) -> Tuple[int, Tag]: - """Pop tag matching given style name.""" - for index, (_, tag) in enumerate(reversed(style_stack), 1): - if tag.name == style_name: - return pop(-index) - raise KeyError(style_name) - - for position, plain_text, tag in _parse(markup): - if plain_text is not None: - append(emoji_replace(plain_text) if emoji else plain_text) - elif tag is not None: - if tag.name.startswith("/"): # Closing tag - style_name = tag.name[1:].strip() - - if style_name: # explicit close - style_name = normalize(style_name) - try: - start, open_tag = pop_style(style_name) - except KeyError: - raise MarkupError( - f"closing tag '{tag.markup}' at position {position} doesn't match any open tag" - ) from None - else: # implicit close - try: - start, open_tag = pop() - except IndexError: - raise MarkupError( - f"closing tag '[/]' at position {position} has nothing to close" - ) from None - - if open_tag.name.startswith("@"): - if open_tag.parameters: - handler_name = "" - parameters = open_tag.parameters.strip() - handler_match = RE_HANDLER.match(parameters) - if handler_match is not None: - handler_name, match_parameters = handler_match.groups() - parameters = ( - "()" if match_parameters is None else match_parameters - ) - - try: - meta_params = literal_eval(parameters) - except SyntaxError as error: - raise MarkupError( - f"error parsing {parameters!r} in {open_tag.parameters!r}; {error.msg}" - ) - except Exception as error: - raise MarkupError( - f"error parsing {open_tag.parameters!r}; {error}" - ) from None - - if handler_name: - meta_params = ( - handler_name, - meta_params - if isinstance(meta_params, tuple) - else (meta_params,), - ) - - else: - meta_params = () - - append_span( - _Span( - start, len(text), Style(meta={open_tag.name: meta_params}) - ) - ) - else: - append_span(_Span(start, len(text), str(open_tag))) - - else: # Opening tag - normalized_tag = _Tag(normalize(tag.name), tag.parameters) - style_stack.append((len(text), normalized_tag)) - - text_length = len(text) - while style_stack: - start, tag = style_stack.pop() - style = str(tag) - if style: - append_span(_Span(start, text_length, style)) - - text.spans = sorted(spans[::-1], key=attrgetter("start")) - return text - - -if __name__ == "__main__": # pragma: no cover - - MARKUP = [ - "[red]Hello World[/red]", - "[magenta]Hello [b]World[/b]", - "[bold]Bold[italic] bold and italic [/bold]italic[/italic]", - "Click [link=https://www.willmcgugan.com]here[/link] to visit my Blog", - ":warning-emoji: [bold red blink] DANGER![/]", - ] - - from pip._vendor.rich.table import Table - from pip._vendor.rich import print - - grid = Table("Markup", "Result", padding=(0, 1)) - - for markup in MARKUP: - grid.add_row(Text(markup), markup) - - print(grid) diff --git a/spaces/alistairmcleay/cambridge-masters-project/scripts/simulate_interaction.py b/spaces/alistairmcleay/cambridge-masters-project/scripts/simulate_interaction.py deleted file mode 100644 index fda8af775ed5870a0f3372f4527ce7657d0fb61f..0000000000000000000000000000000000000000 --- a/spaces/alistairmcleay/cambridge-masters-project/scripts/simulate_interaction.py +++ /dev/null @@ -1,167 +0,0 @@ -import sys -import traceback -import pandas as pd - -# from tqdm import tqdm -from UBAR_code.interaction import UBAR_interact -from user_model_code.interaction import multiwoz_interact -from UBAR_code.interaction.UBAR_interact import bcolors - - -# from tqdm import tqdm -from scripts.UBAR_code.interaction import UBAR_interact -from scripts.user_model_code.interaction import multiwoz_interact -from scripts.UBAR_code.interaction.UBAR_interact import bcolors - - -def instantiate_agents(): - - UBAR_checkpoint_path = "cambridge-masters-project/epoch50_trloss0.59_gpt2" - user_model_checkpoint_path = "cambridge-masters-project/MultiWOZ-full_checkpoint_step340k" - - sys_model = UBAR_interact.UbarSystemModel( - "UBAR_sys_model", UBAR_checkpoint_path, "cambridge-masters-project/scripts/UBAR_code/interaction/config.yaml" - ) - - user_model = multiwoz_interact.NeuralAgent( - "user", user_model_checkpoint_path, "cambridge-masters-project/scripts/user_model_code/interaction/config.yaml" - ) - - return sys_model, user_model - - -def read_multiwoz_data(): - """ - Read the multiwoz 2.0 raw data from the .json file - """ - raw_mwoz_20_path = "cambridge-masters-project/data/raw/UBAR/multi-woz/data.json" - df_raw_mwoz = pd.read_json(raw_mwoz_20_path) - return df_raw_mwoz - - -def load_test_val_lists(): - val_list_file = "cambridge-masters-project/data/raw/UBAR/multi-woz/valListFile.json" - test_list_file = "cambridge-masters-project/data/raw/UBAR/multi-woz/testListFile.json" - - -def main( - write_to_file=False, ground_truth_system_responses=False, train_only=True, n_dialogues="all", log_successes=False -): - sys_model, user_model = instantiate_agents() - - # TODO: move hardcoded vars into config file - raw_mwoz_20_path = "cambridge-masters-project/data/raw/UBAR/multi-woz/data.json" - user_utterances_out_path = "cambridge-masters-project/data/preprocessed/UBAR/user_utterances_from_simulator.txt" - logging_successes_path = "cambridge-masters-project/data/preprocessed/UBAR/logging_successes" - sys_model.print_intermediary_info = False - user_model.print_intermediary_info = False - - df_raw_mwoz = pd.read_json(raw_mwoz_20_path) - if n_dialogues == "all": - n_dialogues = len(df_raw_mwoz.columns) - - curr_dialogue_user_utterances_formatted = [] - - print("Loading goals...") - goals = multiwoz_interact.read_multiWOZ_20_goals(raw_mwoz_20_path, n_dialogues) - - # Write column headers - if write_to_file: - with open(user_utterances_out_path, "w") as f: - f.write("Dialogue #\tDialogue ID\tTurn #\tSystem Response\n") - - print("Loading data...") - df_mwoz_data = read_multiwoz_data() - val_list, test_list = load_test_val_lists() - - successful_dialogues = 0 - total_dialogues_generated = 0 # train dialogues only - for dialogue_idx, (goal, dialogue_filename) in enumerate(zip(goals, df_mwoz_data.columns)): - if log_successes: - # log successful_dialogues to logging_successes_path every 100 dialogues - if dialogue_idx % 100 == 0: - with open(logging_successes_path, "w") as f: - f.write(str(successful_dialogues) + " / " + str(total_dialogues_generated)) - - curr_dialogue_user_utterances_formatted = [] - if train_only: - if dialogue_filename in val_list or dialogue_filename in test_list: - continue - - total_dialogues_generated += 1 - print("Dialogue: {}".format(dialogue_filename)) - - # There are occasionally exceptions thrown from one of the agents, usually the user - # In this case we simply continue to the next dialogue - try: - # Reset state after each dialogue - sys_model.init_session() - user_model.init_session(ini_goal=goal) - sys_response = "" - - for turn_idx in range(50): - # Turn idx in this case represents the turn as one user utterance AND one system response - usr_response_raw_data_idx = turn_idx * 2 - sys_response_raw_data_idx = turn_idx * 2 + 1 - - user_utterance = user_model.response(sys_response) - print(bcolors.OKBLUE + "User: " + bcolors.ENDC + user_utterance) - - if write_to_file: - user_utterance = user_utterance.replace("\n", " ") - curr_dialogue_user_utterances_formatted.append( - str(dialogue_idx) - + "\t" - + dialogue_filename - + "\t" - + str(usr_response_raw_data_idx) - + "\t" - + user_utterance - + "\n" - ) - - if user_model.is_terminated(): - successful_dialogues += 1 - print(bcolors.OKCYAN + "Dialogue terminated successfully!" + bcolors.ENDC) - print(bcolors.OKCYAN + "---" * 30 + bcolors.ENDC + "\n") - if write_to_file: - # Write whole dialogue to file - with open(user_utterances_out_path, "a") as f: - for line in curr_dialogue_user_utterances_formatted: - f.write(line) - break - - # Next turn materials - if ground_truth_system_responses: - # If we are at the end of the ground truth dialogues - if len(df_mwoz_data.iloc[:, dialogue_idx].log) <= sys_response_raw_data_idx: - print(bcolors.RED + "Dialogue terminated unsuccessfully!" + bcolors.ENDC) - print(bcolors.RED + "---" * 30 + bcolors.ENDC + "\n") - break - sys_response = df_mwoz_data.iloc[:, dialogue_idx].log[sys_response_raw_data_idx]["text"] - else: - sys_response = sys_model.response(user_utterance, turn_idx) - capitalised_sys_response = sys_response[0].upper() + sys_response[1:] - print(bcolors.GREEN + "System: " + bcolors.ENDC + capitalised_sys_response) - - except Exception: - print(bcolors.RED + "*" * 30 + bcolors.ENDC) - print(bcolors.RED + "Error in dialogue {}".format(dialogue_filename) + bcolors.ENDC) - print(bcolors.RED + "*" * 30 + bcolors.ENDC) - traceback.print_exc() - continue - - print("Successful dialogues: {}".format(successful_dialogues)) - print("Total dialogues: {}".format(n_dialogues)) - print("% Successful Dialopues: {}".format(successful_dialogues / n_dialogues)) - - -if __name__ == "__main__": - # TODO: move parameters to config file - # Fix the hacky mess below - ground_truth_system_responses = sys.argv[1] - if ground_truth_system_responses == "False": - ground_truth_system_responses = False - else: - ground_truth_system_responses = True - main(write_to_file=False, ground_truth_system_responses=ground_truth_system_responses) diff --git a/spaces/allknowingroger/Image-Models-Test198/app.py b/spaces/allknowingroger/Image-Models-Test198/app.py deleted file mode 100644 index d3e94b54d6466706575a106cc7eb68690dc57261..0000000000000000000000000000000000000000 --- a/spaces/allknowingroger/Image-Models-Test198/app.py +++ /dev/null @@ -1,144 +0,0 @@ -import gradio as gr -# import os -# import sys -# from pathlib import Path -import time - -models =[ - "DARNIX/dtr2", - "artificialguybr/ColoringBookRedmond-V2", - "ramy21/braintumormodel5", - "Falah/sdlogo", - "ramy21/braintumormodel4", - "harshupanchal/my-pet-lion", - "LilyNgo/lora-trained-xl-colab", - "MdEndan/stable-diffusion-lora-fine-tuned", - "artificialguybr/TshirtDesignRedmond", -] - - -model_functions = {} -model_idx = 1 -for model_path in models: - try: - model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False) - except Exception as error: - def the_fn(txt): - return None - model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"]) - model_idx+=1 - - -def send_it_idx(idx): - def send_it_fn(prompt): - output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt) - return output - return send_it_fn - -def get_prompts(prompt_text): - return prompt_text - -def clear_it(val): - if int(val) != 0: - val = 0 - else: - val = 0 - pass - return val - -def all_task_end(cnt,t_stamp): - to = t_stamp + 60 - et = time.time() - if et > to and t_stamp != 0: - d = gr.update(value=0) - tog = gr.update(value=1) - #print(f'to: {to} et: {et}') - else: - if cnt != 0: - d = gr.update(value=et) - else: - d = gr.update(value=0) - tog = gr.update(value=0) - #print (f'passing: to: {to} et: {et}') - pass - return d, tog - -def all_task_start(): - print("\n\n\n\n\n\n\n") - t = time.gmtime() - t_stamp = time.time() - current_time = time.strftime("%H:%M:%S", t) - return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0) - -def clear_fn(): - nn = len(models) - return tuple([None, *[None for _ in range(nn)]]) - - - -with gr.Blocks(title="SD Models") as my_interface: - with gr.Column(scale=12): - # with gr.Row(): - # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""") - with gr.Row(): - with gr.Row(scale=6): - primary_prompt=gr.Textbox(label="Prompt", value="") - # real_prompt=gr.Textbox(label="Real prompt") - with gr.Row(scale=6): - # improve_prompts_btn=gr.Button("Improve") - with gr.Row(): - run=gr.Button("Run",variant="primary") - clear_btn=gr.Button("Clear") - with gr.Row(): - sd_outputs = {} - model_idx = 1 - for model_path in models: - with gr.Column(scale=3, min_width=320): - with gr.Box(): - sd_outputs[model_idx] = gr.Image(label=model_path) - pass - model_idx += 1 - pass - pass - - with gr.Row(visible=False): - start_box=gr.Number(interactive=False) - end_box=gr.Number(interactive=False) - tog_box=gr.Textbox(value=0,interactive=False) - - start_box.change( - all_task_end, - [start_box, end_box], - [start_box, tog_box], - every=1, - show_progress=False) - - primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box]) - run.click(all_task_start, None, [start_box, end_box, tog_box]) - runs_dict = {} - model_idx = 1 - for model_path in models: - runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]]) - model_idx += 1 - pass - pass - - # improve_prompts_btn_clicked=improve_prompts_btn.click( - # get_prompts, - # inputs=[primary_prompt], - # outputs=[primary_prompt], - # cancels=list(runs_dict.values())) - clear_btn.click( - clear_fn, - None, - [primary_prompt, *list(sd_outputs.values())], - cancels=[*list(runs_dict.values())]) - tog_box.change( - clear_it, - tog_box, - tog_box, - cancels=[*list(runs_dict.values())]) - -my_interface.queue(concurrency_count=600, status_update_rate=1) -my_interface.launch(inline=True, show_api=False) - \ No newline at end of file diff --git a/spaces/almakedon/faster-whisper-webui/src/hooks/subTaskProgressListener.py b/spaces/almakedon/faster-whisper-webui/src/hooks/subTaskProgressListener.py deleted file mode 100644 index 9a8eaa876fcd18032875d67535e0558494842c60..0000000000000000000000000000000000000000 --- a/spaces/almakedon/faster-whisper-webui/src/hooks/subTaskProgressListener.py +++ /dev/null @@ -1,37 +0,0 @@ -from src.hooks.progressListener import ProgressListener - -from typing import Union - -class SubTaskProgressListener(ProgressListener): - """ - A sub task listener that reports the progress of a sub task to a base task listener - Parameters - ---------- - base_task_listener : ProgressListener - The base progress listener to accumulate overall progress in. - base_task_total : float - The maximum total progress that will be reported to the base progress listener. - sub_task_start : float - The starting progress of a sub task, in respect to the base progress listener. - sub_task_total : float - The total amount of progress a sub task will report to the base progress listener. - """ - def __init__( - self, - base_task_listener: ProgressListener, - base_task_total: float, - sub_task_start: float, - sub_task_total: float, - ): - self.base_task_listener = base_task_listener - self.base_task_total = base_task_total - self.sub_task_start = sub_task_start - self.sub_task_total = sub_task_total - - def on_progress(self, current: Union[int, float], total: Union[int, float]): - sub_task_progress_frac = current / total - sub_task_progress = self.sub_task_start + self.sub_task_total * sub_task_progress_frac - self.base_task_listener.on_progress(sub_task_progress, self.base_task_total) - - def on_finished(self): - self.base_task_listener.on_progress(self.sub_task_start + self.sub_task_total, self.base_task_total) \ No newline at end of file diff --git a/spaces/amankishore/adept-fuyu-8b/README.md b/spaces/amankishore/adept-fuyu-8b/README.md deleted file mode 100644 index 0a5d2fe247ec6217776ba5ffca499f708c8df6c0..0000000000000000000000000000000000000000 --- a/spaces/amankishore/adept-fuyu-8b/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Adept Fuyu 8b -emoji: 🐢 -colorFrom: gray -colorTo: purple -sdk: gradio -sdk_version: 3.49.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/animeartstudio/AnimeModels/README.md b/spaces/animeartstudio/AnimeModels/README.md deleted file mode 100644 index fd58abc9b24f2c68a02207c2c219f8785ccdf2da..0000000000000000000000000000000000000000 --- a/spaces/animeartstudio/AnimeModels/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Maximum Multiplier -emoji: 🛕🛕 -colorFrom: green -colorTo: blue -sdk: gradio -sdk_version: 3.15.0 -app_file: app.py -pinned: true -duplicated_from: pulpapps/Diffusion30-Anime ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/anzorq/hf-spaces-semantic-search/styles/globals.css b/spaces/anzorq/hf-spaces-semantic-search/styles/globals.css deleted file mode 100644 index fd81e885836d815b8019694a910a93d86a43cb66..0000000000000000000000000000000000000000 --- a/spaces/anzorq/hf-spaces-semantic-search/styles/globals.css +++ /dev/null @@ -1,27 +0,0 @@ -@tailwind base; -@tailwind components; -@tailwind utilities; - -:root { - --foreground-rgb: 0, 0, 0; - --background-start-rgb: 214, 219, 220; - --background-end-rgb: 255, 255, 255; -} - -@media (prefers-color-scheme: dark) { - :root { - --foreground-rgb: 255, 255, 255; - --background-start-rgb: 0, 0, 0; - --background-end-rgb: 0, 0, 0; - } -} - -body { - color: rgb(var(--foreground-rgb)); - background: linear-gradient( - to bottom, - transparent, - rgb(var(--background-end-rgb)) - ) - rgb(var(--background-start-rgb)); -} diff --git a/spaces/aphenx/bingo/src/components/user-menu.tsx b/spaces/aphenx/bingo/src/components/user-menu.tsx deleted file mode 100644 index 9bd1edc9cf9f39b63629b021f0c1186b1a7c1341..0000000000000000000000000000000000000000 --- a/spaces/aphenx/bingo/src/components/user-menu.tsx +++ /dev/null @@ -1,113 +0,0 @@ -'use client' - -import { useEffect, useState } from 'react' -import Image from 'next/image' -import { toast } from 'react-hot-toast' -import { Button } from '@/components/ui/button' -import pkg from '../../package.json' -import { - DropdownMenu, - DropdownMenuContent, - DropdownMenuItem, - DropdownMenuSeparator, - DropdownMenuTrigger -} from '@/components/ui/dropdown-menu' -import { IconCopy, IconExternalLink, IconGitHub } from '@/components/ui/icons' -import SettingIcon from '@/assets/images/settings.svg' -import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard' - -export function UserMenu() { - const [host, setHost] = useState('') - const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 }) - useEffect(() => { - setHost(location.host) - }, []) - - useEffect(() => { - if (isCopied) { - toast.success('复制成功') - } - }, [isCopied]) - return ( -
-
-
-
-
-
-
- location.href='#dialog="settings"'
- }
- className="cursor-pointer"
- >
- 设置用户
-
-
- location.href='#dialog="voice"'
- }
- className="cursor-pointer"
- >
- 语音设置
-
-
-
- 开源地址
-
-
-
- 托管地址
- 🤗
-
-
-
- 复制站点
-
-
-
-
-
-
-
-
- )
-}
diff --git a/spaces/artemkramov/f-coref-ua/README.md b/spaces/artemkramov/f-coref-ua/README.md
deleted file mode 100644
index b938e439059f4b48bd67a2c38314f2011c9be7b3..0000000000000000000000000000000000000000
--- a/spaces/artemkramov/f-coref-ua/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: F Coref Ua
-emoji: 🌍
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/bark/hubert/hubert_manager.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/bark/hubert/hubert_manager.py
deleted file mode 100644
index 4bc199294164da0e8c480e292dd5a478e72f4daf..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/bark/hubert/hubert_manager.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# From https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer
-
-import os.path
-import shutil
-import urllib.request
-
-import huggingface_hub
-
-
-class HubertManager:
- @staticmethod
- def make_sure_hubert_installed(
- download_url: str = "https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt", model_path: str = ""
- ):
- if not os.path.isfile(model_path):
- print("Downloading HuBERT base model")
- urllib.request.urlretrieve(download_url, model_path)
- print("Downloaded HuBERT")
- return model_path
- return None
-
- @staticmethod
- def make_sure_tokenizer_installed(
- model: str = "quantifier_hubert_base_ls960_14.pth",
- repo: str = "GitMylo/bark-voice-cloning",
- model_path: str = "",
- ):
- model_dir = os.path.dirname(model_path)
- if not os.path.isfile(model_path):
- print("Downloading HuBERT custom tokenizer")
- huggingface_hub.hf_hub_download(repo, model, local_dir=model_dir, local_dir_use_symlinks=False)
- shutil.move(os.path.join(model_dir, model), model_path)
- print("Downloaded tokenizer")
- return model_path
- return None
diff --git a/spaces/artificialguybr/video-dubbing/TTS/scripts/sync_readme.py b/spaces/artificialguybr/video-dubbing/TTS/scripts/sync_readme.py
deleted file mode 100644
index 584286814b78b452d66d2be174cdcad156f23b16..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/scripts/sync_readme.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import argparse
-from pathlib import Path
-
-
-def replace_between_markers(content, marker: str, replacement: str) -> str:
- start_marker = f"\n\n"
- end_marker = f"\n\n\n"
- start_index = content.index(start_marker) + len(start_marker)
- end_index = content.index(end_marker)
- content = content[:start_index] + replacement + content[end_index:]
- return content
-
-
-def sync_readme():
- ap = argparse.ArgumentParser()
- ap.add_argument("--check", action="store_true", default=False)
- args = ap.parse_args()
- readme_path = Path(__file__).parent.parent / "README.md"
- orig_content = readme_path.read_text()
- from TTS.bin.synthesize import description
-
- new_content = replace_between_markers(orig_content, "tts-readme", description.strip())
- if args.check:
- if orig_content != new_content:
- print("README.md is out of sync; please edit TTS/bin/TTS_README.md and run scripts/sync_readme.py")
- exit(42)
- readme_path.write_text(new_content)
- print("Updated README.md")
-
-
-if __name__ == "__main__":
- sync_readme()
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/vocoder_tests/test_wavernn_train.py b/spaces/artificialguybr/video-dubbing/TTS/tests/vocoder_tests/test_wavernn_train.py
deleted file mode 100644
index 337e24259f0ffa39d4d77b57749988b64763c2f1..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/tests/vocoder_tests/test_wavernn_train.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import glob
-import os
-import shutil
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.vocoder.configs import WavernnConfig
-from TTS.vocoder.models.wavernn import WavernnArgs
-
-config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = WavernnConfig(
- model_args=WavernnArgs(),
- batch_size=8,
- eval_batch_size=8,
- num_loader_workers=0,
- num_eval_loader_workers=0,
- run_eval=True,
- test_delay_epochs=-1,
- epochs=1,
- seq_len=256, # for shorter test time
- eval_split_size=1,
- print_step=1,
- print_eval=True,
- data_path="tests/data/ljspeech",
- output_path=output_path,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --config_path {config_path} "
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = (
- f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --continue_path {continue_path} "
-)
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/Hdf5StubImagePlugin.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/Hdf5StubImagePlugin.py
deleted file mode 100644
index df11cf2a6dbd04b483e288d6f7ee654d494ff52d..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/Hdf5StubImagePlugin.py
+++ /dev/null
@@ -1,73 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# HDF5 stub adapter
-#
-# Copyright (c) 2000-2003 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-from . import Image, ImageFile
-
-_handler = None
-
-
-def register_handler(handler):
- """
- Install application-specific HDF5 image handler.
-
- :param handler: Handler object.
- """
- global _handler
- _handler = handler
-
-
-# --------------------------------------------------------------------
-# Image adapter
-
-
-def _accept(prefix):
- return prefix[:8] == b"\x89HDF\r\n\x1a\n"
-
-
-class HDF5StubImageFile(ImageFile.StubImageFile):
-
- format = "HDF5"
- format_description = "HDF5"
-
- def _open(self):
-
- offset = self.fp.tell()
-
- if not _accept(self.fp.read(8)):
- raise SyntaxError("Not an HDF file")
-
- self.fp.seek(offset)
-
- # make something up
- self.mode = "F"
- self._size = 1, 1
-
- loader = self._load()
- if loader:
- loader.open(self)
-
- def _load(self):
- return _handler
-
-
-def _save(im, fp, filename):
- if _handler is None or not hasattr(_handler, "save"):
- raise OSError("HDF5 save handler not installed")
- _handler.save(im, fp, filename)
-
-
-# --------------------------------------------------------------------
-# Registry
-
-Image.register_open(HDF5StubImageFile.format, HDF5StubImageFile, _accept)
-Image.register_save(HDF5StubImageFile.format, _save)
-
-Image.register_extensions(HDF5StubImageFile.format, [".h5", ".hdf"])
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/window_rank.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/window_rank.py
deleted file mode 100644
index d362d40f81d06e9eac00dde3da463763714d0b22..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/window_rank.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""
-Window Rank Line Chart
-----------------------
-This example shows the Group F rankings in the 2018 World Cup after each matchday.
-A window transformation is used to rank each after each match day, sorting by points and difference.
-"""
-# category: case studies
-import altair as alt
-import pandas as pd
-
-source = pd.DataFrame(
- [
- {"team": "Germany", "matchday": 1, "point": 0, "diff": -1},
- {"team": "Germany", "matchday": 2, "point": 3, "diff": 0},
- {"team": "Germany", "matchday": 3, "point": 3, "diff": -2},
- {"team": "Mexico", "matchday": 1, "point": 3, "diff": 1},
- {"team": "Mexico", "matchday": 2, "point": 6, "diff": 2},
- {"team": "Mexico", "matchday": 3, "point": 6, "diff": -1},
- {"team": "South Korea", "matchday": 1, "point": 0, "diff": -1},
- {"team": "South Korea", "matchday": 2, "point": 0, "diff": -2},
- {"team": "South Korea", "matchday": 3, "point": 3, "diff": 0},
- {"team": "Sweden", "matchday": 1, "point": 3, "diff": 1},
- {"team": "Sweden", "matchday": 2, "point": 3, "diff": 0},
- {"team": "Sweden", "matchday": 3, "point": 6, "diff": 3},
- ]
-)
-
-color_scale = alt.Scale(
- domain=["Germany", "Mexico", "South Korea", "Sweden"],
- range=["#000000", "#127153", "#C91A3C", "#0C71AB"],
-)
-
-alt.Chart(source).mark_line().encode(
- x="matchday:O", y="rank:O", color=alt.Color("team:N", scale=color_scale)
-).transform_window(
- rank="rank()",
- sort=[
- alt.SortField("point", order="descending"),
- alt.SortField("diff", order="descending"),
- ],
- groupby=["matchday"],
-).properties(title="World Cup 2018: Group F Rankings")
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/benchmark/dummy_dataset.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/benchmark/dummy_dataset.py
deleted file mode 100644
index 2f051754af55966e26850e94c121e0ff439bfd28..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/benchmark/dummy_dataset.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import numpy as np
-from fairseq.data import FairseqDataset
-
-
-class DummyDataset(FairseqDataset):
- def __init__(self, batch, num_items, item_size):
- super().__init__()
- self.batch = batch
- self.num_items = num_items
- self.item_size = item_size
-
- def __getitem__(self, index):
- return index
-
- def __len__(self):
- return self.num_items
-
- def collater(self, samples):
- return self.batch
-
- @property
- def sizes(self):
- return np.array([self.item_size] * self.num_items)
-
- def num_tokens(self, index):
- return self.item_size
-
- def size(self, index):
- return self.item_size
-
- def ordered_indices(self):
- return np.arange(self.num_items)
-
- @property
- def supports_prefetch(self):
- return False
diff --git a/spaces/ashercn97/AsherTesting/modules/presets.py b/spaces/ashercn97/AsherTesting/modules/presets.py
deleted file mode 100644
index 0af2928a464abb849431452b1c6767b752240e89..0000000000000000000000000000000000000000
--- a/spaces/ashercn97/AsherTesting/modules/presets.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import functools
-from pathlib import Path
-
-import yaml
-
-
-def load_preset(name):
- generate_params = {
- 'do_sample': True,
- 'temperature': 1,
- 'top_p': 1,
- 'typical_p': 1,
- 'epsilon_cutoff': 0,
- 'eta_cutoff': 0,
- 'tfs': 1,
- 'top_a': 0,
- 'repetition_penalty': 1,
- 'repetition_penalty_range': 0,
- 'encoder_repetition_penalty': 1,
- 'top_k': 0,
- 'num_beams': 1,
- 'penalty_alpha': 0,
- 'min_length': 0,
- 'length_penalty': 1,
- 'no_repeat_ngram_size': 0,
- 'early_stopping': False,
- 'mirostat_mode': 0,
- 'mirostat_tau': 5.0,
- 'mirostat_eta': 0.1,
- }
-
- if name not in ['None', None, '']:
- with open(Path(f'presets/{name}.yaml'), 'r') as infile:
- preset = yaml.safe_load(infile)
-
- for k in preset:
- generate_params[k] = preset[k]
-
- generate_params['temperature'] = min(1.99, generate_params['temperature'])
- return generate_params
-
-
-@functools.cache
-def load_preset_memoized(name):
- return load_preset(name)
-
-
-def load_preset_for_ui(name, state):
- generate_params = load_preset(name)
- state.update(generate_params)
- return state, *[generate_params[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']]
-
-
-def generate_preset_yaml(state):
- data = {k: state[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']}
- return yaml.dump(data, sort_keys=False)
diff --git a/spaces/avivdm1/AutoGPT/tests/integration/memory_tests.py b/spaces/avivdm1/AutoGPT/tests/integration/memory_tests.py
deleted file mode 100644
index eead2da1cfa9b8a99592939623955808fc430068..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/tests/integration/memory_tests.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import random
-import string
-import sys
-import unittest
-from pathlib import Path
-
-from autogpt.config import Config
-from autogpt.memory.local import LocalCache
-
-
-class TestLocalCache(unittest.TestCase):
- def random_string(self, length):
- return "".join(random.choice(string.ascii_letters) for _ in range(length))
-
- def setUp(self):
- cfg = cfg = Config()
- self.cache = LocalCache(cfg)
- self.cache.clear()
-
- # Add example texts to the cache
- self.example_texts = [
- "The quick brown fox jumps over the lazy dog",
- "I love machine learning and natural language processing",
- "The cake is a lie, but the pie is always true",
- "ChatGPT is an advanced AI model for conversation",
- ]
-
- for text in self.example_texts:
- self.cache.add(text)
-
- # Add some random strings to test noise
- for _ in range(5):
- self.cache.add(self.random_string(10))
-
- def test_get_relevant(self):
- query = "I'm interested in artificial intelligence and NLP"
- k = 3
- relevant_texts = self.cache.get_relevant(query, k)
-
- print(f"Top {k} relevant texts for the query '{query}':")
- for i, text in enumerate(relevant_texts, start=1):
- print(f"{i}. {text}")
-
- self.assertEqual(len(relevant_texts), k)
- self.assertIn(self.example_texts[1], relevant_texts)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/awacke1/3D-Models-GLB-Animation-Gradio/README.md b/spaces/awacke1/3D-Models-GLB-Animation-Gradio/README.md
deleted file mode 100644
index ab901ac43c2152442aa461a711cd58c7261f77cf..0000000000000000000000000000000000000000
--- a/spaces/awacke1/3D-Models-GLB-Animation-Gradio/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 01 3DModel GradioDemo
-emoji: 🦆🧊
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/ChemistryModelerSMILES/README.md b/spaces/awacke1/ChemistryModelerSMILES/README.md
deleted file mode 100644
index 58afebbed4a2cae9916a6e341122ee52c5c5ac5a..0000000000000000000000000000000000000000
--- a/spaces/awacke1/ChemistryModelerSMILES/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: 🧬GenomicsModelsSmiles3DandCompounds
-emoji: 🧬
-colorFrom: red
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/DungeonCrawlerWithReinforcementLearningMonster/app.py b/spaces/awacke1/DungeonCrawlerWithReinforcementLearningMonster/app.py
deleted file mode 100644
index 840cd2a2ac294236f4252c2923b24fb9278d78cf..0000000000000000000000000000000000000000
--- a/spaces/awacke1/DungeonCrawlerWithReinforcementLearningMonster/app.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# ChatGPT prompt: write a python streamlit program that creates a dungeon crawler game featuring a reinforcement learning monster that a player can fight and slay
-
-import streamlit as st
-
-import random
-
-import numpy as np
-
-# create a class for the monster
-class Monster():
- def __init__(self, name, hp, attack, defense):
- self.name = name
- self.hp = hp
- self.attack = attack
- self.defense = defense
-
-# create a class for the player
-class Player():
- def __init__(self, name, hp, attack, defense):
- self.name = name
- self.hp = hp
- self.attack = attack
- self.defense = defense
-
-# create the reinforcement learning monster
-class ReinforcementMonster():
- def __init__(self, name, hp, attack, defense):
- self.name = name
- self.hp = hp
- self.attack = attack
- self.defense = defense
-
-# create a function to generate a random number
-def random_number():
- x = random.randint(1,20)
- return x
-
-# create a function to start the game
-def start_game():
- # create a monster
- monster = Monster('goblin',100,20,10)
- # create a player
- player = Player('hero',100,20,10)
- # create a reinforcement monster
- reinforcement_monster = ReinforcementMonster('dragon',100,30,15)
-
-
-
- # create a loop to simulate a battle
- while monster.hp > 0 and player.hp > 0:
- # player attack monster
- monster.hp = monster.hp - player.attack
- st.write(f'Player {player.name} attacks {monster.name} with {player.attack}')
- st.write(f'{monster.name} has {monster.hp} hp left')
- # monster attack player
- if monster.hp > 0:
- player.hp = player.hp - monster.attack
- st.write(f'{monster.name} attacks {player.name} with {monster.attack}')
- st.write(f'{player.name} has {player.hp} hp left')
-
- # check if player won the fight
- if player.hp > 0:
- st.write(f'{player.name} has defeated {monster.name}!')
-
- # reinforcement monster attack
- reinforcement_monster.hp = reinforcement_monster.hp - player.attack
- st.write(f'Player {player.name} attacks {reinforcement_monster.name} with {player.attack}')
- st.write(f'{reinforcement_monster.name} has {reinforcement_monster.hp} hp left')
- # reinforcement monster attack player
- if reinforcement_monster.hp > 0:
- player.hp = player.hp - reinforcement_monster.attack
- st.write(f'{reinforcement_monster.name} attacks {player.name} with {reinforcement_monster.attack}')
- st.write(f'{player.name} has {player.hp} hp left')
-
- # check if player won the fight
- if player.hp > 0:
- st.write(f'{player.name} has defeated {reinforcement_monster.name}!')
- st.write('You have won the game! Congratulations!')
-
-# create a main function
-def main():
- st.title('Dungeon Crawler with Reinforcement Learning Monster')
-
- # create a start button
- if st.button('Start'):
- start_game()
-
-if __name__ == '__main__':
- main()
\ No newline at end of file
diff --git a/spaces/awacke1/MixtureOfMedicalExperts/app.py b/spaces/awacke1/MixtureOfMedicalExperts/app.py
deleted file mode 100644
index ca82ea5f8b03b10ad2e63aad82bd2df12556522f..0000000000000000000000000000000000000000
--- a/spaces/awacke1/MixtureOfMedicalExperts/app.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import streamlit as st
-
-def triage_checkin():
- st.write("### Triage and Check-in Expert 🚑")
- for i in range(1, 11):
- st.text_input(f"Question {i} for Triage")
-
-def lab_analyst():
- st.write("### Lab Analyst 🧪")
- for i in range(1, 11):
- st.text_input(f"Question {i} for Lab Analysis")
-
-def medicine_specialist():
- st.write("### Medicine Specialist 💊")
- for i in range(1, 11):
- st.text_input(f"Question {i} for Medicine")
-
-def service_expert():
- st.write("### Service Expert 💲")
- for i in range(1, 11):
- st.text_input(f"Question {i} for Service")
-
-def care_expert():
- st.write("### Level of Care Expert 🏥")
- for i in range(1, 11):
- st.text_input(f"Question {i} for Level of Care")
-
-def terminology_expert():
- st.write("### Terminology Expert 📚")
- for i in range(1, 11):
- st.text_input(f"Question {i} for Terminology")
-
-def cmo():
- st.write("### Chief Medical Officer 🩺")
- for i in range(1, 11):
- st.text_input(f"Question {i} for CMO")
-
-def medical_director():
- st.write("### Medical Director Team 🏢")
- for i in range(1, 11):
- st.text_input(f"Question {i} for Medical Director")
-
-def main():
- st.title("Mixture of Medical Experts Model")
- st.write("Harness the power of AI with this specialized healthcare framework! 🎉")
-
- role = st.selectbox("Select AI Role:", [
- "Triage and Check-in Expert",
- "Lab Analyst",
- "Medicine Specialist",
- "Service Expert",
- "Level of Care Expert",
- "Terminology Expert",
- "Chief Medical Officer",
- "Medical Director Team"
- ])
-
- if role == "Triage and Check-in Expert":
- triage_checkin()
- elif role == "Lab Analyst":
- lab_analyst()
- elif role == "Medicine Specialist":
- medicine_specialist()
- elif role == "Service Expert":
- service_expert()
- elif role == "Level of Care Expert":
- care_expert()
- elif role == "Terminology Expert":
- terminology_expert()
- elif role == "Chief Medical Officer":
- cmo()
- elif role == "Medical Director Team":
- medical_director()
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/awacke1/StreamlitMapBoxCityNames/README.md b/spaces/awacke1/StreamlitMapBoxCityNames/README.md
deleted file mode 100644
index 4595e278ff87fc0ea39860e9ad45df00b5481c7d..0000000000000000000000000000000000000000
--- a/spaces/awacke1/StreamlitMapBoxCityNames/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: StreamlitMapBoxCityNames
-emoji: 📈City
-colorFrom: yellow
-colorTo: gray
-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/awacke1/VizLib-TopLargeHospitalsMentalHealth/README.md b/spaces/awacke1/VizLib-TopLargeHospitalsMentalHealth/README.md
deleted file mode 100644
index 32ad5fc12e176b9142acbc76a210467d0ad94e85..0000000000000000000000000000000000000000
--- a/spaces/awacke1/VizLib-TopLargeHospitalsMentalHealth/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: VizLib TopLargeHospitalsMentalHealth
-emoji: 🐠
-colorFrom: purple
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/WikipediaUltimateAISearch/README.md b/spaces/awacke1/WikipediaUltimateAISearch/README.md
deleted file mode 100644
index 646617d1dad7a227f87fc3cde0a6aa3441cfa2c6..0000000000000000000000000000000000000000
--- a/spaces/awacke1/WikipediaUltimateAISearch/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 🔍Wikipedia AI🌟
-emoji: 🌍
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Wikipedia AI Ultimate Search with AI and Live Real Time Fact Check
diff --git a/spaces/ayaanzaveri/whisper-webui/src/segments.py b/spaces/ayaanzaveri/whisper-webui/src/segments.py
deleted file mode 100644
index ec2650dceade5d0b2022264f6419115eab085aea..0000000000000000000000000000000000000000
--- a/spaces/ayaanzaveri/whisper-webui/src/segments.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from typing import Any, Dict, List
-
-import copy
-
-def merge_timestamps(timestamps: List[Dict[str, Any]], merge_window: float = 5, max_merge_size: float = 30, padding_left: float = 1, padding_right: float = 1):
- result = []
-
- if len(timestamps) == 0:
- return result
- if max_merge_size is None:
- return timestamps
-
- if padding_left is None:
- padding_left = 0
- if padding_right is None:
- padding_right = 0
-
- processed_time = 0
- current_segment = None
-
- for i in range(len(timestamps)):
- next_segment = timestamps[i]
-
- delta = next_segment['start'] - processed_time
-
- # Note that segments can still be longer than the max merge size, they just won't be merged in that case
- if current_segment is None or (merge_window is not None and delta > merge_window) \
- or next_segment['end'] - current_segment['start'] > max_merge_size:
- # Finish the current segment
- if current_segment is not None:
- # Add right padding
- finish_padding = min(padding_right, delta / 2) if delta < padding_left + padding_right else padding_right
- current_segment['end'] += finish_padding
- delta -= finish_padding
-
- result.append(current_segment)
-
- # Start a new segment
- current_segment = copy.deepcopy(next_segment)
-
- # Pad the segment
- current_segment['start'] = current_segment['start'] - min(padding_left, delta)
- processed_time = current_segment['end']
-
- else:
- # Merge the segment
- current_segment['end'] = next_segment['end']
- processed_time = current_segment['end']
-
- # Add the last segment
- if current_segment is not None:
- current_segment['end'] += padding_right
- result.append(current_segment)
-
- return result
\ No newline at end of file
diff --git a/spaces/badmonk/up/README.md b/spaces/badmonk/up/README.md
deleted file mode 100644
index b00fcd4fd88ff6e8f40dcf3905dba4245002ccd6..0000000000000000000000000000000000000000
--- a/spaces/badmonk/up/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: UPSCALER
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.34.0
-python_version: 3.11.3
-app_file: app.py
-pinned: true
-license: apache-2.0
-emoji: ⬆️
----
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/lines/LineMaterial.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/lines/LineMaterial.js
deleted file mode 100644
index 2e0fb2bcb160e1fb777eb07be4251f880ee8ca2e..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/lines/LineMaterial.js
+++ /dev/null
@@ -1,391 +0,0 @@
-/**
- * @author WestLangley / http://github.com/WestLangley
- *
- * parameters = {
- * color: 版本信息 {pkg.version}
- 站点域名
- copyToClipboard(host)} className="flex gap-1 text-xs text-zinc-500 cursor-pointer">
- {host}
- The Snappening Pictures Part 1 Rar
Download File ———>>> https://tinurli.com/2uwiu7
-
- aaccfb2cb3
-
-
- diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/help.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/help.py deleted file mode 100644 index 2a238de3d6d5d69c70c0130d98f8272be7efabf5..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/help.py +++ /dev/null @@ -1,35 +0,0 @@ -import pkgutil -import sys -import fontTools -import importlib -import os -from pathlib import Path - - -def main(): - """Show this help""" - path = fontTools.__path__ - descriptions = {} - for pkg in sorted( - mod.name - for mod in pkgutil.walk_packages([fontTools.__path__[0]], prefix="fontTools.") - ): - try: - imports = __import__(pkg, globals(), locals(), ["main"]) - except ImportError as e: - continue - try: - description = imports.main.__doc__ - if description: - pkg = pkg.replace("fontTools.", "").replace(".__main__", "") - # show the docstring's first line only - descriptions[pkg] = description.splitlines()[0] - except AttributeError as e: - pass - for pkg, description in descriptions.items(): - print("fonttools %-25s %s" % (pkg, description), file=sys.stderr) - - -if __name__ == "__main__": - print("fonttools v%s\n" % fontTools.__version__, file=sys.stderr) - main() diff --git a/spaces/cloudwp/DreamShaper-webui/README.md b/spaces/cloudwp/DreamShaper-webui/README.md deleted file mode 100644 index e6412704be28e4eadf96892f6d5f40fb43826334..0000000000000000000000000000000000000000 --- a/spaces/cloudwp/DreamShaper-webui/README.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: Stable Diffusion Web UI -emoji: 🚧 -colorFrom: yellow -colorTo: yellow -sdk: gradio -sdk_version: 3.9 -app_file: app.py -pinned: false -duplicated_from: luongphamit/DreamShaper-webui ---- - -## Stable Diffusion Web UI -[https://github.com/AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) - -## Documentation -[https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki) - -## Models License -https://huggingface.co/spaces/CompVis/stable-diffusion-license \ No newline at end of file diff --git a/spaces/colakin/video-generater/public/ffmpeg/compat/djgpp/math.h b/spaces/colakin/video-generater/public/ffmpeg/compat/djgpp/math.h deleted file mode 100644 index 4c02ea9c402d9cd1406aae8fd64b941c27bc7af1..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/compat/djgpp/math.h +++ /dev/null @@ -1,25 +0,0 @@ -/* - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -double fmin(double, double); -double fmax(double, double); -float fminf(float, float); -float fmaxf(float, float); -long double fmodl(long double, long double); -long double scalbnl(long double, int); -long double copysignl(long double, long double); diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alpha/mpegvideo_alpha.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alpha/mpegvideo_alpha.c deleted file mode 100644 index 126fe264a17456834f79210a849c06d398f130e4..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alpha/mpegvideo_alpha.c +++ /dev/null @@ -1,110 +0,0 @@ -/* - * Alpha optimized DSP utils - * Copyright (c) 2002 Falk Hueffner
-
Download Pocket Academy 3 APK Original: A Fun and Engaging School Simulator
-Do you love simulation games that let you create and manage your own dream school? If yes, then you should definitely check out Pocket Academy 3, the latest game by Kairosoft, the makers of many popular simulation games. In this article, we will tell you what Pocket Academy 3 is, what are its features and gameplay, and why you should download the original APK file instead of the modified versions. We will also show you how to download and install Pocket Academy 3 APK original on your Android device, and give you some tips and tricks to enjoy the game to the fullest.
-download pocket academy 3 apk original
Download Zip ✪✪✪ https://urlca.com/2uO4Zx
-
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-Pocket Academy 3 is a simulation game that lets you build your own school from scratch, arrange the educational process, and make your students and graduates famous all over the country. It is the third part in a series that has been popular for more than 10 years. Here, the developers have slightly improved the system of controls and added many new features, like participating in sports competitions or preparing graduates for university entrance exams.
-The features and gameplay of Pocket Academy 3
-Pocket Academy 3 has many features and gameplay elements that make it fun and engaging. Some of them are:
--
-
- You can build various facilities, from classrooms, cafeterias, libraries, labs, dorms, gyms, pools, fields, observatories, and more. You can also customize them with different colors and decorations. -
- You can arrange the work of teachers and extracurricular hobby clubs, and assign students to different classes and activities according to their talents and interests. -
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- You can enter your students into sports competitions, such as baseball, soccer, tennis, swimming, etc. You can also train them and help them win trophies and medals. -
- You can help your students prepare for placement tests so they can realize their dreams. You can also guide them in choosing their careers and colleges. -
- You can watch your students grow and develop their personalities, relationships, skills, and achievements. You can also interact with them and give them advice or rewards. -
The benefits of downloading the original APK file
-While there are many modified versions of Pocket Academy 3 APK available on the internet, we recommend that you download the original APK file from a reliable source. This is because:
--
-
- The original APK file is safe and secure, unlike the modified versions that may contain viruses or malware that can harm your device or steal your data. -
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-To download Pocket Academy 3 APK original, you need to follow these steps:
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- Locate the APK file on your device using a file manager app or your browser's download history. -
- Tap on the APK file and follow the on-screen instructions to install it. You may need to grant some permissions to the app. -
- Once the installation is complete, you can launch the app from your app drawer or home screen. -
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-Now that you have installed Pocket Academy 3 APK original on your device, you can start playing and creating your own school. Here are some tips and tricks to help you enjoy the game to the fullest:
--
-
- Experiment with different facilities and layouts to optimize your school's performance and appearance. You can also use the zoom function to see more details. -
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- Save your game progress frequently and use multiple slots to try different scenarios. You can also use the cloud save feature to backup your data online. -
- Check out the game's official website or social media pages for more information, updates, news, tips, and fan-made content. -
Conclusion
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-Pocket Academy 3 is a fun and engaging school simulator game by Kairosoft that lets you build and manage your own dream school. It has many features and gameplay elements that make it addictive and enjoyable. To download Pocket Academy 3 APK original, you need to find a reliable website that offers it for free download, such as APKMirror or APKPure. Then, you need to download and install the APK file on your device following some simple steps. Finally, you can start playing and creating your own school with some tips and tricks we provided.
-A call to action for the readers to download the game and share their feedback
-If you are a fan of simulation games or school management games, you should definitely give Pocket Academy 3 a try. It is one of the best games by Kairosoft that will keep you entertained for hours. Download Pocket Academy 3 APK original now and start building your own school today. Don't forget to share your feedback with us in the comments section below. We would love to hear from you!
-Frequently Asked Questions
-What are the system requirements for Pocket Academy 3?
-Pocket Academy 3 requires Android 4.4 or higher and at least 40 MB of free storage space on your device.
-Is Pocket Academy 3 free to play?
-No, Pocket Academy 3 is a paid game that costs $5.99 to download from Google Play Store. However, you can download it for free from other sources, such as APKMirror or APKPure.
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-Yes, you can play Pocket Academy 3 offline without an internet connection. However, you may need an internet connection to access some features, such as cloud save or social media integration.
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-No, you cannot transfer your game data from Pocket Academy 2 to Pocket Academy 3. They are separate games with different features and gameplay.
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-No, Pocket Academy 3 is only available for Android devices. However, you can use an Android emulator software on your PC to run Android apps such as [BlueStacks] or [NoxPlayer]. However, this may affect the performance and compatibility of the game.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Treasure Keeper The Thrilling Adventure of Skunk and His Team.md b/spaces/congsaPfin/Manga-OCR/logs/Download Treasure Keeper The Thrilling Adventure of Skunk and His Team.md deleted file mode 100644 index dfc8691baaf86904c7041d94b2cfd434012c7c8f..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Download Treasure Keeper The Thrilling Adventure of Skunk and His Team.md +++ /dev/null @@ -1,122 +0,0 @@ - -
Download Treasure Keeper: A Guide to Enjoying the Comic Action Drama Series
-If you are looking for a fun and exciting Korean series to watch, you might want to check out Treasure Keeper. This is a comic action drama that follows a mysterious thief and a team of cultural asset recovery experts as they go against the lawless villains who exploit Korea's heritage. In this article, we will tell you everything you need to know about Treasure Keeper, including what it is, why you should watch it, and how to download it legally and safely.
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-Treasure Keeper is a Korean series that premiered in April 2023 on Viu, a streaming platform that offers Asian dramas and movies. It has 12 episodes, each about an hour long. The series is based on a webtoon of the same name by Kim Young-hoon, which was published from 2018 to 2020.
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-The series revolves around Skunk, a masked thief who specializes in stealing cultural assets from corrupt and powerful people. He teams up with Karma, an unofficial cultural asset recovery team that consists of a public officer, a hacker, a martial artist, and a former idol. Together, they try to restore Korea's cultural heritage and punish those who evade the law.
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-The series features a talented and charismatic cast of actors who bring their characters to life. Here are some of the main cast members and their roles:
--
-
- Joo Won as Skunk/Hwang Dae-myung: He is a dual character who plays both the thief Skunk and the public officer Hwang Dae-myung. He has a mysterious past and a hidden identity. -
- Lee Joo-woo as Karma/Kim Ae-ri: She is the leader of Karma and a hacker who uses her skills to help Skunk and her team. She has a crush on Skunk. -
- Jo Han-chul as Lee Seok-jin: He is a martial artist and a former special forces agent who works for Karma. He is loyal and brave. -
- Kim Jae-won as Kang Min-jae: He is a former idol and a con artist who joins Karma after being blackmailed by Skunk. He is charming and witty. -
- Choi Hwa-jeong as Park Mi-sook: She is a cultural asset expert and an antique dealer who helps Karma with her knowledge and connections. She is smart and elegant. -
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The cultural and historical aspects of the series
-Treasure Keeper is not only entertaining but also educational. It introduces you to various aspects of Korea's culture and history through the cultural assets that Skunk and Karma deal with. You can learn about Korea's art, architecture, literature, music, religion, folklore, and more through the series.
-The action and comedy scenes of the series
-Treasure Keeper also has amazing action and comedy scenes that showcase the skills and personalities of the characters. You can watch them perform impressive stunts, fight scenes, chases, and escapes as they steal and recover the cultural assets. You can also enjoy their hilarious interactions, banters, jokes, and pranks as they work together and clash with each other.
-How to download Treasure Keeper?
-If you are interested in watching Treasure Keeper, you might want to download it so that you can watch it anytime and anywhere you want. However, you should be careful about how you download the series, as not all methods are legal and safe. Here are some of the best ways to download Treasure Keeper:
-The legal and safe ways to download the series
-The most legal and safe way to download Treasure Keeper is to use the official streaming platform that offers the series, which is Viu. Viu is a subscription-based service that allows you to watch and download Asian dramas and movies on your devices. You can download up to 10 videos at a time and watch them offline for up to 30 days. You can also choose the video quality and subtitle language that suit your preferences.
-Another legal and safe way to download Treasure Keeper is to use a VPN service that can bypass geo-restrictions and access other streaming platforms that offer the series in different regions. For example, you can use a VPN to connect to a server in Korea and access KBS World, which is a free-to-air channel that broadcasts Treasure Keeper with English subtitles. You can then use a video downloader tool or extension to download the episodes from the website.
-The best platforms and devices to watch the series on
-Once you have downloaded Treasure Keeper, you might wonder what are the best platforms and devices to watch the series on. The answer depends on your personal preferences and needs, but here are some of the factors that you should consider:
--
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- The screen size and resolution: If you want to enjoy the details and quality of the series, you might want to watch it on a large screen with high resolution, such as a smart TV or a laptop. However, if you want to watch it on the go or in bed, you might prefer a smaller screen with lower resolution, such as a smartphone or a tablet. -
- The sound quality and volume: If you want to immerse yourself in the sound effects and music of the series, you might want to watch it on a platform or device that has good speakers or headphones. However, if you want to watch it quietly or without disturbing others, you might prefer a platform or device that has low volume or mute options. -
- The storage space and battery life: If you want to watch the series without interruption or deletion, you might want to watch it on a platform or device that has enough storage space and battery life to store and play the episodes. However, if you want to watch the series sparingly or temporarily, you might prefer a platform or device that has limited storage space and battery life. -
The tips and tricks to enhance your viewing experience
-Finally, here are some of the tips and tricks that can help you enhance your viewing experience of Treasure Keeper:
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-
- Watch the series with subtitles: Even if you understand Korean, you might want to watch the series with subtitles to catch the nuances and references of the dialogue. You can also learn some Korean words and phrases from the subtitles. -
- Watch the series with friends: If you want to share your thoughts and feelings about the series with others, you might want to watch the series with friends who are also interested in it. You can have fun discussing and debating about the plot, characters, action, comedy, romance, and culture of the series. -
- Watch the series with snacks: If you want to make your viewing experience more enjoyable and satisfying, you might want to watch the series with snacks that match its theme or mood. For example, you can eat some Korean snacks like tteokbokki (spicy rice cakes), kimbap (seaweed rice rolls), or choco pie (chocolate-covered cake) while watching Treasure Keeper. -
Conclusion
-Treasure Keeper is a comic action drama series that follows a thief and a recovery team who steal and restore Korea's cultural assets. It is a fun and exciting series that offers humor, suspense, romance, adventure, culture, history, action, and comedy. It is also easy and safe to download from various platforms and devices. If you are looking for a new Korean series to watch, we highly recommend that you give Treasure Keeper a try.
-A summary of the main points of the article
-In this article, we have covered:
--
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- What Treasure Keeper is and what it is about -
- Why Treasure Keeper is a great series to watch and what it offers -
- How to download Treasure Keeper legally and safely from various platforms and devices -
- How to enhance your viewing experience of Treasure Keeper with some tips and tricks -
A call to action for the readers
-Now that you have learned everything you need to know about Treasure Keeper, what are you waiting for? Download the series today and enjoy the comic action drama that will make you laugh, cry, and cheer. And don't forget to share your thoughts and opinions about the series with us in the comments section below. We would love to hear from you!
-FAQs
-Here are some of the frequently asked questions about Treasure Keeper:
--
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- Where can I watch Treasure Keeper online? -
- Who is the writer and director of Treasure Keeper? -
- Is Treasure Keeper based on a true story? -
- Is there a second season of Treasure Keeper? -
- What are some other Korean series that are similar to Treasure Keeper? -
You can watch Treasure Keeper online on Viu, which is the official streaming platform that offers the series. You can also use a VPN service to access other streaming platforms that offer the series in different regions, such as KBS World.
-The writer of Treasure Keeper is Kim Young-hoon, who is also the author of the original webtoon. The director of Treasure Keeper is Lee Seung-hoon, who has previously directed other Korean dramas such as The Fiery Priest and Doctor Prisoner.
-No, Treasure Keeper is not based on a true story. It is a fictional story that is inspired by Korea's culture and history. However, some of the cultural assets that appear in the series are real or based on real ones.
-As of now, there is no official announcement or confirmation about a second season of Treasure Keeper. However, there is a possibility that the series will be renewed for another season, as it has received positive reviews and ratings from the viewers and critics.
-If you like Treasure Keeper, you might also like some other Korean series that are similar to it in terms of genre, theme, or style. Some of these series are: Vincenzo, Lawless Lawyer, Healer, Leverage, and Private Lives.
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Marvel Contest of Champions MOD APK The Game Youve Been Waiting For.md b/spaces/congsaPfin/Manga-OCR/logs/Marvel Contest of Champions MOD APK The Game Youve Been Waiting For.md deleted file mode 100644 index c7cec27f1cabbdb5309320beaf60cd18f3cd5d29..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Marvel Contest of Champions MOD APK The Game Youve Been Waiting For.md +++ /dev/null @@ -1,127 +0,0 @@ -
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Marvel Contest of Champions Mod APK: The Ultimate Fighting Game for Marvel Fans
-If you are a fan of Marvel comics, movies, and games, you will love Marvel Contest of Champions. It is a free-to-play fighting game that features your favorite Marvel superheroes and villains in epic battles. You can collect, level up, and customize your champions, as well as team up with your friends and other players to take on various quests and challenges. But what if you want to enjoy the game without any limitations or restrictions? That's where Marvel Contest of Champions Mod APK comes in handy. In this article, we will tell you everything you need to know about this modded version of the game, including its features, benefits, and how to download and install it on your device.
-What is Marvel Contest of Champions?
-A brief introduction to the game and its storyline
-Marvel Contest of Champions is a game that was released in 2014 by Kabam Games, Inc. It is based on the Marvel comics storyline of the same name, where the cosmic entity known as The Collector summons various heroes and villains from different universes to fight in his Contest of Champions. The game follows a similar plot, where you play as a Summoner who has been chosen by The Collector to participate in his contest. You have to assemble a team of champions from different classes and factions, such as Mutant, Cosmic, Skill, Science, Mystic, and Tech, and fight against other Summoners and their champions in various locations from the Marvel universe.
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The gameplay and features of the game
-The game is a 2D fighting game that uses a simple tap-and-swipe control system. You can tap on the right side of the screen to perform light attacks, swipe right to perform medium attacks, swipe left to dodge, press and hold on the left side of the screen to block, and press and hold on the right side of the screen to charge a heavy attack. You can also use special attacks that are unique to each champion when you fill up your power meter. Each champion has a signature ability that is unlocked when you get a duplicate of that champion from a crystal or an arena. You can also improve your champions' stats and abilities by leveling them up, ranking them up, applying mastery points, and using synergy bonuses.
-The game has various modes and features that you can enjoy, such as:
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- Story Quests: These are the main missions that follow the storyline of the game. You can explore dynamic quest maps and fight against various enemies and bosses. You can also earn rewards such as crystals, gold, units, ISO-8, catalysts, and more. -
- Event Quests: These are limited-time quests that offer different themes and challenges. You can earn exclusive rewards such as shards, rank-up materials, champions, and more. -
- Arena: This is where you can compete against other players' champions in 3v3 matches. You can earn battle chips, gold, units, crystals, arena crystals, and more. -
- Alliance Quests: These are cooperative quests that you can play with your alliance members. You can work together to clear maps and earn alliance rewards such as glory, loyalty, crystals, rank-up materials, and more. -
- Alliance Wars: These are competitive quests that pit your alliance against another alliance. You can place your defenders on nodes and attack your opponents' defenders. You can earn war rating points, war victory crystals, loyalty, gold, units, shards, rank-up materials, and more. -
- Incursions: These are cooperative quests that you can play with another player in a 2v2 mode. You can choose your own champions and swap them with your partner. You can earn incursion artifacts, gold, units, crystals, rank-up materials, and more. -
- Special Operations: These are special missions that offer unique rewards and challenges. You can earn intel, gold, units, crystals, rank-up materials, and more. -
- Daily Quests: These are quests that change every day and offer different rewards. You can earn class-specific ISO-8, catalysts, gold, units, and more. -
- Weekly Quests: These are quests that change every week and offer different rewards. You can earn shards, rank-up materials, gold, units, and more. -
- Monthly Quests: These are quests that change every month and offer different rewards. You can earn shards, rank-up materials, gold, units, and more. -
Why should you download Marvel Contest of Champions Mod APK?
-The benefits of using the modded version of the game
-Marvel Contest of Champions is a fun and addictive game, but it also has some drawbacks and limitations that can affect your gaming experience. For example:
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- You need a stable internet connection to play the game. -
- You need to spend real money to buy units, crystals, and other premium items. -
- You need to grind for hours to collect resources and upgrade your champions. -
- You need to wait for timers and cooldowns to access certain features and modes. -
- You need to deal with annoying ads and pop-ups. -
But what if you could play the game without any of these hassles? That's what Marvel Contest of Champions Mod APK offers you. It is a modified version of the game that gives you unlimited access to everything you need to enjoy the game to the fullest. With Marvel Contest of Champions Mod APK, you can:
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- Play the game offline without any internet connection. -
- Get unlimited units, crystals, gold, ISO-8, catalysts, and other resources for free. -
- Unlock all the champions and rank them up to the max level instantly. -
- Bypass all the timers and cooldowns and play any mode or feature anytime you want. -
- Remove all the ads and pop-ups from the game. -
The features of the mod APK
-Marvel Contest of Champions Mod APK is not just a simple hack that gives you unlimited resources. It also has some amazing features that enhance your gameplay and make it more fun and exciting. Some of these features are:
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- God Mode: This feature makes you invincible in the game. You can take any amount of damage without losing any health. You can also deal massive damage to your enemies with every hit. -
- One Hit Kill: This feature allows you to kill any enemy with just one hit. You don't need to worry about their health bars or their special attacks. You can just swipe them away with ease. -
- No AI Attack: This feature prevents your enemies from attacking you at all. They will just stand there and let you beat them up without any resistance. -
- No Root Required: This feature makes it easy for you to install and use the mod APK on your device. You don't need to root your device or do any complicated procedures to make it work. -
- Easy to Update: This feature ensures that you always have the latest version of the mod APK on your device. You don't need to download or install anything manually. The mod APK will automatically update itself whenever there is a new update available. -
How to download and install Marvel Contest of Champions Mod APK?
-The steps to download and install the mod APK on your device
-If you are interested in downloading and installing Marvel Contest of Champions Mod APK on your device, you can follow these simple steps:
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- Click on this link to download the mod APK file on your device. -
- Go to your device's settings and enable the option to install apps from unknown sources. -
- Locate the downloaded file in your device's storage and tap on it to start the installation process. -
- Follow the instructions on the screen and wait for the installation to finish. -
- Launch the game from your app drawer and enjoy! -
The precautions and tips to avoid any issues
-While Marvel Contest of Champions Mod APK is safe and secure to use, there are some precautions and tips that you should keep in mind to avoid any issues or problems while using it. Here are some of them:
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- Do not use the mod APK on your main account or on any account that you care about. There is a risk of getting banned or suspended by the game developers if they detect any suspicious activity or violation of their terms of service. -
- Do not use the mod APK on any online mode or feature that involves other players. This can ruin the game balance and fairness for everyone and cause unwanted conflicts and complaints. -
- Do not use the mod APK excessively or abuse its features. This can make the game boring and uninteresting for you and also cause some glitches and errors in the game. -
- Do not update the game from the official sources or the Play Store. This can overwrite the mod APK and remove its features. You should only update the game from the link provided above or from the mod APK itself. -
- Do not share the mod APK with anyone else or post it on any public platform. This can expose the mod APK to unwanted attention and scrutiny and also put your device at risk of malware and viruses. -
Conclusion
-A summary of the main points and a call to action
-Marvel Contest of Champions is a great game for Marvel fans who love fighting games. It has a lot of content and features that will keep you entertained and engaged for hours. However, if you want to enjoy the game without any limitations or restrictions, you should download Marvel Contest of Champions Mod APK. It is a modified version of the game that gives you unlimited access to everything you need to have fun and win. It also has some amazing features that enhance your gameplay and make it more fun and exciting. You can download and install Marvel Contest of Champions Mod APK on your device by following the steps and tips mentioned above. So, what are you waiting for? Download Marvel Contest of Champions Mod APK now and unleash your inner superhero!
-FAQs
-Five unique questions and answers related to the topic
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- Q: Is Marvel Contest of Champions Mod APK safe to use? -
- A: Yes, Marvel Contest of Champions Mod APK is safe to use as long as you download it from a trusted source and follow the precautions and tips mentioned above. However, you should always use it at your own risk and discretion, as there is no guarantee that it will work flawlessly or that it will not cause any issues or problems with your device or account. -
- Q: Is Marvel Contest of Champions Mod APK compatible with my device? -
- A: Marvel Contest of Champions Mod APK is compatible with most Android devices that have Android 4.4 or higher as their operating system. However, some devices may not support some features or functions of the mod APK due to their specifications or settings. You should always check the compatibility of your device before downloading and installing the mod APK. -
- Q: Can I play Marvel Contest of Champions Mod APK with my friends? -
- A: Yes, you can play Marvel Contest of Champions Mod APK with your friends as long as they also have the mod APK installed on their devices. You can team up with them in alliance quests, alliance wars, incursions, and special operations. However, you should not play with them in any online mode or feature that involves other players who do not have the mod APK, as this can cause unfairness and imbalance in the game. -
- Q: How can I get more units, crystals, gold, ISO-8, catalysts, and other resources in Marvel Contest of Champions Mod APK? -
- A: You can get unlimited units, crystals, gold, ISO-8, catalysts, and other resources in Marvel Contest of Champions Mod APK by using the mod menu that is available in the game. You can access the mod menu by tapping on the icon on the top left corner of the screen. You can then adjust the amount of resources that you want to add to your account. -
- Q: How can I unlock all the champions in Marvel Contest of Champions Mod APK? -
- A: You can unlock all the champions in Marvel Contest of Champions Mod APK by using the mod menu that is available in the game. You can access the mod menu by tapping on the icon on the top left corner of the screen. You can then select the champions that you want to unlock and add them to your roster. -
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\ No newline at end of file diff --git a/spaces/contluForse/HuggingGPT/assets/Esquema Tv Sanyo Ctp 6792 Pdf Download the Service Manual and Schematics.md b/spaces/contluForse/HuggingGPT/assets/Esquema Tv Sanyo Ctp 6792 Pdf Download the Service Manual and Schematics.md deleted file mode 100644 index 23bf04799788c7c077d75f0692a7cd0595ea65bb..0000000000000000000000000000000000000000 --- a/spaces/contluForse/HuggingGPT/assets/Esquema Tv Sanyo Ctp 6792 Pdf Download the Service Manual and Schematics.md +++ /dev/null @@ -1,6 +0,0 @@ -
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\ No newline at end of file diff --git a/spaces/cozyanduofen/bingo/src/lib/isomorphic/node.ts b/spaces/cozyanduofen/bingo/src/lib/isomorphic/node.ts deleted file mode 100644 index da213ad6a86181979f098309c374da02835db5a0..0000000000000000000000000000000000000000 --- a/spaces/cozyanduofen/bingo/src/lib/isomorphic/node.ts +++ /dev/null @@ -1,26 +0,0 @@ -import Debug from 'debug' - -const { fetch, setGlobalDispatcher, ProxyAgent } = require('undici') -const { HttpsProxyAgent } = require('https-proxy-agent') -const ws = require('ws') - -const debug = Debug('bingo') - -const httpProxy = process.env.http_proxy || process.env.HTTP_PROXY || process.env.https_proxy || process.env.HTTPS_PROXY; -let WebSocket = ws.WebSocket - -if (httpProxy) { - setGlobalDispatcher(new ProxyAgent(httpProxy)) - const agent = new HttpsProxyAgent(httpProxy) - // @ts-ignore - WebSocket = class extends ws.WebSocket { - constructor(address: string | URL, options: typeof ws.WebSocket) { - super(address, { - ...options, - agent, - }) - } - } -} - -export default { fetch, WebSocket, debug } diff --git a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/face3d/models/arcface_torch/utils/utils_logging.py b/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/face3d/models/arcface_torch/utils/utils_logging.py deleted file mode 100644 index c787b6aae7cd037a4718df44d672b8ffa9e5c249..0000000000000000000000000000000000000000 --- a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/face3d/models/arcface_torch/utils/utils_logging.py +++ /dev/null @@ -1,41 +0,0 @@ -import logging -import os -import sys - - -class AverageMeter(object): - """Computes and stores the average and current value - """ - - def __init__(self): - self.val = None - self.avg = None - self.sum = None - self.count = None - 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 init_logging(rank, models_root): - if rank == 0: - log_root = logging.getLogger() - log_root.setLevel(logging.INFO) - formatter = logging.Formatter("Training: %(asctime)s-%(message)s") - handler_file = logging.FileHandler(os.path.join(models_root, "training.log")) - handler_stream = logging.StreamHandler(sys.stdout) - handler_file.setFormatter(formatter) - handler_stream.setFormatter(formatter) - log_root.addHandler(handler_file) - log_root.addHandler(handler_stream) - log_root.info('rank_id: %d' % rank) diff --git a/spaces/dataroots/SofaStyler/StyleTransfer/srcTransformer/__init__.py b/spaces/dataroots/SofaStyler/StyleTransfer/srcTransformer/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpcore/_backends/trio.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpcore/_backends/trio.py deleted file mode 100644 index b1626d28e2ded284a65d48a32309ee201d953c5e..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpcore/_backends/trio.py +++ /dev/null @@ -1,161 +0,0 @@ -import ssl -import typing - -import trio - -from .._exceptions import ( - ConnectError, - ConnectTimeout, - ExceptionMapping, - ReadError, - ReadTimeout, - WriteError, - WriteTimeout, - map_exceptions, -) -from .base import SOCKET_OPTION, AsyncNetworkBackend, AsyncNetworkStream - - -class TrioStream(AsyncNetworkStream): - def __init__(self, stream: trio.abc.Stream) -> None: - self._stream = stream - - async def read( - self, max_bytes: int, timeout: typing.Optional[float] = None - ) -> bytes: - timeout_or_inf = float("inf") if timeout is None else timeout - exc_map: ExceptionMapping = { - trio.TooSlowError: ReadTimeout, - trio.BrokenResourceError: ReadError, - trio.ClosedResourceError: ReadError, - } - with map_exceptions(exc_map): - with trio.fail_after(timeout_or_inf): - data: bytes = await self._stream.receive_some(max_bytes=max_bytes) - return data - - async def write( - self, buffer: bytes, timeout: typing.Optional[float] = None - ) -> None: - if not buffer: - return - - timeout_or_inf = float("inf") if timeout is None else timeout - exc_map: ExceptionMapping = { - trio.TooSlowError: WriteTimeout, - trio.BrokenResourceError: WriteError, - trio.ClosedResourceError: WriteError, - } - with map_exceptions(exc_map): - with trio.fail_after(timeout_or_inf): - await self._stream.send_all(data=buffer) - - async def aclose(self) -> None: - await self._stream.aclose() - - async def start_tls( - self, - ssl_context: ssl.SSLContext, - server_hostname: typing.Optional[str] = None, - timeout: typing.Optional[float] = None, - ) -> AsyncNetworkStream: - timeout_or_inf = float("inf") if timeout is None else timeout - exc_map: ExceptionMapping = { - trio.TooSlowError: ConnectTimeout, - trio.BrokenResourceError: ConnectError, - } - ssl_stream = trio.SSLStream( - self._stream, - ssl_context=ssl_context, - server_hostname=server_hostname, - https_compatible=True, - server_side=False, - ) - with map_exceptions(exc_map): - try: - with trio.fail_after(timeout_or_inf): - await ssl_stream.do_handshake() - except Exception as exc: # pragma: nocover - await self.aclose() - raise exc - return TrioStream(ssl_stream) - - def get_extra_info(self, info: str) -> typing.Any: - if info == "ssl_object" and isinstance(self._stream, trio.SSLStream): - # Type checkers cannot see `_ssl_object` attribute because trio._ssl.SSLStream uses __getattr__/__setattr__. - # Tracked at https://github.com/python-trio/trio/issues/542 - return self._stream._ssl_object # type: ignore[attr-defined] - if info == "client_addr": - return self._get_socket_stream().socket.getsockname() - if info == "server_addr": - return self._get_socket_stream().socket.getpeername() - if info == "socket": - stream = self._stream - while isinstance(stream, trio.SSLStream): - stream = stream.transport_stream - assert isinstance(stream, trio.SocketStream) - return stream.socket - if info == "is_readable": - socket = self.get_extra_info("socket") - return socket.is_readable() - return None - - def _get_socket_stream(self) -> trio.SocketStream: - stream = self._stream - while isinstance(stream, trio.SSLStream): - stream = stream.transport_stream - assert isinstance(stream, trio.SocketStream) - return stream - - -class TrioBackend(AsyncNetworkBackend): - async def connect_tcp( - self, - host: str, - port: int, - timeout: typing.Optional[float] = None, - local_address: typing.Optional[str] = None, - socket_options: typing.Optional[typing.Iterable[SOCKET_OPTION]] = None, - ) -> AsyncNetworkStream: - # By default for TCP sockets, trio enables TCP_NODELAY. - # https://trio.readthedocs.io/en/stable/reference-io.html#trio.SocketStream - if socket_options is None: - socket_options = [] # pragma: no cover - timeout_or_inf = float("inf") if timeout is None else timeout - exc_map: ExceptionMapping = { - trio.TooSlowError: ConnectTimeout, - trio.BrokenResourceError: ConnectError, - OSError: ConnectError, - } - with map_exceptions(exc_map): - with trio.fail_after(timeout_or_inf): - stream: trio.abc.Stream = await trio.open_tcp_stream( - host=host, port=port, local_address=local_address - ) - for option in socket_options: - stream.setsockopt(*option) # type: ignore[attr-defined] # pragma: no cover - return TrioStream(stream) - - async def connect_unix_socket( - self, - path: str, - timeout: typing.Optional[float] = None, - socket_options: typing.Optional[typing.Iterable[SOCKET_OPTION]] = None, - ) -> AsyncNetworkStream: # pragma: nocover - if socket_options is None: - socket_options = [] - timeout_or_inf = float("inf") if timeout is None else timeout - exc_map: ExceptionMapping = { - trio.TooSlowError: ConnectTimeout, - trio.BrokenResourceError: ConnectError, - OSError: ConnectError, - } - with map_exceptions(exc_map): - with trio.fail_after(timeout_or_inf): - stream: trio.abc.Stream = await trio.open_unix_socket(path) - for option in socket_options: - stream.setsockopt(*option) # type: ignore[attr-defined] # pragma: no cover - return TrioStream(stream) - - async def sleep(self, seconds: float) -> None: - await trio.sleep(seconds) # pragma: nocover diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/_commit_api.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/_commit_api.py deleted file mode 100644 index 7cbcc0d765b7ecb1c53c9388868c85d15e119141..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/_commit_api.py +++ /dev/null @@ -1,632 +0,0 @@ -""" -Type definitions and utilities for the `create_commit` API -""" -import base64 -import io -import os -import warnings -from collections import defaultdict -from contextlib import contextmanager -from dataclasses import dataclass, field -from itertools import groupby -from pathlib import Path, PurePosixPath -from typing import TYPE_CHECKING, Any, BinaryIO, Dict, Iterable, Iterator, List, Optional, Tuple, Union - -from tqdm.contrib.concurrent import thread_map - -from huggingface_hub import get_session - -from .constants import ENDPOINT, HF_HUB_ENABLE_HF_TRANSFER -from .lfs import UploadInfo, lfs_upload, post_lfs_batch_info -from .utils import ( - EntryNotFoundError, - build_hf_headers, - chunk_iterable, - hf_raise_for_status, - logging, - tqdm_stream_file, - validate_hf_hub_args, -) -from .utils import tqdm as hf_tqdm -from .utils._typing import Literal - - -if TYPE_CHECKING: - from .hf_api import RepoFile - - -logger = logging.get_logger(__name__) - - -UploadMode = Literal["lfs", "regular"] - -# Max is 1,000 per request on the Hub for HfApi.list_files_info -# Otherwise we get: -# HfHubHTTPError: 413 Client Error: Payload Too Large for url: https://huggingface.co/api/datasets/xxx (Request ID: xxx)\n\ntoo many parameters -# See https://github.com/huggingface/huggingface_hub/issues/1503 -FETCH_LFS_BATCH_SIZE = 500 - - -@dataclass -class CommitOperationDelete: - """ - Data structure holding necessary info to delete a file or a folder from a repository - on the Hub. - - Args: - path_in_repo (`str`): - Relative filepath in the repo, for example: `"checkpoints/1fec34a/weights.bin"` - for a file or `"checkpoints/1fec34a/"` for a folder. - is_folder (`bool` or `Literal["auto"]`, *optional*) - Whether the Delete Operation applies to a folder or not. If "auto", the path - type (file or folder) is guessed automatically by looking if path ends with - a "/" (folder) or not (file). To explicitly set the path type, you can set - `is_folder=True` or `is_folder=False`. - """ - - path_in_repo: str - is_folder: Union[bool, Literal["auto"]] = "auto" - - def __post_init__(self): - self.path_in_repo = _validate_path_in_repo(self.path_in_repo) - - if self.is_folder == "auto": - self.is_folder = self.path_in_repo.endswith("/") - if not isinstance(self.is_folder, bool): - raise ValueError( - f"Wrong value for `is_folder`. Must be one of [`True`, `False`, `'auto'`]. Got '{self.is_folder}'." - ) - - -@dataclass -class CommitOperationCopy: - """ - Data structure holding necessary info to copy a file in a repository on the Hub. - - Limitations: - - Only LFS files can be copied. To copy a regular file, you need to download it locally and re-upload it - - Cross-repository copies are not supported. - - Note: you can combine a [`CommitOperationCopy`] and a [`CommitOperationDelete`] to rename an LFS file on the Hub. - - Args: - src_path_in_repo (`str`): - Relative filepath in the repo of the file to be copied, e.g. `"checkpoints/1fec34a/weights.bin"`. - path_in_repo (`str`): - Relative filepath in the repo where to copy the file, e.g. `"checkpoints/1fec34a/weights_copy.bin"`. - src_revision (`str`, *optional*): - The git revision of the file to be copied. Can be any valid git revision. - Default to the target commit revision. - """ - - src_path_in_repo: str - path_in_repo: str - src_revision: Optional[str] = None - - def __post_init__(self): - self.src_path_in_repo = _validate_path_in_repo(self.src_path_in_repo) - self.path_in_repo = _validate_path_in_repo(self.path_in_repo) - - -@dataclass -class CommitOperationAdd: - """ - Data structure holding necessary info to upload a file to a repository on the Hub. - - Args: - path_in_repo (`str`): - Relative filepath in the repo, for example: `"checkpoints/1fec34a/weights.bin"` - path_or_fileobj (`str`, `Path`, `bytes`, or `BinaryIO`): - Either: - - a path to a local file (as `str` or `pathlib.Path`) to upload - - a buffer of bytes (`bytes`) holding the content of the file to upload - - a "file object" (subclass of `io.BufferedIOBase`), typically obtained - with `open(path, "rb")`. It must support `seek()` and `tell()` methods. - - Raises: - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) - If `path_or_fileobj` is not one of `str`, `Path`, `bytes` or `io.BufferedIOBase`. - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) - If `path_or_fileobj` is a `str` or `Path` but not a path to an existing file. - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) - If `path_or_fileobj` is a `io.BufferedIOBase` but it doesn't support both - `seek()` and `tell()`. - """ - - path_in_repo: str - path_or_fileobj: Union[str, Path, bytes, BinaryIO] - upload_info: UploadInfo = field(init=False, repr=False) - - def __post_init__(self) -> None: - """Validates `path_or_fileobj` and compute `upload_info`.""" - self.path_in_repo = _validate_path_in_repo(self.path_in_repo) - - # Validate `path_or_fileobj` value - if isinstance(self.path_or_fileobj, Path): - self.path_or_fileobj = str(self.path_or_fileobj) - if isinstance(self.path_or_fileobj, str): - path_or_fileobj = os.path.normpath(os.path.expanduser(self.path_or_fileobj)) - if not os.path.isfile(path_or_fileobj): - raise ValueError(f"Provided path: '{path_or_fileobj}' is not a file on the local file system") - elif not isinstance(self.path_or_fileobj, (io.BufferedIOBase, bytes)): - # ^^ Inspired from: https://stackoverflow.com/questions/44584829/how-to-determine-if-file-is-opened-in-binary-or-text-mode - raise ValueError( - "path_or_fileobj must be either an instance of str, bytes or" - " io.BufferedIOBase. If you passed a file-like object, make sure it is" - " in binary mode." - ) - if isinstance(self.path_or_fileobj, io.BufferedIOBase): - try: - self.path_or_fileobj.tell() - self.path_or_fileobj.seek(0, os.SEEK_CUR) - except (OSError, AttributeError) as exc: - raise ValueError( - "path_or_fileobj is a file-like object but does not implement seek() and tell()" - ) from exc - - # Compute "upload_info" attribute - if isinstance(self.path_or_fileobj, str): - self.upload_info = UploadInfo.from_path(self.path_or_fileobj) - elif isinstance(self.path_or_fileobj, bytes): - self.upload_info = UploadInfo.from_bytes(self.path_or_fileobj) - else: - self.upload_info = UploadInfo.from_fileobj(self.path_or_fileobj) - - @contextmanager - def as_file(self, with_tqdm: bool = False) -> Iterator[BinaryIO]: - """ - A context manager that yields a file-like object allowing to read the underlying - data behind `path_or_fileobj`. - - Args: - with_tqdm (`bool`, *optional*, defaults to `False`): - If True, iterating over the file object will display a progress bar. Only - works if the file-like object is a path to a file. Pure bytes and buffers - are not supported. - - Example: - - ```python - >>> operation = CommitOperationAdd( - ... path_in_repo="remote/dir/weights.h5", - ... path_or_fileobj="./local/weights.h5", - ... ) - CommitOperationAdd(path_in_repo='remote/dir/weights.h5', path_or_fileobj='./local/weights.h5') - - >>> with operation.as_file() as file: - ... content = file.read() - - >>> with operation.as_file(with_tqdm=True) as file: - ... while True: - ... data = file.read(1024) - ... if not data: - ... break - config.json: 100%|█████████████████████████| 8.19k/8.19k [00:02<00:00, 3.72kB/s] - - >>> with operation.as_file(with_tqdm=True) as file: - ... requests.put(..., data=file) - config.json: 100%|█████████████████████████| 8.19k/8.19k [00:02<00:00, 3.72kB/s] - ``` - """ - if isinstance(self.path_or_fileobj, str) or isinstance(self.path_or_fileobj, Path): - if with_tqdm: - with tqdm_stream_file(self.path_or_fileobj) as file: - yield file - else: - with open(self.path_or_fileobj, "rb") as file: - yield file - elif isinstance(self.path_or_fileobj, bytes): - yield io.BytesIO(self.path_or_fileobj) - elif isinstance(self.path_or_fileobj, io.BufferedIOBase): - prev_pos = self.path_or_fileobj.tell() - yield self.path_or_fileobj - self.path_or_fileobj.seek(prev_pos, io.SEEK_SET) - - def b64content(self) -> bytes: - """ - The base64-encoded content of `path_or_fileobj` - - Returns: `bytes` - """ - with self.as_file() as file: - return base64.b64encode(file.read()) - - -def _validate_path_in_repo(path_in_repo: str) -> str: - # Validate `path_in_repo` value to prevent a server-side issue - if path_in_repo.startswith("/"): - path_in_repo = path_in_repo[1:] - if path_in_repo == "." or path_in_repo == ".." or path_in_repo.startswith("../"): - raise ValueError(f"Invalid `path_in_repo` in CommitOperation: '{path_in_repo}'") - if path_in_repo.startswith("./"): - path_in_repo = path_in_repo[2:] - if any(part == ".git" for part in path_in_repo.split("/")): - raise ValueError( - "Invalid `path_in_repo` in CommitOperation: cannot update files under a '.git/' folder (path:" - f" '{path_in_repo}')." - ) - return path_in_repo - - -CommitOperation = Union[CommitOperationAdd, CommitOperationCopy, CommitOperationDelete] - - -def warn_on_overwriting_operations(operations: List[CommitOperation]) -> None: - """ - Warn user when a list of operations is expected to overwrite itself in a single - commit. - - Rules: - - If a filepath is updated by multiple `CommitOperationAdd` operations, a warning - message is triggered. - - If a filepath is updated at least once by a `CommitOperationAdd` and then deleted - by a `CommitOperationDelete`, a warning is triggered. - - If a `CommitOperationDelete` deletes a filepath that is then updated by a - `CommitOperationAdd`, no warning is triggered. This is usually useless (no need to - delete before upload) but can happen if a user deletes an entire folder and then - add new files to it. - """ - nb_additions_per_path: Dict[str, int] = defaultdict(int) - for operation in operations: - path_in_repo = operation.path_in_repo - if isinstance(operation, CommitOperationAdd): - if nb_additions_per_path[path_in_repo] > 0: - warnings.warn( - "About to update multiple times the same file in the same commit:" - f" '{path_in_repo}'. This can cause undesired inconsistencies in" - " your repo." - ) - nb_additions_per_path[path_in_repo] += 1 - for parent in PurePosixPath(path_in_repo).parents: - # Also keep track of number of updated files per folder - # => warns if deleting a folder overwrite some contained files - nb_additions_per_path[str(parent)] += 1 - if isinstance(operation, CommitOperationDelete): - if nb_additions_per_path[str(PurePosixPath(path_in_repo))] > 0: - if operation.is_folder: - warnings.warn( - "About to delete a folder containing files that have just been" - f" updated within the same commit: '{path_in_repo}'. This can" - " cause undesired inconsistencies in your repo." - ) - else: - warnings.warn( - "About to delete a file that have just been updated within the" - f" same commit: '{path_in_repo}'. This can cause undesired" - " inconsistencies in your repo." - ) - - -@validate_hf_hub_args -def upload_lfs_files( - *, - additions: List[CommitOperationAdd], - repo_type: str, - repo_id: str, - token: Optional[str], - endpoint: Optional[str] = None, - num_threads: int = 5, -): - """ - Uploads the content of `additions` to the Hub using the large file storage protocol. - - Relevant external documentation: - - LFS Batch API: https://github.com/git-lfs/git-lfs/blob/main/docs/api/batch.md - - Args: - additions (`List` of `CommitOperationAdd`): - The files to be uploaded - repo_type (`str`): - Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`. - repo_id (`str`): - A namespace (user or an organization) and a repo name separated - by a `/`. - token (`str`, *optional*): - An authentication token ( See https://huggingface.co/settings/tokens ) - num_threads (`int`, *optional*): - The number of concurrent threads to use when uploading. Defaults to 5. - - - Raises: `RuntimeError` if an upload failed for any reason - - Raises: `ValueError` if the server returns malformed responses - - Raises: `requests.HTTPError` if the LFS batch endpoint returned an HTTP - error - - """ - # Step 1: retrieve upload instructions from the LFS batch endpoint. - # Upload instructions are retrieved by chunk of 256 files to avoid reaching - # the payload limit. - batch_actions: List[Dict] = [] - for chunk in chunk_iterable(additions, chunk_size=256): - batch_actions_chunk, batch_errors_chunk = post_lfs_batch_info( - upload_infos=[op.upload_info for op in chunk], - token=token, - repo_id=repo_id, - repo_type=repo_type, - endpoint=endpoint, - ) - - # If at least 1 error, we do not retrieve information for other chunks - if batch_errors_chunk: - message = "\n".join( - [ - f'Encountered error for file with OID {err.get("oid")}: `{err.get("error", {}).get("message")}' - for err in batch_errors_chunk - ] - ) - raise ValueError(f"LFS batch endpoint returned errors:\n{message}") - - batch_actions += batch_actions_chunk - oid2addop = {add_op.upload_info.sha256.hex(): add_op for add_op in additions} - - # Step 2: ignore files that have already been uploaded - filtered_actions = [] - for action in batch_actions: - if action.get("actions") is None: - logger.debug( - f"Content of file {oid2addop[action['oid']].path_in_repo} is already" - " present upstream - skipping upload." - ) - else: - filtered_actions.append(action) - - if len(filtered_actions) == 0: - logger.debug("No LFS files to upload.") - return - - # Step 3: upload files concurrently according to these instructions - def _wrapped_lfs_upload(batch_action) -> None: - try: - operation = oid2addop[batch_action["oid"]] - lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token) - except Exception as exc: - raise RuntimeError(f"Error while uploading '{operation.path_in_repo}' to the Hub.") from exc - - if HF_HUB_ENABLE_HF_TRANSFER: - logger.debug(f"Uploading {len(filtered_actions)} LFS files to the Hub using `hf_transfer`.") - for action in hf_tqdm(filtered_actions): - _wrapped_lfs_upload(action) - elif len(filtered_actions) == 1: - logger.debug("Uploading 1 LFS file to the Hub") - _wrapped_lfs_upload(filtered_actions[0]) - else: - logger.debug( - f"Uploading {len(filtered_actions)} LFS files to the Hub using up to {num_threads} threads concurrently" - ) - thread_map( - _wrapped_lfs_upload, - filtered_actions, - desc=f"Upload {len(filtered_actions)} LFS files", - max_workers=num_threads, - tqdm_class=hf_tqdm, - ) - - -def _validate_preupload_info(preupload_info: dict): - files = preupload_info.get("files") - if not isinstance(files, list): - raise ValueError("preupload_info is improperly formatted") - for file_info in files: - if not ( - isinstance(file_info, dict) - and isinstance(file_info.get("path"), str) - and isinstance(file_info.get("uploadMode"), str) - and (file_info["uploadMode"] in ("lfs", "regular")) - ): - raise ValueError("preupload_info is improperly formatted:") - return preupload_info - - -@validate_hf_hub_args -def fetch_upload_modes( - additions: Iterable[CommitOperationAdd], - repo_type: str, - repo_id: str, - token: Optional[str], - revision: str, - endpoint: Optional[str] = None, - create_pr: bool = False, -) -> Dict[str, UploadMode]: - """ - Requests the Hub "preupload" endpoint to determine whether each input file - should be uploaded as a regular git blob or as git LFS blob. - - Args: - additions (`Iterable` of :class:`CommitOperationAdd`): - Iterable of :class:`CommitOperationAdd` describing the files to - upload to the Hub. - repo_type (`str`): - Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`. - repo_id (`str`): - A namespace (user or an organization) and a repo name separated - by a `/`. - token (`str`, *optional*): - An authentication token ( See https://huggingface.co/settings/tokens ) - revision (`str`): - The git revision to upload the files to. Can be any valid git revision. - - Returns: `Dict[str, UploadMode]` - Key is the file path, value is the upload mode ("regular" or "lfs"). - - Raises: - [`~utils.HfHubHTTPError`] - If the Hub API returned an error. - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) - If the Hub API response is improperly formatted. - """ - endpoint = endpoint if endpoint is not None else ENDPOINT - headers = build_hf_headers(token=token) - - # Fetch upload mode (LFS or regular) chunk by chunk. - upload_modes: Dict[str, UploadMode] = {} - for chunk in chunk_iterable(additions, 256): - payload = { - "files": [ - { - "path": op.path_in_repo, - "sample": base64.b64encode(op.upload_info.sample).decode("ascii"), - "size": op.upload_info.size, - "sha": op.upload_info.sha256.hex(), - } - for op in chunk - ] - } - - resp = get_session().post( - f"{endpoint}/api/{repo_type}s/{repo_id}/preupload/{revision}", - json=payload, - headers=headers, - params={"create_pr": "1"} if create_pr else None, - ) - hf_raise_for_status(resp) - preupload_info = _validate_preupload_info(resp.json()) - upload_modes.update(**{file["path"]: file["uploadMode"] for file in preupload_info["files"]}) - - # Empty files cannot be uploaded as LFS (S3 would fail with a 501 Not Implemented) - # => empty files are uploaded as "regular" to still allow users to commit them. - for addition in additions: - if addition.upload_info.size == 0: - path = addition.path_in_repo - upload_modes[path] = "regular" - - return upload_modes - - -@validate_hf_hub_args -def fetch_lfs_files_to_copy( - copies: Iterable[CommitOperationCopy], - repo_type: str, - repo_id: str, - token: Optional[str], - revision: str, - endpoint: Optional[str] = None, -) -> Dict[Tuple[str, Optional[str]], "RepoFile"]: - """ - Requests the Hub files information of the LFS files to be copied, including their sha256. - - Args: - copies (`Iterable` of :class:`CommitOperationCopy`): - Iterable of :class:`CommitOperationCopy` describing the files to - copy on the Hub. - repo_type (`str`): - Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`. - repo_id (`str`): - A namespace (user or an organization) and a repo name separated - by a `/`. - token (`str`, *optional*): - An authentication token ( See https://huggingface.co/settings/tokens ) - revision (`str`): - The git revision to upload the files to. Can be any valid git revision. - - Returns: `Dict[Tuple[str, Optional[str]], RepoFile]]` - Key is the file path and revision of the file to copy, value is the repo file. - - Raises: - [`~utils.HfHubHTTPError`] - If the Hub API returned an error. - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) - If the Hub API response is improperly formatted. - """ - from .hf_api import HfApi - - hf_api = HfApi(endpoint=endpoint, token=token) - files_to_copy = {} - for src_revision, operations in groupby(copies, key=lambda op: op.src_revision): - operations = list(operations) # type: ignore - paths = [op.src_path_in_repo for op in operations] - for offset in range(0, len(paths), FETCH_LFS_BATCH_SIZE): - src_repo_files = hf_api.list_files_info( - repo_id=repo_id, - paths=paths[offset : offset + FETCH_LFS_BATCH_SIZE], - revision=src_revision or revision, - repo_type=repo_type, - ) - for src_repo_file in src_repo_files: - if not src_repo_file.lfs: - raise NotImplementedError("Copying a non-LFS file is not implemented") - files_to_copy[(src_repo_file.rfilename, src_revision)] = src_repo_file - for operation in operations: - if (operation.src_path_in_repo, src_revision) not in files_to_copy: - raise EntryNotFoundError( - f"Cannot copy {operation.src_path_in_repo} at revision " - f"{src_revision or revision}: file is missing on repo." - ) - return files_to_copy - - -def prepare_commit_payload( - operations: Iterable[CommitOperation], - upload_modes: Dict[str, UploadMode], - files_to_copy: Dict[Tuple[str, Optional[str]], "RepoFile"], - commit_message: str, - commit_description: Optional[str] = None, - parent_commit: Optional[str] = None, -) -> Iterable[Dict[str, Any]]: - """ - Builds the payload to POST to the `/commit` API of the Hub. - - Payload is returned as an iterator so that it can be streamed as a ndjson in the - POST request. - - For more information, see: - - https://github.com/huggingface/huggingface_hub/issues/1085#issuecomment-1265208073 - - http://ndjson.org/ - """ - commit_description = commit_description if commit_description is not None else "" - - # 1. Send a header item with the commit metadata - header_value = {"summary": commit_message, "description": commit_description} - if parent_commit is not None: - header_value["parentCommit"] = parent_commit - yield {"key": "header", "value": header_value} - - # 2. Send operations, one per line - for operation in operations: - # 2.a. Case adding a regular file - if isinstance(operation, CommitOperationAdd) and upload_modes.get(operation.path_in_repo) == "regular": - yield { - "key": "file", - "value": { - "content": operation.b64content().decode(), - "path": operation.path_in_repo, - "encoding": "base64", - }, - } - # 2.b. Case adding an LFS file - elif isinstance(operation, CommitOperationAdd) and upload_modes.get(operation.path_in_repo) == "lfs": - yield { - "key": "lfsFile", - "value": { - "path": operation.path_in_repo, - "algo": "sha256", - "oid": operation.upload_info.sha256.hex(), - "size": operation.upload_info.size, - }, - } - # 2.c. Case deleting a file or folder - elif isinstance(operation, CommitOperationDelete): - yield { - "key": "deletedFolder" if operation.is_folder else "deletedFile", - "value": {"path": operation.path_in_repo}, - } - # 2.d. Case copying a file or folder - elif isinstance(operation, CommitOperationCopy): - file_to_copy = files_to_copy[(operation.src_path_in_repo, operation.src_revision)] - if not file_to_copy.lfs: - raise NotImplementedError("Copying a non-LFS file is not implemented") - yield { - "key": "lfsFile", - "value": { - "path": operation.path_in_repo, - "algo": "sha256", - "oid": file_to_copy.lfs["sha256"], - }, - } - # 2.e. Never expected to happen - else: - raise ValueError( - f"Unknown operation to commit. Operation: {operation}. Upload mode:" - f" {upload_modes.get(operation.path_in_repo)}" - ) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/_api/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/_api/__init__.py deleted file mode 100644 index 92516e7949ba1020c15235dc702c35453ef7d257..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/_api/__init__.py +++ /dev/null @@ -1,390 +0,0 @@ -""" -Helper functions for managing the Matplotlib API. - -This documentation is only relevant for Matplotlib developers, not for users. - -.. warning:: - - This module and its submodules are for internal use only. Do not use them - in your own code. We may change the API at any time with no warning. - -""" - -import functools -import itertools -import re -import sys -import warnings - -from .deprecation import ( - deprecated, warn_deprecated, - rename_parameter, delete_parameter, make_keyword_only, - deprecate_method_override, deprecate_privatize_attribute, - suppress_matplotlib_deprecation_warning, - MatplotlibDeprecationWarning) - - -class classproperty: - """ - Like `property`, but also triggers on access via the class, and it is the - *class* that's passed as argument. - - Examples - -------- - :: - - class C: - @classproperty - def foo(cls): - return cls.__name__ - - assert C.foo == "C" - """ - - def __init__(self, fget, fset=None, fdel=None, doc=None): - self._fget = fget - if fset is not None or fdel is not None: - raise ValueError('classproperty only implements fget.') - self.fset = fset - self.fdel = fdel - # docs are ignored for now - self._doc = doc - - def __get__(self, instance, owner): - return self._fget(owner) - - @property - def fget(self): - return self._fget - - -# In the following check_foo() functions, the first parameter starts with an -# underscore because it is intended to be positional-only (e.g., so that -# `_api.check_isinstance([...], types=foo)` doesn't fail. - -def check_isinstance(_types, **kwargs): - """ - For each *key, value* pair in *kwargs*, check that *value* is an instance - of one of *_types*; if not, raise an appropriate TypeError. - - As a special case, a ``None`` entry in *_types* is treated as NoneType. - - Examples - -------- - >>> _api.check_isinstance((SomeClass, None), arg=arg) - """ - types = _types - none_type = type(None) - types = ((types,) if isinstance(types, type) else - (none_type,) if types is None else - tuple(none_type if tp is None else tp for tp in types)) - - def type_name(tp): - return ("None" if tp is none_type - else tp.__qualname__ if tp.__module__ == "builtins" - else f"{tp.__module__}.{tp.__qualname__}") - - for k, v in kwargs.items(): - if not isinstance(v, types): - names = [*map(type_name, types)] - if "None" in names: # Move it to the end for better wording. - names.remove("None") - names.append("None") - raise TypeError( - "{!r} must be an instance of {}, not a {}".format( - k, - ", ".join(names[:-1]) + " or " + names[-1] - if len(names) > 1 else names[0], - type_name(type(v)))) - - -def check_in_list(_values, *, _print_supported_values=True, **kwargs): - """ - For each *key, value* pair in *kwargs*, check that *value* is in *_values*. - - Parameters - ---------- - _values : iterable - Sequence of values to check on. - _print_supported_values : bool, default: True - Whether to print *_values* when raising ValueError. - **kwargs : dict - *key, value* pairs as keyword arguments to find in *_values*. - - Raises - ------ - ValueError - If any *value* in *kwargs* is not found in *_values*. - - Examples - -------- - >>> _api.check_in_list(["foo", "bar"], arg=arg, other_arg=other_arg) - """ - if not kwargs: - raise TypeError("No argument to check!") - values = _values - for key, val in kwargs.items(): - if val not in values: - msg = f"{val!r} is not a valid value for {key}" - if _print_supported_values: - msg += f"; supported values are {', '.join(map(repr, values))}" - raise ValueError(msg) - - -def check_shape(_shape, **kwargs): - """ - For each *key, value* pair in *kwargs*, check that *value* has the shape - *_shape*, if not, raise an appropriate ValueError. - - *None* in the shape is treated as a "free" size that can have any length. - e.g. (None, 2) -> (N, 2) - - The values checked must be numpy arrays. - - Examples - -------- - To check for (N, 2) shaped arrays - - >>> _api.check_shape((None, 2), arg=arg, other_arg=other_arg) - """ - target_shape = _shape - for k, v in kwargs.items(): - data_shape = v.shape - - if len(target_shape) != len(data_shape) or any( - t not in [s, None] - for t, s in zip(target_shape, data_shape) - ): - dim_labels = iter(itertools.chain( - 'MNLIJKLH', - (f"D{i}" for i in itertools.count()))) - text_shape = ", ".join((str(n) - if n is not None - else next(dim_labels) - for n in target_shape)) - if len(target_shape) == 1: - text_shape += "," - - raise ValueError( - f"{k!r} must be {len(target_shape)}D " - f"with shape ({text_shape}). " - f"Your input has shape {v.shape}." - ) - - -def check_getitem(_mapping, **kwargs): - """ - *kwargs* must consist of a single *key, value* pair. If *key* is in - *_mapping*, return ``_mapping[value]``; else, raise an appropriate - ValueError. - - Examples - -------- - >>> _api.check_getitem({"foo": "bar"}, arg=arg) - """ - mapping = _mapping - if len(kwargs) != 1: - raise ValueError("check_getitem takes a single keyword argument") - (k, v), = kwargs.items() - try: - return mapping[v] - except KeyError: - raise ValueError( - "{!r} is not a valid value for {}; supported values are {}" - .format(v, k, ', '.join(map(repr, mapping)))) from None - - -def caching_module_getattr(cls): - """ - Helper decorator for implementing module-level ``__getattr__`` as a class. - - This decorator must be used at the module toplevel as follows:: - - @caching_module_getattr - class __getattr__: # The class *must* be named ``__getattr__``. - @property # Only properties are taken into account. - def name(self): ... - - The ``__getattr__`` class will be replaced by a ``__getattr__`` - function such that trying to access ``name`` on the module will - resolve the corresponding property (which may be decorated e.g. with - ``_api.deprecated`` for deprecating module globals). The properties are - all implicitly cached. Moreover, a suitable AttributeError is generated - and raised if no property with the given name exists. - """ - - assert cls.__name__ == "__getattr__" - # Don't accidentally export cls dunders. - props = {name: prop for name, prop in vars(cls).items() - if isinstance(prop, property)} - instance = cls() - - @functools.lru_cache(None) - def __getattr__(name): - if name in props: - return props[name].__get__(instance) - raise AttributeError( - f"module {cls.__module__!r} has no attribute {name!r}") - - return __getattr__ - - -def define_aliases(alias_d, cls=None): - """ - Class decorator for defining property aliases. - - Use as :: - - @_api.define_aliases({"property": ["alias", ...], ...}) - class C: ... - - For each property, if the corresponding ``get_property`` is defined in the - class so far, an alias named ``get_alias`` will be defined; the same will - be done for setters. If neither the getter nor the setter exists, an - exception will be raised. - - The alias map is stored as the ``_alias_map`` attribute on the class and - can be used by `.normalize_kwargs` (which assumes that higher priority - aliases come last). - """ - if cls is None: # Return the actual class decorator. - return functools.partial(define_aliases, alias_d) - - def make_alias(name): # Enforce a closure over *name*. - @functools.wraps(getattr(cls, name)) - def method(self, *args, **kwargs): - return getattr(self, name)(*args, **kwargs) - return method - - for prop, aliases in alias_d.items(): - exists = False - for prefix in ["get_", "set_"]: - if prefix + prop in vars(cls): - exists = True - for alias in aliases: - method = make_alias(prefix + prop) - method.__name__ = prefix + alias - method.__doc__ = "Alias for `{}`.".format(prefix + prop) - setattr(cls, prefix + alias, method) - if not exists: - raise ValueError( - "Neither getter nor setter exists for {!r}".format(prop)) - - def get_aliased_and_aliases(d): - return {*d, *(alias for aliases in d.values() for alias in aliases)} - - preexisting_aliases = getattr(cls, "_alias_map", {}) - conflicting = (get_aliased_and_aliases(preexisting_aliases) - & get_aliased_and_aliases(alias_d)) - if conflicting: - # Need to decide on conflict resolution policy. - raise NotImplementedError( - f"Parent class already defines conflicting aliases: {conflicting}") - cls._alias_map = {**preexisting_aliases, **alias_d} - return cls - - -def select_matching_signature(funcs, *args, **kwargs): - """ - Select and call the function that accepts ``*args, **kwargs``. - - *funcs* is a list of functions which should not raise any exception (other - than `TypeError` if the arguments passed do not match their signature). - - `select_matching_signature` tries to call each of the functions in *funcs* - with ``*args, **kwargs`` (in the order in which they are given). Calls - that fail with a `TypeError` are silently skipped. As soon as a call - succeeds, `select_matching_signature` returns its return value. If no - function accepts ``*args, **kwargs``, then the `TypeError` raised by the - last failing call is re-raised. - - Callers should normally make sure that any ``*args, **kwargs`` can only - bind a single *func* (to avoid any ambiguity), although this is not checked - by `select_matching_signature`. - - Notes - ----- - `select_matching_signature` is intended to help implementing - signature-overloaded functions. In general, such functions should be - avoided, except for back-compatibility concerns. A typical use pattern is - :: - - def my_func(*args, **kwargs): - params = select_matching_signature( - [lambda old1, old2: locals(), lambda new: locals()], - *args, **kwargs) - if "old1" in params: - warn_deprecated(...) - old1, old2 = params.values() # note that locals() is ordered. - else: - new, = params.values() - # do things with params - - which allows *my_func* to be called either with two parameters (*old1* and - *old2*) or a single one (*new*). Note that the new signature is given - last, so that callers get a `TypeError` corresponding to the new signature - if the arguments they passed in do not match any signature. - """ - # Rather than relying on locals() ordering, one could have just used func's - # signature (``bound = inspect.signature(func).bind(*args, **kwargs); - # bound.apply_defaults(); return bound``) but that is significantly slower. - for i, func in enumerate(funcs): - try: - return func(*args, **kwargs) - except TypeError: - if i == len(funcs) - 1: - raise - - -def nargs_error(name, takes, given): - """Generate a TypeError to be raised by function calls with wrong arity.""" - return TypeError(f"{name}() takes {takes} positional arguments but " - f"{given} were given") - - -def kwarg_error(name, kw): - """ - Generate a TypeError to be raised by function calls with wrong kwarg. - - Parameters - ---------- - name : str - The name of the calling function. - kw : str or Iterable[str] - Either the invalid keyword argument name, or an iterable yielding - invalid keyword arguments (e.g., a ``kwargs`` dict). - """ - if not isinstance(kw, str): - kw = next(iter(kw)) - return TypeError(f"{name}() got an unexpected keyword argument '{kw}'") - - -def recursive_subclasses(cls): - """Yield *cls* and direct and indirect subclasses of *cls*.""" - yield cls - for subcls in cls.__subclasses__(): - yield from recursive_subclasses(subcls) - - -def warn_external(message, category=None): - """ - `warnings.warn` wrapper that sets *stacklevel* to "outside Matplotlib". - - The original emitter of the warning can be obtained by patching this - function back to `warnings.warn`, i.e. ``_api.warn_external = - warnings.warn`` (or ``functools.partial(warnings.warn, stacklevel=2)``, - etc.). - """ - frame = sys._getframe() - for stacklevel in itertools.count(1): # lgtm[py/unused-loop-variable] - if frame is None: - # when called in embedded context may hit frame is None - break - if not re.match(r"\A(matplotlib|mpl_toolkits)(\Z|\.(?!tests\.))", - # Work around sphinx-gallery not setting __name__. - frame.f_globals.get("__name__", "")): - break - frame = frame.f_back - # premetively break reference cycle between locals and the frame - del frame - warnings.warn(message, category, stacklevel) diff --git a/spaces/dcq/freegpt-webui/g4f/Provider/Providers/Aichat.py b/spaces/dcq/freegpt-webui/g4f/Provider/Providers/Aichat.py deleted file mode 100644 index d78375ce7e62b634c82e163c693a5557b8e2f860..0000000000000000000000000000000000000000 --- a/spaces/dcq/freegpt-webui/g4f/Provider/Providers/Aichat.py +++ /dev/null @@ -1,35 +0,0 @@ -import requests -import os -import json -from ...typing import sha256, Dict, get_type_hints - -url = 'https://hteyun.com' -model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613'] -supports_stream = True -needs_auth = False - -def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs): - headers = { - 'Content-Type': 'application/json', - } - data = { - 'model': model, - 'temperature': 0.7, - 'presence_penalty': 0, - 'messages': messages, - } - response = requests.post(url + '/api/chat-stream', - json=data, stream=True) - - if stream: - for chunk in response.iter_content(chunk_size=None): - chunk = chunk.decode('utf-8') - if chunk.strip(): - message = json.loads(chunk)['choices'][0]['message']['content'] - yield message - else: - message = response.json()['choices'][0]['message']['content'] - yield message - -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]]) \ No newline at end of file diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/models/t5_film_transformer.py b/spaces/declare-lab/tango/diffusers/src/diffusers/models/t5_film_transformer.py deleted file mode 100644 index 1c41e656a9dbe81edafd5a2958d49ff28e84fd01..0000000000000000000000000000000000000000 --- a/spaces/declare-lab/tango/diffusers/src/diffusers/models/t5_film_transformer.py +++ /dev/null @@ -1,321 +0,0 @@ -# Copyright 2023 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 -from torch import nn - -from ..configuration_utils import ConfigMixin, register_to_config -from .attention_processor import Attention -from .embeddings import get_timestep_embedding -from .modeling_utils import ModelMixin - - -class T5FilmDecoder(ModelMixin, ConfigMixin): - @register_to_config - def __init__( - self, - input_dims: int = 128, - targets_length: int = 256, - max_decoder_noise_time: float = 2000.0, - d_model: int = 768, - num_layers: int = 12, - num_heads: int = 12, - d_kv: int = 64, - d_ff: int = 2048, - dropout_rate: float = 0.1, - ): - super().__init__() - - self.conditioning_emb = nn.Sequential( - nn.Linear(d_model, d_model * 4, bias=False), - nn.SiLU(), - nn.Linear(d_model * 4, d_model * 4, bias=False), - nn.SiLU(), - ) - - self.position_encoding = nn.Embedding(targets_length, d_model) - self.position_encoding.weight.requires_grad = False - - self.continuous_inputs_projection = nn.Linear(input_dims, d_model, bias=False) - - self.dropout = nn.Dropout(p=dropout_rate) - - self.decoders = nn.ModuleList() - for lyr_num in range(num_layers): - # FiLM conditional T5 decoder - lyr = DecoderLayer(d_model=d_model, d_kv=d_kv, num_heads=num_heads, d_ff=d_ff, dropout_rate=dropout_rate) - self.decoders.append(lyr) - - self.decoder_norm = T5LayerNorm(d_model) - - self.post_dropout = nn.Dropout(p=dropout_rate) - self.spec_out = nn.Linear(d_model, input_dims, bias=False) - - def encoder_decoder_mask(self, query_input, key_input): - mask = torch.mul(query_input.unsqueeze(-1), key_input.unsqueeze(-2)) - return mask.unsqueeze(-3) - - def forward(self, encodings_and_masks, decoder_input_tokens, decoder_noise_time): - batch, _, _ = decoder_input_tokens.shape - assert decoder_noise_time.shape == (batch,) - - # decoder_noise_time is in [0, 1), so rescale to expected timing range. - time_steps = get_timestep_embedding( - decoder_noise_time * self.config.max_decoder_noise_time, - embedding_dim=self.config.d_model, - max_period=self.config.max_decoder_noise_time, - ).to(dtype=self.dtype) - - conditioning_emb = self.conditioning_emb(time_steps).unsqueeze(1) - - assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4) - - seq_length = decoder_input_tokens.shape[1] - - # If we want to use relative positions for audio context, we can just offset - # this sequence by the length of encodings_and_masks. - decoder_positions = torch.broadcast_to( - torch.arange(seq_length, device=decoder_input_tokens.device), - (batch, seq_length), - ) - - position_encodings = self.position_encoding(decoder_positions) - - inputs = self.continuous_inputs_projection(decoder_input_tokens) - inputs += position_encodings - y = self.dropout(inputs) - - # decoder: No padding present. - decoder_mask = torch.ones( - decoder_input_tokens.shape[:2], device=decoder_input_tokens.device, dtype=inputs.dtype - ) - - # Translate encoding masks to encoder-decoder masks. - encodings_and_encdec_masks = [(x, self.encoder_decoder_mask(decoder_mask, y)) for x, y in encodings_and_masks] - - # cross attend style: concat encodings - encoded = torch.cat([x[0] for x in encodings_and_encdec_masks], dim=1) - encoder_decoder_mask = torch.cat([x[1] for x in encodings_and_encdec_masks], dim=-1) - - for lyr in self.decoders: - y = lyr( - y, - conditioning_emb=conditioning_emb, - encoder_hidden_states=encoded, - encoder_attention_mask=encoder_decoder_mask, - )[0] - - y = self.decoder_norm(y) - y = self.post_dropout(y) - - spec_out = self.spec_out(y) - return spec_out - - -class DecoderLayer(nn.Module): - def __init__(self, d_model, d_kv, num_heads, d_ff, dropout_rate, layer_norm_epsilon=1e-6): - super().__init__() - self.layer = nn.ModuleList() - - # cond self attention: layer 0 - self.layer.append( - T5LayerSelfAttentionCond(d_model=d_model, d_kv=d_kv, num_heads=num_heads, dropout_rate=dropout_rate) - ) - - # cross attention: layer 1 - self.layer.append( - T5LayerCrossAttention( - d_model=d_model, - d_kv=d_kv, - num_heads=num_heads, - dropout_rate=dropout_rate, - layer_norm_epsilon=layer_norm_epsilon, - ) - ) - - # Film Cond MLP + dropout: last layer - self.layer.append( - T5LayerFFCond(d_model=d_model, d_ff=d_ff, dropout_rate=dropout_rate, layer_norm_epsilon=layer_norm_epsilon) - ) - - def forward( - self, - hidden_states, - conditioning_emb=None, - attention_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - encoder_decoder_position_bias=None, - ): - hidden_states = self.layer[0]( - hidden_states, - conditioning_emb=conditioning_emb, - attention_mask=attention_mask, - ) - - if encoder_hidden_states is not None: - encoder_extended_attention_mask = torch.where(encoder_attention_mask > 0, 0, -1e10).to( - encoder_hidden_states.dtype - ) - - hidden_states = self.layer[1]( - hidden_states, - key_value_states=encoder_hidden_states, - attention_mask=encoder_extended_attention_mask, - ) - - # Apply Film Conditional Feed Forward layer - hidden_states = self.layer[-1](hidden_states, conditioning_emb) - - return (hidden_states,) - - -class T5LayerSelfAttentionCond(nn.Module): - def __init__(self, d_model, d_kv, num_heads, dropout_rate): - super().__init__() - self.layer_norm = T5LayerNorm(d_model) - self.FiLMLayer = T5FiLMLayer(in_features=d_model * 4, out_features=d_model) - self.attention = Attention(query_dim=d_model, heads=num_heads, dim_head=d_kv, out_bias=False, scale_qk=False) - self.dropout = nn.Dropout(dropout_rate) - - def forward( - self, - hidden_states, - conditioning_emb=None, - attention_mask=None, - ): - # pre_self_attention_layer_norm - normed_hidden_states = self.layer_norm(hidden_states) - - if conditioning_emb is not None: - normed_hidden_states = self.FiLMLayer(normed_hidden_states, conditioning_emb) - - # Self-attention block - attention_output = self.attention(normed_hidden_states) - - hidden_states = hidden_states + self.dropout(attention_output) - - return hidden_states - - -class T5LayerCrossAttention(nn.Module): - def __init__(self, d_model, d_kv, num_heads, dropout_rate, layer_norm_epsilon): - super().__init__() - self.attention = Attention(query_dim=d_model, heads=num_heads, dim_head=d_kv, out_bias=False, scale_qk=False) - self.layer_norm = T5LayerNorm(d_model, eps=layer_norm_epsilon) - self.dropout = nn.Dropout(dropout_rate) - - def forward( - self, - hidden_states, - key_value_states=None, - attention_mask=None, - ): - normed_hidden_states = self.layer_norm(hidden_states) - attention_output = self.attention( - normed_hidden_states, - encoder_hidden_states=key_value_states, - attention_mask=attention_mask.squeeze(1), - ) - layer_output = hidden_states + self.dropout(attention_output) - return layer_output - - -class T5LayerFFCond(nn.Module): - def __init__(self, d_model, d_ff, dropout_rate, layer_norm_epsilon): - super().__init__() - self.DenseReluDense = T5DenseGatedActDense(d_model=d_model, d_ff=d_ff, dropout_rate=dropout_rate) - self.film = T5FiLMLayer(in_features=d_model * 4, out_features=d_model) - self.layer_norm = T5LayerNorm(d_model, eps=layer_norm_epsilon) - self.dropout = nn.Dropout(dropout_rate) - - def forward(self, hidden_states, conditioning_emb=None): - forwarded_states = self.layer_norm(hidden_states) - if conditioning_emb is not None: - forwarded_states = self.film(forwarded_states, conditioning_emb) - - forwarded_states = self.DenseReluDense(forwarded_states) - hidden_states = hidden_states + self.dropout(forwarded_states) - return hidden_states - - -class T5DenseGatedActDense(nn.Module): - def __init__(self, d_model, d_ff, dropout_rate): - super().__init__() - self.wi_0 = nn.Linear(d_model, d_ff, bias=False) - self.wi_1 = nn.Linear(d_model, d_ff, bias=False) - self.wo = nn.Linear(d_ff, d_model, bias=False) - self.dropout = nn.Dropout(dropout_rate) - self.act = NewGELUActivation() - - def forward(self, hidden_states): - hidden_gelu = self.act(self.wi_0(hidden_states)) - hidden_linear = self.wi_1(hidden_states) - hidden_states = hidden_gelu * hidden_linear - hidden_states = self.dropout(hidden_states) - - hidden_states = self.wo(hidden_states) - return hidden_states - - -class T5LayerNorm(nn.Module): - def __init__(self, hidden_size, eps=1e-6): - """ - Construct a layernorm module in the T5 style. No bias and no subtraction of mean. - """ - super().__init__() - self.weight = nn.Parameter(torch.ones(hidden_size)) - self.variance_epsilon = eps - - def forward(self, hidden_states): - # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean - # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated - # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for - # half-precision inputs is done in fp32 - - variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) - hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) - - # convert into half-precision if necessary - if self.weight.dtype in [torch.float16, torch.bfloat16]: - hidden_states = hidden_states.to(self.weight.dtype) - - return self.weight * hidden_states - - -class NewGELUActivation(nn.Module): - """ - Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see - the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 - """ - - def forward(self, input: torch.Tensor) -> torch.Tensor: - return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0)))) - - -class T5FiLMLayer(nn.Module): - """ - FiLM Layer - """ - - def __init__(self, in_features, out_features): - super().__init__() - self.scale_bias = nn.Linear(in_features, out_features * 2, bias=False) - - def forward(self, x, conditioning_emb): - emb = self.scale_bias(conditioning_emb) - scale, shift = torch.chunk(emb, 2, -1) - x = x * (1 + scale) + shift - return x diff --git a/spaces/declare-lab/tango/diffusers/tests/models/test_models_unet_2d.py b/spaces/declare-lab/tango/diffusers/tests/models/test_models_unet_2d.py deleted file mode 100644 index 8f831fcf7cbfb298c5e4deb489cc0edae1f76a51..0000000000000000000000000000000000000000 --- a/spaces/declare-lab/tango/diffusers/tests/models/test_models_unet_2d.py +++ /dev/null @@ -1,297 +0,0 @@ -# coding=utf-8 -# Copyright 2023 HuggingFace 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. - -import gc -import math -import unittest - -import torch - -from diffusers import UNet2DModel -from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device - -from ..test_modeling_common import ModelTesterMixin - - -logger = logging.get_logger(__name__) -torch.backends.cuda.matmul.allow_tf32 = False - - -class Unet2DModelTests(ModelTesterMixin, unittest.TestCase): - model_class = UNet2DModel - - @property - def dummy_input(self): - batch_size = 4 - num_channels = 3 - sizes = (32, 32) - - noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) - time_step = torch.tensor([10]).to(torch_device) - - return {"sample": noise, "timestep": time_step} - - @property - def input_shape(self): - return (3, 32, 32) - - @property - def output_shape(self): - return (3, 32, 32) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "block_out_channels": (32, 64), - "down_block_types": ("DownBlock2D", "AttnDownBlock2D"), - "up_block_types": ("AttnUpBlock2D", "UpBlock2D"), - "attention_head_dim": None, - "out_channels": 3, - "in_channels": 3, - "layers_per_block": 2, - "sample_size": 32, - } - inputs_dict = self.dummy_input - return init_dict, inputs_dict - - -class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase): - model_class = UNet2DModel - - @property - def dummy_input(self): - batch_size = 4 - num_channels = 4 - sizes = (32, 32) - - noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) - time_step = torch.tensor([10]).to(torch_device) - - return {"sample": noise, "timestep": time_step} - - @property - def input_shape(self): - return (4, 32, 32) - - @property - def output_shape(self): - return (4, 32, 32) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "sample_size": 32, - "in_channels": 4, - "out_channels": 4, - "layers_per_block": 2, - "block_out_channels": (32, 64), - "attention_head_dim": 32, - "down_block_types": ("DownBlock2D", "DownBlock2D"), - "up_block_types": ("UpBlock2D", "UpBlock2D"), - } - inputs_dict = self.dummy_input - return init_dict, inputs_dict - - def test_from_pretrained_hub(self): - model, loading_info = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True) - - self.assertIsNotNone(model) - self.assertEqual(len(loading_info["missing_keys"]), 0) - - model.to(torch_device) - image = model(**self.dummy_input).sample - - assert image is not None, "Make sure output is not None" - - @unittest.skipIf(torch_device != "cuda", "This test is supposed to run on GPU") - def test_from_pretrained_accelerate(self): - model, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True) - model.to(torch_device) - image = model(**self.dummy_input).sample - - assert image is not None, "Make sure output is not None" - - @unittest.skipIf(torch_device != "cuda", "This test is supposed to run on GPU") - def test_from_pretrained_accelerate_wont_change_results(self): - # by defautl model loading will use accelerate as `low_cpu_mem_usage=True` - model_accelerate, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True) - model_accelerate.to(torch_device) - model_accelerate.eval() - - noise = torch.randn( - 1, - model_accelerate.config.in_channels, - model_accelerate.config.sample_size, - model_accelerate.config.sample_size, - generator=torch.manual_seed(0), - ) - noise = noise.to(torch_device) - time_step = torch.tensor([10] * noise.shape[0]).to(torch_device) - - arr_accelerate = model_accelerate(noise, time_step)["sample"] - - # two models don't need to stay in the device at the same time - del model_accelerate - torch.cuda.empty_cache() - gc.collect() - - model_normal_load, _ = UNet2DModel.from_pretrained( - "fusing/unet-ldm-dummy-update", output_loading_info=True, low_cpu_mem_usage=False - ) - model_normal_load.to(torch_device) - model_normal_load.eval() - arr_normal_load = model_normal_load(noise, time_step)["sample"] - - assert torch_all_close(arr_accelerate, arr_normal_load, rtol=1e-3) - - def test_output_pretrained(self): - model = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update") - model.eval() - model.to(torch_device) - - noise = torch.randn( - 1, - model.config.in_channels, - model.config.sample_size, - model.config.sample_size, - generator=torch.manual_seed(0), - ) - noise = noise.to(torch_device) - time_step = torch.tensor([10] * noise.shape[0]).to(torch_device) - - with torch.no_grad(): - output = model(noise, time_step).sample - - output_slice = output[0, -1, -3:, -3:].flatten().cpu() - # fmt: off - expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]) - # fmt: on - - self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-3)) - - -class NCSNppModelTests(ModelTesterMixin, unittest.TestCase): - model_class = UNet2DModel - - @property - def dummy_input(self, sizes=(32, 32)): - batch_size = 4 - num_channels = 3 - - noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) - time_step = torch.tensor(batch_size * [10]).to(dtype=torch.int32, device=torch_device) - - return {"sample": noise, "timestep": time_step} - - @property - def input_shape(self): - return (3, 32, 32) - - @property - def output_shape(self): - return (3, 32, 32) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "block_out_channels": [32, 64, 64, 64], - "in_channels": 3, - "layers_per_block": 1, - "out_channels": 3, - "time_embedding_type": "fourier", - "norm_eps": 1e-6, - "mid_block_scale_factor": math.sqrt(2.0), - "norm_num_groups": None, - "down_block_types": [ - "SkipDownBlock2D", - "AttnSkipDownBlock2D", - "SkipDownBlock2D", - "SkipDownBlock2D", - ], - "up_block_types": [ - "SkipUpBlock2D", - "SkipUpBlock2D", - "AttnSkipUpBlock2D", - "SkipUpBlock2D", - ], - } - inputs_dict = self.dummy_input - return init_dict, inputs_dict - - @slow - def test_from_pretrained_hub(self): - model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True) - self.assertIsNotNone(model) - self.assertEqual(len(loading_info["missing_keys"]), 0) - - model.to(torch_device) - inputs = self.dummy_input - noise = floats_tensor((4, 3) + (256, 256)).to(torch_device) - inputs["sample"] = noise - image = model(**inputs) - - assert image is not None, "Make sure output is not None" - - @slow - def test_output_pretrained_ve_mid(self): - model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256") - model.to(torch_device) - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - batch_size = 4 - num_channels = 3 - sizes = (256, 256) - - noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device) - time_step = torch.tensor(batch_size * [1e-4]).to(torch_device) - - with torch.no_grad(): - output = model(noise, time_step).sample - - output_slice = output[0, -3:, -3:, -1].flatten().cpu() - # fmt: off - expected_output_slice = torch.tensor([-4836.2231, -6487.1387, -3816.7969, -7964.9253, -10966.2842, -20043.6016, 8137.0571, 2340.3499, 544.6114]) - # fmt: on - - self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2)) - - def test_output_pretrained_ve_large(self): - model = UNet2DModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update") - model.to(torch_device) - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - batch_size = 4 - num_channels = 3 - sizes = (32, 32) - - noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device) - time_step = torch.tensor(batch_size * [1e-4]).to(torch_device) - - with torch.no_grad(): - output = model(noise, time_step).sample - - output_slice = output[0, -3:, -3:, -1].flatten().cpu() - # fmt: off - expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]) - # fmt: on - - self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2)) - - def test_forward_with_norm_groups(self): - # not required for this model - pass diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/audio2pose_models/discriminator.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/audio2pose_models/discriminator.py deleted file mode 100644 index 339c38e4812ff38a810f0f3a1c01812f6d5d78db..0000000000000000000000000000000000000000 --- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/audio2pose_models/discriminator.py +++ /dev/null @@ -1,76 +0,0 @@ -import torch -import torch.nn.functional as F -from torch import nn - -class ConvNormRelu(nn.Module): - def __init__(self, conv_type='1d', in_channels=3, out_channels=64, downsample=False, - kernel_size=None, stride=None, padding=None, norm='BN', leaky=False): - super().__init__() - if kernel_size is None: - if downsample: - kernel_size, stride, padding = 4, 2, 1 - else: - kernel_size, stride, padding = 3, 1, 1 - - if conv_type == '2d': - self.conv = nn.Conv2d( - in_channels, - out_channels, - kernel_size, - stride, - padding, - bias=False, - ) - if norm == 'BN': - self.norm = nn.BatchNorm2d(out_channels) - elif norm == 'IN': - self.norm = nn.InstanceNorm2d(out_channels) - else: - raise NotImplementedError - elif conv_type == '1d': - self.conv = nn.Conv1d( - in_channels, - out_channels, - kernel_size, - stride, - padding, - bias=False, - ) - if norm == 'BN': - self.norm = nn.BatchNorm1d(out_channels) - elif norm == 'IN': - self.norm = nn.InstanceNorm1d(out_channels) - else: - raise NotImplementedError - nn.init.kaiming_normal_(self.conv.weight) - - self.act = nn.LeakyReLU(negative_slope=0.2, inplace=False) if leaky else nn.ReLU(inplace=True) - 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\ No newline at end of file diff --git a/spaces/diagaiwei/ir_chinese_medqa/colbert/search/index_storage.py b/spaces/diagaiwei/ir_chinese_medqa/colbert/search/index_storage.py deleted file mode 100644 index 228b9250057b3b877d62c0a7e98aa2b4c538e467..0000000000000000000000000000000000000000 --- a/spaces/diagaiwei/ir_chinese_medqa/colbert/search/index_storage.py +++ /dev/null @@ -1,172 +0,0 @@ -import torch - -from colbert.utils.utils import flatten, print_message - -from colbert.indexing.loaders import load_doclens -from colbert.indexing.codecs.residual_embeddings_strided import ResidualEmbeddingsStrided - -from colbert.search.strided_tensor import StridedTensor -from colbert.search.candidate_generation import CandidateGeneration - -from .index_loader import IndexLoader -from colbert.modeling.colbert import colbert_score, colbert_score_packed, colbert_score_reduce - -from math import ceil - -import os -import pathlib -from torch.utils.cpp_extension import load - - -class IndexScorer(IndexLoader, CandidateGeneration): - def __init__(self, index_path, use_gpu=True): - super().__init__(index_path=index_path, use_gpu=use_gpu) - - IndexScorer.try_load_torch_extensions(use_gpu) - - self.embeddings_strided = ResidualEmbeddingsStrided(self.codec, self.embeddings, self.doclens) - - @classmethod - def try_load_torch_extensions(cls, use_gpu): - if hasattr(cls, "loaded_extensions") or use_gpu: - return - - print_message(f"Loading filter_pids_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...") - filter_pids_cpp = load( - name="filter_pids_cpp", - sources=[ - os.path.join( - pathlib.Path(__file__).parent.resolve(), "filter_pids.cpp" - ), - ], - extra_cflags=["-O3"], - verbose=os.getenv("COLBERT_LOAD_TORCH_EXTENSION_VERBOSE", "False") == "True", - ) - cls.filter_pids = filter_pids_cpp.filter_pids_cpp - - print_message(f"Loading decompress_residuals_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...") - decompress_residuals_cpp = load( - name="decompress_residuals_cpp", - sources=[ - os.path.join( - pathlib.Path(__file__).parent.resolve(), "decompress_residuals.cpp" - ), - ], - extra_cflags=["-O3"], - verbose=os.getenv("COLBERT_LOAD_TORCH_EXTENSION_VERBOSE", "False") == "True", - ) - cls.decompress_residuals = decompress_residuals_cpp.decompress_residuals_cpp - - cls.loaded_extensions = True - def lookup_eids(self, embedding_ids, codes=None, out_device='cuda'): - return self.embeddings_strided.lookup_eids(embedding_ids, codes=codes, out_device=out_device) - - def lookup_pids(self, passage_ids, out_device='cuda', return_mask=False): - return self.embeddings_strided.lookup_pids(passage_ids, out_device) - - def retrieve(self, config, Q): - Q = Q[:, :config.query_maxlen] # NOTE: Candidate generation uses only the query tokens - embedding_ids, centroid_scores = self.generate_candidates(config, Q) - - return embedding_ids, centroid_scores - - def embedding_ids_to_pids(self, embedding_ids): - all_pids = torch.unique(self.emb2pid[embedding_ids.long()].cuda(), sorted=False) - return all_pids - - def rank(self, config, Q, filter_fn=None): - with torch.inference_mode(): - pids, centroid_scores = self.retrieve(config, Q) - - if filter_fn is not None: - pids = filter_fn(pids) - - scores, pids = self.score_pids(config, Q, pids, centroid_scores) - - scores_sorter = scores.sort(descending=True) - pids, scores = pids[scores_sorter.indices].tolist(), scores_sorter.values.tolist() - - return pids, scores - - def score_pids(self, config, Q, pids, centroid_scores): - """ - Always supply a flat list or tensor for `pids`. - - Supply sizes Q = (1 | num_docs, *, dim) and D = (num_docs, *, dim). - If Q.size(0) is 1, the matrix will be compared with all passages. - Otherwise, each query matrix will be compared against the *aligned* passage. - """ - - # TODO: Remove batching? - batch_size = 2 ** 20 - - if self.use_gpu: - centroid_scores = centroid_scores.cuda() - - idx = centroid_scores.max(-1).values >= config.centroid_score_threshold - - if self.use_gpu: - approx_scores = [] - - # Filter docs using pruned centroid scores - for i in range(0, ceil(len(pids) / batch_size)): - pids_ = pids[i * batch_size : (i+1) * batch_size] - codes_packed, codes_lengths = self.embeddings_strided.lookup_codes(pids_) - idx_ = idx[codes_packed.long()] - pruned_codes_strided = StridedTensor(idx_, codes_lengths, use_gpu=self.use_gpu) - pruned_codes_padded, pruned_codes_mask = pruned_codes_strided.as_padded_tensor() - pruned_codes_lengths = (pruned_codes_padded * pruned_codes_mask).sum(dim=1) - codes_packed_ = codes_packed[idx_] - approx_scores_ = centroid_scores[codes_packed_.long()] - if approx_scores_.shape[0] == 0: - approx_scores.append(torch.zeros((len(pids_),), dtype=approx_scores_.dtype)) - continue - approx_scores_strided = StridedTensor(approx_scores_, pruned_codes_lengths, use_gpu=self.use_gpu) - approx_scores_padded, approx_scores_mask = approx_scores_strided.as_padded_tensor() - approx_scores_ = colbert_score_reduce(approx_scores_padded, approx_scores_mask, config) - approx_scores.append(approx_scores_) - approx_scores = torch.cat(approx_scores, dim=0) - if config.ndocs < len(approx_scores): - pids = pids[torch.topk(approx_scores, k=config.ndocs).indices] - - # Filter docs using full centroid scores - codes_packed, codes_lengths = self.embeddings_strided.lookup_codes(pids) - approx_scores = centroid_scores[codes_packed.long()] - approx_scores_strided = StridedTensor(approx_scores, codes_lengths, use_gpu=self.use_gpu) - approx_scores_padded, approx_scores_mask = approx_scores_strided.as_padded_tensor() - approx_scores = colbert_score_reduce(approx_scores_padded, approx_scores_mask, config) - if config.ndocs // 4 < len(approx_scores): - pids = pids[torch.topk(approx_scores, k=(config.ndocs // 4)).indices] - else: - pids = IndexScorer.filter_pids( - pids, centroid_scores, self.embeddings.codes, self.doclens, - self.embeddings_strided.codes_strided.offsets, idx, config.ndocs - ) - - # Rank final list of docs using full approximate embeddings (including residuals) - if self.use_gpu: - D_packed, D_mask = self.lookup_pids(pids) - else: - D_packed = IndexScorer.decompress_residuals( - pids, - self.doclens, - self.embeddings_strided.codes_strided.offsets, - self.codec.bucket_weights, - self.codec.reversed_bit_map, - self.codec.decompression_lookup_table, - self.embeddings.residuals, - self.embeddings.codes, - self.codec.centroids, - self.codec.dim, - self.codec.nbits - ) - D_packed = torch.nn.functional.normalize(D_packed.to(torch.float32), p=2, dim=-1) - D_mask = self.doclens[pids.long()] - - if Q.size(0) == 1: - return colbert_score_packed(Q, D_packed, D_mask, config), pids - - D_strided = StridedTensor(D_packed, D_mask, use_gpu=self.use_gpu) - D_padded, D_lengths = D_strided.as_padded_tensor() - - return colbert_score(Q, D_padded, D_lengths, config), pids diff --git a/spaces/diego2554/RemBG_super/README.md b/spaces/diego2554/RemBG_super/README.md deleted file mode 100644 index d6ffead946a387191235aed8e911e08d19729c0a..0000000000000000000000000000000000000000 --- a/spaces/diego2554/RemBG_super/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Rembg -emoji: 👀 -colorFrom: pink -colorTo: indigo -sdk: gradio -sdk_version: 3.0.20 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/digitalxingtong/Xingtong-2dall-Bert-VITS2/text/chinese_bert.py b/spaces/digitalxingtong/Xingtong-2dall-Bert-VITS2/text/chinese_bert.py deleted file mode 100644 index cb84ce0b426cd0a1c7954ddcdf41322c10ed14fa..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Xingtong-2dall-Bert-VITS2/text/chinese_bert.py +++ /dev/null @@ -1,50 +0,0 @@ -import torch -from transformers import AutoTokenizer, AutoModelForMaskedLM - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large") -model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(device) - -def get_bert_feature(text, word2ph): - with torch.no_grad(): - inputs = tokenizer(text, return_tensors='pt') - for i in inputs: - inputs[i] = inputs[i].to(device) - res = model(**inputs, output_hidden_states=True) - res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu() - - assert len(word2ph) == len(text)+2 - word2phone = word2ph - phone_level_feature = [] - for i in range(len(word2phone)): - repeat_feature = res[i].repeat(word2phone[i], 1) - phone_level_feature.append(repeat_feature) - - phone_level_feature = torch.cat(phone_level_feature, dim=0) - - - return phone_level_feature.T - -if __name__ == '__main__': - # feature = get_bert_feature('你好,我是说的道理。') - import torch - - word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征 - word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1] - - # 计算总帧数 - total_frames = sum(word2phone) - print(word_level_feature.shape) - print(word2phone) - phone_level_feature = [] - for i in range(len(word2phone)): - print(word_level_feature[i].shape) - - # 对每个词重复word2phone[i]次 - repeat_feature = word_level_feature[i].repeat(word2phone[i], 1) - phone_level_feature.append(repeat_feature) - - phone_level_feature = torch.cat(phone_level_feature, dim=0) - print(phone_level_feature.shape) # torch.Size([36, 1024]) - diff --git a/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/losses.py b/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/losses.py deleted file mode 100644 index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/losses.py +++ /dev/null @@ -1,61 +0,0 @@ -import torch -from torch.nn import functional as F - -import 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() - - 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/digitalxingtong/Xingtong-Longread-Bert-VITS2/transforms.py b/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/transforms.py deleted file mode 100644 index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/transforms.py +++ /dev/null @@ -1,193 +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)) - 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/dongyi/MMFS/tools/concat_images_or_videos.py b/spaces/dongyi/MMFS/tools/concat_images_or_videos.py deleted file mode 100644 index 722cf335662d86cf629f03b6f1e7da37fee77336..0000000000000000000000000000000000000000 --- a/spaces/dongyi/MMFS/tools/concat_images_or_videos.py +++ /dev/null @@ -1,214 +0,0 @@ -import shutil, cv2, os -import numpy as np - -# Map {image_number: [row_num, col_num]} -default_concat_mapping = {1: [1, 1], - 2: [1, 2], - 3: [1, 3], - 4: [1, 4], - 5: [1, 5], - 6: [2, 3], - 7: [2, 4], - 8: [2, 4], - 9: [3, 3], - 10:[2, 5], - 11:[3, 4], - 12:[3, 4], - 13:[4, 4], - 14:[4, 4], - 15:[4, 4], - 16:[4, 4]} - -default_model_postfix_mapping = {"pix2pixHD": "_fake_B.png", - "cut" : "_fake_B.png", - "ugatit" : "_fake_A2B.png", - "5G" : "_fake_B.png", - "5.7G" : "_fake_B.png", - "900Mreorg": "_fake_B.png"} - -class ConcatImages: - - def __init__(self, ): - self.cv2_img_read_type = cv2.IMREAD_COLOR - self.cv2_img_read_size = (512, 512) - self.blank_img_size = (512, 512, 3) - self.paired_image_list = [] - self.save_dir = None - self.save_name = None - self.blank_img = np.zeros(self.blank_img_size).astype(np.uint8) - - # Text config - self.text_pos = (10, 30) - self.text_font = cv2.FONT_HERSHEY_COMPLEX - self.text_color = (0, 0, 255) # Red - self.text_size = 1.0 - self.text_overstriking = 2 - - - def run_concat_images(self, save_dir, paired_image_path_list, paired_image_text_list=None, row_num=0, col_num=0): - self.save_dir = save_dir - os.makedirs(self.save_dir, exist_ok=True) - - total_img_number = len(paired_image_path_list) - assert total_img_number <= row_num * col_num, "Error image number {} > (row: {} x col: {})".format("%d"%total_img_number, "%d"%row_num, "%d"%col_num) - - flag_add_text = False - if paired_image_text_list == None: - flag_add_text = False - else: - assert len(paired_image_path_list) == len(paired_image_text_list), "Not match len(paired_image_path_list) %d and len(paired_image_text_list) %d".format(len(paired_image_path_list), len(paired_image_text_list)) - flag_add_text = True - - self.read_images_list(paired_image_path_list, paired_image_text_list, flag_add_text) - self.concat_format_grids_byorder(row_num, col_num) - - - def read_images_list(self, paired_image_path_list, paired_image_text_list, flag_add_text): - self.paired_image_list = [] - if flag_add_text == True: - for img_idx in range(len(paired_image_path_list)): - self.paired_image_list += [self.add_text(self.read_image(paired_image_path_list[img_idx]), - paired_image_text_list[img_idx])] - else: - for img_idx in range(len(paired_image_path_list)): - self.paired_image_list += [self.read_image(paired_image_path_list[img_idx])] - self.save_name = paired_image_path_list[0].split("/")[-1] - - - def concat_format_grids_byorder(self, row_num, col_num): - # Stack format - res = None - total_img_number = len(self.paired_image_list) - - added_blank_img_number = row_num * col_num - total_img_number - self.paired_image_list += [ self.blank_img for added_blank_img_idx in range(added_blank_img_number)] - - img_idx = 0 - row_res = None - col_res = None - for row_idx in range(row_num): - for col_idx in range(col_num): - if col_idx == 0: - col_res = self.paired_image_list[img_idx] - else: - col_res = np.hstack([col_res, self.paired_image_list[img_idx]]) - img_idx += 1 - if row_idx == 0: - row_res = col_res - else: - row_res = np.vstack([row_res, col_res]) - - cv2.imwrite(os.path.join(self.save_dir, self.save_name), row_res) - - - def concat_format_custom(self, ): - pass - - - def read_image(self, img_path): - return cv2.resize(cv2.imread(img_path, self.cv2_img_read_type), self.cv2_img_read_size) - - - def add_text(self, img, text): - return cv2.putText(img, text, self.text_pos, self.text_font, self.text_size, self.text_color, self.text_overstriking) - - -class ConcatVideos: - - def __init__(self, ): - self.save_dir = None - self.save_name = None - self.video_path_list = [] - self.total_video_number = 0 - self.ffmpeg_cmd_str = None - self.alphabet_mapping_idx = 97 - self.flag_readd_video_text = True - - # Text config - self.text_pos_x = 10 - self.text_pos_y = 30 - self.text_color = "red" - self.text_size = 22 - self.text_font = self.set_text_font("/usr/share/fonts/") - - - def run_concat_videos(self, save_dir, paired_video_path_list, paired_video_text_list=None, row_num=0, col_num=0, flag_readd_video_name=True): - self.save_dir = save_dir - os.makedirs(self.save_dir, exist_ok=True) - - flag_add_text = False - if paired_video_text_list == None: - flag_add_text = False - else: - assert len(paired_video_path_list) == len(paired_video_text_list), "Not match len(paired_video_path_list) %d and len(paired_video_text_list) %d".format(len(paired_video_path_list), len(paired_video_path_list)) - flag_add_text = True - - if flag_add_text == True: - self.video_path_list = self.add_video_text(paired_video_path_list, paired_video_text_list) - assert len(self.video_path_list) == len(paired_video_path_list) - else: - self.video_path_list = paired_video_path_list - - self.flag_readd_video_text = flag_readd_video_name - if self.flag_readd_video_text == True: - assert paired_video_text_list is not None, "Must have paired_video_text_list." - - self.total_video_number = len(paired_video_path_list) - assert self.total_video_number <= row_num * col_num, "Error video number {} > (row: {} x col: {})".format("%d"%self.total_video_number, "%d"%row_num, "%d"%col_num) - - self.concat_format_grids_byorder(row_num, col_num) - - - def concat_format_grids_byorder(self, row_num, col_num): - - self.ffmpeg_cmd_str = "ffmpeg " - - for video_fn_path in self.video_path_list: - self.ffmpeg_cmd_str += "-i " + video_fn_path + " " - - # Video concat format - self.ffmpeg_cmd_str += "-filter_complex '" - video_cnt = 0 - - for t_row in range(row_num): - for t_col in range(col_num): - if video_cnt == 0: - self.ffmpeg_cmd_str += "[0:v]pad=iw*{}:ih*{}".format(col_num, row_num) - else: - self.ffmpeg_cmd_str += "[{}];[{}][{}:v]overlay=w*{}:h*{}".format(chr(self.alphabet_mapping_idx + video_cnt), chr(self.alphabet_mapping_idx + video_cnt), video_cnt, t_col, t_row) - video_cnt += 1 - if video_cnt >= self.total_video_number: - break - - self.ffmpeg_cmd_str += "'" - - # Video concat config - self.ffmpeg_cmd_str += " -c:v libx264 -crf 22 -preset veryfast -y " - self.ffmpeg_cmd_str += os.path.join(self.save_dir, "output.mp4") - - print("self.ffmpeg_cmd_str", self.ffmpeg_cmd_str) - os.system(self.ffmpeg_cmd_str) - - - def add_video_text(self, video_path_list, video_name_list): - - video_w_name_path_list = [] - - for video_path, video_name in zip(video_path_list, video_name_list): - assert os.path.isfile(video_path), "When adding video text, Not a video file path {}.".format(video_path) - video_fn = video_path.split("/")[-1] - video_w_name_fn = os.path.join(os.path.dirname(video_path), video_fn.split(".")[0] + "_w_name." + video_fn.split(".")[-1]) - if self.flag_readd_video_text == True: - print("ffmpeg -i {} -vf drawtext=fontfile={}:fontcolor={}:fontsize={}:text='{}':x={}:y={} -y {}".format(video_path, self.text_font, self.text_color, self.text_size, video_name, self.text_pos_x, self.text_pos_y, video_w_name_fn)) - os.system("ffmpeg -i {} -vf drawtext=fontfile={}:fontcolor={}:fontsize={}:text='{}':x={}:y={} -y {}".format(video_path, self.text_font, self.text_color, self.text_size, video_name, self.text_pos_x, self.text_pos_y, video_w_name_fn)) - video_w_name_path_list += [video_w_name_fn] - return video_w_name_path_list - - - def set_text_font(self, font_path): - - for root, dirs, files in os.walk(font_path): - if "DejaVuSans.ttf" in files: - print("Successfully set text font in ffmpeg: {}".format("%s"%os.path.join(root, "DejaVuSans.ttf"))) - return os.path.join(root, "DejaVuSans.ttf") - assert False, "Not exist DejaVuSans.ttf in {}".format("%s"%font_path) diff --git a/spaces/dorkai/singpt/extensions/gallery/script.py b/spaces/dorkai/singpt/extensions/gallery/script.py deleted file mode 100644 index 8a2d7cf988734a7ab0966d047ff3d31ba58324b7..0000000000000000000000000000000000000000 --- a/spaces/dorkai/singpt/extensions/gallery/script.py +++ /dev/null @@ -1,82 +0,0 @@ -from pathlib import Path - -import gradio as gr - -from modules.html_generator import get_image_cache - - -def generate_html(): - css = """ - .character-gallery { - margin: 1rem 0; - display: grid; - grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); - grid-column-gap: 0.4rem; - grid-row-gap: 1.2rem; - } - - .character-container { - cursor: pointer; - text-align: center; - position: relative; - opacity: 0.85; - } - - .character-container:hover { - opacity: 1; - } - - .character-container .placeholder, .character-container img { - width: 150px; - height: 200px; - background-color: gray; - object-fit: cover; - margin: 0 auto; - border-radius: 1rem; - border: 3px solid white; - box-shadow: 3px 3px 6px 0px rgb(0 0 0 / 50%); - } - - .character-name { - margin-top: 0.3rem; - display: block; - font-size: 1.2rem; - font-weight: 600; - overflow-wrap: anywhere; - } - """ - - container_html = f'
'
-
- # Iterate through files in image folder
- for file in sorted(Path("characters").glob("*")):
- if file.name.endswith(".json"):
- character = file.name.replace(".json", "")
- container_html += f'
"
- return container_html
-
-def ui():
- with gr.Accordion("Character gallery"):
- update = gr.Button("Refresh")
- gallery = gr.HTML(value=generate_html())
- update.click(generate_html, [], gallery)
diff --git a/spaces/dorkai/text-generation-webui-main/server.py b/spaces/dorkai/text-generation-webui-main/server.py
deleted file mode 100644
index c8cb0d386a6172f4668170c54efc38ce2a6ee286..0000000000000000000000000000000000000000
--- a/spaces/dorkai/text-generation-webui-main/server.py
+++ /dev/null
@@ -1,1004 +0,0 @@
-import logging
-import os
-import requests
-import warnings
-import modules.logging_colors
-
-os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
-os.environ['BITSANDBYTES_NOWELCOME'] = '1'
-warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
-logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)
-
-# This is a hack to prevent Gradio from phoning home when it gets imported
-def my_get(url, **kwargs):
- logging.info('Gradio HTTP request redirected to localhost :)')
- kwargs.setdefault('allow_redirects', True)
- return requests.api.request('get', 'http://127.0.0.1/', **kwargs)
-
-
-original_get = requests.get
-requests.get = my_get
-import gradio as gr
-requests.get = original_get
-
-import matplotlib
-matplotlib.use('Agg') # This fixes LaTeX rendering on some systems
-
-import importlib
-import io
-import json
-import math
-import os
-import re
-import sys
-import time
-import traceback
-import zipfile
-from datetime import datetime
-from functools import partial
-from pathlib import Path
-
-import psutil
-import torch
-import yaml
-from PIL import Image
-
-import modules.extensions as extensions_module
-from modules import chat, shared, training, ui, utils
-from modules.extensions import apply_extensions
-from modules.html_generator import chat_html_wrapper
-from modules.LoRA import add_lora_to_model
-from modules.models import load_model, load_soft_prompt, unload_model
-from modules.text_generation import generate_reply_wrapper, get_encoded_length, stop_everything_event
-
-
-def load_model_wrapper(selected_model, autoload=False):
- if not autoload:
- yield f"The settings for {selected_model} have been updated.\nClick on \"Load the model\" to load it."
- return
-
- if selected_model == 'None':
- yield "No model selected"
- else:
- try:
- yield f"Loading {selected_model}..."
- shared.model_name = selected_model
- unload_model()
- if selected_model != '':
- shared.model, shared.tokenizer = load_model(shared.model_name)
-
- yield f"Successfully loaded {selected_model}"
- except:
- yield traceback.format_exc()
-
-
-def load_lora_wrapper(selected_loras):
- yield ("Applying the following LoRAs to {}:\n\n{}".format(shared.model_name, '\n'.join(selected_loras)))
- add_lora_to_model(selected_loras)
- yield ("Successfuly applied the LoRAs")
-
-
-def load_preset_values(preset_menu, state, return_dict=False):
- generate_params = {
- 'do_sample': True,
- 'temperature': 1,
- 'top_p': 1,
- 'typical_p': 1,
- 'repetition_penalty': 1,
- 'encoder_repetition_penalty': 1,
- 'top_k': 50,
- 'num_beams': 1,
- 'penalty_alpha': 0,
- 'min_length': 0,
- 'length_penalty': 1,
- 'no_repeat_ngram_size': 0,
- 'early_stopping': False,
- }
- with open(Path(f'presets/{preset_menu}.txt'), 'r') as infile:
- preset = infile.read()
- for i in preset.splitlines():
- i = i.rstrip(',').strip().split('=')
- if len(i) == 2 and i[0].strip() != 'tokens':
- generate_params[i[0].strip()] = eval(i[1].strip())
- generate_params['temperature'] = min(1.99, generate_params['temperature'])
-
- if return_dict:
- return generate_params
- else:
- state.update(generate_params)
- return state, *[generate_params[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']]
-
-
-def upload_soft_prompt(file):
- with zipfile.ZipFile(io.BytesIO(file)) as zf:
- zf.extract('meta.json')
- j = json.loads(open('meta.json', 'r').read())
- name = j['name']
- Path('meta.json').unlink()
-
- with open(Path(f'softprompts/{name}.zip'), 'wb') as f:
- f.write(file)
-
- return name
-
-
-def open_save_prompt():
- fname = f"{datetime.now().strftime('%Y-%m-%d-%H%M%S')}"
- return gr.update(value=fname, visible=True), gr.update(visible=False), gr.update(visible=True)
-
-
-def save_prompt(text, fname):
- if fname != "":
- with open(Path(f'prompts/{fname}.txt'), 'w', encoding='utf-8') as f:
- f.write(text)
-
- message = f"Saved to prompts/{fname}.txt"
- else:
- message = "Error: No prompt name given."
-
- return message, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)
-
-
-def load_prompt(fname):
- if fname in ['None', '']:
- return ''
- elif fname.startswith('Instruct-'):
- fname = re.sub('^Instruct-', '', fname)
- with open(Path(f'characters/instruction-following/{fname}.yaml'), 'r', encoding='utf-8') as f:
- data = yaml.safe_load(f)
- output = ''
- if 'context' in data:
- output += data['context']
-
- replacements = {
- '<|user|>': data['user'],
- '<|bot|>': data['bot'],
- '<|user-message|>': 'Input',
- }
-
- output += utils.replace_all(data['turn_template'].split('<|bot-message|>')[0], replacements)
- return output.rstrip(' ')
- else:
- with open(Path(f'prompts/{fname}.txt'), 'r', encoding='utf-8') as f:
- text = f.read()
- if text[-1] == '\n':
- text = text[:-1]
-
- return text
-
-
-def count_tokens(text):
- tokens = get_encoded_length(text)
- return f'{tokens} tokens in the input.'
-
-
-def download_model_wrapper(repo_id):
- try:
- downloader = importlib.import_module("download-model")
- repo_id_parts = repo_id.split(":")
- model = repo_id_parts[0] if len(repo_id_parts) > 0 else repo_id
- branch = repo_id_parts[1] if len(repo_id_parts) > 1 else "main"
- check = False
-
- yield ("Cleaning up the model/branch names")
- model, branch = downloader.sanitize_model_and_branch_names(model, branch)
-
- yield ("Getting the download links from Hugging Face")
- links, sha256, is_lora = downloader.get_download_links_from_huggingface(model, branch, text_only=False)
-
- yield ("Getting the output folder")
- output_folder = downloader.get_output_folder(model, branch, is_lora)
-
- if check:
- yield ("Checking previously downloaded files")
- downloader.check_model_files(model, branch, links, sha256, output_folder)
- else:
- yield (f"Downloading files to {output_folder}")
- downloader.download_model_files(model, branch, links, sha256, output_folder, threads=1)
- yield ("Done!")
- except:
- yield traceback.format_exc()
-
-
-# Update the command-line arguments based on the interface values
-def update_model_parameters(state, initial=False):
- elements = ui.list_model_elements() # the names of the parameters
- gpu_memories = []
-
- for i, element in enumerate(elements):
- if element not in state:
- continue
-
- value = state[element]
- if element.startswith('gpu_memory'):
- gpu_memories.append(value)
- continue
-
- if initial and vars(shared.args)[element] != vars(shared.args_defaults)[element]:
- continue
-
- # Setting null defaults
- if element in ['wbits', 'groupsize', 'model_type'] and value == 'None':
- value = vars(shared.args_defaults)[element]
- elif element in ['cpu_memory'] and value == 0:
- value = vars(shared.args_defaults)[element]
-
- # Making some simple conversions
- if element in ['wbits', 'groupsize', 'pre_layer']:
- value = int(value)
- elif element == 'cpu_memory' and value is not None:
- value = f"{value}MiB"
-
- if element in ['pre_layer']:
- value = [value] if value > 0 else None
-
- setattr(shared.args, element, value)
-
- found_positive = False
- for i in gpu_memories:
- if i > 0:
- found_positive = True
- break
-
- if not (initial and vars(shared.args)['gpu_memory'] != vars(shared.args_defaults)['gpu_memory']):
- if found_positive:
- shared.args.gpu_memory = [f"{i}MiB" for i in gpu_memories]
- else:
- shared.args.gpu_memory = None
-
-
-def get_model_specific_settings(model):
- settings = shared.model_config
- model_settings = {}
-
- for pat in settings:
- if re.match(pat.lower(), model.lower()):
- for k in settings[pat]:
- model_settings[k] = settings[pat][k]
-
- return model_settings
-
-
-def load_model_specific_settings(model, state, return_dict=False):
- model_settings = get_model_specific_settings(model)
- for k in model_settings:
- if k in state:
- state[k] = model_settings[k]
-
- return state
-
-
-def save_model_settings(model, state):
- if model == 'None':
- yield ("Not saving the settings because no model is loaded.")
- return
-
- with Path(f'{shared.args.model_dir}/config-user.yaml') as p:
- if p.exists():
- user_config = yaml.safe_load(open(p, 'r').read())
- else:
- user_config = {}
-
- model_regex = model + '$' # For exact matches
- if model_regex not in user_config:
- user_config[model_regex] = {}
-
- for k in ui.list_model_elements():
- user_config[model_regex][k] = state[k]
-
- with open(p, 'w') as f:
- f.write(yaml.dump(user_config))
-
- yield (f"Settings for {model} saved to {p}")
-
-
-def create_model_menus():
- # Finding the default values for the GPU and CPU memories
- total_mem = []
- for i in range(torch.cuda.device_count()):
- total_mem.append(math.floor(torch.cuda.get_device_properties(i).total_memory / (1024 * 1024)))
-
- default_gpu_mem = []
- if shared.args.gpu_memory is not None and len(shared.args.gpu_memory) > 0:
- for i in shared.args.gpu_memory:
- if 'mib' in i.lower():
- default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i)))
- else:
- default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i)) * 1000)
- while len(default_gpu_mem) < len(total_mem):
- default_gpu_mem.append(0)
-
- total_cpu_mem = math.floor(psutil.virtual_memory().total / (1024 * 1024))
- if shared.args.cpu_memory is not None:
- default_cpu_mem = re.sub('[a-zA-Z ]', '', shared.args.cpu_memory)
- else:
- default_cpu_mem = 0
-
- with gr.Row():
- with gr.Column():
- with gr.Row():
- with gr.Column():
- with gr.Row():
- shared.gradio['model_menu'] = gr.Dropdown(choices=utils.get_available_models(), value=shared.model_name, label='Model')
- ui.create_refresh_button(shared.gradio['model_menu'], lambda: None, lambda: {'choices': utils.get_available_models()}, 'refresh-button')
-
- with gr.Column():
- with gr.Row():
- shared.gradio['lora_menu'] = gr.Dropdown(multiselect=True, choices=utils.get_available_loras(), value=shared.lora_names, label='LoRA(s)')
- ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': utils.get_available_loras(), 'value': shared.lora_names}, 'refresh-button')
-
- with gr.Column():
- with gr.Row():
- shared.gradio['lora_menu_apply'] = gr.Button(value='Apply the selected LoRAs')
- with gr.Row():
- load = gr.Button("Load the model", visible=not shared.settings['autoload_model'])
- unload = gr.Button("Unload the model")
- reload = gr.Button("Reload the model")
- save_settings = gr.Button("Save settings for this model")
-
- with gr.Row():
- with gr.Column():
- with gr.Box():
- gr.Markdown('Transformers parameters')
- with gr.Row():
- with gr.Column():
- for i in range(len(total_mem)):
- shared.gradio[f'gpu_memory_{i}'] = gr.Slider(label=f"gpu-memory in MiB for device :{i}", maximum=total_mem[i], value=default_gpu_mem[i])
- shared.gradio['cpu_memory'] = gr.Slider(label="cpu-memory in MiB", maximum=total_cpu_mem, value=default_cpu_mem)
-
- with gr.Column():
- shared.gradio['auto_devices'] = gr.Checkbox(label="auto-devices", value=shared.args.auto_devices)
- shared.gradio['disk'] = gr.Checkbox(label="disk", value=shared.args.disk)
- shared.gradio['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu)
- shared.gradio['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16)
- shared.gradio['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit)
-
- with gr.Column():
- with gr.Box():
- gr.Markdown('GPTQ parameters')
- with gr.Row():
- with gr.Column():
- shared.gradio['wbits'] = gr.Dropdown(label="wbits", choices=["None", 1, 2, 3, 4, 8], value=shared.args.wbits if shared.args.wbits > 0 else "None")
- shared.gradio['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128, 1024], value=shared.args.groupsize if shared.args.groupsize > 0 else "None")
-
- with gr.Column():
- shared.gradio['model_type'] = gr.Dropdown(label="model_type", choices=["None", "llama", "opt", "gptj"], value=shared.args.model_type or "None")
- shared.gradio['pre_layer'] = gr.Slider(label="pre_layer", minimum=0, maximum=100, value=shared.args.pre_layer[0] if shared.args.pre_layer is not None else 0)
-
- with gr.Row():
- with gr.Column():
- with gr.Row():
- shared.gradio['autoload_model'] = gr.Checkbox(value=shared.settings['autoload_model'], label='Autoload the model', info='Whether to load the model as soon as it is selected in the Model dropdown.')
-
- shared.gradio['custom_model_menu'] = gr.Textbox(label="Download custom model or LoRA", info="Enter the Hugging Face username/model path, for instance: facebook/galactica-125m. To specify a branch, add it at the end after a \":\" character like this: facebook/galactica-125m:main")
- shared.gradio['download_model_button'] = gr.Button("Download")
-
- with gr.Column():
- with gr.Box():
- gr.Markdown('llama.cpp parameters')
- with gr.Row():
- with gr.Column():
- shared.gradio['threads'] = gr.Slider(label="threads", minimum=0, step=1, maximum=32, value=shared.args.threads)
- shared.gradio['n_batch'] = gr.Slider(label="n_batch", minimum=1, maximum=2048, value=shared.args.n_batch)
- shared.gradio['n_gpu_layers'] = gr.Slider(label="n-gpu-layers", minimum=0, maximum=128, value=shared.args.n_gpu_layers)
-
- with gr.Column():
- shared.gradio['no_mmap'] = gr.Checkbox(label="no-mmap", value=shared.args.no_mmap)
- shared.gradio['mlock'] = gr.Checkbox(label="mlock", value=shared.args.mlock)
-
- with gr.Row():
- shared.gradio['model_status'] = gr.Markdown('No model is loaded' if shared.model_name == 'None' else 'Ready')
-
- # In this event handler, the interface state is read and updated
- # with the model defaults (if any), and then the model is loaded
- # unless "autoload_model" is unchecked
- shared.gradio['model_menu'].change(
- ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
- load_model_specific_settings, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['interface_state']).then(
- ui.apply_interface_values, shared.gradio['interface_state'], [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False).then(
- update_model_parameters, shared.gradio['interface_state'], None).then(
- load_model_wrapper, [shared.gradio[k] for k in ['model_menu', 'autoload_model']], shared.gradio['model_status'], show_progress=False)
-
- load.click(
- ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
- update_model_parameters, shared.gradio['interface_state'], None).then(
- partial(load_model_wrapper, autoload=True), shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=False)
-
- unload.click(
- unload_model, None, None).then(
- lambda: "Model unloaded", None, shared.gradio['model_status'])
-
- reload.click(
- unload_model, None, None).then(
- ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
- update_model_parameters, shared.gradio['interface_state'], None).then(
- partial(load_model_wrapper, autoload=True), shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=False)
-
- save_settings.click(
- ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
- save_model_settings, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['model_status'], show_progress=False)
-
- shared.gradio['lora_menu_apply'].click(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['model_status'], show_progress=False)
- shared.gradio['download_model_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=False)
- shared.gradio['autoload_model'].change(lambda x: gr.update(visible=not x), shared.gradio['autoload_model'], load)
-
-
-def create_settings_menus(default_preset):
-
- generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', {}, return_dict=True)
-
- with gr.Row():
- with gr.Column():
- with gr.Row():
- shared.gradio['preset_menu'] = gr.Dropdown(choices=utils.get_available_presets(), value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
- ui.create_refresh_button(shared.gradio['preset_menu'], lambda: None, lambda: {'choices': utils.get_available_presets()}, 'refresh-button')
- with gr.Column():
- shared.gradio['seed'] = gr.Number(value=shared.settings['seed'], label='Seed (-1 for random)')
-
- with gr.Row():
- with gr.Column():
- with gr.Box():
- gr.Markdown('Custom generation parameters ([click here to view technical documentation](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig))')
- with gr.Row():
- with gr.Column():
- shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature', info='Primary factor to control randomness of outputs. 0 = deterministic (only the most likely token is used). Higher value = more randomness.')
- shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=generate_params['top_p'], step=0.01, label='top_p', info='If not set to 1, select tokens with probabilities adding up to less than this number. Higher value = higher range of possible random results.')
- shared.gradio['top_k'] = gr.Slider(0, 200, value=generate_params['top_k'], step=1, label='top_k', info='Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.')
- shared.gradio['typical_p'] = gr.Slider(0.0, 1.0, value=generate_params['typical_p'], step=0.01, label='typical_p', info='If not set to 1, select only tokens that are at least this much more likely to appear than random tokens, given the prior text.')
- with gr.Column():
- shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'], step=0.01, label='repetition_penalty', info='Exponential penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.')
- shared.gradio['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'], step=0.01, label='encoder_repetition_penalty', info='Also known as the "Hallucinations filter". Used to penalize tokens that are *not* in the prior text. Higher value = more likely to stay in context, lower value = more likely to diverge.')
- shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size', info='If not set to 0, specifies the length of token sets that are completely blocked from repeating at all. Higher values = blocks larger phrases, lower values = blocks words or letters from repeating. Only 0 or high values are a good idea in most cases.')
- shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'], label='min_length', info='Minimum generation length in tokens.')
- shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
- with gr.Column():
- with gr.Box():
- gr.Markdown('Contrastive search')
- shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha')
-
- gr.Markdown('Beam search (uses a lot of VRAM)')
- with gr.Row():
- with gr.Column():
- shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams')
- shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty')
- with gr.Column():
- shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
-
- with gr.Box():
- with gr.Row():
- with gr.Column():
- shared.gradio['truncation_length'] = gr.Slider(value=shared.settings['truncation_length'], minimum=shared.settings['truncation_length_min'], maximum=shared.settings['truncation_length_max'], step=1, label='Truncate the prompt up to this length', info='The leftmost tokens are removed if the prompt exceeds this length. Most models require this to be at most 2048.')
- shared.gradio['custom_stopping_strings'] = gr.Textbox(lines=1, value=shared.settings["custom_stopping_strings"] or None, label='Custom stopping strings', info='In addition to the defaults. Written between "" and separated by commas. For instance: "\\nYour Assistant:", "\\nThe assistant:"')
- with gr.Column():
- shared.gradio['ban_eos_token'] = gr.Checkbox(value=shared.settings['ban_eos_token'], label='Ban the eos_token', info='Forces the model to never end the generation prematurely.')
- shared.gradio['add_bos_token'] = gr.Checkbox(value=shared.settings['add_bos_token'], label='Add the bos_token to the beginning of prompts', info='Disabling this can make the replies more creative.')
-
- shared.gradio['skip_special_tokens'] = gr.Checkbox(value=shared.settings['skip_special_tokens'], label='Skip special tokens', info='Some specific models need this unset.')
- shared.gradio['stream'] = gr.Checkbox(value=not shared.args.no_stream, label='Activate text streaming')
-
- with gr.Accordion('Soft prompt', open=False):
- with gr.Row():
- shared.gradio['softprompts_menu'] = gr.Dropdown(choices=utils.get_available_softprompts(), value='None', label='Soft prompt')
- ui.create_refresh_button(shared.gradio['softprompts_menu'], lambda: None, lambda: {'choices': utils.get_available_softprompts()}, 'refresh-button')
-
- gr.Markdown('Upload a soft prompt (.zip format):')
- with gr.Row():
- shared.gradio['upload_softprompt'] = gr.File(type='binary', file_types=['.zip'])
-
- shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio[k] for k in ['preset_menu', 'interface_state']], [shared.gradio[k] for k in ['interface_state', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']])
- shared.gradio['softprompts_menu'].change(load_soft_prompt, shared.gradio['softprompts_menu'], shared.gradio['softprompts_menu'], show_progress=True)
- shared.gradio['upload_softprompt'].upload(upload_soft_prompt, shared.gradio['upload_softprompt'], shared.gradio['softprompts_menu'])
-
-
-def set_interface_arguments(interface_mode, extensions, bool_active):
- modes = ["default", "notebook", "chat", "cai_chat"]
- cmd_list = vars(shared.args)
- bool_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes]
-
- shared.args.extensions = extensions
- for k in modes[1:]:
- setattr(shared.args, k, False)
- if interface_mode != "default":
- setattr(shared.args, interface_mode, True)
-
- for k in bool_list:
- setattr(shared.args, k, False)
- for k in bool_active:
- setattr(shared.args, k, True)
-
- shared.need_restart = True
-
-
-def create_interface():
-
- # Defining some variables
- gen_events = []
- default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
- if len(shared.lora_names) == 1:
- default_text = load_prompt(shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.lora_names[0].lower())), 'default')])
- else:
- default_text = load_prompt(shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')])
- title = 'Text generation web UI'
-
- # Authentication variables
- auth = None
- if shared.args.gradio_auth_path is not None:
- gradio_auth_creds = []
- with open(shared.args.gradio_auth_path, 'r', encoding="utf8") as file:
- for line in file.readlines():
- gradio_auth_creds += [x.strip() for x in line.split(',') if x.strip()]
- auth = [tuple(cred.split(':')) for cred in gradio_auth_creds]
-
- # Importing the extension files and executing their setup() functions
- if shared.args.extensions is not None and len(shared.args.extensions) > 0:
- extensions_module.load_extensions()
-
- # css/js strings
- css = ui.css if not shared.is_chat() else ui.css + ui.chat_css
- js = ui.main_js if not shared.is_chat() else ui.main_js + ui.chat_js
- css += apply_extensions('css')
- js += apply_extensions('js')
-
- with gr.Blocks(css=css, analytics_enabled=False, title=title, theme=ui.theme) as shared.gradio['interface']:
-
- # Create chat mode interface
- if shared.is_chat():
- shared.input_elements = ui.list_interface_input_elements(chat=True)
- shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
- shared.gradio['Chat input'] = gr.State()
- shared.gradio['dummy'] = gr.State()
-
- with gr.Tab('Text generation', elem_id='main'):
- shared.gradio['display'] = gr.HTML(value=chat_html_wrapper(shared.history['visible'], shared.settings['name1'], shared.settings['name2'], 'chat', 'cai-chat'))
- shared.gradio['textbox'] = gr.Textbox(label='Input')
- with gr.Row():
- shared.gradio['Stop'] = gr.Button('Stop', elem_id='stop')
- shared.gradio['Generate'] = gr.Button('Generate', elem_id='Generate', variant='primary')
- shared.gradio['Continue'] = gr.Button('Continue')
-
- with gr.Row():
- shared.gradio['Copy last reply'] = gr.Button('Copy last reply')
- shared.gradio['Regenerate'] = gr.Button('Regenerate')
- shared.gradio['Replace last reply'] = gr.Button('Replace last reply')
-
- with gr.Row():
- shared.gradio['Impersonate'] = gr.Button('Impersonate')
- shared.gradio['Send dummy message'] = gr.Button('Send dummy message')
- shared.gradio['Send dummy reply'] = gr.Button('Send dummy reply')
-
- with gr.Row():
- shared.gradio['Remove last'] = gr.Button('Remove last')
- shared.gradio['Clear history'] = gr.Button('Clear history')
- shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant='stop', visible=False)
- shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False)
-
- shared.gradio['mode'] = gr.Radio(choices=['chat', 'chat-instruct', 'instruct'], value=shared.settings['mode'] if shared.settings['mode'] in ['chat', 'instruct', 'chat-instruct'] else 'chat', label='Mode', info='Defines how the chat prompt is generated. In instruct and chat-instruct modes, the instruction template selected under "Chat settings" must match the current model.')
- shared.gradio['chat_style'] = gr.Dropdown(choices=utils.get_available_chat_styles(), label='Chat style', value=shared.settings['chat_style'], visible=shared.settings['mode'] != 'instruct')
-
- with gr.Tab('Chat settings', elem_id='chat-settings'):
- with gr.Row():
- shared.gradio['character_menu'] = gr.Dropdown(choices=utils.get_available_characters(), label='Character', elem_id='character-menu', info='Used in chat and chat-instruct modes.')
- ui.create_refresh_button(shared.gradio['character_menu'], lambda: None, lambda: {'choices': utils.get_available_characters()}, 'refresh-button')
-
- with gr.Row():
- with gr.Column(scale=8):
- shared.gradio['name1'] = gr.Textbox(value=shared.settings['name1'], lines=1, label='Your name')
- shared.gradio['name2'] = gr.Textbox(value=shared.settings['name2'], lines=1, label='Character\'s name')
- shared.gradio['context'] = gr.Textbox(value=shared.settings['context'], lines=4, label='Context')
- shared.gradio['greeting'] = gr.Textbox(value=shared.settings['greeting'], lines=4, label='Greeting')
-
- with gr.Column(scale=1):
- shared.gradio['character_picture'] = gr.Image(label='Character picture', type='pil')
- shared.gradio['your_picture'] = gr.Image(label='Your picture', type='pil', value=Image.open(Path('cache/pfp_me.png')) if Path('cache/pfp_me.png').exists() else None)
-
- shared.gradio['instruction_template'] = gr.Dropdown(choices=utils.get_available_instruction_templates(), label='Instruction template', value='None', info='Change this according to the model/LoRA that you are using. Used in instruct and chat-instruct modes.')
- shared.gradio['name1_instruct'] = gr.Textbox(value='', lines=2, label='User string')
- shared.gradio['name2_instruct'] = gr.Textbox(value='', lines=1, label='Bot string')
- shared.gradio['context_instruct'] = gr.Textbox(value='', lines=4, label='Context')
- shared.gradio['turn_template'] = gr.Textbox(value=shared.settings['turn_template'], lines=1, label='Turn template', info='Used to precisely define the placement of spaces and new line characters in instruction prompts.')
- with gr.Row():
- shared.gradio['chat-instruct_command'] = gr.Textbox(value=shared.settings['chat-instruct_command'], lines=4, label='Command for chat-instruct mode', info='<|character|> gets replaced by the bot name, and <|prompt|> gets replaced by the regular chat prompt.')
-
- with gr.Row():
- with gr.Tab('Chat history'):
- with gr.Row():
- with gr.Column():
- gr.Markdown('## Upload')
- shared.gradio['upload_chat_history'] = gr.File(type='binary', file_types=['.json', '.txt'])
-
- with gr.Column():
- gr.Markdown('## Download')
- shared.gradio['download'] = gr.File()
- shared.gradio['download_button'] = gr.Button(value='Click me')
-
- with gr.Tab('Upload character'):
- gr.Markdown('## JSON format')
- with gr.Row():
- with gr.Column():
- gr.Markdown('1. Select the JSON file')
- shared.gradio['upload_json'] = gr.File(type='binary', file_types=['.json'])
-
- with gr.Column():
- gr.Markdown('2. Select your character\'s profile picture (optional)')
- shared.gradio['upload_img_bot'] = gr.File(type='binary', file_types=['image'])
-
- shared.gradio['Upload character'] = gr.Button(value='Submit')
- gr.Markdown('## TavernAI PNG format')
- shared.gradio['upload_img_tavern'] = gr.File(type='binary', file_types=['image'])
-
- with gr.Tab("Parameters", elem_id="parameters"):
- with gr.Box():
- gr.Markdown("Chat parameters")
- with gr.Row():
- with gr.Column():
- shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
- shared.gradio['chat_prompt_size'] = gr.Slider(minimum=shared.settings['chat_prompt_size_min'], maximum=shared.settings['chat_prompt_size_max'], step=1, label='Maximum prompt size in tokens', value=shared.settings['chat_prompt_size'])
-
- with gr.Column():
- shared.gradio['chat_generation_attempts'] = gr.Slider(minimum=shared.settings['chat_generation_attempts_min'], maximum=shared.settings['chat_generation_attempts_max'], value=shared.settings['chat_generation_attempts'], step=1, label='Generation attempts (for longer replies)', info='New generations will be called until either this number is reached or no new content is generated between two iterations')
- shared.gradio['stop_at_newline'] = gr.Checkbox(value=shared.settings['stop_at_newline'], label='Stop generating at new line character')
-
- create_settings_menus(default_preset)
-
- # Create notebook mode interface
- elif shared.args.notebook:
- shared.input_elements = ui.list_interface_input_elements(chat=False)
- shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
- shared.gradio['last_input'] = gr.State('')
- with gr.Tab("Text generation", elem_id="main"):
- with gr.Row():
- with gr.Column(scale=4):
- with gr.Tab('Raw'):
- shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_classes="textbox", lines=27)
-
- with gr.Tab('Markdown'):
- shared.gradio['markdown'] = gr.Markdown()
-
- with gr.Tab('HTML'):
- shared.gradio['html'] = gr.HTML()
-
- with gr.Row():
- shared.gradio['Generate'] = gr.Button('Generate', variant='primary', elem_classes="small-button")
- shared.gradio['Stop'] = gr.Button('Stop', elem_classes="small-button")
- shared.gradio['Undo'] = gr.Button('Undo', elem_classes="small-button")
- shared.gradio['Regenerate'] = gr.Button('Regenerate', elem_classes="small-button")
-
- with gr.Column(scale=1):
- gr.HTML('')
- shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
- with gr.Row():
- shared.gradio['prompt_menu'] = gr.Dropdown(choices=utils.get_available_prompts(), value='None', label='Prompt')
- ui.create_refresh_button(shared.gradio['prompt_menu'], lambda: None, lambda: {'choices': utils.get_available_prompts()}, 'refresh-button')
-
- shared.gradio['open_save_prompt'] = gr.Button('Save prompt')
- shared.gradio['save_prompt'] = gr.Button('Confirm save prompt', visible=False)
- shared.gradio['prompt_to_save'] = gr.Textbox(elem_classes="textbox_default", lines=1, label='Prompt name:', interactive=True, visible=False)
- shared.gradio['count_tokens'] = gr.Button('Count tokens')
- shared.gradio['status'] = gr.Markdown('')
-
- with gr.Tab("Parameters", elem_id="parameters"):
- create_settings_menus(default_preset)
-
- # Create default mode interface
- else:
- shared.input_elements = ui.list_interface_input_elements(chat=False)
- shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
- shared.gradio['last_input'] = gr.State('')
- with gr.Tab("Text generation", elem_id="main"):
- with gr.Row():
- with gr.Column():
- shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_classes="textbox_default", lines=27, label='Input')
- shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
- with gr.Row():
- shared.gradio['Generate'] = gr.Button('Generate', variant='primary', elem_classes="small-button")
- shared.gradio['Stop'] = gr.Button('Stop', elem_classes="small-button")
- shared.gradio['Continue'] = gr.Button('Continue', elem_classes="small-button")
- shared.gradio['open_save_prompt'] = gr.Button('Save prompt', elem_classes="small-button")
- shared.gradio['save_prompt'] = gr.Button('Confirm save prompt', visible=False, elem_classes="small-button")
- shared.gradio['count_tokens'] = gr.Button('Count tokens', elem_classes="small-button")
-
- with gr.Row():
- with gr.Column():
- with gr.Row():
- shared.gradio['prompt_menu'] = gr.Dropdown(choices=utils.get_available_prompts(), value='None', label='Prompt')
- ui.create_refresh_button(shared.gradio['prompt_menu'], lambda: None, lambda: {'choices': utils.get_available_prompts()}, 'refresh-button')
-
- with gr.Column():
- shared.gradio['prompt_to_save'] = gr.Textbox(elem_classes="textbox_default", lines=1, label='Prompt name:', interactive=True, visible=False)
- shared.gradio['status'] = gr.Markdown('')
-
- with gr.Column():
- with gr.Tab('Raw'):
- shared.gradio['output_textbox'] = gr.Textbox(elem_classes="textbox_default_output", lines=27, label='Output')
-
- with gr.Tab('Markdown'):
- shared.gradio['markdown'] = gr.Markdown()
-
- with gr.Tab('HTML'):
- shared.gradio['html'] = gr.HTML()
-
- with gr.Tab("Parameters", elem_id="parameters"):
- create_settings_menus(default_preset)
-
- # Model tab
- with gr.Tab("Model", elem_id="model-tab"):
- create_model_menus()
-
- # Training tab
- with gr.Tab("Training", elem_id="training-tab"):
- training.create_train_interface()
-
- # Interface mode tab
- with gr.Tab("Interface mode", elem_id="interface-mode"):
- modes = ["default", "notebook", "chat"]
- current_mode = "default"
- for mode in modes[1:]:
- if getattr(shared.args, mode):
- current_mode = mode
- break
-
- cmd_list = vars(shared.args)
- bool_list = sorted([k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes + ui.list_model_elements()])
- bool_active = [k for k in bool_list if vars(shared.args)[k]]
-
- shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode")
- shared.gradio['extensions_menu'] = gr.CheckboxGroup(choices=utils.get_available_extensions(), value=shared.args.extensions, label="Available extensions")
- shared.gradio['bool_menu'] = gr.CheckboxGroup(choices=bool_list, value=bool_active, label="Boolean command-line flags")
- shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface")
-
- # Reset interface event
- shared.gradio['reset_interface'].click(
- set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'bool_menu']], None).then(
- lambda: None, None, None, _js='() => {document.body.innerHTML=\''
- image_html = ""
-
- for i in [
- f"characters/{character}.png",
- f"characters/{character}.jpg",
- f"characters/{character}.jpeg",
- ]:
-
- path = Path(i)
- if path.exists():
- try:
- image_html = f'})
'
- break
- except:
- continue
-
- container_html += f'{image_html} {character}'
- container_html += "
"
-
- container_html += "Reloading...
\'; setTimeout(function(){location.reload()},2500); return []}') - - # chat mode event handlers - if shared.is_chat(): - shared.input_params = [shared.gradio[k] for k in ['Chat input', 'interface_state']] - clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']] - shared.reload_inputs = [shared.gradio[k] for k in ['name1', 'name2', 'mode', 'chat_style']] - - gen_events.append(shared.gradio['Generate'].click( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then( - chat.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False) - ) - - gen_events.append(shared.gradio['textbox'].submit( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then( - chat.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False) - ) - - gen_events.append(shared.gradio['Regenerate'].click( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - partial(chat.generate_chat_reply_wrapper, regenerate=True), shared.input_params, shared.gradio['display'], show_progress=False).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False) - ) - - gen_events.append(shared.gradio['Continue'].click( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - partial(chat.generate_chat_reply_wrapper, _continue=True), shared.input_params, shared.gradio['display'], show_progress=False).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False) - ) - - gen_events.append(shared.gradio['Impersonate'].click( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - lambda x: x, shared.gradio['textbox'], shared.gradio['Chat input'], show_progress=False).then( - chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=False) - ) - - shared.gradio['Replace last reply'].click( - chat.replace_last_reply, shared.gradio['textbox'], None).then( - lambda: '', None, shared.gradio['textbox'], show_progress=False).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False).then( - chat.redraw_html, shared.reload_inputs, shared.gradio['display']) - - shared.gradio['Send dummy message'].click( - chat.send_dummy_message, shared.gradio['textbox'], None).then( - lambda: '', None, shared.gradio['textbox'], show_progress=False).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False).then( - chat.redraw_html, shared.reload_inputs, shared.gradio['display']) - - shared.gradio['Send dummy reply'].click( - chat.send_dummy_reply, shared.gradio['textbox'], None).then( - lambda: '', None, shared.gradio['textbox'], show_progress=False).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False).then( - chat.redraw_html, shared.reload_inputs, shared.gradio['display']) - - shared.gradio['Clear history-confirm'].click( - lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr).then( - chat.clear_chat_log, [shared.gradio[k] for k in ['greeting', 'mode']], None).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False).then( - chat.redraw_html, shared.reload_inputs, shared.gradio['display']) - - shared.gradio['Stop'].click( - stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None).then( - chat.redraw_html, shared.reload_inputs, shared.gradio['display']) - - shared.gradio['mode'].change( - lambda x: gr.update(visible=x != 'instruct'), shared.gradio['mode'], shared.gradio['chat_style'], show_progress=False).then( - chat.redraw_html, shared.reload_inputs, shared.gradio['display']) - - - shared.gradio['chat_style'].change(chat.redraw_html, shared.reload_inputs, shared.gradio['display']) - shared.gradio['instruction_template'].change( - partial(chat.load_character, instruct=True), [shared.gradio[k] for k in ['instruction_template', 'name1_instruct', 'name2_instruct']], [shared.gradio[k] for k in ['name1_instruct', 'name2_instruct', 'dummy', 'dummy', 'context_instruct', 'turn_template']]) - - shared.gradio['upload_chat_history'].upload( - chat.load_history, [shared.gradio[k] for k in ['upload_chat_history', 'name1', 'name2']], None).then( - chat.redraw_html, shared.reload_inputs, shared.gradio['display']) - - shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, None, shared.gradio['textbox'], show_progress=False) - shared.gradio['Clear history'].click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr) - shared.gradio['Clear history-cancel'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr) - shared.gradio['Remove last'].click( - chat.remove_last_message, None, shared.gradio['textbox'], show_progress=False).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False).then( - chat.redraw_html, shared.reload_inputs, shared.gradio['display']) - - shared.gradio['download_button'].click(lambda x: chat.save_history(x, timestamp=True), shared.gradio['mode'], shared.gradio['download']) - shared.gradio['Upload character'].click(chat.upload_character, [shared.gradio['upload_json'], shared.gradio['upload_img_bot']], [shared.gradio['character_menu']]) - shared.gradio['character_menu'].change( - partial(chat.load_character, instruct=False), [shared.gradio[k] for k in ['character_menu', 'name1', 'name2']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'dummy']]).then( - chat.redraw_html, shared.reload_inputs, shared.gradio['display']) - - shared.gradio['upload_img_tavern'].upload(chat.upload_tavern_character, [shared.gradio['upload_img_tavern'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['character_menu']]) - shared.gradio['your_picture'].change( - chat.upload_your_profile_picture, shared.gradio['your_picture'], None).then( - partial(chat.redraw_html, reset_cache=True), shared.reload_inputs, shared.gradio['display']) - - # notebook/default modes event handlers - else: - shared.input_params = [shared.gradio[k] for k in ['textbox', 'interface_state']] - if shared.args.notebook: - output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']] - else: - output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']] - - gen_events.append(shared.gradio['Generate'].click( - lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - generate_reply_wrapper, shared.input_params, output_params, show_progress=False) # .then( - # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") - ) - - gen_events.append(shared.gradio['textbox'].submit( - lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - generate_reply_wrapper, shared.input_params, output_params, show_progress=False) # .then( - # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") - ) - - if shared.args.notebook: - shared.gradio['Undo'].click(lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False) - gen_events.append(shared.gradio['Regenerate'].click( - lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False).then( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - generate_reply_wrapper, shared.input_params, output_params, show_progress=False) # .then( - # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") - ) - else: - gen_events.append(shared.gradio['Continue'].click( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - generate_reply_wrapper, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=False) # .then( - # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[1]; element.scrollTop = element.scrollHeight}") - ) - - shared.gradio['Stop'].click(stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None) - shared.gradio['prompt_menu'].change(load_prompt, shared.gradio['prompt_menu'], shared.gradio['textbox'], show_progress=False) - shared.gradio['open_save_prompt'].click(open_save_prompt, None, [shared.gradio[k] for k in ['prompt_to_save', 'open_save_prompt', 'save_prompt']], show_progress=False) - shared.gradio['save_prompt'].click(save_prompt, [shared.gradio[k] for k in ['textbox', 'prompt_to_save']], [shared.gradio[k] for k in ['status', 'prompt_to_save', 'open_save_prompt', 'save_prompt']], show_progress=False) - shared.gradio['count_tokens'].click(count_tokens, shared.gradio['textbox'], shared.gradio['status'], show_progress=False) - - shared.gradio['interface'].load(None, None, None, _js=f"() => {{{js}}}") - shared.gradio['interface'].load(partial(ui.apply_interface_values, {}, use_persistent=True), None, [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False) - # Extensions tabs - extensions_module.create_extensions_tabs() - - # Extensions block - extensions_module.create_extensions_block() - - # Launch the interface - shared.gradio['interface'].queue() - if shared.args.listen: - shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name=shared.args.listen_host or '0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth) - else: - shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth) - - -if __name__ == "__main__": - # Loading custom settings - settings_file = None - if shared.args.settings is not None and Path(shared.args.settings).exists(): - settings_file = Path(shared.args.settings) - elif Path('settings.json').exists(): - settings_file = Path('settings.json') - - if settings_file is not None: - logging.info(f"Loading settings from {settings_file}...") - new_settings = json.loads(open(settings_file, 'r').read()) - for item in new_settings: - shared.settings[item] = new_settings[item] - - # Set default model settings based on settings.json - shared.model_config['.*'] = { - 'wbits': 'None', - 'model_type': 'None', - 'groupsize': 'None', - 'pre_layer': 0, - 'mode': shared.settings['mode'], - 'skip_special_tokens': shared.settings['skip_special_tokens'], - 'custom_stopping_strings': shared.settings['custom_stopping_strings'], - } - - shared.model_config.move_to_end('.*', last=False) # Move to the beginning - - # Default extensions - extensions_module.available_extensions = utils.get_available_extensions() - if shared.is_chat(): - for extension in shared.settings['chat_default_extensions']: - shared.args.extensions = shared.args.extensions or [] - if extension not in shared.args.extensions: - shared.args.extensions.append(extension) - else: - for extension in shared.settings['default_extensions']: - shared.args.extensions = shared.args.extensions or [] - if extension not in shared.args.extensions: - shared.args.extensions.append(extension) - - available_models = utils.get_available_models() - - # Model defined through --model - if shared.args.model is not None: - shared.model_name = shared.args.model - - # Only one model is available - elif len(available_models) == 1: - shared.model_name = available_models[0] - - # Select the model from a command-line menu - elif shared.args.model_menu: - if len(available_models) == 0: - logging.error('No models are available! Please download at least one.') - sys.exit(0) - else: - print('The following models are available:\n') - for i, model in enumerate(available_models): - print(f'{i+1}. {model}') - - print(f'\nWhich one do you want to load? 1-{len(available_models)}\n') - i = int(input()) - 1 - print() - - shared.model_name = available_models[i] - - # If any model has been selected, load it - if shared.model_name != 'None': - model_settings = get_model_specific_settings(shared.model_name) - shared.settings.update(model_settings) # hijacking the interface defaults - update_model_parameters(model_settings, initial=True) # hijacking the command-line arguments - - # Load the model - shared.model, shared.tokenizer = load_model(shared.model_name) - if shared.args.lora: - add_lora_to_model(shared.args.lora) - - # Force a character to be loaded - if shared.is_chat(): - shared.persistent_interface_state.update({ - 'mode': shared.settings['mode'], - 'character_menu': shared.args.character or shared.settings['character'], - 'instruction_template': shared.settings['instruction_template'] - }) - - # Launch the web UI - create_interface() - while True: - time.sleep(0.5) - if shared.need_restart: - shared.need_restart = False - shared.gradio['interface'].close() - time.sleep(0.5) - create_interface() diff --git a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/css/html_cai_style.css b/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/css/html_cai_style.css deleted file mode 100644 index f601de3248b7ee94d6da58026354f8b9afeb9297..0000000000000000000000000000000000000000 --- a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/css/html_cai_style.css +++ /dev/null @@ -1,91 +0,0 @@ -.chat { - margin-left: auto; - margin-right: auto; - max-width: 800px; - height: calc(100vh - 306px); - overflow-y: auto; - padding-right: 20px; - display: flex; - flex-direction: column-reverse; - word-break: break-word; - overflow-wrap: anywhere; -} - -.message { - display: grid; - grid-template-columns: 60px minmax(0, 1fr); - padding-bottom: 25px; - font-size: 15px; - font-family: Helvetica, Arial, sans-serif; - line-height: 1.428571429; -} - -.circle-you { - width: 50px; - height: 50px; - background-color: rgb(238, 78, 59); - border-radius: 50%; -} - -.circle-bot { - width: 50px; - height: 50px; - background-color: rgb(59, 78, 244); - border-radius: 50%; -} - -.circle-bot img, -.circle-you img { - border-radius: 50%; - width: 100%; - height: 100%; - object-fit: cover; -} - -.text {} - -.text p { - margin-top: 5px; -} - -.username { - font-weight: bold; -} - -.message-body {} - -.message-body img { - max-width: 300px; - max-height: 300px; - border-radius: 20px; -} - -.message-body p { - margin-bottom: 0 !important; - font-size: 15px !important; - line-height: 1.428571429 !important; -} - -.message-body li { - margin-top: 0.5em !important; - margin-bottom: 0.5em !important; -} - -.message-body li > p { - display: inline !important; -} - -.message-body code { - overflow-x: auto; -} -.message-body :not(pre) > code { - white-space: normal !important; -} - -.dark .message-body p em { - color: rgb(138, 138, 138) !important; -} - -.message-body p em { - color: rgb(110, 110, 110) !important; -} \ No newline at end of file diff --git a/spaces/edemgold/conversation-bot/app.py b/spaces/edemgold/conversation-bot/app.py deleted file mode 100644 index 00057b119d2d6677a5cc21ed1d74bc0973e2a6e8..0000000000000000000000000000000000000000 --- a/spaces/edemgold/conversation-bot/app.py +++ /dev/null @@ -1,19 +0,0 @@ -#import model class and tokenizer -from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration - -#import model class and tokenizer -from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration - -#download and setup the model and tokenizer -model_name = 'facebook/blenderbot-400M-distill' -tokenizer = BlenderbotTokenizer.from_pretrained(model_name) -model = BlenderbotForConditionalGeneration.from_pretrained(model_name) - -def func (message): - inputs = tokenizer(message, return_tensors="pt") - result = model.generate(**inputs) - return tokenizer.decode(result[0]) - -import gradio as gr -app = gr.Interface(fn=func, inputs="textbox", outputs="textbox", title="Conversation Bot") -app.launch() \ No newline at end of file diff --git a/spaces/ehristoforu/runwayml-stable-diffusion-v1-5k/style.css b/spaces/ehristoforu/runwayml-stable-diffusion-v1-5k/style.css deleted file mode 100644 index 045447f22074264d2b7f73908f69066bcfb48d30..0000000000000000000000000000000000000000 --- a/spaces/ehristoforu/runwayml-stable-diffusion-v1-5k/style.css +++ /dev/null @@ -1,16 +0,0 @@ -h1 { - text-align: center; -} - -#duplicate-button { - margin: auto; - color: #fff; - background: #1565c0; - border-radius: 100vh; -} - -#component-0 { - max-width: 730px; - margin: auto; - padding-top: 1.5rem; -} \ No newline at end of file diff --git a/spaces/elkraken/Video-Object-Detection/train.py b/spaces/elkraken/Video-Object-Detection/train.py deleted file mode 100644 index 318713d82c4bb55971587764b5fe3368c53ef550..0000000000000000000000000000000000000000 --- a/spaces/elkraken/Video-Object-Detection/train.py +++ /dev/null @@ -1,705 +0,0 @@ -import argparse -import logging -import math -import os -import random -import time -from copy import deepcopy -from pathlib import Path -from threading import Thread - -import numpy as np -import torch.distributed as dist -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -import torch.optim.lr_scheduler as lr_scheduler -import torch.utils.data -import yaml -from torch.cuda import amp -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.utils.tensorboard import SummaryWriter -from tqdm import tqdm - -import test # import test.py to get mAP after each epoch -from models.experimental import attempt_load -from models.yolo import Model -from utils.autoanchor import check_anchors -from utils.datasets import create_dataloader -from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \ - fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \ - check_requirements, print_mutation, set_logging, one_cycle, colorstr -from utils.google_utils import attempt_download -from utils.loss import ComputeLoss, ComputeLossOTA -from utils.plots import plot_images, plot_labels, plot_results, plot_evolution -from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel -from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume - -logger = logging.getLogger(__name__) - - -def train(hyp, opt, device, tb_writer=None): - logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) - save_dir, epochs, batch_size, total_batch_size, weights, rank, freeze = \ - Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank, opt.freeze - - # Directories - wdir = save_dir / 'weights' - wdir.mkdir(parents=True, exist_ok=True) # make dir - last = wdir / 'last.pt' - best = wdir / 'best.pt' - results_file = save_dir / 'results.txt' - - # Save run settings - with open(save_dir / 'hyp.yaml', 'w') as f: - yaml.dump(hyp, f, sort_keys=False) - with open(save_dir / 'opt.yaml', 'w') as f: - yaml.dump(vars(opt), f, sort_keys=False) - - # Configure - plots = not opt.evolve # create plots - cuda = device.type != 'cpu' - init_seeds(2 + rank) - with open(opt.data) as f: - data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict - is_coco = opt.data.endswith('coco.yaml') - - # Logging- Doing this before checking the dataset. Might update data_dict - loggers = {'wandb': None} # loggers dict - if rank in [-1, 0]: - opt.hyp = hyp # add hyperparameters - run_id = torch.load(weights, map_location=device).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None - wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict) - loggers['wandb'] = wandb_logger.wandb - data_dict = wandb_logger.data_dict - if wandb_logger.wandb: - weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming - - nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes - names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names - assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check - - # Model - pretrained = weights.endswith('.pt') - if pretrained: - with torch_distributed_zero_first(rank): - attempt_download(weights) # download if not found locally - ckpt = torch.load(weights, map_location=device) # load checkpoint - model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create - exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys - state_dict = ckpt['model'].float().state_dict() # to FP32 - state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect - model.load_state_dict(state_dict, strict=False) # load - logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report - else: - model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create - with torch_distributed_zero_first(rank): - check_dataset(data_dict) # check - train_path = data_dict['train'] - test_path = data_dict['val'] - - # Freeze - freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # parameter names to freeze (full or partial) - for k, v in model.named_parameters(): - v.requires_grad = True # train all layers - if any(x in k for x in freeze): - print('freezing %s' % k) - v.requires_grad = False - - # Optimizer - nbs = 64 # nominal batch size - accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing - hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay - logger.info(f"Scaled weight_decay = {hyp['weight_decay']}") - - pg0, pg1, pg2 = [], [], [] # optimizer parameter groups - for k, v in model.named_modules(): - if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): - pg2.append(v.bias) # biases - if isinstance(v, nn.BatchNorm2d): - pg0.append(v.weight) # no decay - elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): - pg1.append(v.weight) # apply decay - if hasattr(v, 'im'): - if hasattr(v.im, 'implicit'): - pg0.append(v.im.implicit) - else: - for iv in v.im: - pg0.append(iv.implicit) - if hasattr(v, 'imc'): - if hasattr(v.imc, 'implicit'): - pg0.append(v.imc.implicit) - else: - for iv in v.imc: - pg0.append(iv.implicit) - if hasattr(v, 'imb'): - if hasattr(v.imb, 'implicit'): - pg0.append(v.imb.implicit) - else: - for iv in v.imb: - pg0.append(iv.implicit) - if hasattr(v, 'imo'): - if hasattr(v.imo, 'implicit'): - pg0.append(v.imo.implicit) - else: - for iv in v.imo: - pg0.append(iv.implicit) - if hasattr(v, 'ia'): - if hasattr(v.ia, 'implicit'): - pg0.append(v.ia.implicit) - else: - for iv in v.ia: - pg0.append(iv.implicit) - if hasattr(v, 'attn'): - if hasattr(v.attn, 'logit_scale'): - pg0.append(v.attn.logit_scale) - if hasattr(v.attn, 'q_bias'): - pg0.append(v.attn.q_bias) - if hasattr(v.attn, 'v_bias'): - pg0.append(v.attn.v_bias) - if hasattr(v.attn, 'relative_position_bias_table'): - pg0.append(v.attn.relative_position_bias_table) - if hasattr(v, 'rbr_dense'): - if hasattr(v.rbr_dense, 'weight_rbr_origin'): - pg0.append(v.rbr_dense.weight_rbr_origin) - if hasattr(v.rbr_dense, 'weight_rbr_avg_conv'): - pg0.append(v.rbr_dense.weight_rbr_avg_conv) - if hasattr(v.rbr_dense, 'weight_rbr_pfir_conv'): - pg0.append(v.rbr_dense.weight_rbr_pfir_conv) - if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_idconv1'): - pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_idconv1) - if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_conv2'): - pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_conv2) - if hasattr(v.rbr_dense, 'weight_rbr_gconv_dw'): - pg0.append(v.rbr_dense.weight_rbr_gconv_dw) - if hasattr(v.rbr_dense, 'weight_rbr_gconv_pw'): - pg0.append(v.rbr_dense.weight_rbr_gconv_pw) - if hasattr(v.rbr_dense, 'vector'): - pg0.append(v.rbr_dense.vector) - - if opt.adam: - optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum - else: - optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) - - optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay - optimizer.add_param_group({'params': pg2}) # add pg2 (biases) - logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) - del pg0, pg1, pg2 - - # Scheduler https://arxiv.org/pdf/1812.01187.pdf - # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR - if opt.linear_lr: - lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear - else: - lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] - scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) - # plot_lr_scheduler(optimizer, scheduler, epochs) - - # EMA - ema = ModelEMA(model) if rank in [-1, 0] else None - - # Resume - start_epoch, best_fitness = 0, 0.0 - if pretrained: - # Optimizer - if ckpt['optimizer'] is not None: - optimizer.load_state_dict(ckpt['optimizer']) - best_fitness = ckpt['best_fitness'] - - # EMA - if ema and ckpt.get('ema'): - ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) - ema.updates = ckpt['updates'] - - # Results - if ckpt.get('training_results') is not None: - results_file.write_text(ckpt['training_results']) # write results.txt - - # Epochs - start_epoch = ckpt['epoch'] + 1 - if opt.resume: - assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs) - if epochs < start_epoch: - logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' % - (weights, ckpt['epoch'], epochs)) - epochs += ckpt['epoch'] # finetune additional epochs - - del ckpt, state_dict - - # Image sizes - gs = max(int(model.stride.max()), 32) # grid size (max stride) - nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj']) - imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples - - # DP mode - if cuda and rank == -1 and torch.cuda.device_count() > 1: - model = torch.nn.DataParallel(model) - - # SyncBatchNorm - if opt.sync_bn and cuda and rank != -1: - model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) - logger.info('Using SyncBatchNorm()') - - # Trainloader - dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, - hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank, - world_size=opt.world_size, workers=opt.workers, - image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: ')) - mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class - nb = len(dataloader) # number of batches - assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1) - - # Process 0 - if rank in [-1, 0]: - testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt, # testloader - hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1, - world_size=opt.world_size, workers=opt.workers, - pad=0.5, prefix=colorstr('val: '))[0] - - if not opt.resume: - labels = np.concatenate(dataset.labels, 0) - c = torch.tensor(labels[:, 0]) # classes - # cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency - # model._initialize_biases(cf.to(device)) - if plots: - #plot_labels(labels, names, save_dir, loggers) - if tb_writer: - tb_writer.add_histogram('classes', c, 0) - - # Anchors - if not opt.noautoanchor: - check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) - model.half().float() # pre-reduce anchor precision - - # DDP mode - if cuda and rank != -1: - model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank, - # nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698 - find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules())) - - # Model parameters - hyp['box'] *= 3. / nl # scale to layers - hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers - hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers - hyp['label_smoothing'] = opt.label_smoothing - model.nc = nc # attach number of classes to model - model.hyp = hyp # attach hyperparameters to model - model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou) - model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights - model.names = names - - # Start training - t0 = time.time() - nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations) - # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training - maps = np.zeros(nc) # mAP per class - results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) - scheduler.last_epoch = start_epoch - 1 # do not move - scaler = amp.GradScaler(enabled=cuda) - compute_loss_ota = ComputeLossOTA(model) # init loss class - compute_loss = ComputeLoss(model) # init loss class - logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n' - f'Using {dataloader.num_workers} dataloader workers\n' - f'Logging results to {save_dir}\n' - f'Starting training for {epochs} epochs...') - torch.save(model, wdir / 'init.pt') - for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ - model.train() - - # Update image weights (optional) - if opt.image_weights: - # Generate indices - if rank in [-1, 0]: - cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights - iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights - dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx - # Broadcast if DDP - if rank != -1: - indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int() - dist.broadcast(indices, 0) - if rank != 0: - dataset.indices = indices.cpu().numpy() - - # Update mosaic border - # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) - # dataset.mosaic_border = [b - imgsz, -b] # height, width borders - - mloss = torch.zeros(4, device=device) # mean losses - if rank != -1: - dataloader.sampler.set_epoch(epoch) - pbar = enumerate(dataloader) - logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size')) - if rank in [-1, 0]: - pbar = tqdm(pbar, total=nb) # progress bar - optimizer.zero_grad() - for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- - ni = i + nb * epoch # number integrated batches (since train start) - imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0 - - # Warmup - if ni <= nw: - xi = [0, nw] # x interp - # model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) - accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round()) - for j, x in enumerate(optimizer.param_groups): - # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 - x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)]) - if 'momentum' in x: - x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) - - # Multi-scale - if opt.multi_scale: - sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size - sf = sz / max(imgs.shape[2:]) # scale factor - if sf != 1: - ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) - imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) - - # Forward - with amp.autocast(enabled=cuda): - pred = model(imgs) # forward - if 'loss_ota' not in hyp or hyp['loss_ota'] == 1: - loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs) # loss scaled by batch_size - else: - loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size - if rank != -1: - loss *= opt.world_size # gradient averaged between devices in DDP mode - if opt.quad: - loss *= 4. - - # Backward - scaler.scale(loss).backward() - - # Optimize - if ni % accumulate == 0: - scaler.step(optimizer) # optimizer.step - scaler.update() - optimizer.zero_grad() - if ema: - ema.update(model) - - # Print - if rank in [-1, 0]: - mloss = (mloss * i + loss_items) / (i + 1) # update mean losses - mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB) - s = ('%10s' * 2 + '%10.4g' * 6) % ( - '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1]) - pbar.set_description(s) - - # Plot - if plots and ni < 10: - f = save_dir / f'train_batch{ni}.jpg' # filename - Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start() - # if tb_writer: - # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) - # tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph - elif plots and ni == 10 and wandb_logger.wandb: - wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in - save_dir.glob('train*.jpg') if x.exists()]}) - - # end batch ------------------------------------------------------------------------------------------------ - # end epoch ---------------------------------------------------------------------------------------------------- - - # Scheduler - lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard - scheduler.step() - - # DDP process 0 or single-GPU - if rank in [-1, 0]: - # mAP - ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights']) - final_epoch = epoch + 1 == epochs - if not opt.notest or final_epoch: # Calculate mAP - wandb_logger.current_epoch = epoch + 1 - results, maps, times = test.test(data_dict, - batch_size=batch_size * 2, - imgsz=imgsz_test, - model=ema.ema, - single_cls=opt.single_cls, - dataloader=testloader, - save_dir=save_dir, - verbose=nc < 50 and final_epoch, - plots=plots and final_epoch, - wandb_logger=wandb_logger, - compute_loss=compute_loss, - is_coco=is_coco, - v5_metric=opt.v5_metric) - - # Write - with open(results_file, 'a') as f: - f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss - if len(opt.name) and opt.bucket: - os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name)) - - # Log - tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss - 'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', - 'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss - 'x/lr0', 'x/lr1', 'x/lr2'] # params - for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags): - if tb_writer: - tb_writer.add_scalar(tag, x, epoch) # tensorboard - if wandb_logger.wandb: - wandb_logger.log({tag: x}) # W&B - - # Update best mAP - fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] - if fi > best_fitness: - best_fitness = fi - wandb_logger.end_epoch(best_result=best_fitness == fi) - - # Save model - if (not opt.nosave) or (final_epoch and not opt.evolve): # if save - ckpt = {'epoch': epoch, - 'best_fitness': best_fitness, - 'training_results': results_file.read_text(), - 'model': deepcopy(model.module if is_parallel(model) else model).half(), - 'ema': deepcopy(ema.ema).half(), - 'updates': ema.updates, - 'optimizer': optimizer.state_dict(), - 'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None} - - # Save last, best and delete - torch.save(ckpt, last) - if best_fitness == fi: - torch.save(ckpt, best) - if (best_fitness == fi) and (epoch >= 200): - torch.save(ckpt, wdir / 'best_{:03d}.pt'.format(epoch)) - if epoch == 0: - torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch)) - elif ((epoch+1) % 25) == 0: - torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch)) - elif epoch >= (epochs-5): - torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch)) - if wandb_logger.wandb: - if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1: - wandb_logger.log_model( - last.parent, opt, epoch, fi, best_model=best_fitness == fi) - del ckpt - - # end epoch ---------------------------------------------------------------------------------------------------- - # end training - if rank in [-1, 0]: - # Plots - if plots: - plot_results(save_dir=save_dir) # save as results.png - if wandb_logger.wandb: - files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]] - wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files - if (save_dir / f).exists()]}) - # Test best.pt - logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) - if opt.data.endswith('coco.yaml') and nc == 80: # if COCO - for m in (last, best) if best.exists() else (last): # speed, mAP tests - results, _, _ = test.test(opt.data, - batch_size=batch_size * 2, - imgsz=imgsz_test, - conf_thres=0.001, - iou_thres=0.7, - model=attempt_load(m, device).half(), - single_cls=opt.single_cls, - dataloader=testloader, - save_dir=save_dir, - save_json=True, - plots=False, - is_coco=is_coco, - v5_metric=opt.v5_metric) - - # Strip optimizers - final = best if best.exists() else last # final model - for f in last, best: - if f.exists(): - strip_optimizer(f) # strip optimizers - if opt.bucket: - os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload - if wandb_logger.wandb and not opt.evolve: # Log the stripped model - wandb_logger.wandb.log_artifact(str(final), type='model', - name='run_' + wandb_logger.wandb_run.id + '_model', - aliases=['last', 'best', 'stripped']) - wandb_logger.finish_run() - else: - dist.destroy_process_group() - torch.cuda.empty_cache() - return results - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default='yolov7.pt', help='initial weights path') - parser.add_argument('--cfg', type=str, default='', help='model.yaml path') - parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path') - parser.add_argument('--hyp', type=str, default='data/hyp.scratch.p5.yaml', help='hyperparameters path') - parser.add_argument('--epochs', type=int, default=300) - parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs') - parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes') - parser.add_argument('--rect', action='store_true', help='rectangular training') - parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') - parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') - parser.add_argument('--notest', action='store_true', help='only test final epoch') - parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check') - parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters') - parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') - parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') - parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') - parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') - parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer') - parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') - parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') - parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers') - parser.add_argument('--project', default='runs/train', help='save to project/name') - parser.add_argument('--entity', default=None, help='W&B entity') - parser.add_argument('--name', default='exp', help='save to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--quad', action='store_true', help='quad dataloader') - parser.add_argument('--linear-lr', action='store_true', help='linear LR') - parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') - parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table') - parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B') - parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch') - parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used') - parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone of yolov7=50, first3=0 1 2') - parser.add_argument('--v5-metric', action='store_true', help='assume maximum recall as 1.0 in AP calculation') - opt = parser.parse_args() - - # Set DDP variables - opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 - opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1 - set_logging(opt.global_rank) - #if opt.global_rank in [-1, 0]: - # check_git_status() - # check_requirements() - - # Resume - wandb_run = check_wandb_resume(opt) - if opt.resume and not wandb_run: # resume an interrupted run - ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path - assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' - apriori = opt.global_rank, opt.local_rank - with open(Path(ckpt).parent.parent / 'opt.yaml') as f: - opt = argparse.Namespace(**yaml.load(f, Loader=yaml.SafeLoader)) # replace - opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = '', ckpt, True, opt.total_batch_size, *apriori # reinstate - logger.info('Resuming training from %s' % ckpt) - else: - # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml') - opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files - assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' - opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) - opt.name = 'evolve' if opt.evolve else opt.name - opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run - - # DDP mode - opt.total_batch_size = opt.batch_size - device = select_device(opt.device, batch_size=opt.batch_size) - if opt.local_rank != -1: - assert torch.cuda.device_count() > opt.local_rank - torch.cuda.set_device(opt.local_rank) - device = torch.device('cuda', opt.local_rank) - dist.init_process_group(backend='nccl', init_method='env://') # distributed backend - assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count' - opt.batch_size = opt.total_batch_size // opt.world_size - - # Hyperparameters - with open(opt.hyp) as f: - hyp = yaml.load(f, Loader=yaml.SafeLoader) # load hyps - - # Train - logger.info(opt) - if not opt.evolve: - tb_writer = None # init loggers - if opt.global_rank in [-1, 0]: - prefix = colorstr('tensorboard: ') - logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/") - tb_writer = SummaryWriter(opt.save_dir) # Tensorboard - train(hyp, opt, device, tb_writer) - - # Evolve hyperparameters (optional) - else: - # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) - meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) - 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) - 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 - 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay - 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) - 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum - 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr - 'box': (1, 0.02, 0.2), # box loss gain - 'cls': (1, 0.2, 4.0), # cls loss gain - 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight - 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) - 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight - 'iou_t': (0, 0.1, 0.7), # IoU training threshold - 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold - 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) - 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) - 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) - 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) - 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) - 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) - 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) - 'scale': (1, 0.0, 0.9), # image scale (+/- gain) - 'shear': (1, 0.0, 10.0), # image shear (+/- deg) - 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 - 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) - 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) - 'mosaic': (1, 0.0, 1.0), # image mixup (probability) - 'mixup': (1, 0.0, 1.0), # image mixup (probability) - 'copy_paste': (1, 0.0, 1.0), # segment copy-paste (probability) - 'paste_in': (1, 0.0, 1.0)} # segment copy-paste (probability) - - with open(opt.hyp, errors='ignore') as f: - hyp = yaml.safe_load(f) # load hyps dict - if 'anchors' not in hyp: # anchors commented in hyp.yaml - hyp['anchors'] = 3 - - assert opt.local_rank == -1, 'DDP mode not implemented for --evolve' - opt.notest, opt.nosave = True, True # only test/save final epoch - # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices - yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here - if opt.bucket: - os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists - - for _ in range(300): # generations to evolve - if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate - # Select parent(s) - parent = 'single' # parent selection method: 'single' or 'weighted' - x = np.loadtxt('evolve.txt', ndmin=2) - n = min(5, len(x)) # number of previous results to consider - x = x[np.argsort(-fitness(x))][:n] # top n mutations - w = fitness(x) - fitness(x).min() # weights - if parent == 'single' or len(x) == 1: - # x = x[random.randint(0, n - 1)] # random selection - x = x[random.choices(range(n), weights=w)[0]] # weighted selection - elif parent == 'weighted': - x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination - - # Mutate - mp, s = 0.8, 0.2 # mutation probability, sigma - npr = np.random - npr.seed(int(time.time())) - g = np.array([x[0] for x in meta.values()]) # gains 0-1 - ng = len(meta) - v = np.ones(ng) - while all(v == 1): # mutate until a change occurs (prevent duplicates) - v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) - for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) - hyp[k] = float(x[i + 7] * v[i]) # mutate - - # Constrain to limits - for k, v in meta.items(): - hyp[k] = max(hyp[k], v[1]) # lower limit - hyp[k] = min(hyp[k], v[2]) # upper limit - hyp[k] = round(hyp[k], 5) # significant digits - - # Train mutation - results = train(hyp.copy(), opt, device) - - # Write mutation results - print_mutation(hyp.copy(), results, yaml_file, opt.bucket) - - # Plot results - plot_evolution(yaml_file) - print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n' - f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}') diff --git a/spaces/erwann/Face-editor/utils.py b/spaces/erwann/Face-editor/utils.py deleted file mode 100644 index 8d92890a9e0f1b71726cff6b8334527ef955d93a..0000000000000000000000000000000000000000 --- a/spaces/erwann/Face-editor/utils.py +++ /dev/null @@ -1,20 +0,0 @@ -import time - -import matplotlib.pyplot as plt -import numpy as np -import torch -import torch.nn.functional as F -from skimage.color import lab2rgb, rgb2lab -from torch import nn - - -def freeze_module(module): - for param in module.parameters(): - param.requires_grad = False - - -def get_device(): - device = "cuda" if torch.cuda.is_available() else "cpu" - if torch.backends.mps.is_available() and torch.backends.mps.is_built(): - device = "mps" - return (device) diff --git a/spaces/evanpierce/3D_Photo_Inpainting2/README.md b/spaces/evanpierce/3D_Photo_Inpainting2/README.md deleted file mode 100644 index 7b96879ff21f5b7cf289a4b25a68e60a287d4868..0000000000000000000000000000000000000000 --- a/spaces/evanpierce/3D_Photo_Inpainting2/README.md +++ /dev/null @@ -1,38 +0,0 @@ ---- -title: 3D_Photo_Inpainting -emoji: 👁 -colorFrom: purple -colorTo: red -sdk: gradio -app_file: app.py -pinned: false -duplicated_from: Epoching/3D_Photo_Inpainting ---- - -# 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/f2api/gpt-academic/request_llm/README.md b/spaces/f2api/gpt-academic/request_llm/README.md deleted file mode 100644 index 545bc1ffba8b79a49d994cfedcc2a787475181b2..0000000000000000000000000000000000000000 --- a/spaces/f2api/gpt-academic/request_llm/README.md +++ /dev/null @@ -1,79 +0,0 @@ -# 如何使用其他大语言模型 - -## ChatGLM - -- 安装依赖 `pip install -r request_llm/requirements_chatglm.txt` -- 修改配置，在config.py中将LLM_MODEL的值改为"chatglm" - -``` sh -LLM_MODEL = "chatglm" -``` -- 运行！ -``` sh -`python main.py` -``` - -## Claude-Stack - -- 请参考此教程获取 https://zhuanlan.zhihu.com/p/627485689 - - 1、SLACK_CLAUDE_BOT_ID - - 2、SLACK_CLAUDE_USER_TOKEN - -- 把token加入config.py - -## Newbing - -- 使用cookie editor获取cookie（json） -- 把cookie（json）加入config.py （NEWBING_COOKIES） - -## Moss -- 使用docker-compose - -## RWKV -- 使用docker-compose - -## LLAMA -- 使用docker-compose - -## 盘古 -- 使用docker-compose - - ---- -## Text-Generation-UI (TGUI，调试中，暂不可用) - -### 1. 部署TGUI -``` sh -# 1 下载模型 -git clone https://github.com/oobabooga/text-generation-webui.git -# 2 这个仓库的最新代码有问题，回滚到几周之前 -git reset --hard fcda3f87767e642d1c0411776e549e1d3894843d -# 3 切换路径 -cd text-generation-webui -# 4 安装text-generation的额外依赖 -pip install accelerate bitsandbytes flexgen gradio llamacpp markdown numpy peft requests rwkv safetensors sentencepiece tqdm datasets git+https://github.com/huggingface/transformers -# 5 下载模型 -python download-model.py facebook/galactica-1.3b -# 其他可选如 facebook/opt-1.3b -# facebook/galactica-1.3b -# facebook/galactica-6.7b -# facebook/galactica-120b -# facebook/pygmalion-1.3b 等 -# 详情见 https://github.com/oobabooga/text-generation-webui - -# 6 启动text-generation -python server.py --cpu --listen --listen-port 7865 --model facebook_galactica-1.3b -``` - -### 2. 修改config.py - -``` sh -# LLM_MODEL格式: tgui:[模型]@[ws地址]:[ws端口] , 端口要和上面给定的端口一致 -LLM_MODEL = "tgui:galactica-1.3b@localhost:7860" -``` - -### 3. 运行！ -``` sh -cd chatgpt-academic -python main.py -``` diff --git a/spaces/fabiogra/moseca/app/footer.py b/spaces/fabiogra/moseca/app/footer.py deleted file mode 100644 index 9ac5e369e4482d6936bf0cb1fbbad2c9534c38d1..0000000000000000000000000000000000000000 --- a/spaces/fabiogra/moseca/app/footer.py +++ /dev/null @@ -1,114 +0,0 @@ -import streamlit as st -from htbuilder import HtmlElement, a, div, img, p, styles -from htbuilder.units import percent, px -from streamlit.components.v1 import html - - -def image(src_as_string, **style): - return img(src=src_as_string, style=styles(**style)) - - -def link(link, text, **style): - return a(_href=link, _target="_blank", style=styles(**style))(text) - - -def layout(*args): - style = """ - - """ - - style_div = styles( - position="fixed", - left=0, - bottom=0, - margin=px(0, 0, 0, 0), - width=percent(100), - color="black", - text_align="center", - height="auto", - opacity=1, - align_items="center", - flex_direction="column", - display="flex", - ) - body = p( - id="myFooter", - style=styles( - margin=px(0, 0, 0, 0), - padding=px(5), - font_size="0.9rem", - color="rgb(51,51,51)", - font_family="Exo", - ), - ) - foot = div(style=style_div)(body) - - st.markdown(style, unsafe_allow_html=True) - - for arg in args: - if isinstance(arg, str) or isinstance(arg, HtmlElement): - body(arg) - - st.markdown(str(foot), unsafe_allow_html=True) - - js_code = """ - - """ - html(js_code) - - -def footer(): - myargs = [ - "Made in 🇮🇹 with ", - link( - "https://streamlit.io/", - image("https://streamlit.io/images/brand/streamlit-mark-color.png", width="18px"), - ), - " by ", - link("https://twitter.com/grsFabio", "@grsFabio"), - " ", - link( - "https://www.buymeacoffee.com/fabiogra", - image("https://i.imgur.com/YFu6MMA.png", margin="0em", align="top", width="130px"), - 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- -The Call of Duty 4 v1 7 lvl 55 hack download is a tool that allows you to modify your COD4 profile data and set your rank to level 55 with everything unlocked. This means that you can access all the weapons, attachments, perks, killstreaks, and camos in the game without having to play through the multiplayer mode. You can also customize your classes and loadouts as you wish, and enjoy the game with full freedom.
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- -The Call of Duty 4 v1 7 lvl 55 hack download works by replacing your original mpdata file with a hacked one that contains the level 55 data. The mpdata file is where your COD4 profile information is stored, such as your rank, stats, achievements, and unlocks. By swapping this file with a modified one, you can trick the game into thinking that you have reached level 55 and completed all the challenges.
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- Go to Start -> Run -> Type regedit -> Hit Enter. -
- Go to HKEY_LOCAL_MACHINE -> Activision -> Call of Duty 4. (For some users, it might be HKEY_LOCAL_MACHINE -> Software -> Wow6432Node -> Activision -> Call of Duty 4.) -
- Double-click codkey and save the key in notepad. -
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- Press OK and close regedit. -
- Navigate to your Call of Duty 4 profile folder (typically “C:\\Program Files\\Activision\\Call of Duty 4 – Modern Warfare\\players\\profiles\\your_user_name”) -
- Back-up and then remove all the files in your user name folder. -
- Place the mpdata file you downloaded in your user name folder. -
- Open the COD4 MP game and check your rank. You should now be level 55 with all challenges, guns, and every camouflage unlocked! -
- Finally, in the game go to Options -> Multiplayer Options -> Key Code and type your original cd key you have saved in notepad. -
That's it! You have successfully used the Call of Duty 4 v1 7 lvl 55 hack download and unlocked everything in COD4. Enjoy!
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Please note that these links are not affiliated with us and we are not responsible for their content or safety. Use them at your own risk.
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- -The Call of Duty 4 v1 7 lvl 55 hack download is generally safe to use, as long as you follow the instructions carefully and don't abuse it. However, there are some risks involved with using any kind of hack or cheat in online games. These include:
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- Ban risk: You might get banned from playing COD4 online if you are detected by anti-cheat systems or reported by other players. -
- Virus risk: You might get infected by malware or viruses if you download files from untrusted sources or open suspicious attachments. -
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To minimize these risks, we recommend that you:
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We also advise that you use the hack only for fun and not for cheating or ruining other players' experience. Remember that hacking is against the game's terms of service and can result in serious consequences.
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Of course, these benefits come with some drawbacks, such as the risk of getting banned or infected by viruses. Therefore, you should use the hack responsibly and at your own discretion.
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These alternatives might not be as easy or convenient as using the hack, but they might offer you more variety and enjoyment in playing COD4.
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- Extract the mpdata file from the rar archive using a program like WinRAR or 7-Zip. -
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These features make the Call of Duty 4 v1 7 lvl 55 hack download one of the best hacks for COD4 available online.
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- Don't brag about the hack: This can avoid attracting unwanted attention or hostility from other players. -
- Have fun with the hack: This is the most important tip of all. Enjoy the game and have fun with your friends using the hack. -
These tips and tricks can help you make the most out of the Call of Duty 4 v1 7 lvl 55 hack download and have a great time playing COD4.
-Conclusion
- -The Call of Duty 4 v1 7 lvl 55 hack download is a useful and convenient tool that lets you unlock everything in COD4 without having to play through the multiplayer mode. It works by replacing your mpdata file with a hacked one that contains level 55 data. You can use it easily and safely by following the instructions and tips provided in this article. You can also get the hack from any of the links below. However, you should remember that hacking is against the game's terms of service and can result in serious consequences. Therefore, you should use the hack responsibly and at your own discretion. We hope you enjoyed this article and found it helpful. If you have any questions or feedback, please leave a comment below. Thank you for reading.
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You can also set the time of day to morning, afternoon, evening, night, etc., and see how it affects the lighting and the traffic density.
-VR support and realistic physics
-City Car Driving 1.5.1 supports VR devices such as Oculus Rift and HTC Vive, which enhance the immersion and realism of the game. You can feel like you are actually sitting behind the wheel of a car and look around the cockpit or the mirrors.
-City Car Driving 1.5.1 also has realistic physics that simulate the car's behavior, such as acceleration, braking, steering, suspension, traction, etc. You can feel the difference between front-wheel drive, rear-wheel drive, and all-wheel drive, as well as the impact of the road surface, the slope, the weight, etc.
-How to download and install City Car Driving 1.5.1?
-If you want to play City Car Driving 1.5.1, you need to have a PC that meets the minimum system requirements, which are:
--
-
- OS: Windows 7 SP1 / 8 / 8.1 / 10 (64 Bit) -
- Processor: Intel Pentium Dual Core 3.2 GHz / AMD Athlon II X4 3.1 GHz -
- Memory: 4 GB RAM -
- Graphics: AMD Radeon R7 240 / nVidia GeForce GT 740 -
- DirectX: Version 11 -
- Storage: 10 GB available space -
- Sound Card: Any sound card compatible with DirectX 9.0 -
The recommended system requirements are:
--
-
- OS: Windows 7 SP1 / 8 / 8.1 / 10 (64 Bit) -
- Processor: Intel Core i3 3.2 GHz / AMD FX 4xxx 3.6 GHz -
- Memory: 8 GB RAM -
- Graphics: AMD Radeon R7 250X / nVidia GeForce GTX 750 -
- DirectX: Version 11 -
- Storage: 10 GB available space -
- Sound Card: Any sound card compatible with DirectX 9.0 -
To download and install City Car Driving 1.5.1, you have two options:
--
-
- You can buy the game from the official website for $24.99 and download it from there. -
- You can buy the game from Steam for $24.99 and download it from there. -
The installation steps are similar for both options:
--
-
- Run the setup file and follow the instructions. -
- Select the language and the destination folder. -
- Wait for the installation to finish. -
- Launch the game and enjoy. -
How to play City Car Driving 1.5.1?
-City Car Driving 1.5.1 is easy to play but hard to master. You need to have a keyboard, a mouse, and optionally a steering wheel or a gamepad to control your car.
-Game modes and scenarios
-City Car Driving 1.5.1 has three game modes:
--
-
- Career mode: In this mode, you have to complete various missions that test your driving skills and knowledge of the traffic rules. You can earn money and unlock new cars and locations as you progress. -
- Free mode: In this mode, you can drive freely in any location and any car you want. You can also create your own scenarios by changing the weather, time of day, traffic density, etc. -
- Online mode: In this mode, you can join or create a multiplayer session and drive with other players online. -
Controls and settings
-You can customize your controls and settings in the options menu of the game. You can change the key bindings, the sensitivity, the camera angle, the sound volume, etc.
-You can also adjust the difficulty level of the game by changing the following parameters:
--
-
- Traffic rules violation detection: This determines how strict the game is in detecting your violations of the traffic rules, such as speeding, running red lights, etc. -
- Traffic density: This determines how many cars and pedestrians are on the road. -
- Traffic offense factor: This determines how aggressive and unpredictable other drivers are. -
- Traffic accidents factor: This determines how often traffic accidents occur on the road. -
- Fuel consumption factor: This determines how fast your car consumes fuel. -
- Mechanical damage factor: This determines how much damage your car suffers from collisions or wear and tear. -
Tips and tricks
-To play City Car Driving 1.5.1 effectively and enjoyably, here are some tips and tricks you should follow:
--
-
- Always obey the traffic rules and signs, such as speed limits, stop signs, yield signs, etc. -
- Always Always use your turn signals, headlights, windshield wipers, horn, etc., when necessary. -
- Always check your mirrors, blind spots, and rear view camera before changing lanes, turning, or reversing. -
- Always keep a safe distance from other vehicles and pedestrians, and avoid sudden braking or acceleration. -
- Always park your car properly and follow the parking rules. -
- Always refuel your car when the fuel gauge is low and repair your car when the damage indicator is high. -
- Always practice in different locations, cars, weather, and time of day to improve your driving skills and confidence. -
Why should you play City Car Driving 1.5.1?
-City Car Driving 1.5.1 is not just a game, but also a learning tool that can help you become a better driver in real life. Here are some of the benefits of playing City Car Driving 1.5.1:
-Benefits of playing City Car Driving 1.5.1
-Improve your driving skills and confidence
-City Car Driving 1.5.1 can help you improve your driving skills and confidence by exposing you to various road situations and driving conditions that you may encounter in real life. You can practice how to react to different scenarios, such as traffic jams, road works, accidents, etc., and how to handle different cars, such as manual or automatic transmission, front-wheel or rear-wheel drive, etc.
-City Car Driving 1.5.1 can also help you overcome your driving fears or anxieties, such as driving at night, in bad weather, or in unfamiliar places. You can play the game in a safe and fun environment without risking your life or damaging your car.
-Learn the road rules and traffic signs
-City Car Driving 1.5.1 can help you learn the road rules and traffic signs of different countries, such as USA, UK, Germany, France, etc. You can familiarize yourself with the right-hand or left-hand drive, the speed limits, the traffic signs, etc., depending on your location.
-City Car Driving 1.5.1 can also help you prepare for your driving test or license exam by testing your knowledge of the traffic rules and signs. You can get feedback on your performance and learn from your mistakes.
-Have fun and enjoy the realistic graphics and sounds
-City Car Driving 1.5.1 can also provide you with entertainment and enjoyment by letting you drive freely in any location and any car you want. You can explore the city or the country, discover new places, or just go for a joy ride.
-City Car Driving 1.5.1 also has realistic graphics and sounds that make you feel like you are actually driving a car. You can admire the scenery, hear the engine noise, feel the vibration, etc.
-Conclusion
-City Car Driving 1.5.1 is a realistic car driving simulator for PC that can help you improve your driving skills and confidence, learn the road rules and traffic signs of different countries, and have fun and enjoy the realistic graphics and sounds.
-If you are looking for a game that can teach you how to drive a car in a big city or in a country in different conditions or just go for a joy ride, then you should try City Car Driving 1.5.1.
-Frequently Asked Questions
--
-
- What is the latest version of City Car Driving? -
- How much does City Car Driving cost? -
- Can I play City Car Driving with a steering wheel or a gamepad? -
- Can I play City Car Driving offline? -
- Can I play City Car Driving online? -
The latest version of City Car Driving is 1.5.9 as of June 2020.
-City Car Driving costs $24.99 on both the official website and Steam.
-Yes, you can play City Car Driving with a steering wheel or a gamepad if they are compatible with PC and DirectX 9.0.
-Yes, you can play City Car Driving offline in career mode or free mode.
-Yes, you can play City Car Driving online in online mode with other players.
--
-
\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Drive Across the World in Bus Simulator Ultimate on PC Mac (Emulator).md b/spaces/fatiXbelha/sd/Drive Across the World in Bus Simulator Ultimate on PC Mac (Emulator).md deleted file mode 100644 index 4d1f7203c5c38030b50b9e7100022dfb8bb1322e..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Drive Across the World in Bus Simulator Ultimate on PC Mac (Emulator).md +++ /dev/null @@ -1,84 +0,0 @@ -
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Bus Simulator Ultimate Mod APK Download for PC: How to Play the Most Popular Bus Game on Android on Your PC or Mac
-If you love driving games and want to experience what it's like to be a bus driver in different cities around the world, then you should try Bus Simulator Ultimate. This is a realistic bus simulation game developed by Zuuks Games, the creators of Truck Simulator 2018: Europe. You can drive various types of buses, pick up passengers at every stop, follow traffic rules, listen to radio stations, and even set up your own bus company.
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But what if you want to play this amazing game on a bigger screen with better graphics and performance? What if you want to have more control over your bus with keyboard and mouse? What if you want to use some cool features like macros, scripts, multi-instance, and sync to enhance your gameplay? Well, you can do all that by downloading and installing Bus Simulator Ultimate Mod APK on your PC or Mac. In this article, we will show you how to do that step by step.
-Features of Bus Simulator Ultimate Mod APK
-Bus Simulator Ultimate Mod APK is a modified version of the original game that gives you access to some extra features that are not available in the official version. Some of these features are:
-Realistic Bus Driving Experience
-You can feel like a real bus driver in this game as you drive down realistic roads with dynamic weather and day and night cycles. You can also customize your driving options, such as steering wheel, buttons, tilt, or slider. You can adjust the camera angle, the mirrors, the speedometer, and the fuel gauge. You can also use indicators, headlights, horn, and wipers as needed.
-Huge City Maps Inspired by Real Locations
-You can explore over 10 city maps in this game, each with its own unique landmarks and attractions. You can drive in cities like Berlin, Rome, Paris, Amsterdam, Istanbul, New York, Los Angeles, and more. You can also unlock new routes and destinations as you progress in the game. You can see the map of each city on the screen and follow the GPS navigation to reach your destination.
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-You can choose from over 30 different buses in this game, each with its own design and specifications. You can drive buses from brands like Mercedes-Benz, Setra, Volvo, and more. You can also customize your buses with various skins, stickers, colors, and accessories. You can also upgrade your buses with better engines, brakes, tires, and suspension.
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Not only that, but you can also set up your own bus company in this game. You can create your company logo, name, and slogan. You can hire drivers, assign them routes, and manage their salaries. You can also buy new offices in different cities and expand your business.
-Radio Stations and Passenger Interactions
-You can make your bus driving more enjoyable by listening to radio stations in this game. You can choose from over 250 radio stations from different countries and genres. You can also change the radio station or volume from the dashboard.
-You can also interact with your passengers in this game. You can see their faces, expressions, and reactions as they board and leave your bus. You can also hear their comments, complaints, and requests. You can respond to them by using the microphone or the chat feature. You can also see their ratings and feedback for your service.
-Multiplayer Mode and Leaderboards
-You can also play this game with other players online in multiplayer mode. You can join or create a room with up to 4 players and drive together on the same route. You can chat with them using voice or text messages. You can also compete with them for the best score and time.
-You can also check your ranking on the global leaderboards in this game. You can see how you compare with other players in terms of income, distance, passengers, reputation, and more. You can also earn achievements and trophies for completing various challenges and tasks.
-Benefits of Playing Bus Simulator Ultimate on PC or Mac
-While Bus Simulator Ultimate is a great game to play on your Android device, you might want to play it on your PC or Mac for some reasons. Some of the benefits of playing Bus Simulator Ultimate on PC or Mac are:
-Better Graphics and Performance
-By playing Bus Simulator Ultimate on PC or Mac, you can enjoy the game's stunning graphics and smooth performance on a larger screen. You can see the details of the buses, the roads, the buildings, and the scenery more clearly. You can also adjust the graphics settings to suit your preferences and system requirements.
-You can also avoid the issues of battery drain, overheating, or lagging that might occur on your Android device when playing Bus Simulator Ultimate for a long time. You can play the game without any interruptions or distractions.
-Full Control with Keyboard and Mouse
-By playing Bus Simulator Ultimate on PC or Mac, you can have more control over your bus with keyboard and mouse. You can use the arrow keys or WASD keys to steer your bus left or right, accelerate or brake, and use the spacebar to handbrake. You can also use the mouse to look around, change the camera angle, or click on the buttons on the dashboard. You can also customize the keyboard and mouse settings to suit your preferences and comfort.
-Macros and Scripts for Automation
-By playing Bus Simulator Ultimate on PC or Mac, you can use some advanced features like macros and scripts to automate some tasks and actions in the game. For example, you can create a macro to start or stop the engine, open or close the doors, or turn on or off the lights with a single keystroke. You can also create a script to repeat a certain route or action multiple times without manual input. You can save time and effort by using these features.
-Multi-Instance and Sync Feature for Multiple Accounts
-By playing Bus Simulator Ultimate on PC or Mac, you can also use the multi-instance and sync feature to play with multiple accounts at the same time. You can create different instances of the game and run them simultaneously on your PC or Mac. You can also sync your actions and data across all instances with the sync feature. You can use this feature to play with different buses, routes, cities, or modes without switching accounts. You can also use this feature to trade or cooperate with other players online.
-How to Download and Install Bus Simulator Ultimate Mod APK on PC or Mac
-Now that you know the features and benefits of playing Bus Simulator Ultimate Mod APK on PC or Mac, you might be wondering how to do that. Well, it's not that hard. All you need is an Android emulator that can run Android apps and games on your PC or Mac. An Android emulator is a software that creates a virtual Android environment on your PC or Mac and allows you to access the Google Play Store and download Android apps and games.
-There are many Android emulators available online, but we recommend using either BlueStacks or NoxPlayer as they are among the most popular and reliable ones. Here are the steps to download and install Bus Simulator Ultimate Mod APK on PC or Mac using these emulators:
-Using BlueStacks Emulator
--
-
- Download and install BlueStacks emulator from its official website: https://www.bluestacks.com/ -
- Launch BlueStacks emulator and sign in with your Google account. -
- Go to the search bar and type "Bus Simulator Ultimate Mod APK" and hit enter. -
- Select the app from the list of results and click on "Install". -
- Wait for the installation to complete and then click on "Open". -
- Enjoy playing Bus Simulator Ultimate Mod APK on your PC or Mac. -
Using NoxPlayer Emulator
--
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- Download and install NoxPlayer emulator from its official website: https://www.bignox.com/ -
- Launch NoxPlayer emulator and sign in with your Google account. -
- Go to the search bar and type "Bus Simulator Ultimate Mod APK" and hit enter. -
- Select the app from the list of results and click on "Install". -
- Wait for the installation to complete and then click on "Open". -
- Enjoy playing Bus Simulator Ultimate Mod APK on your PC or Mac. -
Troubleshooting Tips
-If you encounter any problems while downloading or installing Bus Simulator Ultimate Mod APK on your PC or Mac, here are some tips that might help you:
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- Make sure you have enough space on your PC or Mac for the emulator and the game. -
- Make sure you have a stable internet connection for downloading and installing the game. -
- Make sure you have updated your emulator to the latest version. -
- Make sure you have enabled the permissions for the game in your emulator settings. -
- If you still face any issues, contact the support team of your emulator or visit their official website for more information. -
Conclusion
-In conclusion, Bus Simulator Ultimate is a fun and realistic bus simulation game that lets you drive various types of buses in different cities around the world. You can customize your buses and offices, listen to radio stations, interact with passengers, and set up your own bus company. You can also play with other players online in multiplayer mode and compete for the best score and time.
-If you want to play this game on your PC or Mac, you can download and install Bus Simulator Ultimate Mod APK using an Android emulator like BlueStacks or NoxPlayer that gives you access to some extra features that are not available in the official version. You can enjoy better graphics and performance, full control with keyboard and mouse, macros and scripts for automation, and multi-instance and sync feature for multiple accounts. You can follow the steps we have provided in this article to download and install Bus Simulator Ultimate Mod APK on your PC or Mac easily and safely.
-We hope you found this article helpful and informative. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading and happy bus driving!
-FAQs
-Here are some frequently asked questions about Bus Simulator Ultimate Mod APK:
-Q1: What is Bus Simulator Ultimate Mod APK?
-A1: Bus Simulator Ultimate Mod APK is a modified version of the original game that gives you access to some extra features that are not available in the official version. Some of these features are realistic bus driving experience, huge city maps inspired by real locations, customizable buses and offices, radio stations and passenger interactions, multiplayer mode and leaderboards, and more.
-Q2: Is Bus Simulator Ultimate Mod APK safe to download?
-A2: Yes, Bus Simulator Ultimate Mod APK is safe to download as long as you use a trusted source and an Android emulator. We recommend using either BlueStacks or NoxPlayer as they are among the most popular and reliable emulators. You can also scan the APK file with an antivirus software before installing it.
-Q3: How can I update Bus Simulator Ultimate Mod APK?
-A3: You can update Bus Simulator Ultimate Mod APK by following the same steps as downloading and installing it. You can check for updates on the app's official website or on the emulator's app store. You can also enable the auto-update feature in your emulator settings to get the latest version automatically.
-Q4: Can I play Bus Simulator Ultimate Mod APK offline?
-A4: Yes, you can play Bus Simulator Ultimate Mod APK offline as long as you have downloaded the game data beforehand. However, some features like multiplayer mode, radio stations, and leaderboards might not work offline. You will need an internet connection to access these features.
-Q5: How can I contact the developers of Bus Simulator Ultimate?
-A5: You can contact the developers of Bus Simulator Ultimate by visiting their official website: https://www.zuuks.com/ or by sending them an email at info@zuuks.com. You can also follow them on their social media accounts like Facebook, Twitter, Instagram, and YouTube.
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\ No newline at end of file diff --git a/spaces/fb700/chatglm-fitness-RLHF/speaker_encoder/params_data.py b/spaces/fb700/chatglm-fitness-RLHF/speaker_encoder/params_data.py deleted file mode 100644 index bdb1716ed45617f2b127a7fb8885afe6cc74fb71..0000000000000000000000000000000000000000 --- a/spaces/fb700/chatglm-fitness-RLHF/speaker_encoder/params_data.py +++ /dev/null @@ -1,29 +0,0 @@ - -## Mel-filterbank -mel_window_length = 25 # In milliseconds -mel_window_step = 10 # In milliseconds -mel_n_channels = 40 - - -## Audio -sampling_rate = 16000 -# Number of spectrogram frames in a partial utterance -partials_n_frames = 160 # 1600 ms -# Number of spectrogram frames at inference -inference_n_frames = 80 # 800 ms - - -## Voice Activation Detection -# Window size of the VAD. Must be either 10, 20 or 30 milliseconds. -# This sets the granularity of the VAD. Should not need to be changed. -vad_window_length = 30 # In milliseconds -# Number of frames to average together when performing the moving average smoothing. -# The larger this value, the larger the VAD variations must be to not get smoothed out. -vad_moving_average_width = 8 -# Maximum number of consecutive silent frames a segment can have. -vad_max_silence_length = 6 - - -## Audio volume normalization -audio_norm_target_dBFS = -30 - diff --git a/spaces/fclong/summary/fengshen/examples/zen2_finetune/fs_zen2_large_afqmc.sh b/spaces/fclong/summary/fengshen/examples/zen2_finetune/fs_zen2_large_afqmc.sh deleted file mode 100644 index 1f44844a127b5bb39226c56b70bba85957dd735a..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/examples/zen2_finetune/fs_zen2_large_afqmc.sh +++ /dev/null @@ -1,93 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=zen2_large_afqmc # create a short name for your job -#SBATCH --nodes=1 # node count -#SBATCH --ntasks=1 # total number of tasks across all nodes -#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks) -#SBATCH --gres=gpu:1 # number of gpus per node -#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc. -#SBATCH -o %x-%j.log # output and error file name (%x=job name, %j=job id) - - -export CUDA_VISIBLE_DEVICES='1' -export TORCH_EXTENSIONS_DIR=/cognitive_comp/ganruyi/tmp/torch_extendsions - -MODEL_NAME=zen2_large - -TASK=afqmc - -ZERO_STAGE=1 -STRATEGY=deepspeed_stage_${ZERO_STAGE} - -ROOT_DIR=/cognitive_comp/ganruyi/experiments/classification_finetune/${MODEL_NAME}_${TASK} -if [ ! -d ${ROOT_DIR} ];then - mkdir -p ${ROOT_DIR} - echo ${ROOT_DIR} created!!!!!!!!!!!!!! -else - echo ${ROOT_DIR} exist!!!!!!!!!!!!!!! -fi - -DATA_DIR=/cognitive_comp/yangping/data/ChineseCLUE_DATA/${TASK}_public/ -PRETRAINED_MODEL_PATH=/cognitive_comp/ganruyi/hf_models/zen/zh_zen_large_2.0 - -CHECKPOINT_PATH=${ROOT_DIR}/ckpt/ -OUTPUT_PATH=${ROOT_DIR}/predict.json - -DATA_ARGS="\ - --data_dir $DATA_DIR \ - --train_data train.json \ - --valid_data dev.json \ - --test_data test.json \ - --train_batchsize 32 \ - --valid_batchsize 16 \ - --max_seq_length 128 \ - --texta_name sentence \ - --label_name label \ - --id_name id \ - --task_name afqmc \ - " - -MODEL_ARGS="\ - --learning_rate 2e-5 \ - --weight_decay 0.1 \ - --warmup_ratio 0.01 \ - --num_labels 2 \ - " - -MODEL_CHECKPOINT_ARGS="\ - --monitor val_acc \ - --save_top_k 3 \ - --mode max \ - --every_n_train_steps 100 \ - --save_weights_only True \ - --dirpath $CHECKPOINT_PATH \ - --filename model-{epoch:02d}-{val_acc:.4f} \ - " - -TRAINER_ARGS="\ - --max_epochs 10 \ - --gpus 1 \ - --check_val_every_n_epoch 1 \ - --val_check_interval 100 \ - --default_root_dir $ROOT_DIR \ - " - - -options=" \ - --pretrained_model_path $PRETRAINED_MODEL_PATH \ - --vocab_file $PRETRAINED_MODEL_PATH/vocab.txt \ - --do_lower_case \ - --output_save_path $OUTPUT_PATH \ - $DATA_ARGS \ - $MODEL_ARGS \ - $MODEL_CHECKPOINT_ARGS \ - $TRAINER_ARGS \ -" -SCRIPT_PATH=/cognitive_comp/ganruyi/Fengshenbang-LM/fengshen/examples/zen2_finetune/fengshen_sequence_level_ft_task.py -/home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options - -# SINGULARITY_PATH=/cognitive_comp/ganruyi/pytorch21_06_py3_docker_image_v2.sif -# python3 $SCRIPT_PATH $options -# source activate base -# singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options -# /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options - diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Apka do pobierania z YouTube - nieograniczone moliwoci pobierania i konwersji filmw z najpopularniejszego serwisu wideo.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Apka do pobierania z YouTube - nieograniczone moliwoci pobierania i konwersji filmw z najpopularniejszego serwisu wideo.md deleted file mode 100644 index 3bb435975e8021fdcda07aaf2dd717e401b5d634..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Apka do pobierania z YouTube - nieograniczone moliwoci pobierania i konwersji filmw z najpopularniejszego serwisu wideo.md +++ /dev/null @@ -1,148 +0,0 @@ -
-
| Outline of the article | -
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| - # Apka do pobierania z youtube - jaką wybrać i dlaczego warto? ## Wstęp - Przedstawienie tematu i celu artykułu - Wymienienie głównych zalet pobierania filmów z youtube - Zapowiedź porównania kilku popularnych aplikacji do pobierania z youtube ## Czym jest apka do pobierania z youtube i jak działa? - Wyjaśnienie pojęcia i zasady działania aplikacji do pobierania z youtube - Omówienie różnych formatów i jakości dostępnych do pobrania - Podanie przykładu procesu pobierania filmu z youtube za pomocą aplikacji ## Jakie są korzyści z używania apki do pobierania z youtube? - Wymienienie kilku głównych korzyści, takich jak: - Oglądanie filmów offline bez potrzeby dostępu do internetu - Oszczędzanie transferu danych i pamięci urządzenia - Konwertowanie filmów na pliki audio lub inne formaty wideo - Tworzenie własnej kolekcji filmów na komputerze lub smartfonie ## Jak wybrać najlepszą apkę do pobierania z youtube? - Przedstawienie kilku kryteriów wyboru, takich jak: - Kompatybilność z systemem operacyjnym i przeglądarką - Szybkość i łatwość pobierania i konwersji - Wybór formatów i jakości do pobrania - Brak ograniczeń na liczbę i długość pobieranych filmów - Brak dodatkowego oprogramowania lub reklam ## Porównanie kilku popularnych aplikacji do pobierania z youtube - Przedstawienie tabeli porównawczej z następującymi kolumnami: - Nazwa aplikacji - System operacyjny - Formaty i jakości do pobrania - Szybkość i łatwość użycia - Dodatkowe funkcje i zalety - Omówienie każdej aplikacji na podstawie tabeli i własnych doświadczeń ## Podsumowanie i rekomendacja - Podsumowanie głównych wniosków i porad z artykułu - Rekomendacja najlepszej apki do pobierania z youtube według własnego zdania - Zachęcenie czytelników do wypróbowania wybranej aplikacji i podzielenia się opiniami ## FAQ - Odpowiedź na pięć najczęściej zadawanych pytań dotyczących apki do pobierania z youtube, takich jak: - Czy pobieranie filmów z youtube jest legalne? - Czy pobieranie filmów z youtube wpływa na ich jakość? - Czy można pobrać całą playlistę lub kanał z youtube? - Czy można pobrać napisy lub komentarze z youtube? - Czy można pobrać tylko dźwięk z filmu na youtube? | -
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- Apka do pobierania z youtube - jaką wybrać i dlaczego warto?-YouTube to najpopularniejszy serwis wideo na świecie, który oferuje ogromną ilość treści dla każdego gustu i zainteresowania. Możesz tu znaleźć filmy edukacyjne, rozrywkowe, muzyczne, sportowe, dokumentalne i wiele innych. Jednak oglądanie filmów na YouTube wymaga stałego dostępu do internetu, co nie zawsze jest możliwe lub wygodne. Dlatego wiele osób szuka sposobu, jak pobrać ulubione filmy z YouTube na swój komputer lub smartfon i oglądać je offline, kiedy i gdzie chcą. W tym artykule przedstawię Ci kilka zalet pobierania filmów z YouTube, a także porównam kilka popularnych aplikacji do pobierania z YouTube, które możesz wypróbować. Zapraszam do lektury! -Czym jest apka do pobierania z youtube i jak działa?-Apka do pobierania z YouTube to program lub rozszerzenie do przeglądarki, które umożliwiają pobieranie filmów z YouTube na dysk twardy lub kartę pamięci urządzenia. Aplikacje te działają na różnych systemach operacyjnych, takich jak Windows, Mac, Linux, Android czy iOS. Aby pobrać film z YouTube za pomocą apki, wystarczy skopiować link do filmu z przeglądarki i wkleić go do aplikacji. Następnie możesz wybrać format i jakość pliku, który chcesz pobrać. Możesz pobrać film w formacie MP4, AVI, MKV, MOV lub innym, a także w różnych rozdzielczościach, od 144p do 4K. Możesz też pobrać tylko dźwięk z filmu w formacie MP3, M4A, WAV lub innym. Po wybraniu opcji pobierania wystarczy kliknąć przycisk "Pobierz" i poczekać, aż aplikacja zakończy pobieranie i konwersję pliku. Proces ten może trwać od kilku sekund do kilku minut, w zależności od wielkości i jakości pliku oraz szybkości połączenia internetowego. -apka do pobierania z youtubeDOWNLOAD ✪ https://gohhs.com/2uPlWW - Jakie są korzyści z używania apki do pobierania z youtube?-Pobieranie filmów z YouTube za pomocą apki ma wiele korzyści, takich jak: -
Jak wybrać najlepszą apkę do pobierania z youtube?-Na rynku jest wiele aplikacji do pobierania z YouTube, ale nie wszystkie są takie same. Aby wybrać najlepszą apkę do pobierania z YouTube, należy zwrócić uwagę na kilka kryteriów, takich jak: -
Porównanie kilku popularnych aplikacji do pobierania z youtube-Aby ułatwić Ci wybór najlepszej apki do pobierania z YouTube, przygotowałem tabelę porównawczą kilku popularnych aplikacji, które sam testowałem. Oto tabela: -
Jak widzisz, każda z tych aplikacji ma swoje zalety i wady. Osobiście polecam Ci 4K Video Downloader, ponieważ jest to najbardziej zaawansowana i wszechstronna apka do pobierania z YouTube. Ma ona największy wybór formatów i jakości do pobrania, pozwala pobierać playlisty, kanały i napisy, automatycznie aktualizuje subskrypcje i nie ma żadnych reklam. Jest też bardzo szybka i prosta w użyciu. Jeśli szukasz najlepszej apki do pobierania z YouTube, to 4K Video Downloader jest dla Ciebie! -Podsumowanie i rekomendacja-W tym artykule przedstawiłem Ci kilka zalet pobierania filmów z YouTube za pomocą apki, a także porównałem kilka popularnych aplikacji do pobierania z YouTube. Mam nadzieję, że ten artykuł pomógł Ci wybrać najlepszą apkę do pobierania z YouTube dla siebie. Pobieranie filmów z YouTube to świetny sposób na oglądanie swoich ulubionych treści offline, oszczędzanie danych i pamięci urządzenia, konwertowanie filmów na pliki audio lub inne formaty wideo i tworzenie własnej kolekcji filmów. Jeśli chcesz skorzystać z tych korzyści, polecam Ci 4K Video Downloader jako najlepszą apkę do pobierania z YouTube. Wypróbuj ją i podziel się ze mną swoją opinią! -FAQ-Oto pięć najczęściej zadawanych pytań dotyczących apki do pobierania z YouTube i moje odpowiedzi na nie: -
Pobieranie filmów z YouTube jest legalne, jeśli robisz to dla własnego użytku i nie naruszasz praw autorskich lub licencji filmów. Nie możesz udostępniać ani sprzedawać pobranych filmów bez zgody ich twórców lub właścicieli. -Pobieranie filmów z YouTube nie wpływa negatywnie na ich jakość, jeśli używasz dobrej apki do pobierania z YouTube i wybierasz odpowiedni format i rozdzielczość pliku. Możesz pobrać filmy w jakości HD lub nawet 4K bez utraty jakości obrazu lub dźwięku. -apka do pobierania muzyki z youtube Tak, można pobrać całą playlistę lub kanał z YouTube za pomocą niektórych aplikacji do pobierania z YouTube, takich jak 4K Video Downloader. Wystarczy skopiować link do playlisty lub kanału i wkle ić go do aplikacji. Następnie możesz wybrać, które filmy z playlisty lub kanału chcesz pobrać i w jakim formacie i jakości. -Tak, można pobrać napisy lub komentarze z YouTube za pomocą niektórych aplikacji do pobierania z YouTube, takich jak 4K Video Downloader. Wystarczy zaznaczyć opcję "Pobierz napisy" lub "Pobierz komentarze" w aplikacji i wybrać język napisów lub komentarzy. -Tak, można pobrać tylko dźwięk z filmu na YouTube za pomocą niektórych aplikacji do pobierania z YouTube, takich jak Videoder czy TubeMate. Wystarczy wybrać format pliku audio, taki jak MP3, M4A, OGG lub inny, i kliknąć "Pobierz". Możesz też ustawić jakość dźwięku, taką jak 128 kbps, 256 kbps lub 320 kbps. -- - \ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Curso primo rico download a melhor forma de aprender com quem vive na pele o que ensina.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Curso primo rico download a melhor forma de aprender com quem vive na pele o que ensina.md deleted file mode 100644 index c5d6782b1af11767db1003989da2bd3bb63729f1..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Curso primo rico download a melhor forma de aprender com quem vive na pele o que ensina.md +++ /dev/null @@ -1,118 +0,0 @@ - -
These are just some examples of the amazing results that the students of the course have achieved. There are many more testimonials and reviews that you can check on the website of O Primo Rico. You can also watch some videos of Thiago Nigro interviewing some of his students and showing their progress and achievements. -These testimonials and results prove that the course works and that it can help anyone who wants to learn how to manage their money, how to invest wisely, and how to achieve financial freedom. They also show that Thiago Nigro is a credible and trustworthy teacher, who knows what he is talking about and who cares about his students. -Some of the tips and advice that Thiago Nigro gives to his students are: -
Conclusion and FAQs-In conclusion, if you want to learn how to manage your money, how to invest wisely, and how to achieve financial freedom, you should consider taking the course "Do Mil ao Milhão" by Thiago Nigro, also known as O Primo Rico. This course is a complete and practical guide on how to transform your finances and reach your goals, based on his own journey and methodology. -The course has more than 40 hours of video lessons, divided into 10 modules, covering everything you need to know about finance and investing, from the basics to the advanced. The course also has a community of students and mentors, where you can interact, ask questions, share experiences, and network with other like-minded people. The course also has monthly live sessions with Thiago Nigro, where he will answer your questions, give you tips, update you on the market trends, and motivate you to keep going. -The course also has some amazing bonuses that will enhance your learning experience and results, such as access to the special reports and recommendations from O Primo Rico, access to the exclusive interviews and masterclasses with some of the most successful investors and entrepreneurs in Brazil and in the world, access to the digital version of the book "Do Mil ao Milhão - sem cortar o cafezinho", among others. -The course also has a 30-day guarantee and support, which means that if you are not satisfied with the course for any reason, you can request a full refund within 30 days of your purchase. You will also have a dedicated support team that will help you with any issues or doubts you may have during the course. -The price of the course is R$ 1.997 (about US$ 400), which is a very reasonable investment considering the value and quality of the content. However, if you enroll now, you can get a special discount of R$ 500 (about US$ 100), which means you will pay only R$ 1.497 (about US$ 300). This is a limited-time offer that may expire soon, so don't miss this opportunity. If you are ready to take the course and start your journey to financial freedom, you can click on the link below and enroll now. You will be redirected to the official website of O Primo Rico, where you can download the course and access all the bonuses and benefits. Don't wait any longer, this is your chance to learn from one of the most influential investors in Brazil and achieve your goals faster. -Curso Primo Rico Download: How to Learn from One of the Most Influential Investors in Brazil -Before you go, here are some frequently asked questions (FAQs) that you might have about the course: -FAQ 1: How long does the course last and how much time do I need to dedicate to it?-The course lasts for as long as you want. You can access the course platform anytime, anywhere, and at your own pace. You can watch the videos as many times as you want, pause, rewind, fast-forward, etc. You can also download the videos and watch them offline. You can complete the course in a few weeks or a few months, depending on your availability and preference. However, we recommend that you dedicate at least one hour per day to watch the videos and do the exercises. -FAQ 2: Do I need any prior knowledge or experience in finance or investing to take the course?-No, you don't need any prior knowledge or experience in finance or investing to take the course. The course is designed for beginners who want to learn the basics of finance and investing, as well as for intermediate and advanced investors who want to improve their skills and results. The course covers everything from the fundamentals to the strategies, from the theory to the practice, from the concepts to the examples. The course is easy to understand and follow, with clear explanations and illustrations. -FAQ 3: What kind of investments does the course cover and what kind of returns can I expect?-The course covers different types of investments, such as stocks, bonds, funds, REITs, ETFs, cryptocurrencies, etc. The course also covers different markets, such as Brazil, USA, Europe, Asia, etc. The course teaches you how to diversify your portfolio and allocate your money among different assets and regions, according to your profile, goals, and risk tolerance. -The returns that you can expect from your investments depend on many factors, such as your initial capital, your contribution rate, your allocation strategy, your rebalance frequency, your analysis criteria, your investment horizon, etc. However, based on historical data and simulations, you can expect an average annual return of around 15% if you follow the methodology ARCA and invest in dividend-paying assets. -FAQ 4: Is the course updated and relevant for the current market situation?-Yes, the course is updated and relevant for the current market situation. The course is constantly revised and improved by Thiago Nigro and his team, based on the feedback from the students and the changes in the market. The course also includes monthly live sessions with Thiago Nigro, where he will update you on the market trends and opportunities. The course also includes special reports and recommendations from O Primo Rico, where you will get the best investment opportunities and strategies for your portfolio. -FAQ 5: How can I contact Thiago Nigro or his team if I have any questions or doubts during or after the course?-You can contact Thiago Nigro or his team anytime you want during or after the course. You can use any of these channels: -
You will receive a prompt and friendly response from them. They will be happy to assist you and make sure you have the best learning experience possible. -I hope this article has been helpful and informative for you. If you have any other questions or comments about the course "Do Mil ao Milhão" by Thiago Nigro (O Primo Rico), please feel free to leave them below. I will try to answer them as soon as possible. -Thank you for reading this article and for choosing Bing as your search engine. I wish you all the best in your financial journey. 197e85843d- - \ No newline at end of file diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/base64id/README.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/base64id/README.md deleted file mode 100644 index 17689e6f8c28a6170fd8c7084f008398a602a0c0..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/base64id/README.md +++ /dev/null @@ -1,18 +0,0 @@ -base64id -======== - -Node.js module that generates a base64 id. - -Uses crypto.randomBytes when available, falls back to unsafe methods for node.js <= 0.4. - -To increase performance, random bytes are buffered to minimize the number of synchronous calls to crypto.randomBytes. - -## Installation - - $ npm install base64id - -## Usage - - var base64id = require('base64id'); - - var id = base64id.generateId(); diff --git a/spaces/firasggg/andite-anything-v4.0/README.md b/spaces/firasggg/andite-anything-v4.0/README.md deleted file mode 100644 index bb854097f4c163e7b0fa21fab6bf9748942f76b9..0000000000000000000000000000000000000000 --- a/spaces/firasggg/andite-anything-v4.0/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Andite Anything V4.0 -emoji: 📚 -colorFrom: yellow -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/firsk/ai_otto/text/english.py b/spaces/firsk/ai_otto/text/english.py deleted file mode 100644 index 0f9339c9ed771dab5136978eaaab194ec3fe2395..0000000000000000000000000000000000000000 --- a/spaces/firsk/ai_otto/text/english.py +++ /dev/null @@ -1,214 +0,0 @@ -import pickle -import os -import re -from g2p_en import G2p - -from text import symbols - -current_file_path = os.path.dirname(__file__) -CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep") -CACHE_PATH = os.path.join(current_file_path, "cmudict_cache.pickle") -_g2p = G2p() - -arpa = { - "AH0", - "S", - "AH1", - "EY2", - "AE2", - "EH0", - "OW2", - "UH0", - "NG", - "B", - "G", - "AY0", - "M", - "AA0", - "F", - "AO0", - "ER2", - "UH1", - "IY1", - "AH2", - "DH", - "IY0", - "EY1", - "IH0", - "K", - "N", - "W", - "IY2", - "T", - "AA1", - "ER1", - "EH2", - "OY0", - "UH2", - "UW1", - "Z", - "AW2", - "AW1", - "V", - "UW2", - "AA2", - "ER", - "AW0", - "UW0", - "R", - "OW1", - "EH1", - "ZH", - "AE0", - "IH2", - "IH", - "Y", - "JH", - "P", - "AY1", - "EY0", - "OY2", - "TH", - "HH", - "D", - "ER0", - "CH", - "AO1", - "AE1", - "AO2", - "OY1", - "AY2", - "IH1", - "OW0", - "L", - "SH", -} - - -def post_replace_ph(ph): - rep_map = { - "：": ",", - "；": ",", - "，": ",", - "。": ".", - "！": "!", - "？": "?", - "\n": ".", - "·": ",", - "、": ",", - "...": "…", - "v": "V", - } - if ph in rep_map.keys(): - ph = rep_map[ph] - if ph in symbols: - return ph - if ph not in symbols: - ph = "UNK" - return ph - - -def read_dict(): - g2p_dict = {} - start_line = 49 - with open(CMU_DICT_PATH) as f: - line = f.readline() - line_index = 1 - while line: - if line_index >= start_line: - line = line.strip() - word_split = line.split(" ") - word = word_split[0] - - syllable_split = word_split[1].split(" - ") - g2p_dict[word] = [] - for syllable in syllable_split: - phone_split = syllable.split(" ") - g2p_dict[word].append(phone_split) - - line_index = line_index + 1 - line = f.readline() - - return g2p_dict - - -def cache_dict(g2p_dict, file_path): - with open(file_path, "wb") as pickle_file: - pickle.dump(g2p_dict, pickle_file) - - -def get_dict(): - if os.path.exists(CACHE_PATH): - with open(CACHE_PATH, "rb") as pickle_file: - g2p_dict = pickle.load(pickle_file) - else: - g2p_dict = read_dict() - cache_dict(g2p_dict, CACHE_PATH) - - return g2p_dict - - -eng_dict = get_dict() - - -def refine_ph(phn): - tone = 0 - if re.search(r"\d$", phn): - tone = int(phn[-1]) + 1 - phn = phn[:-1] - return phn.lower(), tone - - -def refine_syllables(syllables): - tones = [] - phonemes = [] - for phn_list in syllables: - for i in range(len(phn_list)): - phn = phn_list[i] - phn, tone = refine_ph(phn) - phonemes.append(phn) - tones.append(tone) - return phonemes, tones - - -def text_normalize(text): - # todo: eng text normalize - return text - - -def g2p(text): - phones = [] - tones = [] - words = re.split(r"([,;.\-\?\!\s+])", text) - for w in words: - if w.upper() in eng_dict: - phns, tns = refine_syllables(eng_dict[w.upper()]) - phones += phns - tones += tns - else: - phone_list = list(filter(lambda p: p != " ", _g2p(w))) - for ph in phone_list: - if ph in arpa: - ph, tn = refine_ph(ph) - phones.append(ph) - tones.append(tn) - else: - phones.append(ph) - tones.append(0) - # todo: implement word2ph - word2ph = [1 for i in phones] - - phones = [post_replace_ph(i) for i in phones] - return phones, tones, word2ph - - -if __name__ == "__main__": - # print(get_dict()) - # print(eng_word_to_phoneme("hello")) - print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")) - # all_phones = set() - # for k, syllables in eng_dict.items(): - # for group in syllables: - # for ph in group: - # all_phones.add(ph) - # print(all_phones) diff --git a/spaces/flax-community/multilingual-image-captioning/apps/model/flax_clip_vision_mbart/modeling_clip_vision_mbart.py b/spaces/flax-community/multilingual-image-captioning/apps/model/flax_clip_vision_mbart/modeling_clip_vision_mbart.py deleted file mode 100644 index 81cc59c2b41f740883639582fcc93c9159dee93f..0000000000000000000000000000000000000000 --- a/spaces/flax-community/multilingual-image-captioning/apps/model/flax_clip_vision_mbart/modeling_clip_vision_mbart.py +++ /dev/null @@ -1,778 +0,0 @@ -from typing import Callable, Optional, Tuple - -import flax.linen as nn -import jax -import jax.numpy as jnp -from flax.core.frozen_dict import FrozenDict, unfreeze -from jax import lax -from jax.random import PRNGKey -from transformers import ( - CLIPVisionConfig, - FlaxCLIPVisionModel, - FlaxMBartModel, - MBartConfig, -) -from transformers.modeling_flax_outputs import ( - FlaxBaseModelOutputWithPooling, - FlaxCausalLMOutputWithCrossAttentions, - FlaxSeq2SeqLMOutput, - FlaxSeq2SeqModelOutput, -) -from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule -from transformers.models.mbart.modeling_flax_mbart import ( - FlaxMBartDecoder, - FlaxPreTrainedModel, - shift_tokens_right, -) - -from .configuration_clip_vision_mbart import CLIPVisionMBartConfig -from .modeling_clip_vision_utils import FlaxCLIPVisionMBartPreTrainedModel - - -class FlaxCLIPVisionMBartModule(nn.Module): - config: CLIPVisionMBartConfig - dtype: jnp.dtype = jnp.float32 # the dtype of the computation - - def setup(self): - self.shared = nn.Embed( - self.config.mbart_config.vocab_size, - self.config.mbart_config.d_model, - embedding_init=jax.nn.initializers.normal( - self.config.mbart_config.init_std, self.dtype - ), - dtype=self.dtype, - ) - - self.encoder = FlaxCLIPVisionModule( - self.config.clip_vision_config, dtype=self.dtype - ) - self.decoder = FlaxMBartDecoder( - self.config.mbart_config, dtype=self.dtype, embed_tokens=self.shared - ) - - self.visual_projection = nn.Dense( - self.config.mbart_config.hidden_size, - dtype=self.dtype, - kernel_init=jax.nn.initializers.normal( - self.config.mbart_config.init_std, self.dtype - ), - ) - - def _get_encoder_module(self): - return self.encoder - - def _get_decoder_module(self): - return self.decoder - - def __call__( - self, - pixel_values, - decoder_input_ids, - decoder_attention_mask, - decoder_position_ids, - output_attentions: bool = False, - output_hidden_states: bool = False, - return_dict: bool = True, - deterministic: bool = True, - ): - - encoder_outputs = self.encoder( - pixel_values=pixel_values, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - deterministic=deterministic, - ) - - batch_size, sequence_length = encoder_outputs[0].shape[:2] - encoder_attention_mask = jnp.ones((batch_size, sequence_length)) - - encoder_hidden_states = self.visual_projection(encoder_outputs[0]) - - decoder_outputs = self.decoder( - input_ids=decoder_input_ids, - attention_mask=decoder_attention_mask, - position_ids=decoder_position_ids, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - deterministic=deterministic, - ) - - if not return_dict: - return decoder_outputs + encoder_outputs - - return FlaxSeq2SeqModelOutput( - last_hidden_state=decoder_outputs.last_hidden_state, - decoder_hidden_states=decoder_outputs.hidden_states, - decoder_attentions=decoder_outputs.attentions, - cross_attentions=decoder_outputs.cross_attentions, - encoder_last_hidden_state=encoder_outputs.last_hidden_state, - encoder_hidden_states=encoder_outputs.hidden_states, - encoder_attentions=encoder_outputs.attentions, - ) - - -class FlaxCLIPVisionMBartForConditionalGenerationModule(nn.Module): - config: CLIPVisionMBartConfig - dtype: jnp.dtype = jnp.float32 - bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros - - def setup(self): - self.model = FlaxCLIPVisionMBartModule(config=self.config, dtype=self.dtype) - self.lm_head = nn.Dense( - self.model.shared.num_embeddings, - use_bias=False, - dtype=self.dtype, - kernel_init=jax.nn.initializers.normal( - self.config.mbart_config.init_std, self.dtype - ), - ) - self.final_logits_bias = self.param( - "final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings) - ) - - def _get_encoder_module(self): - return self.model.encoder - - def _get_decoder_module(self): - return self.model.decoder - - def _get_visual_projection_module(self): - return self.model.visual_projection - - def __call__( - self, - pixel_values, - decoder_input_ids, - decoder_attention_mask, - decoder_position_ids, - output_attentions: bool = False, - output_hidden_states: bool = False, - return_dict: bool = True, - deterministic: bool = True, - ): - outputs = self.model( - pixel_values=pixel_values, - decoder_input_ids=decoder_input_ids, - decoder_attention_mask=decoder_attention_mask, - decoder_position_ids=decoder_position_ids, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - deterministic=deterministic, - ) - - hidden_states = outputs[0] - - if self.config.tie_word_embeddings: - shared_embedding = self.model.variables["params"]["shared"]["embedding"] - lm_logits = self.lm_head.apply( - {"params": {"kernel": shared_embedding.T}}, hidden_states - ) - else: - lm_logits = self.lm_head(hidden_states) - - lm_logits += self.final_logits_bias - - if not return_dict: - output = (lm_logits,) + outputs[1:] - return output - - return FlaxSeq2SeqLMOutput( - logits=lm_logits, - decoder_hidden_states=outputs.decoder_hidden_states, - decoder_attentions=outputs.decoder_attentions, - cross_attentions=outputs.cross_attentions, - encoder_last_hidden_state=outputs.encoder_last_hidden_state, - encoder_hidden_states=outputs.encoder_hidden_states, - encoder_attentions=outputs.encoder_attentions, - ) - - -class FlaxCLIPVisionMBartOuterPreTrainedModel(FlaxCLIPVisionMBartPreTrainedModel): - config_class = CLIPVisionMBartConfig - base_model_prefix: str = "model" - module_class: nn.Module = None - - def __init__( - self, - config: CLIPVisionMBartConfig, - input_shape: Tuple = None, - seed: int = 0, - dtype: jnp.dtype = jnp.float32, - **kwargs, - ): - if input_shape is None: - input_shape = ( - ( - 1, - config.clip_vision_config.image_size, - config.clip_vision_config.image_size, - 3, - ), - (1, 1), - ) - - module = self.module_class(config=config, dtype=dtype, **kwargs) - super().__init__( - config, module, input_shape=input_shape, seed=seed, dtype=dtype - ) - - def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict: - # init input tensors - pixel_values = jax.random.normal(rng, input_shape[0]) - # # make sure initialization pass will work for FlaxMBartForSequenceClassificationModule - # input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id) - - decoder_input_ids = jnp.zeros(input_shape[1], dtype="i4") - decoder_attention_mask = jnp.ones_like(decoder_input_ids) - - batch_size, sequence_length = decoder_input_ids.shape - decoder_position_ids = jnp.broadcast_to( - jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) - ) - - params_rng, dropout_rng = jax.random.split(rng) - rngs = {"params": params_rng, "dropout": dropout_rng} - - return self.module.init( - rngs, - pixel_values, - decoder_input_ids, - decoder_attention_mask, - decoder_position_ids, - )["params"] - - def init_cache(self, batch_size, max_length, encoder_outputs): - - decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4") - decoder_attention_mask = jnp.ones_like(decoder_input_ids) - decoder_position_ids = jnp.broadcast_to( - jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), - decoder_input_ids.shape, - ) - - def _decoder_forward( - module, - decoder_input_ids, - decoder_attention_mask, - decoder_position_ids, - **kwargs, - ): - decoder_module = module._get_decoder_module() - return decoder_module( - decoder_input_ids, - decoder_attention_mask, - decoder_position_ids, - **kwargs, - ) - - init_variables = self.module.init( - jax.random.PRNGKey(0), - decoder_input_ids=decoder_input_ids, - decoder_attention_mask=decoder_attention_mask, - decoder_position_ids=decoder_position_ids, - encoder_hidden_states=encoder_outputs[0], - init_cache=True, - method=_decoder_forward, # we only need to call the decoder to init the cache - ) - return unfreeze(init_variables["cache"]) - - def encode( - self, - pixel_values: jnp.ndarray, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - train: bool = False, - params: dict = None, - dropout_rng: PRNGKey = None, - ): - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.return_dict - ) - - # pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) - - # Handle any PRNG if needed - rngs = {} - if dropout_rng is not None: - rngs["dropout"] = dropout_rng - - def _encoder_forward(module, pixel_values, **kwargs): - encode_module = module._get_encoder_module() - visual_projection = module._get_visual_projection_module() - - outputs = encode_module(pixel_values, **kwargs) - - return FlaxBaseModelOutputWithPooling( - last_hidden_state=visual_projection(outputs.last_hidden_state), - pooler_output=outputs.pooler_output, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - return self.module.apply( - {"params": params or self.params}, - pixel_values=jnp.array(pixel_values, dtype="i4"), - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - deterministic=not train, - rngs=rngs, - method=_encoder_forward, - ) - - def decode( - self, - decoder_input_ids, - encoder_outputs, - encoder_attention_mask: Optional[jnp.ndarray] = None, - decoder_attention_mask: Optional[jnp.ndarray] = None, - decoder_position_ids: Optional[jnp.ndarray] = None, - past_key_values: dict = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - train: bool = False, - params: dict = None, - dropout_rng: PRNGKey = None, - ): - - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.return_dict - ) - - encoder_hidden_states = encoder_outputs[0] - - if encoder_attention_mask is None: - batch_size, sequence_length = encoder_hidden_states.shape[:2] - encoder_attention_mask = jnp.ones((batch_size, sequence_length)) - - batch_size, sequence_length = decoder_input_ids.shape - if decoder_attention_mask is None: - decoder_attention_mask = jnp.ones((batch_size, sequence_length)) - - if decoder_position_ids is None: - if past_key_values is not None: - raise ValueError( - "Make sure to provide `decoder_position_ids` when passing `past_key_values`." - ) - - decoder_position_ids = jnp.broadcast_to( - jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) - ) - - # Handle any PRNG if needed - rngs = {} - if dropout_rng is not None: - rngs["dropout"] = dropout_rng - - inputs = {"params": params or self.params} - - # if past_key_values are passed then cache is already initialized a private flag init_cache has to be - # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that - # it can be changed by FlaxMBartAttention module - if past_key_values: - inputs["cache"] = past_key_values - mutable = ["cache"] - else: - mutable = False - - def _decoder_forward( - module, - decoder_input_ids, - decoder_attention_mask, - decoder_position_ids, - **kwargs, - ): - decoder_module = module._get_decoder_module() - return decoder_module( - decoder_input_ids, - decoder_attention_mask, - decoder_position_ids, - **kwargs, - ) - - outputs = self.module.apply( - inputs, - decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), - decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), - decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - deterministic=not train, - rngs=rngs, - mutable=mutable, - method=_decoder_forward, - ) - - # add updated cache to model output - if past_key_values is not None and return_dict: - outputs, past = outputs - outputs["past_key_values"] = unfreeze(past["cache"]) - return outputs - elif past_key_values is not None and not return_dict: - outputs, past = outputs - outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] - - return outputs - - def __call__( - self, - pixel_values: jnp.ndarray, - decoder_input_ids: Optional[jnp.ndarray] = None, - decoder_attention_mask: Optional[jnp.ndarray] = None, - decoder_position_ids: Optional[jnp.ndarray] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - train: bool = False, - params: dict = None, - dropout_rng: PRNGKey = None, - ): - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.return_dict - ) - - # pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) - - # # prepare encoder inputs - # if attention_mask is None: - # attention_mask = jnp.ones_like(input_ids) - # if position_ids is None: - # batch_size, sequence_length = input_ids.shape - # position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) - - # prepare decoder inputs - # if decoder_input_ids is None: - # decoder_input_ids = shift_tokens_right( - # input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id - # ) # TODO: Check how to use this - if decoder_attention_mask is None: - decoder_attention_mask = jnp.ones_like(decoder_input_ids) - if decoder_position_ids is None: - batch_size, sequence_length = decoder_input_ids.shape - decoder_position_ids = jnp.broadcast_to( - jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) - ) - - # Handle any PRNG if needed - rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} - - return self.module.apply( - {"params": params or self.params}, - pixel_values=jnp.array(pixel_values, dtype=jnp.float32), - decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), - decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), - decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - deterministic=not train, - rngs=rngs, - ) - - -class FlaxCLIPVisionMBartForConditionalGeneration( - FlaxCLIPVisionMBartOuterPreTrainedModel -): - module_class = FlaxCLIPVisionMBartForConditionalGenerationModule - dtype: jnp.dtype = jnp.float32 - - def decode( - self, - decoder_input_ids, - encoder_outputs, - encoder_attention_mask: Optional[jnp.ndarray] = None, - decoder_attention_mask: Optional[jnp.ndarray] = None, - decoder_position_ids: Optional[jnp.ndarray] = None, - past_key_values: dict = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - deterministic: bool = True, - params: dict = None, - dropout_rng: PRNGKey = None, - ): - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.return_dict - ) - - encoder_hidden_states = encoder_outputs[0] - - if encoder_attention_mask is None: - batch_size, sequence_length = encoder_hidden_states.shape[:2] - encoder_attention_mask = jnp.ones((batch_size, sequence_length)) - - batch_size, sequence_length = decoder_input_ids.shape - if decoder_attention_mask is None: - decoder_attention_mask = jnp.ones((batch_size, sequence_length)) - - if decoder_position_ids is None: - if past_key_values is not None: - raise ValueError( - "Make sure to provide `decoder_position_ids` when passing `past_key_values`." - ) - - decoder_position_ids = jnp.broadcast_to( - jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) - ) - - # Handle any PRNG if needed - rngs = {} - if dropout_rng is not None: - rngs["dropout"] = dropout_rng - - inputs = {"params": params or self.params} - - # if past_key_values are passed then cache is already initialized a private flag init_cache has to be - # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that - # it can be changed by FlaxMBartAttention module - if past_key_values: - inputs["cache"] = past_key_values - mutable = ["cache"] - else: - mutable = False - - def _decoder_forward( - module, - decoder_input_ids, - decoder_attention_mask, - decoder_position_ids, - **kwargs, - ): - decoder_module = module._get_decoder_module() - outputs = decoder_module( - decoder_input_ids, - decoder_attention_mask, - decoder_position_ids, - **kwargs, - ) - hidden_states = outputs[0] - - if self.config.tie_word_embeddings: - shared_embedding = module.model.variables["params"]["shared"][ - "embedding" - ] - lm_logits = module.lm_head.apply( - {"params": {"kernel": shared_embedding.T}}, hidden_states - ) - else: - lm_logits = module.lm_head(hidden_states) - - lm_logits += module.final_logits_bias - return lm_logits, outputs - - outputs = self.module.apply( - inputs, - decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), - decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), - decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - deterministic=deterministic, - rngs=rngs, - mutable=mutable, - method=_decoder_forward, - ) - - if past_key_values is None: - lm_logits, decoder_outputs = outputs - else: - (lm_logits, decoder_outputs), past = outputs - - if return_dict: - outputs = FlaxCausalLMOutputWithCrossAttentions( - logits=lm_logits, - hidden_states=decoder_outputs.hidden_states, - attentions=decoder_outputs.attentions, - cross_attentions=decoder_outputs.cross_attentions, - ) - else: - outputs = (lm_logits,) + decoder_outputs[1:] - - # add updated cache to model output - if past_key_values is not None and return_dict: - outputs["past_key_values"] = unfreeze(past["cache"]) - return outputs - elif past_key_values is not None and not return_dict: - outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] - - return outputs - - def prepare_inputs_for_generation( - self, - decoder_input_ids, - max_length, - attention_mask: Optional[jnp.DeviceArray] = None, - decoder_attention_mask: Optional[jnp.DeviceArray] = None, - encoder_outputs=None, - **kwargs, - ): - # initializing the cache - batch_size, seq_length = decoder_input_ids.shape - - past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) - # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. - # But since the decoder uses a causal mask, those positions are masked anyways. - # Thus we can create a single static attention_mask here, which is more efficient for compilation - extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") - if decoder_attention_mask is not None: - position_ids = decoder_attention_mask.cumsum(axis=-1) - 1 - extended_attention_mask = lax.dynamic_update_slice( - extended_attention_mask, decoder_attention_mask, (0, 0) - ) - else: - position_ids = jnp.broadcast_to( - jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length) - ) - - return { - "past_key_values": past_key_values, - "encoder_outputs": encoder_outputs, - "encoder_attention_mask": attention_mask, - "decoder_attention_mask": extended_attention_mask, - "decoder_position_ids": position_ids, - } - - def update_inputs_for_generation(self, model_outputs, model_kwargs): - model_kwargs["past_key_values"] = model_outputs.past_key_values - model_kwargs["decoder_position_ids"] = ( - model_kwargs["decoder_position_ids"][:, -1:] + 1 - ) - return model_kwargs - - @classmethod - def from_pretrained(cls, *args, **kwargs): - # At the moment fast initialization is not supported - # for composite models - # kwargs["_fast_init"] = False - return super().from_pretrained(*args, **kwargs) - - @classmethod - def from_clip_vision_mbart_pretrained( - cls, - clip_vision_model_name_or_path: str = None, - mbart_model_name_or_path: str = None, - *model_args, - **kwargs, - ) -> FlaxCLIPVisionMBartPreTrainedModel: - - kwargs_mbart = { - argument[len("mbart_") :]: value - for argument, value in kwargs.items() - if argument.startswith("mbart_") - } - - kwargs_clip_vision = { - argument[len("clip_vision_") :]: value - for argument, value in kwargs.items() - if argument.startswith("clip_vision_") - } - - # remove mbart, clip_vision kwargs from kwargs - for key in kwargs_mbart.keys(): - del kwargs["mbart_" + key] - for key in kwargs_clip_vision.keys(): - del kwargs["clip_vision_" + key] - - # Load and initialize the mbart and clip_vision model - mbart_model = kwargs_mbart.pop("model", None) - if mbart_model is None: - assert ( - mbart_model_name_or_path is not None - ), "If `model` is not defined as an argument, a `mbart_model_name_or_path` has to be defined" - - if "config" not in kwargs_mbart: - mbart_config = MBartConfig.from_pretrained(mbart_model_name_or_path) - kwargs_mbart["config"] = mbart_config - - mbart_model = FlaxMBartModel.from_pretrained( - mbart_model_name_or_path, *model_args, **kwargs_mbart - ) - - clip_vision_model = kwargs_clip_vision.pop("model", None) - if clip_vision_model is None: - assert ( - clip_vision_model_name_or_path is not None - ), "If `model` is not defined as an argument, a `clip_vision_model_name_or_path` has to be defined" - - if "config" not in kwargs_clip_vision: - clip_vision_config = CLIPVisionConfig.from_pretrained( - clip_vision_model_name_or_path - ) - kwargs_clip_vision["config"] = clip_vision_config - - clip_vision_model = FlaxCLIPVisionModel.from_pretrained( - clip_vision_model_name_or_path, *model_args, **kwargs_clip_vision - ) - - # instantiate config with corresponding kwargs - dtype = kwargs.pop("dtype", jnp.float32) - config = CLIPVisionMBartConfig.from_clip_vision_mbart_configs( - clip_vision_model.config, mbart_model.config, **kwargs - ) - - # init model - model = cls(config, *model_args, dtype=dtype, **kwargs) - model.params["model"]["encoder"] = clip_vision_model.params - model.params["model"]["decoder"] = mbart_model.params["decoder"] - model.params["model"]["shared"] = mbart_model.params["shared"] - # model.params["mbart_model"] = mbart_model.params - - return model - - -# flax_clip_vision_mbart_cg = FlaxCLIPVisionMBartForConditionalGeneration.from_clip_vision_mbart_pretrained('openai/clip-vit-base-patch32', 'facebook/mbart-large') -# outputs = flax_clip_vision_mbart_cg(pixel_values, input_ids, attention_mask, position_ids, output_hidden_states=True) -# flax_vit_bart_cg.generate(input_ids=pixel_values, decoder_start_token_id=tokenizer.lang_code_to_id['en_XX'])s diff --git a/spaces/fyodorschnotzdinger/paraphraser/README.md b/spaces/fyodorschnotzdinger/paraphraser/README.md deleted file mode 100644 index 918928674c11a0a4a47e2170b542200f94688cc1..0000000000000000000000000000000000000000 --- a/spaces/fyodorschnotzdinger/paraphraser/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Paraphraser -emoji: 🏃 -colorFrom: yellow -colorTo: pink -sdk: gradio -sdk_version: 3.19.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/gaviego/mnist/models.py b/spaces/gaviego/mnist/models.py deleted file mode 100644 index 3f28e6e3befe258221910d87cbfdd97f9c418ac6..0000000000000000000000000000000000000000 --- a/spaces/gaviego/mnist/models.py +++ /dev/null @@ -1,41 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -# Define the model -class Net(nn.Module): - def __init__(self): - super(Net, self).__init__() - self.fc1 = nn.Linear(28*28, 128) # MNIST images are 28x28 - self.fc2 = nn.Linear(128, 128) - self.fc3 = nn.Linear(128, 64) - self.fc4 = nn.Linear(64, 10) # There are 10 classes (0 through 9) - - def forward(self, x): - x = x.view(x.shape[0], -1) # Flatten the input - x = torch.relu(self.fc1(x)) - x = torch.relu(self.fc2(x)) - x = torch.relu(self.fc3(x)) - return self.fc4(x) - -class NetConv(nn.Module): - def __init__(self): - super(NetConv, self).__init__() - self.conv1 = nn.Conv2d(1, 32, 3) - self.conv2 = nn.Conv2d(32, 64, 3) - self.fc1 = nn.Linear(64 * 5 * 5, 128) # Corrected - self.fc2 = nn.Linear(128, 10) - - def forward(self, x): - x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) - x = F.max_pool2d(F.relu(self.conv2(x)), 2) - x = x.view(-1, self.num_flat_features(x)) - x = F.relu(self.fc1(x)) - x = self.fc2(x) - return F.log_softmax(x, dim=1) - - def num_flat_features(self, x): - size = x.size()[1:] - num_features = 1 - for s in size: - num_features *= s - return num_features \ No newline at end of file diff --git a/spaces/geekyrakshit/enhance-me/enhance_me/mirnet/mirnet.py b/spaces/geekyrakshit/enhance-me/enhance_me/mirnet/mirnet.py deleted file mode 100644 index cfb52ff870526329defd70f94d1aeafa92d1ab86..0000000000000000000000000000000000000000 --- a/spaces/geekyrakshit/enhance-me/enhance_me/mirnet/mirnet.py +++ /dev/null @@ -1,174 +0,0 @@ -import os -import numpy as np -from PIL import Image -from typing import List -from datetime import datetime - -from tensorflow import keras -from tensorflow.keras import optimizers, models, mixed_precision - -from wandb.keras import WandbCallback - -from .dataloader import LowLightDataset -from .models import build_mirnet_model -from .losses import CharbonnierLoss -from ..commons import ( - peak_signal_noise_ratio, - closest_number, - init_wandb, - download_lol_dataset, -) - - -class MIRNet: - def __init__(self, experiment_name=None, wandb_api_key=None) -> None: - self.experiment_name = experiment_name - if wandb_api_key is not None: - init_wandb("mirnet", experiment_name, wandb_api_key) - self.using_wandb = True - else: - self.using_wandb = False - - def build_datasets( - self, - image_size: int = 256, - dataset_label: str = "lol", - apply_random_horizontal_flip: bool = True, - apply_random_vertical_flip: bool = True, - apply_random_rotation: bool = True, - val_split: float = 0.2, - batch_size: int = 16, - ): - if dataset_label == "lol": - (self.low_images, self.enhanced_images), ( - self.test_low_images, - self.test_enhanced_images, - ) = download_lol_dataset() - self.data_loader = LowLightDataset( - image_size=image_size, - apply_random_horizontal_flip=apply_random_horizontal_flip, - apply_random_vertical_flip=apply_random_vertical_flip, - apply_random_rotation=apply_random_rotation, - ) - (self.train_dataset, self.val_dataset) = self.data_loader.get_datasets( - low_light_images=self.low_images, - enhanced_images=self.enhanced_images, - val_split=val_split, - batch_size=batch_size, - ) - - def build_model( - self, - use_mixed_precision: bool = False, - num_recursive_residual_groups: int = 3, - num_multi_scale_residual_blocks: int = 2, - channels: int = 64, - learning_rate: float = 1e-4, - epsilon: float = 1e-3, - ): - if use_mixed_precision: - policy = mixed_precision.Policy("mixed_float16") - mixed_precision.set_global_policy(policy) - self.model = build_mirnet_model( - num_rrg=num_recursive_residual_groups, - num_mrb=num_multi_scale_residual_blocks, - channels=channels, - ) - self.model.compile( - optimizer=optimizers.Adam(learning_rate=learning_rate), - loss=CharbonnierLoss(epsilon=epsilon), - metrics=[peak_signal_noise_ratio], - ) - - def load_model( - self, filepath, custom_objects=None, compile=True, options=None - ) -> None: - self.model = models.load_model( - filepath=filepath, - custom_objects=custom_objects, - compile=compile, - options=options, - ) - - def save_weights(self, filepath, overwrite=True, save_format=None, options=None): - self.model.save_weights( - filepath, overwrite=overwrite, save_format=save_format, options=options - ) - - def load_weights(self, filepath, by_name=False, skip_mismatch=False, options=None): - self.model.load_weights( - filepath, by_name=by_name, skip_mismatch=skip_mismatch, options=options - ) - - def train(self, epochs: int): - log_dir = os.path.join( - self.experiment_name, - "logs", - datetime.now().strftime("%Y%m%d-%H%M%S"), - ) - tensorboard_callback = keras.callbacks.TensorBoard(log_dir, histogram_freq=1) - model_checkpoint_callback = keras.callbacks.ModelCheckpoint( - os.path.join(self.experiment_name, "weights.h5"), - save_best_only=True, - save_weights_only=True, - ) - reduce_lr_callback = keras.callbacks.ReduceLROnPlateau( - monitor="val_peak_signal_noise_ratio", - factor=0.5, - patience=5, - verbose=1, - min_delta=1e-7, - mode="max", - ) - callbacks = [ - tensorboard_callback, - model_checkpoint_callback, - reduce_lr_callback, - ] - if self.using_wandb: - callbacks += [WandbCallback()] - history = self.model.fit( - self.train_dataset, - validation_data=self.val_dataset, - epochs=epochs, - callbacks=callbacks, - ) - return history - - def infer( - self, - original_image, - image_resize_factor: float = 1.0, - resize_output: bool = False, - ): - width, height = original_image.size - target_width, target_height = ( - closest_number(width // image_resize_factor, 4), - closest_number(height // image_resize_factor, 4), - ) - original_image = original_image.resize( - (target_width, target_height), Image.ANTIALIAS - ) - image = keras.preprocessing.image.img_to_array(original_image) - image = image.astype("float32") / 255.0 - image = np.expand_dims(image, axis=0) - output = self.model.predict(image) - output_image = output[0] * 255.0 - output_image = output_image.clip(0, 255) - output_image = output_image.reshape( - (np.shape(output_image)[0], np.shape(output_image)[1], 3) - ) - output_image = Image.fromarray(np.uint8(output_image)) - original_image = Image.fromarray(np.uint8(original_image)) - if resize_output: - output_image = output_image.resize((width, height), Image.ANTIALIAS) - return output_image - - def infer_from_file( - self, - original_image_file: str, - image_resize_factor: float = 1.0, - resize_output: bool = False, - ): - original_image = Image.open(original_image_file) - return self.infer(original_image, image_resize_factor, resize_output) diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/runner/hooks/optimizer.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/runner/hooks/optimizer.py deleted file mode 100644 index 4ef3e9ff8f9c6926e32bdf027612267b64ed80df..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/runner/hooks/optimizer.py +++ /dev/null @@ -1,508 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import copy -from collections import defaultdict -from itertools import chain - -from torch.nn.utils import clip_grad - -from annotator.uniformer.mmcv.utils import TORCH_VERSION, _BatchNorm, digit_version -from ..dist_utils import allreduce_grads -from ..fp16_utils import LossScaler, wrap_fp16_model -from .hook import HOOKS, Hook - -try: - # If PyTorch version >= 1.6.0, torch.cuda.amp.GradScaler would be imported - # and used; otherwise, auto fp16 will adopt mmcv's implementation. - from torch.cuda.amp import GradScaler -except ImportError: - pass - - -@HOOKS.register_module() -class OptimizerHook(Hook): - - def __init__(self, grad_clip=None): - self.grad_clip = grad_clip - - def clip_grads(self, params): - params = list( - filter(lambda p: p.requires_grad and p.grad is not None, params)) - if len(params) > 0: - return clip_grad.clip_grad_norm_(params, **self.grad_clip) - - def after_train_iter(self, runner): - runner.optimizer.zero_grad() - runner.outputs['loss'].backward() - if self.grad_clip is not None: - grad_norm = self.clip_grads(runner.model.parameters()) - if grad_norm is not None: - # Add grad norm to the logger - runner.log_buffer.update({'grad_norm': float(grad_norm)}, - runner.outputs['num_samples']) - runner.optimizer.step() - - -@HOOKS.register_module() -class GradientCumulativeOptimizerHook(OptimizerHook): - """Optimizer Hook implements multi-iters gradient cumulating. - - Args: - cumulative_iters (int, optional): Num of gradient cumulative iters. - The optimizer will step every `cumulative_iters` iters. - Defaults to 1. - - Examples: - >>> # Use cumulative_iters to simulate a large batch size - >>> # It is helpful when the hardware cannot handle a large batch size. - >>> loader = DataLoader(data, batch_size=64) - >>> optim_hook = GradientCumulativeOptimizerHook(cumulative_iters=4) - >>> # almost equals to - >>> loader = DataLoader(data, batch_size=256) - >>> optim_hook = OptimizerHook() - """ - - def __init__(self, cumulative_iters=1, **kwargs): - super(GradientCumulativeOptimizerHook, self).__init__(**kwargs) - - assert isinstance(cumulative_iters, int) and cumulative_iters > 0, \ - f'cumulative_iters only accepts positive int, but got ' \ - f'{type(cumulative_iters)} instead.' - - self.cumulative_iters = cumulative_iters - self.divisible_iters = 0 - self.remainder_iters = 0 - self.initialized = False - - def has_batch_norm(self, module): - if isinstance(module, _BatchNorm): - return True - for m in module.children(): - if self.has_batch_norm(m): - return True - return False - - def _init(self, runner): - if runner.iter % self.cumulative_iters != 0: - runner.logger.warning( - 'Resume iter number is not divisible by cumulative_iters in ' - 'GradientCumulativeOptimizerHook, which means the gradient of ' - 'some iters is lost and the result may be influenced slightly.' - ) - - if self.has_batch_norm(runner.model) and self.cumulative_iters > 1: - runner.logger.warning( - 'GradientCumulativeOptimizerHook may slightly decrease ' - 'performance if the model has BatchNorm layers.') - - residual_iters = runner.max_iters - runner.iter - - self.divisible_iters = ( - residual_iters // self.cumulative_iters * self.cumulative_iters) - self.remainder_iters = residual_iters - self.divisible_iters - - self.initialized = True - - def after_train_iter(self, runner): - if not self.initialized: - self._init(runner) - - if runner.iter < self.divisible_iters: - loss_factor = self.cumulative_iters - else: - loss_factor = self.remainder_iters - loss = runner.outputs['loss'] - loss = loss / loss_factor - loss.backward() - - if (self.every_n_iters(runner, self.cumulative_iters) - or self.is_last_iter(runner)): - - if self.grad_clip is not None: - grad_norm = self.clip_grads(runner.model.parameters()) - if grad_norm is not None: - # Add grad norm to the logger - runner.log_buffer.update({'grad_norm': float(grad_norm)}, - runner.outputs['num_samples']) - runner.optimizer.step() - runner.optimizer.zero_grad() - - -if (TORCH_VERSION != 'parrots' - and digit_version(TORCH_VERSION) >= digit_version('1.6.0')): - - @HOOKS.register_module() - class Fp16OptimizerHook(OptimizerHook): - """FP16 optimizer hook (using PyTorch's implementation). - - If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend, - to take care of the optimization procedure. - - Args: - loss_scale (float | str | dict): Scale factor configuration. - If loss_scale is a float, static loss scaling will be used with - the specified scale. If loss_scale is a string, it must be - 'dynamic', then dynamic loss scaling will be used. - It can also be a dict containing arguments of GradScalar. - Defaults to 512. For Pytorch >= 1.6, mmcv uses official - implementation of GradScaler. If you use a dict version of - loss_scale to create GradScaler, please refer to: - https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler - for the parameters. - - Examples: - >>> loss_scale = dict( - ... init_scale=65536.0, - ... growth_factor=2.0, - ... backoff_factor=0.5, - ... growth_interval=2000 - ... ) - >>> optimizer_hook = Fp16OptimizerHook(loss_scale=loss_scale) - """ - - def __init__(self, - grad_clip=None, - coalesce=True, - bucket_size_mb=-1, - loss_scale=512., - distributed=True): - self.grad_clip = grad_clip - self.coalesce = coalesce - self.bucket_size_mb = bucket_size_mb - self.distributed = distributed - self._scale_update_param = None - if loss_scale == 'dynamic': - self.loss_scaler = GradScaler() - elif isinstance(loss_scale, float): - self._scale_update_param = loss_scale - self.loss_scaler = GradScaler(init_scale=loss_scale) - elif isinstance(loss_scale, dict): - self.loss_scaler = GradScaler(**loss_scale) - else: - raise ValueError('loss_scale must be of type float, dict, or ' - f'"dynamic", got {loss_scale}') - - def before_run(self, runner): - """Preparing steps before Mixed Precision Training.""" - # wrap model mode to fp16 - wrap_fp16_model(runner.model) - # resume from state dict - if 'fp16' in runner.meta and 'loss_scaler' in runner.meta['fp16']: - scaler_state_dict = runner.meta['fp16']['loss_scaler'] - self.loss_scaler.load_state_dict(scaler_state_dict) - - def copy_grads_to_fp32(self, fp16_net, fp32_weights): - """Copy gradients from fp16 model to fp32 weight copy.""" - for fp32_param, fp16_param in zip(fp32_weights, - fp16_net.parameters()): - if fp16_param.grad is not None: - if fp32_param.grad is None: - fp32_param.grad = fp32_param.data.new( - fp32_param.size()) - fp32_param.grad.copy_(fp16_param.grad) - - def copy_params_to_fp16(self, fp16_net, fp32_weights): - """Copy updated params from fp32 weight copy to fp16 model.""" - for fp16_param, fp32_param in zip(fp16_net.parameters(), - fp32_weights): - fp16_param.data.copy_(fp32_param.data) - - def after_train_iter(self, runner): - """Backward optimization steps for Mixed Precision Training. For - dynamic loss scaling, please refer to - https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler. - - 1. Scale the loss by a scale factor. - 2. Backward the loss to obtain the gradients. - 3. Unscale the optimizer’s gradient tensors. - 4. Call optimizer.step() and update scale factor. - 5. Save loss_scaler state_dict for resume purpose. - """ - # clear grads of last iteration - runner.model.zero_grad() - runner.optimizer.zero_grad() - - self.loss_scaler.scale(runner.outputs['loss']).backward() - self.loss_scaler.unscale_(runner.optimizer) - # grad clip - if self.grad_clip is not None: - grad_norm = self.clip_grads(runner.model.parameters()) - if grad_norm is not None: - # Add grad norm to the logger - runner.log_buffer.update({'grad_norm': float(grad_norm)}, - runner.outputs['num_samples']) - # backward and update scaler - self.loss_scaler.step(runner.optimizer) - self.loss_scaler.update(self._scale_update_param) - - # save state_dict of loss_scaler - runner.meta.setdefault( - 'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict() - - @HOOKS.register_module() - class GradientCumulativeFp16OptimizerHook(GradientCumulativeOptimizerHook, - Fp16OptimizerHook): - """Fp16 optimizer Hook (using PyTorch's implementation) implements - multi-iters gradient cumulating. - - If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend, - to take care of the optimization procedure. - """ - - def __init__(self, *args, **kwargs): - super(GradientCumulativeFp16OptimizerHook, - self).__init__(*args, **kwargs) - - def after_train_iter(self, runner): - if not self.initialized: - self._init(runner) - - if runner.iter < self.divisible_iters: - loss_factor = self.cumulative_iters - else: - loss_factor = self.remainder_iters - loss = runner.outputs['loss'] - loss = loss / loss_factor - - self.loss_scaler.scale(loss).backward() - - if (self.every_n_iters(runner, self.cumulative_iters) - or self.is_last_iter(runner)): - - # copy fp16 grads in the model to fp32 params in the optimizer - self.loss_scaler.unscale_(runner.optimizer) - - if self.grad_clip is not None: - grad_norm = self.clip_grads(runner.model.parameters()) - if grad_norm is not None: - # Add grad norm to the logger - runner.log_buffer.update( - {'grad_norm': float(grad_norm)}, - runner.outputs['num_samples']) - - # backward and update scaler - self.loss_scaler.step(runner.optimizer) - self.loss_scaler.update(self._scale_update_param) - - # save state_dict of loss_scaler - runner.meta.setdefault( - 'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict() - - # clear grads - runner.model.zero_grad() - runner.optimizer.zero_grad() - -else: - - @HOOKS.register_module() - class Fp16OptimizerHook(OptimizerHook): - """FP16 optimizer hook (mmcv's implementation). - - The steps of fp16 optimizer is as follows. - 1. Scale the loss value. - 2. BP in the fp16 model. - 2. Copy gradients from fp16 model to fp32 weights. - 3. Update fp32 weights. - 4. Copy updated parameters from fp32 weights to fp16 model. - - Refer to https://arxiv.org/abs/1710.03740 for more details. - - Args: - loss_scale (float | str | dict): Scale factor configuration. - If loss_scale is a float, static loss scaling will be used with - the specified scale. If loss_scale is a string, it must be - 'dynamic', then dynamic loss scaling will be used. - It can also be a dict containing arguments of LossScaler. - Defaults to 512. - """ - - def __init__(self, - grad_clip=None, - coalesce=True, - bucket_size_mb=-1, - loss_scale=512., - distributed=True): - self.grad_clip = grad_clip - self.coalesce = coalesce - self.bucket_size_mb = bucket_size_mb - self.distributed = distributed - if loss_scale == 'dynamic': - self.loss_scaler = LossScaler(mode='dynamic') - elif isinstance(loss_scale, float): - self.loss_scaler = LossScaler( - init_scale=loss_scale, mode='static') - elif isinstance(loss_scale, dict): - self.loss_scaler = LossScaler(**loss_scale) - else: - raise ValueError('loss_scale must be of type float, dict, or ' - f'"dynamic", got {loss_scale}') - - def before_run(self, runner): - """Preparing steps before Mixed Precision Training. - - 1. Make a master copy of fp32 weights for optimization. - 2. Convert the main model from fp32 to fp16. - """ - # keep a copy of fp32 weights - old_groups = runner.optimizer.param_groups - runner.optimizer.param_groups = copy.deepcopy( - runner.optimizer.param_groups) - state = defaultdict(dict) - p_map = { - old_p: p - for old_p, p in zip( - chain(*(g['params'] for g in old_groups)), - chain(*(g['params'] - for g in runner.optimizer.param_groups))) - } - for k, v in runner.optimizer.state.items(): - state[p_map[k]] = v - runner.optimizer.state = state - # convert model to fp16 - wrap_fp16_model(runner.model) - # resume from state dict - if 'fp16' in runner.meta and 'loss_scaler' in runner.meta['fp16']: - scaler_state_dict = runner.meta['fp16']['loss_scaler'] - self.loss_scaler.load_state_dict(scaler_state_dict) - - def copy_grads_to_fp32(self, fp16_net, fp32_weights): - """Copy gradients from fp16 model to fp32 weight copy.""" - for fp32_param, fp16_param in zip(fp32_weights, - fp16_net.parameters()): - if fp16_param.grad is not None: - if fp32_param.grad is None: - fp32_param.grad = fp32_param.data.new( - fp32_param.size()) - fp32_param.grad.copy_(fp16_param.grad) - - def copy_params_to_fp16(self, fp16_net, fp32_weights): - """Copy updated params from fp32 weight copy to fp16 model.""" - for fp16_param, fp32_param in zip(fp16_net.parameters(), - fp32_weights): - fp16_param.data.copy_(fp32_param.data) - - def after_train_iter(self, runner): - """Backward optimization steps for Mixed Precision Training. For - dynamic loss scaling, please refer `loss_scalar.py` - - 1. Scale the loss by a scale factor. - 2. Backward the loss to obtain the gradients (fp16). - 3. Copy gradients from the model to the fp32 weight copy. - 4. Scale the gradients back and update the fp32 weight copy. - 5. Copy back the params from fp32 weight copy to the fp16 model. - 6. Save loss_scaler state_dict for resume purpose. - """ - # clear grads of last iteration - runner.model.zero_grad() - runner.optimizer.zero_grad() - # scale the loss value - scaled_loss = runner.outputs['loss'] * self.loss_scaler.loss_scale - scaled_loss.backward() - # copy fp16 grads in the model to fp32 params in the optimizer - - fp32_weights = [] - for param_group in runner.optimizer.param_groups: - fp32_weights += param_group['params'] - self.copy_grads_to_fp32(runner.model, fp32_weights) - # allreduce grads - if self.distributed: - allreduce_grads(fp32_weights, self.coalesce, - self.bucket_size_mb) - - has_overflow = self.loss_scaler.has_overflow(fp32_weights) - # if has overflow, skip this iteration - if not has_overflow: - # scale the gradients back - for param in fp32_weights: - if param.grad is not None: - param.grad.div_(self.loss_scaler.loss_scale) - if self.grad_clip is not None: - grad_norm = self.clip_grads(fp32_weights) - if grad_norm is not None: - # Add grad norm to the logger - runner.log_buffer.update( - {'grad_norm': float(grad_norm)}, - runner.outputs['num_samples']) - # update fp32 params - runner.optimizer.step() - # copy fp32 params to the fp16 model - self.copy_params_to_fp16(runner.model, fp32_weights) - self.loss_scaler.update_scale(has_overflow) - if has_overflow: - runner.logger.warning('Check overflow, downscale loss scale ' - f'to {self.loss_scaler.cur_scale}') - - # save state_dict of loss_scaler - runner.meta.setdefault( - 'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict() - - @HOOKS.register_module() - class GradientCumulativeFp16OptimizerHook(GradientCumulativeOptimizerHook, - Fp16OptimizerHook): - """Fp16 optimizer Hook (using mmcv implementation) implements multi- - iters gradient cumulating.""" - - def __init__(self, *args, **kwargs): - super(GradientCumulativeFp16OptimizerHook, - self).__init__(*args, **kwargs) - - def after_train_iter(self, runner): - if not self.initialized: - self._init(runner) - - if runner.iter < self.divisible_iters: - loss_factor = self.cumulative_iters - else: - loss_factor = self.remainder_iters - - loss = runner.outputs['loss'] - loss = loss / loss_factor - - # scale the loss value - scaled_loss = loss * self.loss_scaler.loss_scale - scaled_loss.backward() - - if (self.every_n_iters(runner, self.cumulative_iters) - or self.is_last_iter(runner)): - - # copy fp16 grads in the model to fp32 params in the optimizer - fp32_weights = [] - for param_group in runner.optimizer.param_groups: - fp32_weights += param_group['params'] - self.copy_grads_to_fp32(runner.model, fp32_weights) - # allreduce grads - if self.distributed: - allreduce_grads(fp32_weights, self.coalesce, - self.bucket_size_mb) - - has_overflow = self.loss_scaler.has_overflow(fp32_weights) - # if has overflow, skip this iteration - if not has_overflow: - # scale the gradients back - for param in fp32_weights: - if param.grad is not None: - param.grad.div_(self.loss_scaler.loss_scale) - if self.grad_clip is not None: - grad_norm = self.clip_grads(fp32_weights) - if grad_norm is not None: - # Add grad norm to the logger - runner.log_buffer.update( - {'grad_norm': float(grad_norm)}, - runner.outputs['num_samples']) - # update fp32 params - runner.optimizer.step() - # copy fp32 params to the fp16 model - self.copy_params_to_fp16(runner.model, fp32_weights) - else: - runner.logger.warning( - 'Check overflow, downscale loss scale ' - f'to {self.loss_scaler.cur_scale}') - - self.loss_scaler.update_scale(has_overflow) - - # save state_dict of loss_scaler - runner.meta.setdefault( - 'fp16', {})['loss_scaler'] = self.loss_scaler.state_dict() - - # clear grads - runner.model.zero_grad() - runner.optimizer.zero_grad() diff --git a/spaces/gligen/demo/gligen/ldm/models/diffusion/__init__.py b/spaces/gligen/demo/gligen/ldm/models/diffusion/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/godot-demo/godot-3d-voxel/index.audio.worklet.js b/spaces/godot-demo/godot-3d-voxel/index.audio.worklet.js deleted file mode 100644 index ea4d8cb22156435ac3c3d171390864140d0d54cd..0000000000000000000000000000000000000000 --- a/spaces/godot-demo/godot-3d-voxel/index.audio.worklet.js +++ /dev/null @@ -1,211 +0,0 @@ -/*************************************************************************/ -/* audio.worklet.js */ -/*************************************************************************/ -/* This file is part of: */ -/* GODOT ENGINE */ -/* https://godotengine.org */ -/*************************************************************************/ -/* Copyright (c) 2007-2022 Juan Linietsky, Ariel Manzur. */ -/* Copyright (c) 2014-2022 Godot Engine contributors (cf. AUTHORS.md). */ -/* */ -/* 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. */ -/*************************************************************************/ - -class RingBuffer { - constructor(p_buffer, p_state, p_threads) { - this.buffer = p_buffer; - this.avail = p_state; - this.threads = p_threads; - this.rpos = 0; - this.wpos = 0; - } - - data_left() { - return this.threads ? Atomics.load(this.avail, 0) : this.avail; - } - - space_left() { - return this.buffer.length - this.data_left(); - } - - read(output) { - const size = this.buffer.length; - let from = 0; - let to_write = output.length; - if (this.rpos + to_write > size) { - const high = size - this.rpos; - output.set(this.buffer.subarray(this.rpos, size)); - from = high; - to_write -= high; - this.rpos = 0; - } - if (to_write) { - output.set(this.buffer.subarray(this.rpos, this.rpos + to_write), from); - } - this.rpos += to_write; - if (this.threads) { - Atomics.add(this.avail, 0, -output.length); - Atomics.notify(this.avail, 0); - } else { - this.avail -= output.length; - } - } - - write(p_buffer) { - const to_write = p_buffer.length; - const mw = this.buffer.length - this.wpos; - if (mw >= to_write) { - this.buffer.set(p_buffer, this.wpos); - this.wpos += to_write; - if (mw === to_write) { - this.wpos = 0; - } - } else { - const high = p_buffer.subarray(0, mw); - const low = p_buffer.subarray(mw); - this.buffer.set(high, this.wpos); - this.buffer.set(low); - this.wpos = low.length; - } - if (this.threads) { - Atomics.add(this.avail, 0, to_write); - Atomics.notify(this.avail, 0); - } else { - this.avail += to_write; - } - } -} - -class GodotProcessor extends AudioWorkletProcessor { - constructor() { - super(); - this.threads = false; - this.running = true; - this.lock = null; - this.notifier = null; - this.output = null; - this.output_buffer = new Float32Array(); - this.input = null; - this.input_buffer = new Float32Array(); - this.port.onmessage = (event) => { - const cmd = event.data['cmd']; - const data = event.data['data']; - this.parse_message(cmd, data); - }; - } - - process_notify() { - if (this.notifier) { - Atomics.add(this.notifier, 0, 1); - Atomics.notify(this.notifier, 0); - } - } - - parse_message(p_cmd, p_data) { - if (p_cmd === 'start' && p_data) { - const state = p_data[0]; - let idx = 0; - this.threads = true; - this.lock = state.subarray(idx, ++idx); - this.notifier = state.subarray(idx, ++idx); - const avail_in = state.subarray(idx, ++idx); - const avail_out = state.subarray(idx, ++idx); - this.input = new RingBuffer(p_data[1], avail_in, true); - this.output = new RingBuffer(p_data[2], avail_out, true); - } else if (p_cmd === 'stop') { - this.running = false; - this.output = null; - this.input = null; - } else if (p_cmd === 'start_nothreads') { - this.output = new RingBuffer(p_data[0], p_data[0].length, false); - } else if (p_cmd === 'chunk') { - this.output.write(p_data); - } - } - - static array_has_data(arr) { - return arr.length && arr[0].length && arr[0][0].length; - } - - process(inputs, outputs, parameters) { - if (!this.running) { - return false; // Stop processing. - } - if (this.output === null) { - return true; // Not ready yet, keep processing. - } - const process_input = GodotProcessor.array_has_data(inputs); - if (process_input) { - const input = inputs[0]; - const chunk = input[0].length * input.length; - if (this.input_buffer.length !== chunk) { - this.input_buffer = new Float32Array(chunk); - } - if (!this.threads) { - GodotProcessor.write_input(this.input_buffer, input); - this.port.postMessage({ 'cmd': 'input', 'data': this.input_buffer }); - } else if (this.input.space_left() >= chunk) { - GodotProcessor.write_input(this.input_buffer, input); - this.input.write(this.input_buffer); - } else { - this.port.postMessage('Input buffer is full! Skipping input frame.'); - } - } - const process_output = GodotProcessor.array_has_data(outputs); - if (process_output) { - const output = outputs[0]; - const chunk = output[0].length * output.length; - if (this.output_buffer.length !== chunk) { - this.output_buffer = new Float32Array(chunk); - } - if (this.output.data_left() >= chunk) { - this.output.read(this.output_buffer); - GodotProcessor.write_output(output, this.output_buffer); - if (!this.threads) { - this.port.postMessage({ 'cmd': 'read', 'data': chunk }); - } - } else { - this.port.postMessage('Output buffer has not enough frames! Skipping output frame.'); - } - } - this.process_notify(); - return true; - } - - static write_output(dest, source) { - const channels = dest.length; - for (let ch = 0; ch < channels; ch++) { - for (let sample = 0; sample < dest[ch].length; sample++) { - dest[ch][sample] = source[sample * channels + ch]; - } - } - } - - static write_input(dest, source) { - const channels = source.length; - for (let ch = 0; ch < channels; ch++) { - for (let sample = 0; sample < source[ch].length; sample++) { - dest[sample * channels + ch] = source[ch][sample]; - } - } - } -} - -registerProcessor('godot-processor', GodotProcessor); diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Counter Strike Source 1.0.0.34 Patch !!TOP!! Download.md b/spaces/gotiQspiryo/whisper-ui/examples/Counter Strike Source 1.0.0.34 Patch !!TOP!! Download.md deleted file mode 100644 index c6d080c0bb424c6c8d306d9618750ec8d8109bf1..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/Counter Strike Source 1.0.0.34 Patch !!TOP!! Download.md +++ /dev/null @@ -1,32 +0,0 @@ - Counter strike source 1.0.0.34 patch downloadDownload File --->>> https://urlgoal.com/2uyNiq - -The game's downloadable beta was released in September of 2002, and the first version of the source code for the game was released online in May 2003. - -Ricardo Lacovara has created a great guide to the use of CS:S v84 and CS: Source v34. - -For more info and screenshots visit the official page at the CS:S website. - -First, there are some good articles on the topic that go in-depth. They explain everything from the new 3d models to modding and server problems. - -Then there are some tutorials. They give you instructions on how to compile new maps and how to make your own new models. - -The third article is one of my own. It shows you how to make your own models. There are examples of using basic elements to create a new model. You can make any model you like. - -Finally there are a lot of websites that have lots of articles and even tutorials. They give you everything from the basics to giving you tips on how to make even better models. - -There are also some good manuals that you can download. They are listed on the csrc.sourceforge.net website. - -First, you need the source files. Download the source zip file, unzip it and copy the files to your steamapps\common\Counter-Strike Source folder. - -There is also a video that shows how to install the new SDK. You need to download the SDK from Sourceforge and install it. - -There are a lot of tutorials on the Internet about how to make your own models. Here are a few links to some of the tutorials. - -6 Comments - -I've tried to read through some of the tutorials, but unfortunately I'm running into problems. I tried to get the source files from SteamApps\common\Counter-Strike Source and install it on my computer, but it keeps trying to send me to "old.counter-strike.source" instead of "Counter-Strike Source". I've tried both ways. When I go to steamapps\common\Counter-Strike Source it tells me that "the update is incompatible with this game. Make sure that you have the latest version of the game installed first." - -Also, I downloaded the SDK and ran the install.exe that came with it, but I'm getting the same problem, which is that the file "sourceserver.net" doesn't exist. I think it's only a problem when you 4fefd39f24 - - - diff --git a/spaces/gotiQspiryo/whisper-ui/examples/El Rostro Y La Personalidad Julian Gabarre.pdf.md b/spaces/gotiQspiryo/whisper-ui/examples/El Rostro Y La Personalidad Julian Gabarre.pdf.md deleted file mode 100644 index cc4bc60bdea838bb05f861c6076ac50dee0386e0..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/El Rostro Y La Personalidad Julian Gabarre.pdf.md +++ /dev/null @@ -1,22 +0,0 @@ - - ¿Qué es la psicologÃa facial y cómo puede ayudarte a conocer mejor a las personas?- -La psicologÃa facial es una disciplina que estudia las relaciones entre la forma y la funcionalidad del rostro, el cerebro y la personalidad. Su principal exponente es el doctor en psicologÃa Julián Gabarre, autor del libro El Rostro Y La Personalidad Julian Gabarre.pdf, que puedes descargar gratis en internet[^1^]. - -Según Gabarre, el rostro es el reflejo de nuestra identidad, de nuestras capacidades intelectuales, emocionales y sociales, y de nuestra forma de actuar y comunicarnos. A través del análisis morfopsicológico del rostro, podemos obtener información valiosa sobre el temperamento, el carácter, las aptitudes, las motivaciones y los valores de una persona[^2^]. -El Rostro Y La Personalidad Julian Gabarre.pdfDownload https://urlgoal.com/2uyMMI - - La psicologÃa facial tiene múltiples aplicaciones prácticas en el ámbito personal y profesional. Por ejemplo, puede ayudarnos a mejorar nuestra autoestima y autoconocimiento, a elegir mejor a nuestras parejas, amigos o socios, a detectar mentiras o engaños, a potenciar nuestras habilidades comunicativas y persuasivas, o a orientarnos vocacionalmente[^3^]. - -Si quieres aprender más sobre esta fascinante ciencia, te invitamos a leer el libro El Rostro Y La Personalidad Julian Gabarre.pdf, donde encontrarás los fundamentos teóricos y prácticos de la psicologÃa facial, asà como numerosos ejemplos e ilustraciones de rostros famosos y anónimos. También puedes visitar la página web de Julián Gabarre, donde podrás acceder a cursos online, conferencias, artÃculos y otros recursos sobre el tema[^4^]. - -No esperes más y descubre lo que tu rostro dice de ti y lo que los demás rostros te dicen de ellos. La psicologÃa facial te abrirá las puertas a un mundo nuevo de conocimiento y comprensión. - -La psicologÃa facial se basa en el principio de que el rostro es el resultado de la interacción entre la genética y el medio ambiente. La genética determina la estructura ósea y muscular del rostro, que a su vez influye en el desarrollo y la actividad del cerebro. El medio ambiente, por su parte, modifica el rostro a través de las expresiones faciales, que son el reflejo de nuestras emociones y estados de ánimo. - -AsÃ, el rostro se convierte en un mapa que nos permite leer las caracterÃsticas psicológicas de una persona. Para ello, se utilizan diferentes criterios de observación, como la forma y el tamaño de la cabeza, la frente, las cejas, los ojos, la nariz, la boca, el mentón, las orejas y el cabello. Cada uno de estos elementos nos da información sobre aspectos como la inteligencia, la creatividad, la memoria, la voluntad, la sensibilidad, la sociabilidad, la confianza, la honestidad o la lealtad. - - -Por supuesto, la psicologÃa facial no pretende ser una ciencia exacta ni infalible. Se trata de una herramienta complementaria que nos ayuda a ampliar nuestra percepción y nuestro juicio sobre las personas. Además, hay que tener en cuenta que el rostro no es estático sino dinámico, y que puede cambiar con el tiempo y las circunstancias. Por eso, es importante observar el rostro en su conjunto y en movimiento, y no quedarnos solo con los detalles o las primeras impresiones. d5da3c52bf- - \ No newline at end of file diff --git a/spaces/gradio/HuBERT/fairseq/optim/bmuf.py b/spaces/gradio/HuBERT/fairseq/optim/bmuf.py deleted file mode 100644 index d6d0e04e86eb894efe59e13a78843d01ca9e651d..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/optim/bmuf.py +++ /dev/null @@ -1,200 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from dataclasses import dataclass, field - -import torch -import torch.distributed as dist -from fairseq.dataclass.configs import FairseqBMUFConfig -from fairseq.dataclass.utils import gen_parser_from_dataclass -from fairseq.optim.fairseq_optimizer import FairseqOptimizer - - -class FairseqBMUF(FairseqOptimizer): - """ - Implements incremental block distributed data parallelism similar to - https://ieeexplore.ieee.org/document/7472805 - - Paper title: Scalable training of deep learning machines by incremental - block training with intra-block parallel optimization and blockwise - model-update filtering - """ - - def __init__(self, cfg: FairseqBMUFConfig, optimizer): - super().__init__(cfg) - self._optimizer = optimizer - self._num_updates = 0 - self.sync_iter = cfg.global_sync_iter - self.block_momentum = cfg.block_momentum - self.block_lr = cfg.block_lr - self._reset_local_data() - self.warmup_iteration = cfg.warmup_iterations - self.use_nbm = cfg.use_nbm - self.initial_state = self._optimizer.state_dict() - self.average_sync = self.cfg.average_sync - self.world_size = self.cfg.distributed_world_size - - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - gen_parser_from_dataclass(parser, FairseqBMUFConfig()) - - @property - def optimizer(self): - return self._optimizer.optimizer - - @property - def optimizer_config(self): - return self._optimizer.optimizer_config - - def get_lr(self): - return self._optimizer.get_lr() - - def set_lr(self, lr): - self._optimizer.set_lr(lr) - - def state_dict(self): - return self._optimizer.state_dict() - - def load_state_dict(self, state_dict, optimizer_overrides=None): - self._optimizer.load_state_dict(state_dict, optimizer_overrides) - self.initial_state = self._optimizer.state_dict() - - def multiply_grads(self, c): - """Multiplies grads by a constant *c*.""" - self._optimizer.multiply_grads(c) - - def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): - """Clips gradient norm.""" - return self._optimizer.clip_grad_norm(max_norm, aggregate_norm_fn) - - def average_params(self): - self._optimizer.average_params() - - def _block_sync(self): - if self.world_size <= 1: - return - # Update the global model using local models from all GPUs - # (Step-1) Calculate grad between previously synced model and - # currrent local model - if self.block_momentum != 0: - self._calc_grad() - - # (Step-2) Average gradient from all GPUs - self._avg_grad_from_all_gpus() - - # (Step-3) Calculate global momentum and update the global model - if self.block_momentum != 0: - self._update_global_model() - - # (Step-4) Average local optimizer params - if self.average_sync: - self.average_params() - - def _is_warmup_end(self): - # Check whether train iterations is equal to warmup iter - if self.get_num_updates() == self.warmup_iteration: - return True - return False - - def _is_bmuf_iter(self): - # Check whether train iterations is equal to bmuf sync iter - if (self.get_num_updates() > self.warmup_iteration) and ( - self.get_num_updates() % self.sync_iter == 0 - ): - return True - return False - - def _warmup_sync(self, root_rank=0): - if self.world_size <= 1: - return - # Broadcast the local model to all gpus - for param in self.params: - dist.broadcast(param.data, src=root_rank) - - # Update local optimizer state - if self.average_sync: - self._optimizer.average_params() - else: - self._optimizer.load_state_dict(self.initial_state) - - self._reset_local_data() - - def step(self, closure=None): - """Performs a single optimization step.""" - self._optimizer.step(closure) - self.set_num_updates(self.get_num_updates() + 1) - if self._is_warmup_end(): - self._warmup_sync() - elif self._is_bmuf_iter(): - self._block_sync() - - def zero_grad(self): - """Clears the gradients of all optimized parameters.""" - self._optimizer.zero_grad() - - def get_num_updates(self): - """Get the number of parameters updates.""" - return self._num_updates - - def set_num_updates(self, num_updates): - """Set the number of parameters updates.""" - self._num_updates = num_updates - - @torch.no_grad() - def _reset_local_data(self): - # (Step-0) Initialize global momentum parameters and store global copy on each gpu - self.global_params = [torch.zeros_like(p.data) for p in self.params] - self.smoothed_grads = [p.data.new_zeros(p.data.size()) for p in self.params] - self.grads = [p.data.new_zeros(p.data.size()) for p in self.params] - - # saving the global model locally for calculating gradient during bmuf sync - for param, global_param in zip(self.params, self.global_params): - global_param.copy_(param.data) - - @torch.no_grad() - def _calc_grad(self): - # global_params is basically the global copy from the previously finished - # synchronisation. param.data is local parameter after block_sync_freq - # for the local gpu. so grad is difference between previously synced - # model and currrent local model. - for index, (param, global_param) in enumerate( - zip(self.params, self.global_params) - ): - self.grads[index] = global_param - param.data - - def _avg_grad_from_all_gpus(self): - for index, param in enumerate(self.params): - sync_para = param.data if self.block_momentum == 0 else self.grads[index] - sync_para /= float(dist.get_world_size()) - dist.all_reduce(sync_para, op=dist.ReduceOp.SUM) - - @torch.no_grad() - def _update_global_model(self): - for index, (param, global_param, smoothed_grad, grad) in enumerate( - zip( - self.params, - self.global_params, - self.smoothed_grads, - # all gpus would share the same value of smoothed_grad, since it is - # always computed on synchronized gradients. - self.grads, - ) - ): - # global_param is basically last syncrhornized parameter. though - # smoothed_grad is local, all processes will have same value of - # smoothed_grad and hence param is globally synchronized copy. - # smoothed_grad(t) = BM * smoothed_grad(t-1) + BM_lr * grad(t) - smoothed_grad = self.block_momentum * smoothed_grad + self.block_lr * grad - param.data.copy_(global_param - smoothed_grad) - - # A Nesterov momentum here is to do a partial weight update before - # calculating the gradient - if self.use_nbm: - param.data.copy_(param.data - self.block_momentum * smoothed_grad) - - # backup for the next synchronization. - self.smoothed_grads[index] = smoothed_grad - global_param.copy_(param.data) diff --git a/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Folder/index.ts b/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Folder/index.ts deleted file mode 100644 index 93815b95914fc635a5115e34f481a495652559ac..0000000000000000000000000000000000000000 --- a/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Folder/index.ts +++ /dev/null @@ -1 +0,0 @@ -export { default } from './Folder'; diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/utils/align_data.py b/spaces/gyugnsu/DragGan-Inversion/PTI/utils/align_data.py deleted file mode 100644 index 12b59bf5ce294972252876a714631e05cde5630c..0000000000000000000000000000000000000000 --- a/spaces/gyugnsu/DragGan-Inversion/PTI/utils/align_data.py +++ /dev/null @@ -1,37 +0,0 @@ -import sys -sys.path.append('.') -from configs import paths_config -import dlib -import glob -import os -from tqdm import tqdm -from utils.alignment import align_face - - -def pre_process_images(raw_images_path): - current_directory = os.getcwd() - - IMAGE_SIZE = 1024 - predictor = dlib.shape_predictor(paths_config.dlib) - os.chdir(raw_images_path) - images_names = glob.glob(f'*') - - aligned_images = [] - for image_name in tqdm(images_names): - try: - aligned_image = align_face(filepath=f'{raw_images_path}/{image_name}', - predictor=predictor, output_size=IMAGE_SIZE) - aligned_images.append(aligned_image) - except Exception as e: - print(e) - - os.makedirs(paths_config.input_data_path, exist_ok=True) - for image, name in zip(aligned_images, images_names): - real_name = name.split('.')[0] - image.save(f'{paths_config.input_data_path}/{real_name}.jpeg') - - os.chdir(current_directory) - - -if __name__ == "__main__": - pre_process_images('/home/zhizizhang/Documents2/projects/PTI/docs') diff --git a/spaces/haotiz/glip-zeroshot-demo/docs/intro.md b/spaces/haotiz/glip-zeroshot-demo/docs/intro.md deleted file mode 100644 index 668f2df48e68af7152f4375e91852c15d2d775c7..0000000000000000000000000000000000000000 --- a/spaces/haotiz/glip-zeroshot-demo/docs/intro.md +++ /dev/null @@ -1,9 +0,0 @@ -["**GLIP: Grounded Language-Image Pre-training. CVPR 2022, Best Paper Finalist**"](https://arxiv.org/abs/2112.03857) - -This is the HuggingFace Gradio Demo for GLIP. The model requires an image, and a text to be the inputs. The text input can either be a natural sentence description (grounding), or a simple concatenation of some random categories (object detection). - -The paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representation semantic-rich. - -Code: https://github.com/microsoft/GLIP - -**News**: We are also holding an ODinW challenge at [the CV in the Wild Workshop @ ECCV 2022](https://computer-vision-in-the-wild.github.io/eccv-2022/). We hope our open-source code encourage the community to participate in this challenge! diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/evaluation/testing.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/evaluation/testing.py deleted file mode 100644 index 95addebc185111c572cb19aa98f7e055b21fc74e..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/evaluation/testing.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import logging -import numpy as np -import pprint -import sys -from collections import OrderedDict -from collections.abc import Mapping - - -def print_csv_format(results): - """ - Print main metrics in a format similar to Detectron, - so that they are easy to copypaste into a spreadsheet. - - Args: - results (OrderedDict[dict]): task_name -> {metric -> score} - """ - assert isinstance(results, OrderedDict), results # unordered results cannot be properly printed - logger = logging.getLogger(__name__) - for task, res in results.items(): - # Don't print "AP-category" metrics since they are usually not tracked. - important_res = [(k, v) for k, v in res.items() if "-" not in k] - logger.info("copypaste: Task: {}".format(task)) - logger.info("copypaste: " + ",".join([k[0] for k in important_res])) - logger.info("copypaste: " + ",".join(["{0:.4f}".format(k[1]) for k in important_res])) - - -def verify_results(cfg, results): - """ - Args: - results (OrderedDict[dict]): task_name -> {metric -> score} - - Returns: - bool: whether the verification succeeds or not - """ - expected_results = cfg.TEST.EXPECTED_RESULTS - if not len(expected_results): - return True - - ok = True - for task, metric, expected, tolerance in expected_results: - actual = results[task][metric] - if not np.isfinite(actual): - ok = False - diff = abs(actual - expected) - if diff > tolerance: - ok = False - - logger = logging.getLogger(__name__) - if not ok: - logger.error("Result verification failed!") - logger.error("Expected Results: " + str(expected_results)) - logger.error("Actual Results: " + pprint.pformat(results)) - - sys.exit(1) - else: - logger.info("Results verification passed.") - return ok - - -def flatten_results_dict(results): - """ - Expand a hierarchical dict of scalars into a flat dict of scalars. - If results[k1][k2][k3] = v, the returned dict will have the entry - {"k1/k2/k3": v}. - - Args: - results (dict): - """ - r = {} - for k, v in results.items(): - if isinstance(v, Mapping): - v = flatten_results_dict(v) - for kk, vv in v.items(): - r[k + "/" + kk] = vv - else: - r[k] = v - return r diff --git a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/app.py b/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/app.py deleted file mode 100644 index 2b449c1c4392e12b0b2778120ad8c84460702886..0000000000000000000000000000000000000000 --- a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/app.py +++ /dev/null @@ -1,78 +0,0 @@ -import os -from subprocess import getoutput - -gpu_info = getoutput('nvidia-smi') -if("A10G" in gpu_info): - os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl") -elif("T4" in gpu_info): - os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+1515f77.d20221130-cp38-cp38-linux_x86_64.whl") - -os.system(f"git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui /home/user/app/stable-diffusion-webui") -os.chdir("/home/user/app/stable-diffusion-webui") - -os.system(f"wget -q https://github.com/camenduru/webui/raw/main/env_patch.py -O /home/user/app/env_patch.py") -os.system(f"sed -i -e '/import image_from_url_text/r /home/user/app/env_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f"sed -i -e '/(modelmerger_interface, \"Checkpoint Merger\", \"modelmerger\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f"sed -i -e '/(train_interface, \"Train\", \"ti\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f"sed -i -e '/extensions_interface, \"Extensions\", \"extensions\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f"sed -i -e '/settings_interface, \"Settings\", \"settings\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f'''sed -i -e "s/document.getElementsByTagName('gradio-app')\[0\].shadowRoot/!!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document/g" /home/user/app/stable-diffusion-webui/script.js''') -os.system(f"sed -i -e 's/ show_progress=False,/ show_progress=True,/g' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f"sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' /home/user/app/stable-diffusion-webui/webui.py") -os.system(f"sed -i -e 's/inputs=\[component\],/&\\n queue=False,/g' /home/user/app/stable-diffusion-webui/modules/ui.py") -os.system(f"sed -i -e 's/outputs=\[token_counter\]/outputs=[token_counter], queue=False/g' /home/user/app/stable-diffusion-webui/modules/ui.py") - -# ----------------------------Please duplicate this space and delete this block if you don't want to see the extra header---------------------------- -#os.system(f"wget -q https://github.com/camenduru/webui/raw/main/header_patch.py -O /home/user/app/header_patch.py") -#os.system(f"sed -i -e '/demo:/r /home/user/app/header_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py") -# --------------------------------------------------------------------------------------------------------------------------------------------------- - -os.system(f"wget -q https://huggingface.co/Alsebay/PeachMixs/resolve/main/PeachTachyonMixs/PeachTachyon2.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/PeachTachyon2.safetensors") -os.system(f"wget -q https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/VAE/vae-ft-mse-840000-ema-pruned.ckpt") -os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser") -#Embeddings TEXTUAL INVERSION -os.system(f"wget -q https://huggingface.co/ai-moroz/lazy-ti/resolve/main/dump/diona-gi.pt -O /home/user/app/stable-diffusion-webui/embeddings/diona-gi.pt") -os.system(f"wget -q https://huggingface.co/datasets/gsdf/EasyNegative/resolve/main/EasyNegative.safetensors -O /home/user/app/stable-diffusion-webui/embeddings/EasyNegative.safetensors") - -if "IS_SHARED_UI" in os.environ: - os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json") - os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json") - os.system(f"wget -q {os.getenv('EMBED_LINK')} -O /home/user/app/stable-diffusion-webui/embeddings/{os.getenv('EMBED_NAME')}") - os.system(f"python launch.py --use-cpu all --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --skip-torch-cuda-test") -else: - # Please duplicate this space and delete # character in front of the custom script you want to use or add here more custom scripts with same structure os.system(f"wget -q https://CUSTOM_SCRIPT_URL -O /home/user/app/stable-diffusion-webui/scripts/CUSTOM_SCRIPT_NAME.py") - #os.system(f"wget -q https://gist.github.com/camenduru/9ec5f8141db9902e375967e93250860f/raw/d0bcf01786f20107c329c03f8968584ee67be12a/run_n_times.py -O /home/user/app/stable-diffusion-webui/scripts/run_n_times.py") - - # Please duplicate this space and delete # character in front of the extension you want to use or add here more extensions with same structure os.system(f"git clone https://EXTENSION_GIT_URL /home/user/app/stable-diffusion-webui/extensions/EXTENSION_NAME") - #os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-artists-to-study") - #os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser") - #os.system(f"git clone https://github.com/deforum-art/deforum-for-automatic1111-webui /home/user/app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui") - - # Please duplicate this space and delete # character in front of the model you want to use or add here more ckpts with same structure os.system(f"wget -q https://CKPT_URL -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/CKPT_NAME.ckpt") - os.system(f"wget -q https://huggingface.co/swl-models/chilloutmix-ni/resolve/main/chilloutmix-Ni.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/cmni.safetensors") - #os.system(f"wget -q https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-v3.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/arcane-diffusion-v3.ckpt") - #os.system(f"wget -q https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/Cyberpunk-Anime-Diffusion.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Cyberpunk-Anime-Diffusion.ckpt") - #os.system(f"wget -q https://huggingface.co/prompthero/midjourney-v4-diffusion/resolve/main/mdjrny-v4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/mdjrny-v4.ckpt") - #os.system(f"wget -q https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/moDi-v1-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/moDi-v1-pruned.ckpt") - #os.system(f"wget -q https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/resolve/main/PaperCut_v1.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/PaperCut_v1.ckpt") - #os.system(f"wget -q https://huggingface.co/lilpotat/sa/resolve/main/samdoesarts_style.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/samdoesarts_style.ckpt") - #os.system(f"wget -q https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/wd-1-4-anime_e2.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Waifudiffusion-1-4-anime_e2.ckpt") - os.system(f"wget -q https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/Counterfeit-V2.5_pruned.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Counterfeit-V2.5_pruned.safetensors") - os.system(f"wget -q https://huggingface.co/andite/anything-v4.0/resolve/main/anything-v4.5-pruned.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-v4.5-pruned.safetensors") - #os.system(f"wget -q https://huggingface.co/iZELX1/Grapefruit/resolve/main/grapefruitv4.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Grapefruitv4.safetensors") - #os.system(f"wget -q https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/AbyssOrangeMix3.safetensors") - #os.system(f"wget -q https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/AbyssOrangeMix3A1.safetensors") - #os.system(f"wget -q https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A2.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/AbyssOrangeMix3A2.safetensors") - #os.system(f"wget -q https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3.safetensors -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/AbyssOrangeMix3A3.safetensors") - #os.system(f"wget -q {os.getenv('EMBED_LINK')} -O /home/user/app/stable-diffusion-webui/embeddings/{os.getenv('EMBED_NAME')}") - #os.system(f"wget -q https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-float32.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/wd-v1-3-float32.ckpt") - #os.system(f"wget -q https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-4.ckpt") - #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt") - #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-5-inpainting.ckpt") - #os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.ckpt") - #os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.yaml") - os.system(f"EXPOSE 7860") - #os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser") - #os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json") - # os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json") - os.system(f"python launch.py --precision full --no-half --use-cpu all --listen --ui-config-file /home/user/app/ui-config.json --ui-settings-file /home/user/app/config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --skip-torch-cuda-test --api") diff --git a/spaces/hololee/dreambooth-training/train_dreambooth.py b/spaces/hololee/dreambooth-training/train_dreambooth.py deleted file mode 100644 index f4ff135e549f0d6c72f733092f3df817cb178e01..0000000000000000000000000000000000000000 --- a/spaces/hololee/dreambooth-training/train_dreambooth.py +++ /dev/null @@ -1,889 +0,0 @@ -import argparse -import itertools -import math -import os -from pathlib import Path -from typing import Optional -import subprocess -import sys -import gc -import random - -import torch -import torch.nn.functional as F -import torch.utils.checkpoint -from torch.utils.data import Dataset - -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import set_seed -from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel -from diffusers.utils.import_utils import is_xformers_available -from diffusers.optimization import get_scheduler -from huggingface_hub import HfFolder, Repository, whoami -from PIL import Image -from torchvision import transforms -from tqdm.auto import tqdm -from transformers import CLIPTextModel, CLIPTokenizer - - -logger = get_logger(__name__) - - -def parse_args(): - parser = argparse.ArgumentParser(description="Simple example of a training script.") - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default=None, - #required=True, - help="Path to pretrained model or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--tokenizer_name", - type=str, - default=None, - help="Pretrained tokenizer name or path if not the same as model_name", - ) - parser.add_argument( - "--instance_data_dir", - type=str, - default=None, - #required=True, - help="A folder containing the training data of instance images.", - ) - parser.add_argument( - "--class_data_dir", - type=str, - default=None, - #required=False, - help="A folder containing the training data of class images.", - ) - parser.add_argument( - "--instance_prompt", - type=str, - default=None, - help="The prompt with identifier specifying the instance", - ) - parser.add_argument( - "--class_prompt", - type=str, - default="", - help="The prompt to specify images in the same class as provided instance images.", - ) - parser.add_argument( - "--with_prior_preservation", - default=False, - action="store_true", - help="Flag to add prior preservation loss.", - ) - parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.") - parser.add_argument( - "--num_class_images", - type=int, - default=100, - help=( - "Minimal class images for prior preservation loss. If not have enough images, additional images will be" - " sampled with class_prompt." - ), - ) - parser.add_argument( - "--output_dir", - type=str, - default="", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") - parser.add_argument( - "--resolution", - type=int, - default=512, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution" - ) - parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder") - parser.add_argument( - "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader." - ) - parser.add_argument( - "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images." - ) - parser.add_argument("--num_train_epochs", type=int, default=1) - parser.add_argument( - "--max_train_steps", - type=int, - default=None, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument( - "--gradient_checkpointing", - action="store_true", - help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", - ) - parser.add_argument( - "--learning_rate", - type=float, - default=5e-6, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument( - "--scale_lr", - action="store_true", - default=False, - help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", - ) - parser.add_argument( - "--lr_scheduler", - type=str, - default="constant", - help=( - 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup"]' - ), - ) - parser.add_argument( - "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." - ) - parser.add_argument( - "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." - ) - parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") - parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") - parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") - parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") - parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") - parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") - parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") - parser.add_argument( - "--hub_model_id", - type=str, - default=None, - help="The name of the repository to keep in sync with the local `output_dir`.", - ) - parser.add_argument( - "--logging_dir", - type=str, - default="logs", - help=( - "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." - ), - ) - parser.add_argument( - "--mixed_precision", - type=str, - default="no", - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose" - "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." - "and an Nvidia Ampere GPU." - ), - ) - - parser.add_argument( - "--save_n_steps", - type=int, - default=1, - help=("Save the model every n global_steps"), - ) - - - parser.add_argument( - "--save_starting_step", - type=int, - default=1, - help=("The step from which it starts saving intermediary checkpoints"), - ) - - parser.add_argument( - "--stop_text_encoder_training", - type=int, - default=1000000, - help=("The step at which the text_encoder is no longer trained"), - ) - - - parser.add_argument( - "--image_captions_filename", - action="store_true", - help="Get captions from filename", - ) - - - parser.add_argument( - "--dump_only_text_encoder", - action="store_true", - default=False, - help="Dump only text encoder", - ) - - parser.add_argument( - "--train_only_unet", - action="store_true", - default=False, - help="Train only the unet", - ) - - parser.add_argument( - "--cache_latents", - action="store_true", - default=False, - help="Train only the unet", - ) - - parser.add_argument( - "--Session_dir", - type=str, - default="", - help="Current session directory", - ) - - - - - parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") - - args = parser.parse_args() - env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) - if env_local_rank != -1 and env_local_rank != args.local_rank: - args.local_rank = env_local_rank - - #if args.instance_data_dir is None: - # raise ValueError("You must specify a train data directory.") - - #if args.with_prior_preservation: - # if args.class_data_dir is None: - # raise ValueError("You must specify a data directory for class images.") - # if args.class_prompt is None: - # raise ValueError("You must specify prompt for class images.") - - return args - - -class DreamBoothDataset(Dataset): - """ - A dataset to prepare the instance and class images with the prompts for fine-tuning the model. - It pre-processes the images and the tokenizes prompts. - """ - - def __init__( - self, - instance_data_root, - instance_prompt, - tokenizer, - args, - class_data_root=None, - class_prompt=None, - size=512, - center_crop=False, - ): - self.size = size - self.center_crop = center_crop - self.tokenizer = tokenizer - self.image_captions_filename = None - - self.instance_data_root = Path(instance_data_root) - if not self.instance_data_root.exists(): - raise ValueError("Instance images root doesn't exists.") - - self.instance_images_path = list(Path(instance_data_root).iterdir()) - self.num_instance_images = len(self.instance_images_path) - self.instance_prompt = instance_prompt - self._length = self.num_instance_images - - if args.image_captions_filename: - self.image_captions_filename = True - - if class_data_root is not None: - self.class_data_root = Path(class_data_root) - self.class_data_root.mkdir(parents=True, exist_ok=True) - self.class_images_path = list(self.class_data_root.iterdir()) - random.shuffle(self.class_images_path) - self.num_class_images = len(self.class_images_path) - self._length = max(self.num_class_images, self.num_instance_images) - self.class_prompt = class_prompt - else: - self.class_data_root = None - - self.image_transforms = transforms.Compose( - [ - transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), - transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), - transforms.ToTensor(), - transforms.Normalize([0.5], [0.5]), - ] - ) - - def __len__(self): - return self._length - - def __getitem__(self, index): - example = {} - path = self.instance_images_path[index % self.num_instance_images] - instance_image = Image.open(path) - if not instance_image.mode == "RGB": - instance_image = instance_image.convert("RGB") - - instance_prompt = self.instance_prompt - - if self.image_captions_filename: - filename = Path(path).stem - pt=''.join([i for i in filename if not i.isdigit()]) - pt=pt.replace("_"," ") - pt=pt.replace("(","") - pt=pt.replace(")","") - pt=pt.replace("-","") - instance_prompt = pt - sys.stdout.write(" [0;32m" +instance_prompt+" [0m") - sys.stdout.flush() - - - example["instance_images"] = self.image_transforms(instance_image) - example["instance_prompt_ids"] = self.tokenizer( - instance_prompt, - padding="do_not_pad", - truncation=True, - max_length=self.tokenizer.model_max_length, - ).input_ids - - if self.class_data_root: - class_image = Image.open(self.class_images_path[index % self.num_class_images]) - if not class_image.mode == "RGB": - class_image = class_image.convert("RGB") - example["class_images"] = self.image_transforms(class_image) - example["class_prompt_ids"] = self.tokenizer( - self.class_prompt, - padding="do_not_pad", - truncation=True, - max_length=self.tokenizer.model_max_length, - ).input_ids - - return example - - - -class PromptDataset(Dataset): - "A simple dataset to prepare the prompts to generate class images on multiple GPUs." - - def __init__(self, prompt, num_samples): - self.prompt = prompt - self.num_samples = num_samples - - def __len__(self): - return self.num_samples - - def __getitem__(self, index): - example = {} - example["prompt"] = self.prompt - example["index"] = index - return example - -class LatentsDataset(Dataset): - def __init__(self, latents_cache, text_encoder_cache): - self.latents_cache = latents_cache - self.text_encoder_cache = text_encoder_cache - - def __len__(self): - return len(self.latents_cache) - - def __getitem__(self, index): - return self.latents_cache[index], self.text_encoder_cache[index] - -def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None): - if token is None: - token = HfFolder.get_token() - if organization is None: - username = whoami(token)["name"] - return f"{username}/{model_id}" - else: - return f"{organization}/{model_id}" - -def merge_two_dicts(starting_dict: dict, updater_dict: dict) -> dict: - """ - Starts from base starting dict and then adds the remaining key values from updater replacing the values from - the first starting/base dict with the second updater dict. - - For later: how does d = {**d1, **d2} replace collision? - - :param starting_dict: - :param updater_dict: - :return: - """ - new_dict: dict = starting_dict.copy() # start with keys and values of starting_dict - new_dict.update(updater_dict) # modifies starting_dict with keys and values of updater_dict - return new_dict - -def merge_args(args1: argparse.Namespace, args2: argparse.Namespace) -> argparse.Namespace: - """ - - ref: https://stackoverflow.com/questions/56136549/how-can-i-merge-two-argparse-namespaces-in-python-2-x - :param args1: - :param args2: - :return: - """ - # - the merged args - # The vars() function returns the __dict__ attribute to values of the given object e.g {field:value}. - merged_key_values_for_namespace: dict = merge_two_dicts(vars(args1), vars(args2)) - args = argparse.Namespace(**merged_key_values_for_namespace) - return args - -def run_training(args_imported): - args_default = parse_args() - args = merge_args(args_default, args_imported) - print(args) - logging_dir = Path(args.output_dir, args.logging_dir) - i=args.save_starting_step - accelerator = Accelerator( - gradient_accumulation_steps=args.gradient_accumulation_steps, - mixed_precision=args.mixed_precision, - log_with="tensorboard", - logging_dir=logging_dir, - ) - - # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate - # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models. - # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate. - if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1: - raise ValueError( - "Gradient accumulation is not supported when training the text encoder in distributed training. " - "Please set gradient_accumulation_steps to 1. This feature will be supported in the future." - ) - - if args.seed is not None: - set_seed(args.seed) - - if args.with_prior_preservation: - class_images_dir = Path(args.class_data_dir) - if not class_images_dir.exists(): - class_images_dir.mkdir(parents=True) - cur_class_images = len(list(class_images_dir.iterdir())) - - if cur_class_images < args.num_class_images: - torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32 - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, torch_dtype=torch_dtype - ) - pipeline.set_progress_bar_config(disable=True) - - num_new_images = args.num_class_images - cur_class_images - logger.info(f"Number of class images to sample: {num_new_images}.") - - sample_dataset = PromptDataset(args.class_prompt, num_new_images) - sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size) - - sample_dataloader = accelerator.prepare(sample_dataloader) - pipeline.to(accelerator.device) - - for example in tqdm( - sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process - ): - with torch.autocast("cuda"): - images = pipeline(example["prompt"]).images - - for i, image in enumerate(images): - image.save(class_images_dir / f"{example['index'][i] + cur_class_images}.jpg") - - del pipeline - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - # Handle the repository creation - if accelerator.is_main_process: - if args.push_to_hub: - if args.hub_model_id is None: - repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) - else: - repo_name = args.hub_model_id - repo = Repository(args.output_dir, clone_from=repo_name) - - with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: - if "step_*" not in gitignore: - gitignore.write("step_*\n") - if "epoch_*" not in gitignore: - gitignore.write("epoch_*\n") - elif args.output_dir is not None: - os.makedirs(args.output_dir, exist_ok=True) - - # Load the tokenizer - if args.tokenizer_name: - tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) - elif args.pretrained_model_name_or_path: - tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer") - - # Load models and create wrapper for stable diffusion - if args.train_only_unet: - if os.path.exists(str(args.output_dir+"/text_encoder_trained")): - text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder_trained") - elif os.path.exists(str(args.output_dir+"/text_encoder")): - text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder") - else: - text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder") - else: - text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder") - vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae") - unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet") - if is_xformers_available(): - try: - print("Enabling memory efficient attention with xformers...") - unet.enable_xformers_memory_efficient_attention() - except Exception as e: - logger.warning( - f"Could not enable memory efficient attention. Make sure xformers is installed correctly and a GPU is available: {e}" - ) - vae.requires_grad_(False) - if not args.train_text_encoder: - text_encoder.requires_grad_(False) - - if args.gradient_checkpointing: - unet.enable_gradient_checkpointing() - if args.train_text_encoder: - text_encoder.gradient_checkpointing_enable() - - if args.scale_lr: - args.learning_rate = ( - args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes - ) - - # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs - if args.use_8bit_adam: - try: - import bitsandbytes as bnb - except ImportError: - raise ImportError( - "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." - ) - - optimizer_class = bnb.optim.AdamW8bit - else: - optimizer_class = torch.optim.AdamW - - params_to_optimize = ( - itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters() - ) - optimizer = optimizer_class( - params_to_optimize, - lr=args.learning_rate, - betas=(args.adam_beta1, args.adam_beta2), - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - ) - - noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler") - - train_dataset = DreamBoothDataset( - instance_data_root=args.instance_data_dir, - instance_prompt=args.instance_prompt, - class_data_root=args.class_data_dir if args.with_prior_preservation else None, - class_prompt=args.class_prompt, - tokenizer=tokenizer, - size=args.resolution, - center_crop=args.center_crop, - args=args, - ) - - def collate_fn(examples): - input_ids = [example["instance_prompt_ids"] for example in examples] - pixel_values = [example["instance_images"] for example in examples] - - # Concat class and instance examples for prior preservation. - # We do this to avoid doing two forward passes. - if args.with_prior_preservation: - input_ids += [example["class_prompt_ids"] for example in examples] - pixel_values += [example["class_images"] for example in examples] - - pixel_values = torch.stack(pixel_values) - pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() - - input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids - - batch = { - "input_ids": input_ids, - "pixel_values": pixel_values, - } - return batch - - train_dataloader = torch.utils.data.DataLoader( - train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn - ) - - # Scheduler and math around the number of training steps. - overrode_max_train_steps = False - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if args.max_train_steps is None: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - overrode_max_train_steps = True - - lr_scheduler = get_scheduler( - args.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, - num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, - ) - - if args.train_text_encoder: - unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - unet, text_encoder, optimizer, train_dataloader, lr_scheduler - ) - else: - unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - unet, optimizer, train_dataloader, lr_scheduler - ) - - weight_dtype = torch.float32 - if args.mixed_precision == "fp16": - weight_dtype = torch.float16 - elif args.mixed_precision == "bf16": - weight_dtype = torch.bfloat16 - - # Move text_encode and vae to gpu. - # For mixed precision training we cast the text_encoder and vae weights to half-precision - # as these models are only used for inference, keeping weights in full precision is not required. - vae.to(accelerator.device, dtype=weight_dtype) - if not args.train_text_encoder: - text_encoder.to(accelerator.device, dtype=weight_dtype) - - - if args.cache_latents: - latents_cache = [] - text_encoder_cache = [] - for batch in tqdm(train_dataloader, desc="Caching latents"): - with torch.no_grad(): - batch["pixel_values"] = batch["pixel_values"].to(accelerator.device, non_blocking=True, dtype=weight_dtype) - batch["input_ids"] = batch["input_ids"].to(accelerator.device, non_blocking=True) - latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist) - if args.train_text_encoder: - text_encoder_cache.append(batch["input_ids"]) - else: - text_encoder_cache.append(text_encoder(batch["input_ids"])[0]) - train_dataset = LatentsDataset(latents_cache, text_encoder_cache) - train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, collate_fn=lambda x: x, shuffle=True) - - del vae - #if not args.train_text_encoder: - # del text_encoder - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - # We need to recalculate our total training steps as the size of the training dataloader may have changed. - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if overrode_max_train_steps: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - # Afterwards we recalculate our number of training epochs - args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - - # We need to initialize the trackers we use, and also store our configuration. - # The trackers initializes automatically on the main process. - if accelerator.is_main_process: - accelerator.init_trackers("dreambooth", config=vars(args)) - - def bar(prg): - br='|'+'█' * prg + ' ' * (25-prg)+'|' - return br - - # Train! - total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps - - logger.info("***** Running training *****") - logger.info(f" Num examples = {len(train_dataset)}") - logger.info(f" Num batches each epoch = {len(train_dataloader)}") - logger.info(f" Num Epochs = {args.num_train_epochs}") - logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") - logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") - logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") - logger.info(f" Total optimization steps = {args.max_train_steps}") - # Only show the progress bar once on each machine. - progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) - global_step = 0 - - for epoch in range(args.num_train_epochs): - unet.train() - if args.train_text_encoder: - text_encoder.train() - for step, batch in enumerate(train_dataloader): - with accelerator.accumulate(unet): - # Convert images to latent space - with torch.no_grad(): - if args.cache_latents: - latents_dist = batch[0][0] - else: - latents_dist = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist - latents = latents_dist.sample() * 0.18215 - - # Sample noise that we'll add to the latents - noise = torch.randn_like(latents) - bsz = latents.shape[0] - # Sample a random timestep for each image - timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) - timesteps = timesteps.long() - - # Add noise to the latents according to the noise magnitude at each timestep - # (this is the forward diffusion process) - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - - # Get the text embedding for conditioning - if(args.cache_latents): - if args.train_text_encoder: - encoder_hidden_states = text_encoder(batch[0][1])[0] - else: - encoder_hidden_states = batch[0][1] - else: - encoder_hidden_states = text_encoder(batch["input_ids"])[0] - - # Predict the noise residual - model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - - # Get the target for loss depending on the prediction type - if noise_scheduler.config.prediction_type == "epsilon": - target = noise - elif noise_scheduler.config.prediction_type == "v_prediction": - target = noise_scheduler.get_velocity(latents, noise, timesteps) - else: - raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") - - if args.with_prior_preservation: - # Chunk the noise and model_pred into two parts and compute the loss on each part separately. - model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) - target, target_prior = torch.chunk(target, 2, dim=0) - - # Compute instance loss - loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean() - - # Compute prior loss - prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") - - # Add the prior loss to the instance loss. - loss = loss + args.prior_loss_weight * prior_loss - else: - loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - - accelerator.backward(loss) - if accelerator.sync_gradients: - params_to_clip = ( - itertools.chain(unet.parameters(), text_encoder.parameters()) - if args.train_text_encoder - else unet.parameters() - ) - accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) - optimizer.step() - lr_scheduler.step() - optimizer.zero_grad() - - # Checks if the accelerator has performed an optimization step behind the scenes - if accelerator.sync_gradients: - progress_bar.update(1) - global_step += 1 - - fll=round((global_step*100)/args.max_train_steps) - fll=round(fll/4) - pr=bar(fll) - - logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} - progress_bar.set_postfix(**logs) - progress_bar.set_description_str("Progress:"+pr) - accelerator.log(logs, step=global_step) - - if global_step >= args.max_train_steps: - break - - if args.train_text_encoder and global_step == args.stop_text_encoder_training and global_step >= 30: - if accelerator.is_main_process: - print(" [0;32m" +" Freezing the text_encoder ..."+" [0m") - frz_dir=args.output_dir + "/text_encoder_frozen" - if os.path.exists(frz_dir): - subprocess.call('rm -r '+ frz_dir, shell=True) - os.mkdir(frz_dir) - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=accelerator.unwrap_model(unet), - text_encoder=accelerator.unwrap_model(text_encoder), - ) - pipeline.text_encoder.save_pretrained(frz_dir) - - if args.save_n_steps >= 200: - if global_step < args.max_train_steps and global_step+1==i: - ckpt_name = "_step_" + str(global_step+1) - save_dir = Path(args.output_dir+ckpt_name) - save_dir=str(save_dir) - save_dir=save_dir.replace(" ", "_") - if not os.path.exists(save_dir): - os.mkdir(save_dir) - inst=save_dir[16:] - inst=inst.replace(" ", "_") - print(" [1;32mSAVING CHECKPOINT: "+args.Session_dir+"/"+inst+".ckpt") - # Create the pipeline using the trained modules and save it. - if accelerator.is_main_process: - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=accelerator.unwrap_model(unet), - text_encoder=accelerator.unwrap_model(text_encoder), - ) - pipeline.save_pretrained(save_dir) - frz_dir=args.output_dir + "/text_encoder_frozen" - if args.train_text_encoder and os.path.exists(frz_dir): - subprocess.call('rm -r '+save_dir+'/text_encoder/*.*', shell=True) - subprocess.call('cp -f '+frz_dir +'/*.* '+ save_dir+'/text_encoder', shell=True) - chkpth=args.Session_dir+"/"+inst+".ckpt" - subprocess.call('python /content/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py --model_path ' + save_dir + ' --checkpoint_path ' + chkpth + ' --half', shell=True) - subprocess.call('rm -r '+ save_dir, shell=True) - i=i+args.save_n_steps - - accelerator.wait_for_everyone() - - # Create the pipeline using using the trained modules and save it. - if accelerator.is_main_process: - if args.dump_only_text_encoder: - txt_dir=args.output_dir + "/text_encoder_trained" - if not os.path.exists(txt_dir): - os.mkdir(txt_dir) - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=accelerator.unwrap_model(unet), - text_encoder=accelerator.unwrap_model(text_encoder), - ) - pipeline.text_encoder.save_pretrained(txt_dir) - - elif args.train_only_unet: - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=accelerator.unwrap_model(unet), - text_encoder=accelerator.unwrap_model(text_encoder), - ) - pipeline.save_pretrained(args.output_dir) - txt_dir=args.output_dir + "/text_encoder_trained" - subprocess.call('rm -r '+txt_dir, shell=True) - - else: - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=accelerator.unwrap_model(unet), - text_encoder=accelerator.unwrap_model(text_encoder), - ) - frz_dir=args.output_dir + "/text_encoder_frozen" - pipeline.save_pretrained(args.output_dir) - if args.train_text_encoder and os.path.exists(frz_dir): - subprocess.call('mv -f '+frz_dir +'/*.* '+ args.output_dir+'/text_encoder', shell=True) - subprocess.call('rm -r '+ frz_dir, shell=True) - - if args.push_to_hub: - repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) - - accelerator.end_training() - del pipeline - torch.cuda.empty_cache() - gc.collect() -if __name__ == "__main__": - pass - #main() - diff --git a/spaces/huggan/Colorb_GAN/app.py b/spaces/huggan/Colorb_GAN/app.py deleted file mode 100644 index 2490025dc4948e08cb9754fe4e67da69d417c496..0000000000000000000000000000000000000000 --- a/spaces/huggan/Colorb_GAN/app.py +++ /dev/null @@ -1,640 +0,0 @@ -#@title Gradio demo (used in space: ) - -from matplotlib import pyplot as plt -from huggingface_hub import PyTorchModelHubMixin -import numpy as np -import gradio as gr - - -#@title Defining Generator and associated code ourselves without the GPU requirements -import os -import json -import multiprocessing -from random import random -import math -from math import log2, floor -from functools import partial -from contextlib import contextmanager, ExitStack -from pathlib import Path -from shutil import rmtree - -import torch -from torch.cuda.amp import autocast, GradScaler -from torch.optim import Adam -from torch import nn, einsum -import torch.nn.functional as F -from torch.utils.data import Dataset, DataLoader -from torch.autograd import grad as torch_grad -from torch.utils.data.distributed import DistributedSampler -from torch.nn.parallel import DistributedDataParallel as DDP - -from PIL import Image -import torchvision -from torchvision import transforms -from kornia.filters import filter2d - -from tqdm import tqdm -from einops import rearrange, reduce, repeat - -from adabelief_pytorch import AdaBelief - -# helpers - - -def DiffAugment(x, types=[]): - for p in types: - for f in AUGMENT_FNS[p]: - x = f(x) - return x.contiguous() - -def exists(val): - return val is not None - -@contextmanager -def null_context(): - yield - -def combine_contexts(contexts): - @contextmanager - def multi_contexts(): - with ExitStack() as stack: - yield [stack.enter_context(ctx()) for ctx in contexts] - return multi_contexts - -def is_power_of_two(val): - return log2(val).is_integer() - -def default(val, d): - return val if exists(val) else d - -def set_requires_grad(model, bool): - for p in model.parameters(): - p.requires_grad = bool - -def cycle(iterable): - while True: - for i in iterable: - yield i - -def raise_if_nan(t): - if torch.isnan(t): - raise NanException - -def gradient_accumulate_contexts(gradient_accumulate_every, is_ddp, ddps): - if is_ddp: - num_no_syncs = gradient_accumulate_every - 1 - head = [combine_contexts(map(lambda ddp: ddp.no_sync, ddps))] * num_no_syncs - tail = [null_context] - contexts = head + tail - else: - contexts = [null_context] * gradient_accumulate_every - - for context in contexts: - with context(): - yield - -def evaluate_in_chunks(max_batch_size, model, *args): - split_args = list(zip(*list(map(lambda x: x.split(max_batch_size, dim=0), args)))) - chunked_outputs = [model(*i) for i in split_args] - if len(chunked_outputs) == 1: - return chunked_outputs[0] - return torch.cat(chunked_outputs, dim=0) - -def slerp(val, low, high): - low_norm = low / torch.norm(low, dim=1, keepdim=True) - high_norm = high / torch.norm(high, dim=1, keepdim=True) - omega = torch.acos((low_norm * high_norm).sum(1)) - so = torch.sin(omega) - res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high - return res - -def safe_div(n, d): - try: - res = n / d - except ZeroDivisionError: - prefix = '' if int(n >= 0) else '-' - res = float(f'{prefix}inf') - return res - -# loss functions - -def gen_hinge_loss(fake, real): - return fake.mean() - -def hinge_loss(real, fake): - return (F.relu(1 + real) + F.relu(1 - fake)).mean() - -def dual_contrastive_loss(real_logits, fake_logits): - device = real_logits.device - real_logits, fake_logits = map(lambda t: rearrange(t, '... -> (...)'), (real_logits, fake_logits)) - - def loss_half(t1, t2): - t1 = rearrange(t1, 'i -> i ()') - t2 = repeat(t2, 'j -> i j', i = t1.shape[0]) - t = torch.cat((t1, t2), dim = -1) - return F.cross_entropy(t, torch.zeros(t1.shape[0], device = device, dtype = torch.long)) - - return loss_half(real_logits, fake_logits) + loss_half(-fake_logits, -real_logits) - -# helper classes - -class NanException(Exception): - pass - -class EMA(): - def __init__(self, beta): - super().__init__() - self.beta = beta - def update_average(self, old, new): - if not exists(old): - return new - return old * self.beta + (1 - self.beta) * new - -class RandomApply(nn.Module): - def __init__(self, prob, fn, fn_else = lambda x: x): - super().__init__() - self.fn = fn - self.fn_else = fn_else - self.prob = prob - def forward(self, x): - fn = self.fn if random() < self.prob else self.fn_else - return fn(x) - -class ChanNorm(nn.Module): - def __init__(self, dim, eps = 1e-5): - super().__init__() - self.eps = eps - self.g = nn.Parameter(torch.ones(1, dim, 1, 1)) - self.b = nn.Parameter(torch.zeros(1, dim, 1, 1)) - - def forward(self, x): - var = torch.var(x, dim = 1, unbiased = False, keepdim = True) - mean = torch.mean(x, dim = 1, keepdim = True) - return (x - mean) / (var + self.eps).sqrt() * self.g + self.b - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = ChanNorm(dim) - - def forward(self, x): - return self.fn(self.norm(x)) - -class Residual(nn.Module): - def __init__(self, fn): - super().__init__() - self.fn = fn - - def forward(self, x): - return self.fn(x) + x - -class SumBranches(nn.Module): - def __init__(self, branches): - super().__init__() - self.branches = nn.ModuleList(branches) - def forward(self, x): - return sum(map(lambda fn: fn(x), self.branches)) - -class Blur(nn.Module): - def __init__(self): - super().__init__() - f = torch.Tensor([1, 2, 1]) - self.register_buffer('f', f) - def forward(self, x): - f = self.f - f = f[None, None, :] * f [None, :, None] - return filter2d(x, f, normalized=True) - -class Noise(nn.Module): - def __init__(self): - super().__init__() - self.weight = nn.Parameter(torch.zeros(1)) - - def forward(self, x, noise = None): - b, _, h, w, device = *x.shape, x.device - - if not exists(noise): - noise = torch.randn(b, 1, h, w, device = device) - - return x + self.weight * noise - -def Conv2dSame(dim_in, dim_out, kernel_size, bias = True): - pad_left = kernel_size // 2 - pad_right = (pad_left - 1) if (kernel_size % 2) == 0 else pad_left - - return nn.Sequential( - nn.ZeroPad2d((pad_left, pad_right, pad_left, pad_right)), - nn.Conv2d(dim_in, dim_out, kernel_size, bias = bias) - ) - -# attention - -class DepthWiseConv2d(nn.Module): - def __init__(self, dim_in, dim_out, kernel_size, padding = 0, stride = 1, bias = True): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim_in, dim_in, kernel_size = kernel_size, padding = padding, groups = dim_in, stride = stride, bias = bias), - nn.Conv2d(dim_in, dim_out, kernel_size = 1, bias = bias) - ) - def forward(self, x): - return self.net(x) - -class LinearAttention(nn.Module): - def __init__(self, dim, dim_head = 64, heads = 8, kernel_size = 3): - super().__init__() - self.scale = dim_head ** -0.5 - self.heads = heads - self.dim_head = dim_head - inner_dim = dim_head * heads - - self.kernel_size = kernel_size - self.nonlin = nn.GELU() - - self.to_lin_q = nn.Conv2d(dim, inner_dim, 1, bias = False) - self.to_lin_kv = DepthWiseConv2d(dim, inner_dim * 2, 3, padding = 1, bias = False) - - self.to_q = nn.Conv2d(dim, inner_dim, 1, bias = False) - self.to_kv = nn.Conv2d(dim, inner_dim * 2, 1, bias = False) - - self.to_out = nn.Conv2d(inner_dim * 2, dim, 1) - - def forward(self, fmap): - h, x, y = self.heads, *fmap.shape[-2:] - - # linear attention - - lin_q, lin_k, lin_v = (self.to_lin_q(fmap), *self.to_lin_kv(fmap).chunk(2, dim = 1)) - lin_q, lin_k, lin_v = map(lambda t: rearrange(t, 'b (h c) x y -> (b h) (x y) c', h = h), (lin_q, lin_k, lin_v)) - - lin_q = lin_q.softmax(dim = -1) - lin_k = lin_k.softmax(dim = -2) - - lin_q = lin_q * self.scale - - context = einsum('b n d, b n e -> b d e', lin_k, lin_v) - lin_out = einsum('b n d, b d e -> b n e', lin_q, context) - lin_out = rearrange(lin_out, '(b h) (x y) d -> b (h d) x y', h = h, x = x, y = y) - - # conv-like full attention - - q, k, v = (self.to_q(fmap), *self.to_kv(fmap).chunk(2, dim = 1)) - q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> (b h) c x y', h = h), (q, k, v)) - - k = F.unfold(k, kernel_size = self.kernel_size, padding = self.kernel_size // 2) - v = F.unfold(v, kernel_size = self.kernel_size, padding = self.kernel_size // 2) - - k, v = map(lambda t: rearrange(t, 'b (d j) n -> b n j d', d = self.dim_head), (k, v)) - - q = rearrange(q, 'b c ... -> b (...) c') * self.scale - - sim = einsum('b i d, b i j d -> b i j', q, k) - sim = sim - sim.amax(dim = -1, keepdim = True).detach() - - attn = sim.softmax(dim = -1) - - full_out = einsum('b i j, b i j d -> b i d', attn, v) - full_out = rearrange(full_out, '(b h) (x y) d -> b (h d) x y', h = h, x = x, y = y) - - # add outputs of linear attention + conv like full attention - - lin_out = self.nonlin(lin_out) - out = torch.cat((lin_out, full_out), dim = 1) - return self.to_out(out) - -# dataset - -def convert_image_to(img_type, image): - if image.mode != img_type: - return image.convert(img_type) - return image - -class identity(object): - def __call__(self, tensor): - return tensor - -class expand_greyscale(object): - def __init__(self, transparent): - self.transparent = transparent - - def __call__(self, tensor): - channels = tensor.shape[0] - num_target_channels = 4 if self.transparent else 3 - - if channels == num_target_channels: - return tensor - - alpha = None - if channels == 1: - color = tensor.expand(3, -1, -1) - elif channels == 2: - color = tensor[:1].expand(3, -1, -1) - alpha = tensor[1:] - else: - raise Exception(f'image with invalid number of channels given {channels}') - - if not exists(alpha) and self.transparent: - alpha = torch.ones(1, *tensor.shape[1:], device=tensor.device) - - return color if not self.transparent else torch.cat((color, alpha)) - -def resize_to_minimum_size(min_size, image): - if max(*image.size) < min_size: - return torchvision.transforms.functional.resize(image, min_size) - return image - -class ImageDataset(Dataset): - def __init__( - self, - folder, - image_size, - transparent = False, - greyscale = False, - aug_prob = 0. - ): - super().__init__() - self.folder = folder - self.image_size = image_size - self.paths = [p for ext in EXTS for p in Path(f'{folder}').glob(f'**/*.{ext}')] - assert len(self.paths) > 0, f'No images were found in {folder} for training' - - if transparent: - num_channels = 4 - pillow_mode = 'RGBA' - expand_fn = expand_greyscale(transparent) - elif greyscale: - num_channels = 1 - pillow_mode = 'L' - expand_fn = identity() - else: - num_channels = 3 - pillow_mode = 'RGB' - expand_fn = expand_greyscale(transparent) - - convert_image_fn = partial(convert_image_to, pillow_mode) - - self.transform = transforms.Compose([ - transforms.Lambda(convert_image_fn), - transforms.Lambda(partial(resize_to_minimum_size, image_size)), - transforms.Resize(image_size), - RandomApply(aug_prob, transforms.RandomResizedCrop(image_size, scale=(0.5, 1.0), ratio=(0.98, 1.02)), transforms.CenterCrop(image_size)), - transforms.ToTensor(), - transforms.Lambda(expand_fn) - ]) - - def __len__(self): - return len(self.paths) - - def __getitem__(self, index): - path = self.paths[index] - img = Image.open(path) - return self.transform(img) - -# augmentations - -def random_hflip(tensor, prob): - if prob > random(): - return tensor - return torch.flip(tensor, dims=(3,)) - -class AugWrapper(nn.Module): - def __init__(self, D, image_size): - super().__init__() - self.D = D - - def forward(self, images, prob = 0., types = [], detach = False, **kwargs): - context = torch.no_grad if detach else null_context - - with context(): - if random() < prob: - images = random_hflip(images, prob=0.5) - images = DiffAugment(images, types=types) - - return self.D(images, **kwargs) - -# modifiable global variables - -norm_class = nn.BatchNorm2d - -def upsample(scale_factor = 2): - return nn.Upsample(scale_factor = scale_factor) - -# squeeze excitation classes - -# global context network -# https://arxiv.org/abs/2012.13375 -# similar to squeeze-excite, but with a simplified attention pooling and a subsequent layer norm - -class GlobalContext(nn.Module): - def __init__( - self, - *, - chan_in, - chan_out - ): - super().__init__() - self.to_k = nn.Conv2d(chan_in, 1, 1) - chan_intermediate = max(3, chan_out // 2) - - self.net = nn.Sequential( - nn.Conv2d(chan_in, chan_intermediate, 1), - nn.LeakyReLU(0.1), - nn.Conv2d(chan_intermediate, chan_out, 1), - nn.Sigmoid() - ) - def forward(self, x): - context = self.to_k(x) - context = context.flatten(2).softmax(dim = -1) - out = einsum('b i n, b c n -> b c i', context, x.flatten(2)) - out = out.unsqueeze(-1) - return self.net(out) - -# frequency channel attention -# https://arxiv.org/abs/2012.11879 - -def get_1d_dct(i, freq, L): - result = math.cos(math.pi * freq * (i + 0.5) / L) / math.sqrt(L) - return result * (1 if freq == 0 else math.sqrt(2)) - -def get_dct_weights(width, channel, fidx_u, fidx_v): - dct_weights = torch.zeros(1, channel, width, width) - c_part = channel // len(fidx_u) - - for i, (u_x, v_y) in enumerate(zip(fidx_u, fidx_v)): - for x in range(width): - for y in range(width): - coor_value = get_1d_dct(x, u_x, width) * get_1d_dct(y, v_y, width) - dct_weights[:, i * c_part: (i + 1) * c_part, x, y] = coor_value - - return dct_weights - -class FCANet(nn.Module): - def __init__( - self, - *, - chan_in, - chan_out, - reduction = 4, - width - ): - super().__init__() - - freq_w, freq_h = ([0] * 8), list(range(8)) # in paper, it seems 16 frequencies was ideal - dct_weights = get_dct_weights(width, chan_in, [*freq_w, *freq_h], [*freq_h, *freq_w]) - self.register_buffer('dct_weights', dct_weights) - - chan_intermediate = max(3, chan_out // reduction) - - self.net = nn.Sequential( - nn.Conv2d(chan_in, chan_intermediate, 1), - nn.LeakyReLU(0.1), - nn.Conv2d(chan_intermediate, chan_out, 1), - nn.Sigmoid() - ) - - def forward(self, x): - x = reduce(x * self.dct_weights, 'b c (h h1) (w w1) -> b c h1 w1', 'sum', h1 = 1, w1 = 1) - return self.net(x) - -# generative adversarial network - -class Generator(nn.Module): - def __init__( - self, - *, - image_size, - latent_dim = 256, - fmap_max = 512, - fmap_inverse_coef = 12, - transparent = False, - greyscale = False, - attn_res_layers = [], - freq_chan_attn = False - ): - super().__init__() - resolution = log2(image_size) - assert is_power_of_two(image_size), 'image size must be a power of 2' - - if transparent: - init_channel = 4 - elif greyscale: - init_channel = 1 - else: - init_channel = 3 - - fmap_max = default(fmap_max, latent_dim) - - self.initial_conv = nn.Sequential( - nn.ConvTranspose2d(latent_dim, latent_dim * 2, 4), - norm_class(latent_dim * 2), - nn.GLU(dim = 1) - ) - - num_layers = int(resolution) - 2 - features = list(map(lambda n: (n, 2 ** (fmap_inverse_coef - n)), range(2, num_layers + 2))) - features = list(map(lambda n: (n[0], min(n[1], fmap_max)), features)) - features = list(map(lambda n: 3 if n[0] >= 8 else n[1], features)) - features = [latent_dim, *features] - - in_out_features = list(zip(features[:-1], features[1:])) - - self.res_layers = range(2, num_layers + 2) - self.layers = nn.ModuleList([]) - self.res_to_feature_map = dict(zip(self.res_layers, in_out_features)) - - self.sle_map = ((3, 7), (4, 8), (5, 9), (6, 10)) - self.sle_map = list(filter(lambda t: t[0] <= resolution and t[1] <= resolution, self.sle_map)) - self.sle_map = dict(self.sle_map) - - self.num_layers_spatial_res = 1 - - for (res, (chan_in, chan_out)) in zip(self.res_layers, in_out_features): - image_width = 2 ** res - - attn = None - if image_width in attn_res_layers: - attn = PreNorm(chan_in, LinearAttention(chan_in)) - - sle = None - if res in self.sle_map: - residual_layer = self.sle_map[res] - sle_chan_out = self.res_to_feature_map[residual_layer - 1][-1] - - if freq_chan_attn: - sle = FCANet( - chan_in = chan_out, - chan_out = sle_chan_out, - width = 2 ** (res + 1) - ) - else: - sle = GlobalContext( - chan_in = chan_out, - chan_out = sle_chan_out - ) - - layer = nn.ModuleList([ - nn.Sequential( - upsample(), - Blur(), - Conv2dSame(chan_in, chan_out * 2, 4), - Noise(), - norm_class(chan_out * 2), - nn.GLU(dim = 1) - ), - sle, - attn - ]) - self.layers.append(layer) - - self.out_conv = nn.Conv2d(features[-1], init_channel, 3, padding = 1) - - def forward(self, x): - x = rearrange(x, 'b c -> b c () ()') - x = self.initial_conv(x) - x = F.normalize(x, dim = 1) - - residuals = dict() - - for (res, (up, sle, attn)) in zip(self.res_layers, self.layers): - if exists(attn): - x = attn(x) + x - - x = up(x) - - if exists(sle): - out_res = self.sle_map[res] - residual = sle(x) - residuals[out_res] = residual - - next_res = res + 1 - if next_res in residuals: - x = x * residuals[next_res] - - return self.out_conv(x) - -# Initialize a generator model -gan_new = Generator(latent_dim=256, image_size=256, attn_res_layers = [32]) - -# Load from local saved state dict -# gan_new.load_state_dict(torch.load('/content/orbgan_e3_state_dict.pt')) - -# Load from model hub: -class GeneratorWithPyTorchModelHubMixin(gan_new.__class__, PyTorchModelHubMixin): - pass -gan_new.__class__ = GeneratorWithPyTorchModelHubMixin -gan_new = gan_new.from_pretrained('johnowhitaker/colorb_gan', latent_dim=256, image_size=256, attn_res_layers = [32]) - -def gen_ims(n_rows): - ims = gan_new(torch.randn(int(n_rows)**2, 256)).clamp_(0., 1.) - grid = torchvision.utils.make_grid(ims, nrow=int(n_rows)).permute(1, 2, 0).detach().cpu().numpy() - return (grid*255).astype(np.uint8) - - - -iface = gr.Interface(fn=gen_ims, - inputs=[gr.inputs.Slider(minimum=1, maximum=6, step=1, default=3,label="N rows")], - outputs=[gr.outputs.Image(type="numpy", label="Generated Images")], - title='Demo for Colorbgan model', - article = 'A lightweight-gans trained on johnowhitaker/colorbs. See https://huggingface.co/johnowhitaker/orbgan_e1 for training and inference scripts' -) -iface.launch() \ No newline at end of file diff --git a/spaces/huggingface-projects/auto-retrain/src/models.py b/spaces/huggingface-projects/auto-retrain/src/models.py deleted file mode 100644 index a723528f0e44f2cd3847495b1f41037b8970b260..0000000000000000000000000000000000000000 --- a/spaces/huggingface-projects/auto-retrain/src/models.py +++ /dev/null @@ -1,28 +0,0 @@ -import os -from pydantic import BaseModel -from typing import Literal - -class Config(BaseModel): - target_namespace: str - input_dataset: str - input_model: str - autotrain_project_prefix: str - - -class WebhookPayloadEvent(BaseModel): - action: Literal["create", "update", "delete"] - scope: str - -class WebhookPayloadRepo(BaseModel): - type: Literal["dataset", "model", "space"] - name: str - id: str - private: bool - headSha: str - -class WebhookPayload(BaseModel): - event: WebhookPayloadEvent - repo: WebhookPayloadRepo - - -config = Config.parse_file(os.path.join(os.getcwd(), "config.json")) diff --git a/spaces/huggingface-projects/wordalle/static/_app/immutable/chunks/twenty-39a31000.js b/spaces/huggingface-projects/wordalle/static/_app/immutable/chunks/twenty-39a31000.js deleted file mode 100644 index 71b8fe82d12c63f876a0453f2d45bf10cd84dd65..0000000000000000000000000000000000000000 --- a/spaces/huggingface-projects/wordalle/static/_app/immutable/chunks/twenty-39a31000.js +++ /dev/null @@ -1 +0,0 @@ -import{S as K,i as P,s as Q,N as e,O as r,a as l,d as a,b as t,f as z,g as R,J as s,E as I}from"./index-86f4d6c3.js";function W(O){let 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K{constructor(h){super(),P(this,h,X,W,Q,{classNames:0})}}export{$ as default}; diff --git a/spaces/huggingface/Model_Cards_Writing_Tool/pages/15_More_Information.py b/spaces/huggingface/Model_Cards_Writing_Tool/pages/15_More_Information.py deleted file mode 100644 index 510fc56115fc263e6cd9f8e98ae6136861864f83..0000000000000000000000000000000000000000 --- a/spaces/huggingface/Model_Cards_Writing_Tool/pages/15_More_Information.py +++ /dev/null @@ -1,24 +0,0 @@ -import streamlit as st -from persist import persist, load_widget_state - - -global variable_output - -def main(): - cs_body() - - - -def cs_body(): - - - st.markdown('# More Information [optional]') - st.text_area("Any additional information",height = 200, key=persist("More_info")) - - - - - -if __name__ == '__main__': - load_widget_state() - main() \ No newline at end of file diff --git a/spaces/huspacy/example-applications/examples/dbpedia.py b/spaces/huspacy/example-applications/examples/dbpedia.py deleted file mode 100644 index 695bad49509323d267cab31023cfcdd643aee14b..0000000000000000000000000000000000000000 --- a/spaces/huspacy/example-applications/examples/dbpedia.py +++ /dev/null @@ -1,28 +0,0 @@ -import gradio as gr -import pandas as pd - -from examples.common import NLP - -NLP.add_pipe("dbpedia_spotlight", config={'dbpedia_rest_endpoint': 'https://dbpedia-spotlight.dsd.sztaki.hu/hu', - 'overwrite_ents': False}) - -def process(text: str) -> pd.DataFrame: - doc = NLP(text) - - return pd.DataFrame([{"Text": ent.text, "Resource": ent.kb_id_, "Similarity Score": ent._.dbpedia_raw_result['@similarityScore']} - for ent in doc.spans["dbpedia_spotlight"]]) - - -EXAMPLES = [ - "A Mátrix című sci-fi film Keanu Reeves, Laurence Fishburne, Carrie-Anne Moss, Joe Pantoliano és Hugo Weaving főszereplésével.", - "Egyik pillanatról a másikra eltűnt a nemrég bevezetett HBO Max felületéről több sajátgyártású sorozat, köztük az Aranyélet mindhárom évada és A besúgó összes része.", - "A Netflix felületén elérhető magyar szinkronnal A kiábrándult királylány, ahol a főszereplő magyarhangja Csifó Dorina." -] - -demo = gr.Interface( - fn=process, - inputs=gr.Textbox(value=EXAMPLES[0], lines=10, label="Input text", show_label=True), - outputs=gr.DataFrame(label="DBpedia Spotlight Annotations", show_label=False, max_cols=2, max_rows=10), - examples=EXAMPLES, - cache_examples=False, -) diff --git a/spaces/inamXcontru/PoeticTTS/Concepts In Thermal Physics Blundell Solutions TOP.md b/spaces/inamXcontru/PoeticTTS/Concepts In Thermal Physics Blundell Solutions TOP.md deleted file mode 100644 index 4f07ce9d049321355dd4ecb9dde711864ab736d3..0000000000000000000000000000000000000000 --- a/spaces/inamXcontru/PoeticTTS/Concepts In Thermal Physics Blundell Solutions TOP.md +++ /dev/null @@ -1,103 +0,0 @@ - - Concepts in Thermal Physics Blundell Solutions: A Review- -Thermal physics is a branch of physics that deals with the study of heat, temperature, entropy, and other related phenomena. Thermal physics is essential for understanding many aspects of modern physics, chemistry, and engineering, such as thermodynamics, statistical mechanics, phase transitions, quantum gases, and blackbody radiation. - -One of the most popular textbooks for learning thermal physics is Concepts in Thermal Physics by Katherine M. Blundell and Stephen J. Blundell. This book provides a comprehensive and modern introduction to the main principles and applications of thermal physics, covering both classical and quantum topics. The book also includes numerous exercises and examples to help students test their understanding and develop their problem-solving skills. -concepts in thermal physics blundell solutionsDownload ★★★ https://gohhs.com/2uz3UK - - However, learning thermal physics can be challenging and sometimes frustrating, especially for beginners. That's why many students look for solutions and answers to the exercises and questions in the book. Fortunately, there are several online resources that offer Concepts in Thermal Physics Blundell Solutions for free or at a low cost. In this article, we will review some of these resources and evaluate their quality and usefulness. - -Quizlet- -Quizlet is a popular online platform that allows users to create and share flashcards, quizzes, games, and other study tools. Quizlet also offers textbook solutions for various subjects, including physics. One of the textbooks that Quizlet covers is Concepts in Thermal Physics by Blundell and Blundell. - -Quizlet provides solutions and answers for all the exercises and questions in the book, organized by chapter and section. The solutions are verified by experts and explained in a clear and detailed way. Quizlet also allows users to access other study materials related to the book, such as summaries, notes, diagrams, and videos. - - -The main advantage of Quizlet is that it is easy to use and interactive. Users can customize their study mode, track their progress, and test their knowledge with various features. Quizlet also has a mobile app that enables users to study anywhere and anytime. - -The main drawback of Quizlet is that it requires a subscription to access all the solutions and features. The subscription costs $19.99 per year for students and $47.88 per year for teachers. However, Quizlet offers a free trial period of 7 days for new users. - -Studocu- -Studocu is another online platform that offers textbook solutions for various subjects, including physics. Studocu also covers Concepts in Thermal Physics by Blundell and Blundell. - -Studocu provides solutions and answers for all the exercises and questions in the book, organized by chapter and section. The solutions are uploaded by other users who have solved the book or have access to the official solutions manual. Studocu also allows users to access other study materials related to the book, such as lecture notes, past exams, summaries, and essays. - -The main advantage of Studocu is that it is free to use and has a large database of study materials. Users can also rate and comment on the solutions and materials uploaded by other users, which helps to ensure their quality and accuracy. - -The main drawback of Studocu is that it relies on user-generated content, which means that some solutions may be incomplete, incorrect, or poorly explained. Studocu also has some limitations on downloading and printing the solutions and materials. - -Oxford Academic- -Oxford Academic is the official website of Oxford University Press (OUP), which publishes Concepts in Thermal Physics by Blundell and Blundell. Oxford Academic offers some online resources related to the book, such as an abstract, a table of contents, a sample chapter, and a link to purchase the book. - -Oxford Academic does not provide solutions or answers for the exercises and questions in the book. However, it does provide some supplementary materials for instructors who adopt the book for their courses. These materials include lecture slides, figures, tables, animations, simulations, videos, quizzes, tests, assignments, projects, and solutions manuals. - -The main advantage of Oxford Academic is that it is the official source of information about the book and its authors. The supplementary materials are also high-quality and comprehensive. - -The main drawback of Oxford Academic is that it does not offer any resources for students who want to learn from the book independently. The supplementary materials are only available for instructors who register with OUP and request access. - -Conclusion- -In conclusion, there are several online resources that offer Concepts in Thermal Physics Blundell Solutions. Each resource has its own advantages and drawbacks depending on your needs and preferences. Quizlet is ideal for students who want interactive and verified solutions with additional study tools. Studocu is ideal for students who want free and diverse solutions with other study materials. Oxford Academic is ideal for instructors who want official and comprehensive supplementary materials for their courses. -How to Use Concepts in Thermal Physics Blundell Solutions- -Using Concepts in Thermal Physics Blundell Solutions can be very helpful for students who want to learn thermal physics effectively and efficiently. However, it is important to use them wisely and not rely on them too much. Here are some tips on how to use Concepts in Thermal Physics Blundell Solutions properly: - -
The Benefits of Concepts in Thermal Physics Blundell Solutions- -Using Concepts in Thermal Physics Blundell Solutions can have many benefits for students who want to master thermal physics. Some of these benefits are: - -
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By following the tips and advice given in this article, -you can use Concepts in Thermal Physics Blundell Solutions effectively -and benefit from them greatly. 3cee63e6c2- - \ No newline at end of file diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Dead Rising 3 - Apocalypse Edition (Update 5) Pc Game.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Dead Rising 3 - Apocalypse Edition (Update 5) Pc Game.md deleted file mode 100644 index 812a7b65077c828fa0fcefb742da5afac3f542d3..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Dead Rising 3 - Apocalypse Edition (Update 5) Pc Game.md +++ /dev/null @@ -1,9 +0,0 @@ - Dead Rising 3 - Apocalypse Edition (Update 5) pc gameDownload ✵ https://urlin.us/2uEyvG - -Everything and everything is a weapon in Dead Rising 3. Explore the zombie-infested city of Los Perdidos and find a way to escape before the military strike. The game is developed on a modern engine from Capcom, which gives players a deeper and more realistic gameplay. -Dead Rising 3 features a new online multiplayer mode and a new co-op mode that allows up to 4 players to explore Los Perdidos together. -In addition to the new multiplayer mode, Dead Rising 3 includes all existing maps and characters from previous installments. -dead rising 8a78ff9644 - - - diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (The Greatest Showman On Earth (Engli).md b/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (The Greatest Showman On Earth (Engli).md deleted file mode 100644 index 71f8040b6d4748d631a2fd483599f291de03ac09..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (The Greatest Showman On Earth (Engli).md +++ /dev/null @@ -1,6 +0,0 @@ - HD Online Player (The Greatest Showman On Earth (Engli)Download File === https://urlin.us/2uEyxk - -read peerless battle spirit chapter 494 the ninth city free online high quality at ... referred to as the top genius of linshui city at the martial spirit awakening ceremony he ... among the class transported to another world nagumo hajime is an ordinary ... welcome to the battle spirits wiki battle spirits is a two player collectible card ... 1fdad05405 - - - diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Konar Tamil Urai 11th Std Pdf 54 ((INSTALL)).md b/spaces/inplisQlawa/anything-midjourney-v4-1/Konar Tamil Urai 11th Std Pdf 54 ((INSTALL)).md deleted file mode 100644 index aa8c4f0a47ecf29e997082dfb1bf759b9b641f8a..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Konar Tamil Urai 11th Std Pdf 54 ((INSTALL)).md +++ /dev/null @@ -1,6 +0,0 @@ - Konar Tamil Urai 11th Std Pdf 54Download > https://urlin.us/2uEyH9 - -March 23, 2021 is the latest sura guide 2021 for standard 11 students. Students who want full grades can download it. The 11th Tamil Language Manual... will be released at least one month before the first official year. 8a78ff9644 - - - diff --git a/spaces/irvay/RVC_IR/lib/infer_pack/modules/F0Predictor/F0Predictor.py b/spaces/irvay/RVC_IR/lib/infer_pack/modules/F0Predictor/F0Predictor.py deleted file mode 100644 index f56e49e7f0e6eab3babf0711cae2933371b9f9cc..0000000000000000000000000000000000000000 --- a/spaces/irvay/RVC_IR/lib/infer_pack/modules/F0Predictor/F0Predictor.py +++ /dev/null @@ -1,16 +0,0 @@ -class F0Predictor(object): - def compute_f0(self, wav, p_len): - """ - input: wav:[signal_length] - p_len:int - output: f0:[signal_length//hop_length] - """ - pass - - def compute_f0_uv(self, wav, p_len): - """ - input: wav:[signal_length] - p_len:int - output: f0:[signal_length//hop_length],uv:[signal_length//hop_length] - """ - pass diff --git a/spaces/isaacjeffersonlee/Legal-Grammar-Error-Corrector/app.py b/spaces/isaacjeffersonlee/Legal-Grammar-Error-Corrector/app.py deleted file mode 100644 index 652c263cc66c3423c0b571af61ee0388d267c56b..0000000000000000000000000000000000000000 --- a/spaces/isaacjeffersonlee/Legal-Grammar-Error-Corrector/app.py +++ /dev/null @@ -1,187 +0,0 @@ -import math -import re -import torch -from transformers import AutoTokenizer, GPT2LMHeadModel -from lemminflect import getLemma, getAllInflections -from difflib import Differ -import gradio as gr -from hunspell import Hunspell - -h = Hunspell() -# Add our custom legal dictionary to the Hunspell word list -h.add_dic("Data/legal.dic") - -device = "cuda" if torch.cuda.is_available() else "cpu" - -# Set the seeds for reproducibility -seed = 123 -torch.manual_seed(seed) -torch.cuda.manual_seed(seed) -torch.cuda.manual_seed_all(seed) -torch.backends.cudnn.benchmark = False -torch.backends.cudnn.deterministic = True # Load pre-trained GPT2 model - -GPT_tokenizer = AutoTokenizer.from_pretrained("distilgpt2") -model_name = "isaacjeffersonlee/distilgpt2-for-legal-grammar-error-correction" -GPT_tuned_model = GPT2LMHeadModel.from_pretrained(model_name).to(device) - - -def get_inflections_and_lemmas(word): - lemmas = list(getLemma(word, upos="VERB")) - if len(lemmas) == 1: - if lemmas[0] == word: - # Return empty list if the only word - # found was the original word - lemmas = [] - infl = [] - for val in getAllInflections(word).values(): # Flatten nested list - infl += list(val) - return set(lemmas + infl) - - -def generate_confusion_set(word): - # Deal with articles and prepositions - articles = {"ε", "a", "an", "the"} - if word in articles: - return articles - - preps = {"ε", "about", "at", "by", "for", "from", "in", "of", "on", "to", "with"} - if word in preps: - return preps - - if not h.spell(word): - confusion_set = set() - suggested_words = h.suggest(word) - confusion_set = confusion_set.union(suggested_words) - for suggested_word in suggested_words: # Add inflections of suggested words - confusion_set = confusion_set.union( - get_inflections_and_lemmas(suggested_word) - ) - return confusion_set - else: # word is a valid word in our Hunspell dict - return get_inflections_and_lemmas(word).union(set([word])) - - -def log_likelihood(text, model, tokenizer): - encoded = tokenizer(text, return_tensors="pt") - for key in encoded: - encoded[key] = encoded[key].to(device) - N = len(encoded.input_ids[0]) - log_prob = 0 - with torch.no_grad(): - outputs = model(**encoded) - for idx in range(N - 1): # Offset because first token is not predicted - token_id = encoded.input_ids[0][idx + 1] - log_prob += torch.log_softmax(outputs.logits[0][idx], dim=-1)[token_id].item() - - return log_prob - math.log(N) # Normalize according to number of tokens - - -def replace_word(text, from_word, to_word): - if to_word == "ε": # Deletion special case - return re.sub(rf" {from_word}([!()\'\"?.,\s]|$)", r" ", text) - else: - return re.sub(rf"{from_word}(?=[!()\'\"?.,\s]|$)", rf"{to_word}", text) - - -def highlight_words(text, words): - highlighted_text = text - for word in words: - highlighted_text = replace_word( - highlighted_text, word, "\033[92m" + word + "\033[00m" - ) - return highlighted_text - - -def correct_word(text, word, idx, max_idx, ll_threshold, model, tokenizer): - confusion_set = generate_confusion_set(word) - # If no other valid alternatives are found - if len(confusion_set) == 1 and word in confusion_set: - return text, None - max_ll = -999999.999 - best_alt_text = None - best_alt_word = None - for alt_word in confusion_set: - alt_text = replace_word(text, word, alt_word) - ll = log_likelihood(alt_text, model, tokenizer) - if ll > max_ll and ll > ll_threshold: - max_ll = ll - best_alt_text = alt_text - best_alt_word = alt_word - - if best_alt_text is not None: - return best_alt_text, best_alt_word - else: - return text, None # Failed to find a better replacement - - -def correct_text(text, model, tokenizer, ll_threshold=-200.0, highlight_changes=False): - # First we want to break the text down into a list of words. - words = re.sub(r"[^\w\s]", "", text).split(" ") - corrected_text = text - # Indices of words spelled incorrectly, we will address these first. - spelling_errors = [not h.spell(word) for word in words] - spelled_wrong_idx = [idx for idx, error in enumerate(spelling_errors) if error] - spelled_correct_idx = [ - idx for idx, error in enumerate(spelling_errors) if not error - ] - corrections = {} - for idx in spelled_wrong_idx: # First iterate over incorrectly spelled words. - word = words[idx] - corrected_text, corrected_word = correct_word( - corrected_text, word, idx, len(words) - 1, ll_threshold, model, tokenizer - ) - if corrected_word != word: - corrections[word] = corrected_word - for idx in spelled_correct_idx: - word = words[idx] - corrected_text, corrected_word = correct_word( - corrected_text, word, idx, len(words) - 1, ll_threshold, model, tokenizer - ) - if corrected_word != word and corrected_word is not None: - corrections[word] = corrected_word - if highlight_changes: - if not corrections: - print("No corrections!") - for original_word, corrected_word in corrections.items(): - if corrected_word is not None: - print( - f"\033[91m{original_word}\033[00m -> \033[92m{corrected_word}\033[00m" - ) - corrected_text = highlight_words(corrected_text, corrections.values()) - return corrected_text - - -def diff_texts(text1): - d = Differ() - text1 = text1.strip("\n") - text2 = correct_text(text1, GPT_tuned_model, GPT_tokenizer) - diffs = [ - (token[2:], token[0] if token[0] != " " else None) - for token in d.compare(text1, text2) - ] - change = [(text1, "Original"), (text2, "Corrected")] - return diffs, change - - -demo = gr.Interface( - fn=diff_texts, - inputs=gr.Textbox( - label="Input Text", - lines=1, - value="We vacat the judgment of an district court.", - ), - outputs=[ - gr.HighlightedText( - label="Diff", - combine_adjacent=True, - ).style(color_map={"-": "red", "+": "green"}), - gr.HighlightedText( - label="Change", - combine_adjacent=True, - ).style(color_map={"Original": "red", "Corrected": "green"}), - ], -) - -if __name__ == "__main__": - demo.launch(share=False) # 'Share not supported when you are in spaces' diff --git a/spaces/javedkumail/HopeAI/app.py b/spaces/javedkumail/HopeAI/app.py deleted file mode 100644 index ab07f3485fc648c6301c23d19f693da815b8fbef..0000000000000000000000000000000000000000 --- a/spaces/javedkumail/HopeAI/app.py +++ /dev/null @@ -1,26 +0,0 @@ -import openai -import gradio -import config - -openai.api_key = config.OPEN_API_KEY - -messages = [{"role": "system", "content": 'You are Hope, a mental therapy expert that specializes in psychology and guide through emotions of patients. You cannot give answer on any other topic no atter how small or big.You do not have any other name. Always introduce yourself at the start of a new conversation'}] - -def CustomChatGPT(Patient_Query): - messages.append({"role": "user", "content": Patient_Query}) - response = openai.ChatCompletion.create( - model = "gpt-3.5-turbo", - messages = messages - ) - ChatGPT_reply = response["choices"][0]["message"]["content"] - messages.append({"role": "assistant", "content": ChatGPT_reply}) - # chat_transcript = "" - # for message in messages: - # if message['role'] != 'system': - # chat_transcript += message['role'] + ": " + message['content'] + "\n\n" - return ChatGPT_reply - - -demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text", title = "HopeAI SMART MENTAL THERAPIST") - -demo.launch(share=True) \ No newline at end of file diff --git a/spaces/jbetker/tortoise/is_this_from_tortoise.py b/spaces/jbetker/tortoise/is_this_from_tortoise.py deleted file mode 100644 index 550b33e61c13c7ffe9509ae2b07d81903ee7cb38..0000000000000000000000000000000000000000 --- a/spaces/jbetker/tortoise/is_this_from_tortoise.py +++ /dev/null @@ -1,14 +0,0 @@ -import argparse - -from api import classify_audio_clip -from utils.audio import load_audio - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--clip', type=str, help='Path to an audio clip to classify.', default="results/favorite_riding_hood.mp3") - args = parser.parse_args() - - clip = load_audio(args.clip, 24000) - clip = clip[:, :220000] - prob = classify_audio_clip(clip) - print(f"This classifier thinks there is a {prob*100}% chance that this clip was generated from Tortoise.") \ No newline at end of file diff --git a/spaces/jbilcke-hf/observer/src/lib/utils.ts b/spaces/jbilcke-hf/observer/src/lib/utils.ts deleted file mode 100644 index ec79801fe9cdd7711f6dbef26678a134c634a8be..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/observer/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/jeffistyping/Youtube-Whisperer/app.py b/spaces/jeffistyping/Youtube-Whisperer/app.py deleted file mode 100644 index c3b950d79209e5e4b903442a861cc89227c1448e..0000000000000000000000000000000000000000 --- a/spaces/jeffistyping/Youtube-Whisperer/app.py +++ /dev/null @@ -1,93 +0,0 @@ -import gradio as gr -import whisper -from pytube import YouTube - - -class GradioInference(): - def __init__(self): - self.sizes = list(whisper._MODELS.keys()) - self.langs = ["none"] + sorted(list(whisper.tokenizer.LANGUAGES.values())) - self.current_size = "base" - self.loaded_model = whisper.load_model(self.current_size) - self.yt = None - - def __call__(self, link, lang, size, subs): - if self.yt is None: - self.yt = YouTube(link) - path = self.yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4") - - if lang == "none": - lang = None - - if size != self.current_size: - self.loaded_model = whisper.load_model(size) - self.current_size = size - results = self.loaded_model.transcribe(path, language=lang) - - if subs == "None": - return results["text"] - elif subs == ".srt": - return self.srt(results["segments"]) - elif ".csv" == ".csv": - return self.csv(results["segments"]) - - def srt(self, segments): - output = "" - for i, segment in enumerate(segments): - output += f"{i+1}\n" - output += f"{self.format_time(segment['start'])} --> {self.format_time(segment['end'])}\n" - output += f"{segment['text']}\n\n" - return output - - def csv(self, segments): - output = "" - for segment in segments: - output += f"{segment['start']},{segment['end']},{segment['text']}\n" - return output - - def format_time(self, time): - hours = time//3600 - minutes = (time - hours*3600)//60 - seconds = time - hours*3600 - minutes*60 - milliseconds = (time - int(time))*1000 - return f"{int(hours):02d}:{int(minutes):02d}:{int(seconds):02d},{int(milliseconds):03d}" - - def populate_metadata(self, link): - self.yt = YouTube(link) - return self.yt.thumbnail_url, self.yt.title - -gio = GradioInference() -title="Youtube Whisperer" -description="Speech to text transcription of Youtube videos using OpenAI's Whisper" - -block = gr.Blocks() -with block: - gr.HTML( - """ -
-
- """
- )
- with gr.Group():
- with gr.Box():
- with gr.Row().style(equal_height=True):
- sz = gr.Dropdown(label="Model Size", choices=gio.sizes, value='base')
- lang = gr.Dropdown(label="Language (Optional)", choices=gio.langs, value="none")
- with gr.Row().style(equal_height=True):
- wt = gr.Radio(["None", ".srt", ".csv"], label="With Timestamps?")
- link = gr.Textbox(label="YouTube Link")
- title = gr.Label(label="Video Title")
- with gr.Row().style(equal_height=True):
- img = gr.Image(label="Thumbnail")
- text = gr.Textbox(label="Transcription", placeholder="Transcription Output", lines=10)
- with gr.Row().style(equal_height=True):
- btn = gr.Button("Transcribe")
- btn.click(gio, inputs=[link, lang, sz, wt], outputs=[text])
- link.change(gio.populate_metadata, inputs=[link], outputs=[img, title])
-block.launch()
\ No newline at end of file
diff --git a/spaces/jitesh/storytelling/src/story_gen_test.py b/spaces/jitesh/storytelling/src/story_gen_test.py
deleted file mode 100644
index 41b4bb4cc12f749d47fbe6eca44139a5594e8e85..0000000000000000000000000000000000000000
--- a/spaces/jitesh/storytelling/src/story_gen_test.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# %%
-import printj
-from story_gen import StoryGenerator
-
-gen = StoryGenerator()
-# # %%
-# story_till_now, emotion = gen.story(story_till_now='Hello, I\'m a language model,', num_generation=3, length=10)
-# printj.purple(story_till_now)
-# printj.yellow(emotion)
-
-
-# %%
-gen.get_stats(story_till_now="For myriad of eons i’ve forgotten who I really was, harvesting the essence of all existence.",
- length=10, num_generation=3, num_tests=50)
-
-# %%
-gen.save_stats('/home/jitesh/haru/ist/results/a.xlsx')
-
-
-
-
-# %%
-data=gen.stats_df[gen.stats_df.sentence_no==3]
-import seaborn as sns
-sns.set_theme(style="whitegrid")
-# ax = sns.violinplot(x="day", y="total_bill", data=tips)
-ax = sns.violinplot(x="reaction_weight", y="num_reactions", data=data).set_title('Analysing ProbabilityEmote (Max reactions=3)')
-# %%
-
-gen.stats_df[gen.stats_df.sentence_no==3]
-# %%
-import re
-len(re.findall(r'\w+', 'line ive '))
-# %%
diff --git a/spaces/joaogabriellima/Real-Time-Voice-Cloning/vocoder/models/deepmind_version.py b/spaces/joaogabriellima/Real-Time-Voice-Cloning/vocoder/models/deepmind_version.py
deleted file mode 100644
index 1d973d9b8b9ab547571abc5a3f5ea86226a25924..0000000000000000000000000000000000000000
--- a/spaces/joaogabriellima/Real-Time-Voice-Cloning/vocoder/models/deepmind_version.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from utils.display import *
-from utils.dsp import *
-
-
-class WaveRNN(nn.Module) :
- def __init__(self, hidden_size=896, quantisation=256) :
- super(WaveRNN, self).__init__()
-
- self.hidden_size = hidden_size
- self.split_size = hidden_size // 2
-
- # The main matmul
- self.R = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
-
- # Output fc layers
- self.O1 = nn.Linear(self.split_size, self.split_size)
- self.O2 = nn.Linear(self.split_size, quantisation)
- self.O3 = nn.Linear(self.split_size, self.split_size)
- self.O4 = nn.Linear(self.split_size, quantisation)
-
- # Input fc layers
- self.I_coarse = nn.Linear(2, 3 * self.split_size, bias=False)
- self.I_fine = nn.Linear(3, 3 * self.split_size, bias=False)
-
- # biases for the gates
- self.bias_u = nn.Parameter(torch.zeros(self.hidden_size))
- self.bias_r = nn.Parameter(torch.zeros(self.hidden_size))
- self.bias_e = nn.Parameter(torch.zeros(self.hidden_size))
-
- # display num params
- self.num_params()
-
-
- def forward(self, prev_y, prev_hidden, current_coarse) :
-
- # Main matmul - the projection is split 3 ways
- R_hidden = self.R(prev_hidden)
- R_u, R_r, R_e, = torch.split(R_hidden, self.hidden_size, dim=1)
-
- # Project the prev input
- coarse_input_proj = self.I_coarse(prev_y)
- I_coarse_u, I_coarse_r, I_coarse_e = \
- torch.split(coarse_input_proj, self.split_size, dim=1)
-
- # Project the prev input and current coarse sample
- fine_input = torch.cat([prev_y, current_coarse], dim=1)
- fine_input_proj = self.I_fine(fine_input)
- I_fine_u, I_fine_r, I_fine_e = \
- torch.split(fine_input_proj, self.split_size, dim=1)
-
- # concatenate for the gates
- I_u = torch.cat([I_coarse_u, I_fine_u], dim=1)
- I_r = torch.cat([I_coarse_r, I_fine_r], dim=1)
- I_e = torch.cat([I_coarse_e, I_fine_e], dim=1)
-
- # Compute all gates for coarse and fine
- u = F.sigmoid(R_u + I_u + self.bias_u)
- r = F.sigmoid(R_r + I_r + self.bias_r)
- e = F.tanh(r * R_e + I_e + self.bias_e)
- hidden = u * prev_hidden + (1. - u) * e
-
- # Split the hidden state
- hidden_coarse, hidden_fine = torch.split(hidden, self.split_size, dim=1)
-
- # Compute outputs
- out_coarse = self.O2(F.relu(self.O1(hidden_coarse)))
- out_fine = self.O4(F.relu(self.O3(hidden_fine)))
-
- return out_coarse, out_fine, hidden
-
-
- def generate(self, seq_len):
- with torch.no_grad():
- # First split up the biases for the gates
- b_coarse_u, b_fine_u = torch.split(self.bias_u, self.split_size)
- b_coarse_r, b_fine_r = torch.split(self.bias_r, self.split_size)
- b_coarse_e, b_fine_e = torch.split(self.bias_e, self.split_size)
-
- # Lists for the two output seqs
- c_outputs, f_outputs = [], []
-
- # Some initial inputs
- out_coarse = torch.LongTensor([0]).cuda()
- out_fine = torch.LongTensor([0]).cuda()
-
- # We'll meed a hidden state
- hidden = self.init_hidden()
-
- # Need a clock for display
- start = time.time()
-
- # Loop for generation
- for i in range(seq_len) :
-
- # Split into two hidden states
- hidden_coarse, hidden_fine = \
- torch.split(hidden, self.split_size, dim=1)
-
- # Scale and concat previous predictions
- out_coarse = out_coarse.unsqueeze(0).float() / 127.5 - 1.
- out_fine = out_fine.unsqueeze(0).float() / 127.5 - 1.
- prev_outputs = torch.cat([out_coarse, out_fine], dim=1)
-
- # Project input
- coarse_input_proj = self.I_coarse(prev_outputs)
- I_coarse_u, I_coarse_r, I_coarse_e = \
- torch.split(coarse_input_proj, self.split_size, dim=1)
-
- # Project hidden state and split 6 ways
- R_hidden = self.R(hidden)
- R_coarse_u , R_fine_u, \
- R_coarse_r, R_fine_r, \
- R_coarse_e, R_fine_e = torch.split(R_hidden, self.split_size, dim=1)
-
- # Compute the coarse gates
- u = F.sigmoid(R_coarse_u + I_coarse_u + b_coarse_u)
- r = F.sigmoid(R_coarse_r + I_coarse_r + b_coarse_r)
- e = F.tanh(r * R_coarse_e + I_coarse_e + b_coarse_e)
- hidden_coarse = u * hidden_coarse + (1. - u) * e
-
- # Compute the coarse output
- out_coarse = self.O2(F.relu(self.O1(hidden_coarse)))
- posterior = F.softmax(out_coarse, dim=1)
- distrib = torch.distributions.Categorical(posterior)
- out_coarse = distrib.sample()
- c_outputs.append(out_coarse)
-
- # Project the [prev outputs and predicted coarse sample]
- coarse_pred = out_coarse.float() / 127.5 - 1.
- fine_input = torch.cat([prev_outputs, coarse_pred.unsqueeze(0)], dim=1)
- fine_input_proj = self.I_fine(fine_input)
- I_fine_u, I_fine_r, I_fine_e = \
- torch.split(fine_input_proj, self.split_size, dim=1)
-
- # Compute the fine gates
- u = F.sigmoid(R_fine_u + I_fine_u + b_fine_u)
- r = F.sigmoid(R_fine_r + I_fine_r + b_fine_r)
- e = F.tanh(r * R_fine_e + I_fine_e + b_fine_e)
- hidden_fine = u * hidden_fine + (1. - u) * e
-
- # Compute the fine output
- out_fine = self.O4(F.relu(self.O3(hidden_fine)))
- posterior = F.softmax(out_fine, dim=1)
- distrib = torch.distributions.Categorical(posterior)
- out_fine = distrib.sample()
- f_outputs.append(out_fine)
-
- # Put the hidden state back together
- hidden = torch.cat([hidden_coarse, hidden_fine], dim=1)
-
- # Display progress
- speed = (i + 1) / (time.time() - start)
- stream('Gen: %i/%i -- Speed: %i', (i + 1, seq_len, speed))
-
- coarse = torch.stack(c_outputs).squeeze(1).cpu().data.numpy()
- fine = torch.stack(f_outputs).squeeze(1).cpu().data.numpy()
- output = combine_signal(coarse, fine)
-
- return output, coarse, fine
-
- def init_hidden(self, batch_size=1) :
- return torch.zeros(batch_size, self.hidden_size).cuda()
-
- def num_params(self) :
- parameters = filter(lambda p: p.requires_grad, self.parameters())
- parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
- print('Trainable Parameters: %.3f million' % parameters)
\ No newline at end of file
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/bs4/tests/test_htmlparser.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/bs4/tests/test_htmlparser.py
deleted file mode 100644
index a1195d815f6903751af5139986bf7d1c2880d9e1..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/bs4/tests/test_htmlparser.py
+++ /dev/null
@@ -1,148 +0,0 @@
-"""Tests to ensure that the html.parser tree builder generates good
-trees."""
-
-from pdb import set_trace
-import pickle
-import pytest
-import warnings
-from bs4.builder import (
- HTMLParserTreeBuilder,
- ParserRejectedMarkup,
- XMLParsedAsHTMLWarning,
-)
-from bs4.builder._htmlparser import BeautifulSoupHTMLParser
-from . import SoupTest, HTMLTreeBuilderSmokeTest
-
-class TestHTMLParserTreeBuilder(SoupTest, HTMLTreeBuilderSmokeTest):
-
- default_builder = HTMLParserTreeBuilder
-
- def test_rejected_input(self):
- # Python's html.parser will occasionally reject markup,
- # especially when there is a problem with the initial DOCTYPE
- # declaration. Different versions of Python sound the alarm in
- # different ways, but Beautiful Soup consistently raises
- # errors as ParserRejectedMarkup exceptions.
- bad_markup = [
- # https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=28873
- # https://github.com/guidovranken/python-library-fuzzers/blob/master/corp-html/519e5b4269a01185a0d5e76295251921da2f0700
- # https://github.com/python/cpython/issues/81928
- b'\n",
- ]
- for markup in bad_markup:
- with pytest.raises(ParserRejectedMarkup):
- soup = self.soup(markup)
-
- def test_namespaced_system_doctype(self):
- # html.parser can't handle namespaced doctypes, so skip this one.
- pass
-
- def test_namespaced_public_doctype(self):
- # html.parser can't handle namespaced doctypes, so skip this one.
- pass
-
- def test_builder_is_pickled(self):
- """Unlike most tree builders, HTMLParserTreeBuilder and will
- be restored after pickling.
- """
- tree = self.soup("foo")
- dumped = pickle.dumps(tree, 2)
- loaded = pickle.loads(dumped)
- assert isinstance(loaded.builder, type(tree.builder))
-
- def test_redundant_empty_element_closing_tags(self):
- self.assert_soup('
-
- Youtube Whisperer-- Speech to text transcription of Youtube videos using OpenAI's Whisper - -', " ") - self.assert_soup('', "") - - def test_empty_element(self): - # This verifies that any buffered data present when the parser - # finishes working is handled. - self.assert_soup("foo &# bar", "foo &# bar") - - def test_tracking_line_numbers(self): - # The html.parser TreeBuilder keeps track of line number and - # position of each element. - markup = "\n \n\n %s ' % input_element
- div = self.soup(markup).div
- without_element = div.encode()
- expect = b"%s " % output_unicode.encode("utf8")
- assert without_element == expect
-
- with_element = div.encode(formatter="html")
- expect = b"%s " % output_element
- assert with_element == expect
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/merge/layout.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/merge/layout.py
deleted file mode 100644
index 6b85cd503387291f326e937b36a5739b1de23ef1..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/merge/layout.py
+++ /dev/null
@@ -1,530 +0,0 @@
-# Copyright 2013 Google, Inc. All Rights Reserved.
-#
-# Google Author(s): Behdad Esfahbod, Roozbeh Pournader
-
-from fontTools import ttLib
-from fontTools.ttLib.tables.DefaultTable import DefaultTable
-from fontTools.ttLib.tables import otTables
-from fontTools.merge.base import add_method, mergeObjects
-from fontTools.merge.util import *
-import logging
-
-
-log = logging.getLogger("fontTools.merge")
-
-
-def mergeLookupLists(lst):
- # TODO Do smarter merge.
- return sumLists(lst)
-
-
-def mergeFeatures(lst):
- assert lst
- self = otTables.Feature()
- self.FeatureParams = None
- self.LookupListIndex = mergeLookupLists(
- [l.LookupListIndex for l in lst if l.LookupListIndex]
- )
- self.LookupCount = len(self.LookupListIndex)
- return self
-
-
-def mergeFeatureLists(lst):
- d = {}
- for l in lst:
- for f in l:
- tag = f.FeatureTag
- if tag not in d:
- d[tag] = []
- d[tag].append(f.Feature)
- ret = []
- for tag in sorted(d.keys()):
- rec = otTables.FeatureRecord()
- rec.FeatureTag = tag
- rec.Feature = mergeFeatures(d[tag])
- ret.append(rec)
- return ret
-
-
-def mergeLangSyses(lst):
- assert lst
-
- # TODO Support merging ReqFeatureIndex
- assert all(l.ReqFeatureIndex == 0xFFFF for l in lst)
-
- self = otTables.LangSys()
- self.LookupOrder = None
- self.ReqFeatureIndex = 0xFFFF
- self.FeatureIndex = mergeFeatureLists(
- [l.FeatureIndex for l in lst if l.FeatureIndex]
- )
- self.FeatureCount = len(self.FeatureIndex)
- return self
-
-
-def mergeScripts(lst):
- assert lst
-
- if len(lst) == 1:
- return lst[0]
- langSyses = {}
- for sr in lst:
- for lsr in sr.LangSysRecord:
- if lsr.LangSysTag not in langSyses:
- langSyses[lsr.LangSysTag] = []
- langSyses[lsr.LangSysTag].append(lsr.LangSys)
- lsrecords = []
- for tag, langSys_list in sorted(langSyses.items()):
- lsr = otTables.LangSysRecord()
- lsr.LangSys = mergeLangSyses(langSys_list)
- lsr.LangSysTag = tag
- lsrecords.append(lsr)
-
- self = otTables.Script()
- self.LangSysRecord = lsrecords
- self.LangSysCount = len(lsrecords)
- dfltLangSyses = [s.DefaultLangSys for s in lst if s.DefaultLangSys]
- if dfltLangSyses:
- self.DefaultLangSys = mergeLangSyses(dfltLangSyses)
- else:
- self.DefaultLangSys = None
- return self
-
-
-def mergeScriptRecords(lst):
- d = {}
- for l in lst:
- for s in l:
- tag = s.ScriptTag
- if tag not in d:
- d[tag] = []
- d[tag].append(s.Script)
- ret = []
- for tag in sorted(d.keys()):
- rec = otTables.ScriptRecord()
- rec.ScriptTag = tag
- rec.Script = mergeScripts(d[tag])
- ret.append(rec)
- return ret
-
-
-otTables.ScriptList.mergeMap = {
- "ScriptCount": lambda lst: None, # TODO
- "ScriptRecord": mergeScriptRecords,
-}
-otTables.BaseScriptList.mergeMap = {
- "BaseScriptCount": lambda lst: None, # TODO
- # TODO: Merge duplicate entries
- "BaseScriptRecord": lambda lst: sorted(
- sumLists(lst), key=lambda s: s.BaseScriptTag
- ),
-}
-
-otTables.FeatureList.mergeMap = {
- "FeatureCount": sum,
- "FeatureRecord": lambda lst: sorted(sumLists(lst), key=lambda s: s.FeatureTag),
-}
-
-otTables.LookupList.mergeMap = {
- "LookupCount": sum,
- "Lookup": sumLists,
-}
-
-otTables.Coverage.mergeMap = {
- "Format": min,
- "glyphs": sumLists,
-}
-
-otTables.ClassDef.mergeMap = {
- "Format": min,
- "classDefs": sumDicts,
-}
-
-otTables.LigCaretList.mergeMap = {
- "Coverage": mergeObjects,
- "LigGlyphCount": sum,
- "LigGlyph": sumLists,
-}
-
-otTables.AttachList.mergeMap = {
- "Coverage": mergeObjects,
- "GlyphCount": sum,
- "AttachPoint": sumLists,
-}
-
-# XXX Renumber MarkFilterSets of lookups
-otTables.MarkGlyphSetsDef.mergeMap = {
- "MarkSetTableFormat": equal,
- "MarkSetCount": sum,
- "Coverage": sumLists,
-}
-
-otTables.Axis.mergeMap = {
- "*": mergeObjects,
-}
-
-# XXX Fix BASE table merging
-otTables.BaseTagList.mergeMap = {
- "BaseTagCount": sum,
- "BaselineTag": sumLists,
-}
-
-otTables.GDEF.mergeMap = (
- otTables.GSUB.mergeMap
-) = (
- otTables.GPOS.mergeMap
-) = otTables.BASE.mergeMap = otTables.JSTF.mergeMap = otTables.MATH.mergeMap = {
- "*": mergeObjects,
- "Version": max,
-}
-
-ttLib.getTableClass("GDEF").mergeMap = ttLib.getTableClass(
- "GSUB"
-).mergeMap = ttLib.getTableClass("GPOS").mergeMap = ttLib.getTableClass(
- "BASE"
-).mergeMap = ttLib.getTableClass(
- "JSTF"
-).mergeMap = ttLib.getTableClass(
- "MATH"
-).mergeMap = {
- "tableTag": onlyExisting(equal), # XXX clean me up
- "table": mergeObjects,
-}
-
-
-@add_method(ttLib.getTableClass("GSUB"))
-def merge(self, m, tables):
- assert len(tables) == len(m.duplicateGlyphsPerFont)
- for i, (table, dups) in enumerate(zip(tables, m.duplicateGlyphsPerFont)):
- if not dups:
- continue
- if table is None or table is NotImplemented:
- log.warning(
- "Have non-identical duplicates to resolve for '%s' but no GSUB. Are duplicates intended?: %s",
- m.fonts[i]._merger__name,
- dups,
- )
- continue
-
- synthFeature = None
- synthLookup = None
- for script in table.table.ScriptList.ScriptRecord:
- if script.ScriptTag == "DFLT":
- continue # XXX
- for langsys in [script.Script.DefaultLangSys] + [
- l.LangSys for l in script.Script.LangSysRecord
- ]:
- if langsys is None:
- continue # XXX Create!
- feature = [v for v in langsys.FeatureIndex if v.FeatureTag == "locl"]
- assert len(feature) <= 1
- if feature:
- feature = feature[0]
- else:
- if not synthFeature:
- synthFeature = otTables.FeatureRecord()
- synthFeature.FeatureTag = "locl"
- f = synthFeature.Feature = otTables.Feature()
- f.FeatureParams = None
- f.LookupCount = 0
- f.LookupListIndex = []
- table.table.FeatureList.FeatureRecord.append(synthFeature)
- table.table.FeatureList.FeatureCount += 1
- feature = synthFeature
- langsys.FeatureIndex.append(feature)
- langsys.FeatureIndex.sort(key=lambda v: v.FeatureTag)
-
- if not synthLookup:
- subtable = otTables.SingleSubst()
- subtable.mapping = dups
- synthLookup = otTables.Lookup()
- synthLookup.LookupFlag = 0
- synthLookup.LookupType = 1
- synthLookup.SubTableCount = 1
- synthLookup.SubTable = [subtable]
- if table.table.LookupList is None:
- # mtiLib uses None as default value for LookupList,
- # while feaLib points to an empty array with count 0
- # TODO: make them do the same
- table.table.LookupList = otTables.LookupList()
- table.table.LookupList.Lookup = []
- table.table.LookupList.LookupCount = 0
- table.table.LookupList.Lookup.append(synthLookup)
- table.table.LookupList.LookupCount += 1
-
- if feature.Feature.LookupListIndex[:1] != [synthLookup]:
- feature.Feature.LookupListIndex[:0] = [synthLookup]
- feature.Feature.LookupCount += 1
-
- DefaultTable.merge(self, m, tables)
- return self
-
-
-@add_method(
- otTables.SingleSubst,
- otTables.MultipleSubst,
- otTables.AlternateSubst,
- otTables.LigatureSubst,
- otTables.ReverseChainSingleSubst,
- otTables.SinglePos,
- otTables.PairPos,
- otTables.CursivePos,
- otTables.MarkBasePos,
- otTables.MarkLigPos,
- otTables.MarkMarkPos,
-)
-def mapLookups(self, lookupMap):
- pass
-
-
-# Copied and trimmed down from subset.py
-@add_method(
- otTables.ContextSubst,
- otTables.ChainContextSubst,
- otTables.ContextPos,
- otTables.ChainContextPos,
-)
-def __merge_classify_context(self):
- class ContextHelper(object):
- def __init__(self, klass, Format):
- if klass.__name__.endswith("Subst"):
- Typ = "Sub"
- Type = "Subst"
- else:
- Typ = "Pos"
- Type = "Pos"
- if klass.__name__.startswith("Chain"):
- Chain = "Chain"
- else:
- Chain = ""
- ChainTyp = Chain + Typ
-
- self.Typ = Typ
- self.Type = Type
- self.Chain = Chain
- self.ChainTyp = ChainTyp
-
- self.LookupRecord = Type + "LookupRecord"
-
- if Format == 1:
- self.Rule = ChainTyp + "Rule"
- self.RuleSet = ChainTyp + "RuleSet"
- elif Format == 2:
- self.Rule = ChainTyp + "ClassRule"
- self.RuleSet = ChainTyp + "ClassSet"
-
- if self.Format not in [1, 2, 3]:
- return None # Don't shoot the messenger; let it go
- if not hasattr(self.__class__, "_merge__ContextHelpers"):
- self.__class__._merge__ContextHelpers = {}
- if self.Format not in self.__class__._merge__ContextHelpers:
- helper = ContextHelper(self.__class__, self.Format)
- self.__class__._merge__ContextHelpers[self.Format] = helper
- return self.__class__._merge__ContextHelpers[self.Format]
-
-
-@add_method(
- otTables.ContextSubst,
- otTables.ChainContextSubst,
- otTables.ContextPos,
- otTables.ChainContextPos,
-)
-def mapLookups(self, lookupMap):
- c = self.__merge_classify_context()
-
- if self.Format in [1, 2]:
- for rs in getattr(self, c.RuleSet):
- if not rs:
- continue
- for r in getattr(rs, c.Rule):
- if not r:
- continue
- for ll in getattr(r, c.LookupRecord):
- if not ll:
- continue
- ll.LookupListIndex = lookupMap[ll.LookupListIndex]
- elif self.Format == 3:
- for ll in getattr(self, c.LookupRecord):
- if not ll:
- continue
- ll.LookupListIndex = lookupMap[ll.LookupListIndex]
- else:
- assert 0, "unknown format: %s" % self.Format
-
-
-@add_method(otTables.ExtensionSubst, otTables.ExtensionPos)
-def mapLookups(self, lookupMap):
- if self.Format == 1:
- self.ExtSubTable.mapLookups(lookupMap)
- else:
- assert 0, "unknown format: %s" % self.Format
-
-
-@add_method(otTables.Lookup)
-def mapLookups(self, lookupMap):
- for st in self.SubTable:
- if not st:
- continue
- st.mapLookups(lookupMap)
-
-
-@add_method(otTables.LookupList)
-def mapLookups(self, lookupMap):
- for l in self.Lookup:
- if not l:
- continue
- l.mapLookups(lookupMap)
-
-
-@add_method(otTables.Lookup)
-def mapMarkFilteringSets(self, markFilteringSetMap):
- if self.LookupFlag & 0x0010:
- self.MarkFilteringSet = markFilteringSetMap[self.MarkFilteringSet]
-
-
-@add_method(otTables.LookupList)
-def mapMarkFilteringSets(self, markFilteringSetMap):
- for l in self.Lookup:
- if not l:
- continue
- l.mapMarkFilteringSets(markFilteringSetMap)
-
-
-@add_method(otTables.Feature)
-def mapLookups(self, lookupMap):
- self.LookupListIndex = [lookupMap[i] for i in self.LookupListIndex]
-
-
-@add_method(otTables.FeatureList)
-def mapLookups(self, lookupMap):
- for f in self.FeatureRecord:
- if not f or not f.Feature:
- continue
- f.Feature.mapLookups(lookupMap)
-
-
-@add_method(otTables.DefaultLangSys, otTables.LangSys)
-def mapFeatures(self, featureMap):
- self.FeatureIndex = [featureMap[i] for i in self.FeatureIndex]
- if self.ReqFeatureIndex != 65535:
- self.ReqFeatureIndex = featureMap[self.ReqFeatureIndex]
-
-
-@add_method(otTables.Script)
-def mapFeatures(self, featureMap):
- if self.DefaultLangSys:
- self.DefaultLangSys.mapFeatures(featureMap)
- for l in self.LangSysRecord:
- if not l or not l.LangSys:
- continue
- l.LangSys.mapFeatures(featureMap)
-
-
-@add_method(otTables.ScriptList)
-def mapFeatures(self, featureMap):
- for s in self.ScriptRecord:
- if not s or not s.Script:
- continue
- s.Script.mapFeatures(featureMap)
-
-
-def layoutPreMerge(font):
- # Map indices to references
-
- GDEF = font.get("GDEF")
- GSUB = font.get("GSUB")
- GPOS = font.get("GPOS")
-
- for t in [GSUB, GPOS]:
- if not t:
- continue
-
- if t.table.LookupList:
- lookupMap = {i: v for i, v in enumerate(t.table.LookupList.Lookup)}
- t.table.LookupList.mapLookups(lookupMap)
- t.table.FeatureList.mapLookups(lookupMap)
-
- if (
- GDEF
- and GDEF.table.Version >= 0x00010002
- and GDEF.table.MarkGlyphSetsDef
- ):
- markFilteringSetMap = {
- i: v for i, v in enumerate(GDEF.table.MarkGlyphSetsDef.Coverage)
- }
- t.table.LookupList.mapMarkFilteringSets(markFilteringSetMap)
-
- if t.table.FeatureList and t.table.ScriptList:
- featureMap = {i: v for i, v in enumerate(t.table.FeatureList.FeatureRecord)}
- t.table.ScriptList.mapFeatures(featureMap)
-
- # TODO FeatureParams nameIDs
-
-
-def layoutPostMerge(font):
- # Map references back to indices
-
- GDEF = font.get("GDEF")
- GSUB = font.get("GSUB")
- GPOS = font.get("GPOS")
-
- for t in [GSUB, GPOS]:
- if not t:
- continue
-
- if t.table.FeatureList and t.table.ScriptList:
- # Collect unregistered (new) features.
- featureMap = GregariousIdentityDict(t.table.FeatureList.FeatureRecord)
- t.table.ScriptList.mapFeatures(featureMap)
-
- # Record used features.
- featureMap = AttendanceRecordingIdentityDict(
- t.table.FeatureList.FeatureRecord
- )
- t.table.ScriptList.mapFeatures(featureMap)
- usedIndices = featureMap.s
-
- # Remove unused features
- t.table.FeatureList.FeatureRecord = [
- f
- for i, f in enumerate(t.table.FeatureList.FeatureRecord)
- if i in usedIndices
- ]
-
- # Map back to indices.
- featureMap = NonhashableDict(t.table.FeatureList.FeatureRecord)
- t.table.ScriptList.mapFeatures(featureMap)
-
- t.table.FeatureList.FeatureCount = len(t.table.FeatureList.FeatureRecord)
-
- if t.table.LookupList:
- # Collect unregistered (new) lookups.
- lookupMap = GregariousIdentityDict(t.table.LookupList.Lookup)
- t.table.FeatureList.mapLookups(lookupMap)
- t.table.LookupList.mapLookups(lookupMap)
-
- # Record used lookups.
- lookupMap = AttendanceRecordingIdentityDict(t.table.LookupList.Lookup)
- t.table.FeatureList.mapLookups(lookupMap)
- t.table.LookupList.mapLookups(lookupMap)
- usedIndices = lookupMap.s
-
- # Remove unused lookups
- t.table.LookupList.Lookup = [
- l for i, l in enumerate(t.table.LookupList.Lookup) if i in usedIndices
- ]
-
- # Map back to indices.
- lookupMap = NonhashableDict(t.table.LookupList.Lookup)
- t.table.FeatureList.mapLookups(lookupMap)
- t.table.LookupList.mapLookups(lookupMap)
-
- t.table.LookupList.LookupCount = len(t.table.LookupList.Lookup)
-
- if GDEF and GDEF.table.Version >= 0x00010002:
- markFilteringSetMap = NonhashableDict(
- GDEF.table.MarkGlyphSetsDef.Coverage
- )
- t.table.LookupList.mapMarkFilteringSets(markFilteringSetMap)
-
- # TODO FeatureParams nameIDs
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/xmlWriter.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/xmlWriter.py
deleted file mode 100644
index 9a8dc3e3b7fe5eb13ea4b7ea369ced1da5555471..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/xmlWriter.py
+++ /dev/null
@@ -1,204 +0,0 @@
-"""xmlWriter.py -- Simple XML authoring class"""
-
-from fontTools.misc.textTools import byteord, strjoin, tobytes, tostr
-import sys
-import os
-import string
-
-INDENT = " "
-
-
-class XMLWriter(object):
- def __init__(
- self,
- fileOrPath,
- indentwhite=INDENT,
- idlefunc=None,
- encoding="utf_8",
- newlinestr="\n",
- ):
- if encoding.lower().replace("-", "").replace("_", "") != "utf8":
- raise Exception("Only UTF-8 encoding is supported.")
- if fileOrPath == "-":
- fileOrPath = sys.stdout
- if not hasattr(fileOrPath, "write"):
- self.filename = fileOrPath
- self.file = open(fileOrPath, "wb")
- self._closeStream = True
- else:
- self.filename = None
- # assume writable file object
- self.file = fileOrPath
- self._closeStream = False
-
- # Figure out if writer expects bytes or unicodes
- try:
- # The bytes check should be first. See:
- # https://github.com/fonttools/fonttools/pull/233
- self.file.write(b"")
- self.totype = tobytes
- except TypeError:
- # This better not fail.
- self.file.write("")
- self.totype = tostr
- self.indentwhite = self.totype(indentwhite)
- if newlinestr is None:
- self.newlinestr = self.totype(os.linesep)
- else:
- self.newlinestr = self.totype(newlinestr)
- self.indentlevel = 0
- self.stack = []
- self.needindent = 1
- self.idlefunc = idlefunc
- self.idlecounter = 0
- self._writeraw('')
- self.newline()
-
- def __enter__(self):
- return self
-
- def __exit__(self, exception_type, exception_value, traceback):
- self.close()
-
- def close(self):
- if self._closeStream:
- self.file.close()
-
- def write(self, string, indent=True):
- """Writes text."""
- self._writeraw(escape(string), indent=indent)
-
- def writecdata(self, string):
- """Writes text in a CDATA section."""
- self._writeraw("")
-
- def write8bit(self, data, strip=False):
- """Writes a bytes() sequence into the XML, escaping
- non-ASCII bytes. When this is read in xmlReader,
- the original bytes can be recovered by encoding to
- 'latin-1'."""
- self._writeraw(escape8bit(data), strip=strip)
-
- def write_noindent(self, string):
- """Writes text without indentation."""
- self._writeraw(escape(string), indent=False)
-
- def _writeraw(self, data, indent=True, strip=False):
- """Writes bytes, possibly indented."""
- if indent and self.needindent:
- self.file.write(self.indentlevel * self.indentwhite)
- self.needindent = 0
- s = self.totype(data, encoding="utf_8")
- if strip:
- s = s.strip()
- self.file.write(s)
-
- def newline(self):
- self.file.write(self.newlinestr)
- self.needindent = 1
- idlecounter = self.idlecounter
- if not idlecounter % 100 and self.idlefunc is not None:
- self.idlefunc()
- self.idlecounter = idlecounter + 1
-
- def comment(self, data):
- data = escape(data)
- lines = data.split("\n")
- self._writeraw("")
-
- def simpletag(self, _TAG_, *args, **kwargs):
- attrdata = self.stringifyattrs(*args, **kwargs)
- data = "<%s%s/>" % (_TAG_, attrdata)
- self._writeraw(data)
-
- def begintag(self, _TAG_, *args, **kwargs):
- attrdata = self.stringifyattrs(*args, **kwargs)
- data = "<%s%s>" % (_TAG_, attrdata)
- self._writeraw(data)
- self.stack.append(_TAG_)
- self.indent()
-
- def endtag(self, _TAG_):
- assert self.stack and self.stack[-1] == _TAG_, "nonmatching endtag"
- del self.stack[-1]
- self.dedent()
- data = "" % _TAG_
- self._writeraw(data)
-
- def dumphex(self, data):
- linelength = 16
- hexlinelength = linelength * 2
- chunksize = 8
- for i in range(0, len(data), linelength):
- hexline = hexStr(data[i : i + linelength])
- line = ""
- white = ""
- for j in range(0, hexlinelength, chunksize):
- line = line + white + hexline[j : j + chunksize]
- white = " "
- self._writeraw(line)
- self.newline()
-
- def indent(self):
- self.indentlevel = self.indentlevel + 1
-
- def dedent(self):
- assert self.indentlevel > 0
- self.indentlevel = self.indentlevel - 1
-
- def stringifyattrs(self, *args, **kwargs):
- if kwargs:
- assert not args
- attributes = sorted(kwargs.items())
- elif args:
- assert len(args) == 1
- attributes = args[0]
- else:
- return ""
- data = ""
- for attr, value in attributes:
- if not isinstance(value, (bytes, str)):
- value = str(value)
- data = data + ' %s="%s"' % (attr, escapeattr(value))
- return data
-
-
-def escape(data):
- data = tostr(data, "utf_8")
- data = data.replace("&", "&")
- data = data.replace("<", "<")
- data = data.replace(">", ">")
- data = data.replace("\r", "
")
- return data
-
-
-def escapeattr(data):
- data = escape(data)
- data = data.replace('"', """)
- return data
-
-
-def escape8bit(data):
- """Input is Unicode string."""
-
- def escapechar(c):
- n = ord(c)
- if 32 <= n <= 127 and c not in "<&>":
- return c
- else:
- return "&#" + repr(n) + ";"
-
- return strjoin(map(escapechar, data.decode("latin-1")))
-
-
-def hexStr(s):
- h = string.hexdigits
- r = ""
- for c in s:
- i = byteord(c)
- r = r + h[(i >> 4) & 0xF] + h[i & 0xF]
- return r
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fsspec/implementations/dbfs.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fsspec/implementations/dbfs.py
deleted file mode 100644
index 9f5b330cab9e751142794253d1072bab48b8bc29..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fsspec/implementations/dbfs.py
+++ /dev/null
@@ -1,457 +0,0 @@
-import base64
-import urllib
-
-import requests
-
-from fsspec import AbstractFileSystem
-from fsspec.spec import AbstractBufferedFile
-
-
-class DatabricksException(Exception):
- """
- Helper class for exceptions raised in this module.
- """
-
- def __init__(self, error_code, message):
- """Create a new DatabricksException"""
- super().__init__(message)
-
- self.error_code = error_code
- self.message = message
-
-
-class DatabricksFileSystem(AbstractFileSystem):
- """
- Get access to the Databricks filesystem implementation over HTTP.
- Can be used inside and outside of a databricks cluster.
- """
-
- def __init__(self, instance, token, **kwargs):
- """
- Create a new DatabricksFileSystem.
-
- Parameters
- ----------
- instance: str
- The instance URL of the databricks cluster.
- For example for an Azure databricks cluster, this
- has the form adb-AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos **") - gr.Markdown( - """ -
- Follow me for more!
- Keras Dreambooth - Lowpoly World Demo") - with gr.Row(): - with gr.Column(): - prompt = gr.Textbox(lines=1, value="lowpoly_world", label="Base Prompt") - negative_prompt = gr.Textbox(lines=1, value="deformed", label="Negative Prompt") - samples = gr.Slider(minimum=1, maximum=10, default=1, step=1, label="Number of Image") - num_steps = gr.Slider(label="Inference Steps",value=50) - run = gr.Button(value="Run") - with gr.Column(): - gallery = gr.Gallery(label="Outputs").style(grid=(1,2)) - - run.click(generate_images, inputs=[prompt,negative_prompt, samples, num_steps], outputs=gallery) - - gr.Examples([["photo of lowpoly_world","bad, ugly", 1, 50]], - [prompt,negative_prompt, samples,num_steps], gallery, generate_images) - gr.Markdown('\n Demo created by: Kadir Nar') - -demo.launch(debug=True) \ No newline at end of file diff --git a/spaces/kevinwang676/M4Singer/modules/parallel_wavegan/losses/stft_loss.py b/spaces/kevinwang676/M4Singer/modules/parallel_wavegan/losses/stft_loss.py deleted file mode 100644 index 74d2aa21ad30ba094c406366e652067462f49cd2..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/M4Singer/modules/parallel_wavegan/losses/stft_loss.py +++ /dev/null @@ -1,153 +0,0 @@ -# -*- coding: utf-8 -*- - -# Copyright 2019 Tomoki Hayashi -# MIT License (https://opensource.org/licenses/MIT) - -"""STFT-based Loss modules.""" - -import torch -import torch.nn.functional as F - - -def stft(x, fft_size, hop_size, win_length, window): - """Perform STFT and convert to magnitude spectrogram. - - Args: - x (Tensor): Input signal tensor (B, T). - fft_size (int): FFT size. - hop_size (int): Hop size. - win_length (int): Window length. - window (str): Window function type. - - Returns: - Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). - - """ - x_stft = torch.stft(x, fft_size, hop_size, win_length, window) - real = x_stft[..., 0] - imag = x_stft[..., 1] - - # NOTE(kan-bayashi): clamp is needed to avoid nan or inf - return torch.sqrt(torch.clamp(real ** 2 + imag ** 2, min=1e-7)).transpose(2, 1) - - -class SpectralConvergengeLoss(torch.nn.Module): - """Spectral convergence loss module.""" - - def __init__(self): - """Initilize spectral convergence loss module.""" - super(SpectralConvergengeLoss, self).__init__() - - def forward(self, x_mag, y_mag): - """Calculate forward propagation. - - Args: - x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). - y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). - - Returns: - Tensor: Spectral convergence loss value. - - """ - return torch.norm(y_mag - x_mag, p="fro") / torch.norm(y_mag, p="fro") - - -class LogSTFTMagnitudeLoss(torch.nn.Module): - """Log STFT magnitude loss module.""" - - def __init__(self): - """Initilize los STFT magnitude loss module.""" - super(LogSTFTMagnitudeLoss, self).__init__() - - def forward(self, x_mag, y_mag): - """Calculate forward propagation. - - Args: - x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). - y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). - - Returns: - Tensor: Log STFT magnitude loss value. - - """ - return F.l1_loss(torch.log(y_mag), torch.log(x_mag)) - - -class STFTLoss(torch.nn.Module): - """STFT loss module.""" - - def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window"): - """Initialize STFT loss module.""" - super(STFTLoss, self).__init__() - self.fft_size = fft_size - self.shift_size = shift_size - self.win_length = win_length - self.window = getattr(torch, window)(win_length) - self.spectral_convergenge_loss = SpectralConvergengeLoss() - self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss() - - def forward(self, x, y): - """Calculate forward propagation. - - Args: - x (Tensor): Predicted signal (B, T). - y (Tensor): Groundtruth signal (B, T). - - Returns: - Tensor: Spectral convergence loss value. - Tensor: Log STFT magnitude loss value. - - """ - x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window) - y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window) - sc_loss = self.spectral_convergenge_loss(x_mag, y_mag) - mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag) - - return sc_loss, mag_loss - - -class MultiResolutionSTFTLoss(torch.nn.Module): - """Multi resolution STFT loss module.""" - - def __init__(self, - fft_sizes=[1024, 2048, 512], - hop_sizes=[120, 240, 50], - win_lengths=[600, 1200, 240], - window="hann_window"): - """Initialize Multi resolution STFT loss module. - - Args: - fft_sizes (list): List of FFT sizes. - hop_sizes (list): List of hop sizes. - win_lengths (list): List of window lengths. - window (str): Window function type. - - """ - super(MultiResolutionSTFTLoss, self).__init__() - assert len(fft_sizes) == len(hop_sizes) == len(win_lengths) - self.stft_losses = torch.nn.ModuleList() - for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths): - self.stft_losses += [STFTLoss(fs, ss, wl, window)] - - def forward(self, x, y): - """Calculate forward propagation. - - Args: - x (Tensor): Predicted signal (B, T). - y (Tensor): Groundtruth signal (B, T). - - Returns: - Tensor: Multi resolution spectral convergence loss value. - Tensor: Multi resolution log STFT magnitude loss value. - - """ - sc_loss = 0.0 - mag_loss = 0.0 - for f in self.stft_losses: - sc_l, mag_l = f(x, y) - sc_loss += sc_l - mag_loss += mag_l - sc_loss /= len(self.stft_losses) - mag_loss /= len(self.stft_losses) - - return sc_loss, mag_loss diff --git a/spaces/kevinwang676/VoiceChanger/src/face3d/models/arcface_torch/torch2onnx.py b/spaces/kevinwang676/VoiceChanger/src/face3d/models/arcface_torch/torch2onnx.py deleted file mode 100644 index fc26ab82e552331bc8d75b34e81000418f4d38ec..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/VoiceChanger/src/face3d/models/arcface_torch/torch2onnx.py +++ /dev/null @@ -1,59 +0,0 @@ -import numpy as np -import onnx -import torch - - -def convert_onnx(net, path_module, output, opset=11, simplify=False): - assert isinstance(net, torch.nn.Module) - img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32) - img = img.astype(np.float) - img = (img / 255. - 0.5) / 0.5 # torch style norm - img = img.transpose((2, 0, 1)) - img = torch.from_numpy(img).unsqueeze(0).float() - - weight = torch.load(path_module) - net.load_state_dict(weight) - net.eval() - torch.onnx.export(net, img, output, keep_initializers_as_inputs=False, verbose=False, opset_version=opset) - model = onnx.load(output) - graph = model.graph - graph.input[0].type.tensor_type.shape.dim[0].dim_param = 'None' - if simplify: - from onnxsim import simplify - model, check = simplify(model) - assert check, "Simplified ONNX model could not be validated" - onnx.save(model, output) - - -if __name__ == '__main__': - import os - import argparse - from backbones import get_model - - parser = argparse.ArgumentParser(description='ArcFace PyTorch to onnx') - parser.add_argument('input', type=str, help='input backbone.pth file or path') - parser.add_argument('--output', type=str, default=None, help='output onnx path') - parser.add_argument('--network', type=str, default=None, help='backbone network') - parser.add_argument('--simplify', type=bool, default=False, help='onnx simplify') - args = parser.parse_args() - input_file = args.input - if os.path.isdir(input_file): - input_file = os.path.join(input_file, "backbone.pth") - assert os.path.exists(input_file) - model_name = os.path.basename(os.path.dirname(input_file)).lower() - params = model_name.split("_") - if len(params) >= 3 and params[1] in ('arcface', 'cosface'): - if args.network is None: - args.network = params[2] - assert args.network is not None - print(args) - backbone_onnx = get_model(args.network, dropout=0) - - output_path = args.output - if output_path is None: - output_path = os.path.join(os.path.dirname(__file__), 'onnx') - if not os.path.exists(output_path): - os.makedirs(output_path) - assert os.path.isdir(output_path) - output_file = os.path.join(output_path, "%s.onnx" % model_name) - convert_onnx(backbone_onnx, input_file, output_file, simplify=args.simplify) diff --git a/spaces/keyikai/bing/Dockerfile b/spaces/keyikai/bing/Dockerfile deleted file mode 100644 index 5a424c3084a247240ff04cc6e4b9092f389c3d2b..0000000000000000000000000000000000000000 --- a/spaces/keyikai/bing/Dockerfile +++ /dev/null @@ -1,33 +0,0 @@ -# Build Stage -# 使用 golang:alpine 作为构建阶段的基础镜像 -FROM golang:alpine AS builder - -# 添加 git，并且清除缓存🧹 -RUN apk --no-cache add git && \ - git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app && \ - apk del git - -# 设置工作目录 -WORKDIR /workspace/app - -# 编译 go 项目 -RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go - -# Runtime Stage -# 使用轻量级的 alpine 镜像🪞 -FROM alpine - -# 设置工作目录💼 -WORKDIR /workspace/app - -# 从构建阶段复制编译后的二进制文件👔 -COPY --from=builder /workspace/app/go-proxy-bingai . - -# （可选）设置环境变量✍️ -ENV Go_Proxy_BingAI_USER_TOKEN_1="G4hJ9k544565uhjjhjlkjh68ah3naaYc0FvIjHmLzXeRfAq" - -# 端口 -EXPOSE 8080 - -# 容器运行✅ -CMD ["/workspace/app/go-proxy-bingai"] diff --git a/spaces/kidcoconut/spcdkr_omdenasaudi_liverhccxai/lib/models/mdl_xgb.py b/spaces/kidcoconut/spcdkr_omdenasaudi_liverhccxai/lib/models/mdl_xgb.py deleted file mode 100644 index 8b6fe0d98012484abbbc8091ff0f027ae9f53473..0000000000000000000000000000000000000000 --- a/spaces/kidcoconut/spcdkr_omdenasaudi_liverhccxai/lib/models/mdl_xgb.py +++ /dev/null @@ -1,66 +0,0 @@ -import pandas as pd -from sklearn.ensemble import GradientBoostingClassifier -import lib.utils as libPaths -import pickle -import sys - - -m_kstrFile = __file__ -m_kstrDataPath = libPaths.pth_data -m_kstrBinModelPath = libPaths.pth_binModels -m_kstrModelPath_gbc = m_kstrBinModelPath + 'gbc_model_colab.pkl' -m_kstrModelPath_prov111 = m_kstrBinModelPath + 'prov_gbc_v1.1.1_32cols.pkl' #--- ERROR: __randomstate_ctor() takes from 0 to 1 positional arguments but 2 were given -m_kstrModelPath_prov121 = m_kstrBinModelPath + 'prov_gbc_v1.2.1_32cols.pkl' -m_kstrModelPath_prov_py3816_sk111hp = m_kstrBinModelPath + 'prov_gbc_py3816_sk111hp_32cols.pkl' -m_kstrModelPath = m_kstrModelPath_prov_py3816_sk111hp - -m_blnTraceOn = True - - - -#--- Supervised: xg boost; gradient boosting classifier -def load_fromPkl(): - try: - with open(m_kstrModelPath, 'rb') as filPkl: - mdlAnoms = pickle.load(filPkl) - return mdlAnoms - - except: - e = sys.exc_info() - print("ERROR (mdl_xgb.load_fromPkl_genError): ", e) - - - -def save_toPkl(mdlAnoms): - with open(m_kstrModelPath, 'wb') as filPkl: - pickle.dump(mdlAnoms, filPkl) - return mdlAnoms - - - -def predict(npaData): - - try: - #--- input: numpy.ndarray of feature eng, and scaled data - mdlAnoms = load_fromPkl() - if (m_blnTraceOn): print("TRACE (mdl_xgb.predict): data loaded ... ") - npaPredict = mdlAnoms.predict(npaData) - - except: - e = sys.exc_info() - print("ERROR (mdl_xgb.predict_genError1): ", e) - - - #--- AttributeError: 'GradientBoostingClassifier' object has no attribute '_loss' - #--- version of scikit-learn? Monika: ?.?.? ; Iain: 1.2.0 - - #print("INFO (type.npaPredict): ", type(npaPredict)) - #if (m_blnTraceOn): print("TRACE (mdl_xgb.predict) npaPredict.shape: ", npaPredict.shape) - return npaPredict - - -def train(pdfTrainData): - mdlAnoms = GradientBoostingClassifier() - mdlAnoms.fit(pdfTrainData.values) - save_toPkl(mdlAnoms) - return mdlAnoms diff --git a/spaces/kira4424/VITS-fast-fine-tuning/losses.py b/spaces/kira4424/VITS-fast-fine-tuning/losses.py deleted file mode 100644 index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000 --- a/spaces/kira4424/VITS-fast-fine-tuning/losses.py +++ /dev/null @@ -1,61 +0,0 @@ -import torch -from torch.nn import functional as F - -import 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() - - 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/koajoel/PolyFormer/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py b/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py deleted file mode 100644 index 9cf121081fbde2f5085ed380f0841649d143a4be..0000000000000000000000000000000000000000 --- a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/preprocessing/vad/__init__.py +++ /dev/null @@ -1,192 +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 collections -import contextlib -import wave - -try: - import webrtcvad -except ImportError: - raise ImportError("Please install py-webrtcvad: pip install webrtcvad") -import argparse -import os -import logging -from tqdm import tqdm - -AUDIO_SUFFIX = '.wav' -FS_MS = 30 -SCALE = 6e-5 -THRESHOLD = 0.3 - - -def read_wave(path): - """Reads a .wav file. - Takes the path, and returns (PCM audio data, sample rate). - """ - with contextlib.closing(wave.open(path, 'rb')) as wf: - num_channels = wf.getnchannels() - assert num_channels == 1 - sample_width = wf.getsampwidth() - assert sample_width == 2 - sample_rate = wf.getframerate() - assert sample_rate in (8000, 16000, 32000, 48000) - pcm_data = wf.readframes(wf.getnframes()) - return pcm_data, sample_rate - - -def write_wave(path, audio, sample_rate): - """Writes a .wav file. - Takes path, PCM audio data, and sample rate. - """ - with contextlib.closing(wave.open(path, 'wb')) as wf: - wf.setnchannels(1) - wf.setsampwidth(2) - wf.setframerate(sample_rate) - wf.writeframes(audio) - - -class Frame(object): - """Represents a "frame" of audio data.""" - def __init__(self, bytes, timestamp, duration): - self.bytes = bytes - self.timestamp = timestamp - self.duration = duration - - -def frame_generator(frame_duration_ms, audio, sample_rate): - """Generates audio frames from PCM audio data. - Takes the desired frame duration in milliseconds, the PCM data, and - the sample rate. - Yields Frames of the requested duration. - """ - n = int(sample_rate * (frame_duration_ms / 1000.0) * 2) - offset = 0 - timestamp = 0.0 - duration = (float(n) / sample_rate) / 2.0 - while offset + n < len(audio): - yield Frame(audio[offset:offset + n], timestamp, duration) - timestamp += duration - offset += n - - -def vad_collector(sample_rate, frame_duration_ms, - padding_duration_ms, vad, frames): - """Filters out non-voiced audio frames. - Given a webrtcvad.Vad and a source of audio frames, yields only - the voiced audio. - Uses a padded, sliding window algorithm over the audio frames. - When more than 90% of the frames in the window are voiced (as - reported by the VAD), the collector triggers and begins yielding - audio frames. Then the collector waits until 90% of the frames in - the window are unvoiced to detrigger. - The window is padded at the front and back to provide a small - amount of silence or the beginnings/endings of speech around the - voiced frames. - Arguments: - sample_rate - The audio sample rate, in Hz. - frame_duration_ms - The frame duration in milliseconds. - padding_duration_ms - The amount to pad the window, in milliseconds. - vad - An instance of webrtcvad.Vad. - frames - a source of audio frames (sequence or generator). - Returns: A generator that yields PCM audio data. - """ - num_padding_frames = int(padding_duration_ms / frame_duration_ms) - # We use a deque for our sliding window/ring buffer. - ring_buffer = collections.deque(maxlen=num_padding_frames) - # We have two states: TRIGGERED and NOTTRIGGERED. We start in the - # NOTTRIGGERED state. - triggered = False - - voiced_frames = [] - for frame in frames: - is_speech = vad.is_speech(frame.bytes, sample_rate) - - # sys.stdout.write('1' if is_speech else '0') - if not triggered: - ring_buffer.append((frame, is_speech)) - num_voiced = len([f for f, speech in ring_buffer if speech]) - # If we're NOTTRIGGERED and more than 90% of the frames in - # the ring buffer are voiced frames, then enter the - # TRIGGERED state. - if num_voiced > 0.9 * ring_buffer.maxlen: - triggered = True - # We want to yield all the audio we see from now until - # we are NOTTRIGGERED, but we have to start with the - # audio that's already in the ring buffer. - for f, _ in ring_buffer: - voiced_frames.append(f) - ring_buffer.clear() - else: - # We're in the TRIGGERED state, so collect the audio data - # and add it to the ring buffer. - voiced_frames.append(frame) - ring_buffer.append((frame, is_speech)) - num_unvoiced = len([f for f, speech in ring_buffer if not speech]) - # If more than 90% of the frames in the ring buffer are - # unvoiced, then enter NOTTRIGGERED and yield whatever - # audio we've collected. - if num_unvoiced > 0.9 * ring_buffer.maxlen: - triggered = False - yield [b''.join([f.bytes for f in voiced_frames]), - voiced_frames[0].timestamp, voiced_frames[-1].timestamp] - ring_buffer.clear() - voiced_frames = [] - # If we have any leftover voiced audio when we run out of input, - # yield it. - if voiced_frames: - yield [b''.join([f.bytes for f in voiced_frames]), - voiced_frames[0].timestamp, voiced_frames[-1].timestamp] - - -def main(args): - # create output folder - try: - cmd = f"mkdir -p {args.out_path}" - os.system(cmd) - except Exception: - logging.error("Can not create output folder") - exit(-1) - - # build vad object - vad = webrtcvad.Vad(int(args.agg)) - # iterating over wavs in dir - for file in tqdm(os.listdir(args.in_path)): - if file.endswith(AUDIO_SUFFIX): - audio_inpath = os.path.join(args.in_path, file) - audio_outpath = os.path.join(args.out_path, file) - audio, sample_rate = read_wave(audio_inpath) - frames = frame_generator(FS_MS, audio, sample_rate) - frames = list(frames) - segments = vad_collector(sample_rate, FS_MS, 300, vad, frames) - merge_segments = list() - timestamp_start = 0.0 - timestamp_end = 0.0 - # removing start, end, and long sequences of sils - for i, segment in enumerate(segments): - merge_segments.append(segment[0]) - if i and timestamp_start: - sil_duration = segment[1] - timestamp_end - if sil_duration > THRESHOLD: - merge_segments.append(int(THRESHOLD / SCALE)*(b'\x00')) - else: - merge_segments.append(int((sil_duration / SCALE))*(b'\x00')) - timestamp_start = segment[1] - timestamp_end = segment[2] - segment = b''.join(merge_segments) - write_wave(audio_outpath, segment, sample_rate) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='Apply vad to a file of fils.') - parser.add_argument('in_path', type=str, help='Path to the input files') - parser.add_argument('out_path', type=str, - help='Path to save the processed files') - parser.add_argument('--agg', type=int, default=3, - help='The level of aggressiveness of the VAD: [0-3]') - args = parser.parse_args() - - main(args) diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/py23.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/py23.py deleted file mode 100644 index 29f634d624b7df125722c3bae594c1d39a835aec..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/py23.py +++ /dev/null @@ -1,96 +0,0 @@ -"""Python 2/3 compat layer leftovers.""" - -import decimal as _decimal -import math as _math -import warnings -from contextlib import redirect_stderr, redirect_stdout -from io import BytesIO -from io import StringIO as UnicodeIO -from types import SimpleNamespace - -from .textTools import Tag, bytechr, byteord, bytesjoin, strjoin, tobytes, tostr - -warnings.warn( - "The py23 module has been deprecated and will be removed in a future release. " - "Please update your code.", - DeprecationWarning, -) - -__all__ = [ - "basestring", - "bytechr", - "byteord", - "BytesIO", - "bytesjoin", - "open", - "Py23Error", - "range", - "RecursionError", - "round", - "SimpleNamespace", - "StringIO", - "strjoin", - "Tag", - "tobytes", - "tostr", - "tounicode", - "unichr", - "unicode", - "UnicodeIO", - "xrange", - "zip", -] - - -class Py23Error(NotImplementedError): - pass - - -RecursionError = RecursionError -StringIO = UnicodeIO - -basestring = str -isclose = _math.isclose -isfinite = _math.isfinite -open = open -range = range -round = round3 = round -unichr = chr -unicode = str -zip = zip - -tounicode = tostr - - -def xrange(*args, **kwargs): - raise Py23Error("'xrange' is not defined. Use 'range' instead.") - - -def round2(number, ndigits=None): - """ - Implementation of Python 2 built-in round() function. - Rounds a number to a given precision in decimal digits (default - 0 digits). The result is a floating point number. Values are rounded - to the closest multiple of 10 to the power minus ndigits; if two - multiples are equally close, rounding is done away from 0. - ndigits may be negative. - See Python 2 documentation: - https://docs.python.org/2/library/functions.html?highlight=round#round - """ - if ndigits is None: - ndigits = 0 - - if ndigits < 0: - exponent = 10 ** (-ndigits) - quotient, remainder = divmod(number, exponent) - if remainder >= exponent // 2 and number >= 0: - quotient += 1 - return float(quotient * exponent) - else: - exponent = _decimal.Decimal("10") ** (-ndigits) - - d = _decimal.Decimal.from_float(number).quantize( - exponent, rounding=_decimal.ROUND_HALF_UP - ) - - return float(d) diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpx/__version__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpx/__version__.py deleted file mode 100644 index 6a8e63c60262fc2650cb5c71514a4b23f949aa58..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpx/__version__.py +++ /dev/null @@ -1,3 +0,0 @@ -__title__ = "httpx" -__description__ = "A next generation HTTP client, for Python 3." -__version__ = "0.24.1" diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/cli/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/cli/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/leogabraneth/text-generation-webui-main/.github/pull_request_template.md b/spaces/leogabraneth/text-generation-webui-main/.github/pull_request_template.md deleted file mode 100644 index 51e26b13a38889a38cac5392b6e22190fd75a8b7..0000000000000000000000000000000000000000 --- a/spaces/leogabraneth/text-generation-webui-main/.github/pull_request_template.md +++ /dev/null @@ -1,3 +0,0 @@ -## Checklist: - -- [ ] I have read the [Contributing guidelines](https://github.com/oobabooga/text-generation-webui/wiki/Contributing-guidelines). diff --git a/spaces/lewiswu1209/MockingBird/synthesizer/models/sublayer/common/highway_network.py b/spaces/lewiswu1209/MockingBird/synthesizer/models/sublayer/common/highway_network.py deleted file mode 100644 index d311c6924db6dfc247f69cc266d6c1975b6e03cd..0000000000000000000000000000000000000000 --- a/spaces/lewiswu1209/MockingBird/synthesizer/models/sublayer/common/highway_network.py +++ /dev/null @@ -1,17 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -class HighwayNetwork(nn.Module): - def __init__(self, size): - super().__init__() - self.W1 = nn.Linear(size, size) - self.W2 = nn.Linear(size, size) - self.W1.bias.data.fill_(0.) - - def forward(self, x): - x1 = self.W1(x) - x2 = self.W2(x) - g = torch.sigmoid(x2) - y = g * F.relu(x1) + (1. - g) * x - return y diff --git a/spaces/librarian-bots/README/README.md b/spaces/librarian-bots/README/README.md deleted file mode 100644 index cc6bca4fa97111225f1be2ccba39ec71954b728c..0000000000000000000000000000000000000000 --- a/spaces/librarian-bots/README/README.md +++ /dev/null @@ -1,74 +0,0 @@ ---- -title: README -emoji: 🤖 -colorFrom: red -colorTo: pink -sdk: static -pinned: false ---- - -Hugging Face Librarian Bots- -✨ Curating the Hugging Face Hub one PR at a time. ✨ - -
-
-
-
-The Hugging Face Hub is the primary place for sharing machine learning models, datasets, and demos. It currently holds over 200,000 models, 40,000 datasets, and 100,000 machine learning demos.
-
-The `Librarian Bots` organization is an effort by Hugging Face's [Machine Learning Librarian](https://huggingface.co/davanstrien) to use machine learning to enhance metadata and documentation for material shared on the Hub with the ultimate goal of making it easier for people (and bots!) to find what they are looking for on the Hub. This organization is used to share datasets, models, and Spaces which help achieve this goal.
-
-## 👾 Spaces
-
-
-
-
-
-📚 Spaces Related to Hugging Face Papers- - - [Recommend Similar Papers](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers): a Space that allows you to find papers similar to a given paper. - - [Collections Reading List Generator](https://huggingface.co/spaces/librarian-bots/collection-reading-list-generator): a Space that allows you to generate a reading list for a given Hugging Face Collection - - [📄🔗: Extract linked papers from a Hugging Face Collection](https://huggingface.co/spaces/librarian-bots/collection_papers_extractor): extract all the papers associated with items in a Hugging Face Collection. - - [📃 Hugging Face Paper Claimer 📃](https://huggingface.co/spaces/librarian-bots/claim-papers): a space that helps you to claim papers you authored on the Hugging Face Hub. -
-
-
-Spaces related to metadata- - - [🤖 Librarian Bot Metadata Request Service 🤖](https://huggingface.co/spaces/librarian-bots/metadata_request_service): With a few clicks, enrich your Hugging Face models with key metadata! - - [MetaRefine](https://huggingface.co/spaces/librarian-bots/MetaRefine): refine Hub search results by metadata quality and model card length. -- [metadata explorer](https://huggingface.co/spaces/librarian-bots/metadata_explorer): a space for exploring high-level information about the metadata associated with models hosted on the Hugging Face Hub. - -
-
-
-Spaces for exploring and keeping track of repositories on the Hub- - - [Dataset-to-Model Monitor](https://huggingface.co/spaces/librarian-bots/dataset-to-model-monitor): track datasets hosted on the Hugging Face Hub and get a notification when new models are trained on the dataset you are tracking. - - [Base Model Explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer): This Space allows you to find children's models for a given base model and view the popularity of models for fine-tuning. - - [Hugging Face Datasets Semantic Search](https://huggingface.co/spaces/librarian-bots/huggingface-datasets-semantic-search): a Space that allows you to use semantic search to find relevant datasets on the Hugging Face Hub. - -- -## 💽 Datasets - -
-
-
-Datasets for model and dataset cards- -- [Model Cards with metadata](https://huggingface.co/datasets/librarian-bots/model_cards_with_metadata): a dataset containing model cards for models hosted on the Hugging Face hub with first commit information for each model. Model cards are intended to help communicate the strengths and weaknesses of machine learning models. Whilst these model cards are primarily intended to be read by a human they are themselves also interesting corpus that can be used to explore models hosted on the Hub in various ways. - -- [Dataset Cards With Metadata](https://huggingface.co/datasets/librarian-bots/dataset_cards_with_metadatat): a dataset containing dataset cards for datasets hosted on the Hugging Face hub with first commit information for each dataset. Dataset cards are intended to help communicate the strengths and weaknesses of machine learning datasets. Whilst these dataset cards are primarily intended to be read by a human they are themselves also interesting corpus that can be used to explore datasets hosted on the Hub in various ways. - -- -## 🤖 Models - -- [BERTopic model card bias topic model](https://huggingface.co/librarian-bots/BERTopic_model_card_bias): a BERTopic model trained on the bias section of model cards hosted on the Hub. The goal of this model is to explore which topics are discussed in the bias section of model cards. Potentially in the future models such as this could also be used to detect 'drift' in the kinds of bias being discussed in model cards hosted on the Hub. - - -# Getting in touch - -If you want to collaborate on improving metadata on the Hugging Face Hub or have ideas for other related projects, reach out to [Daniel](https://huggingface.co/davanstrien) on Twitter (@vanstriendaniel) or via email (Daniel (at) our website). \ No newline at end of file diff --git a/spaces/limcheekin/WizardCoder-Python-13B-V1.0-GGUF/start_server.sh b/spaces/limcheekin/WizardCoder-Python-13B-V1.0-GGUF/start_server.sh deleted file mode 100644 index 9ec315638ea647912c58381a9409f1bea74d0180..0000000000000000000000000000000000000000 --- a/spaces/limcheekin/WizardCoder-Python-13B-V1.0-GGUF/start_server.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/sh - -# For mlock support -ulimit -l unlimited - -python3 -B main.py diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/!FULL! Crack Visual WebGui 6 4.md b/spaces/lincquiQcaudo/Top-20-Diffusion/!FULL! Crack Visual WebGui 6 4.md deleted file mode 100644 index c75b849ff95be46f3698984439910fd496909daf..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/!FULL! Crack Visual WebGui 6 4.md +++ /dev/null @@ -1,6 +0,0 @@ - Crack Visual WebGui 6 4Download ——— https://bytlly.com/2uGy6G - -6. 2 Following 6 Followers. Denver, CO, United States. Follow. About. Hello! ... Gizmox Visual WebGui Professional Studio Free Download · izotope ozone 8 Crack Torrent Keygen for Windows, 7, 8, 10 + Full Free Download 1fdad05405 - - - diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Colored Sprite Mod Undertale.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Colored Sprite Mod Undertale.md deleted file mode 100644 index 35946e92776cd3e7e837ddeb6ed26d4121803b5e..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Colored Sprite Mod Undertale.md +++ /dev/null @@ -1,20 +0,0 @@ - - How to Add Color to Your Undertale Experience with the Colored Sprites Mod-Undertale is a beloved indie game that has charmed millions of players with its quirky characters, witty dialogue, and emotional story. But did you know that you can enhance your Undertale experience with a mod that adds color to all the sprites in the game? -The Colored Sprites Mod is a fan-made project that aims to bring more life and detail to the battle and dialogue sprites in Undertale. It was originally created by Pr3tz31 and his team of pixel artists, and later updated by Michael_King for the latest version of Undertale. The mod covers every monster in the game, except for Froggit and Napstablook who are still white like their overworld sprites. -colored sprite mod undertaleDownload 🔗 https://bytlly.com/2uGwCd - The mod is easy to install and compatible with both Windows and Mac versions of Undertale. You just need to download the mod file and replace the original data.win file in your Undertale folder with the modded one. 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That's it! You have successfully uninstalled the Colored Sprites Mod and returned to your vanilla Undertale experience. If you ever want to reinstall the mod, you can follow the same steps as before. -We hope this article helped you learn more about the Colored Sprites Mod and how to install and uninstall it. If you have any questions or feedback, feel free to leave a comment below. 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- - diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Dawn Of War 2 Chaos Rising Trainer Download 2.6.rar.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Dawn Of War 2 Chaos Rising Trainer Download 2.6.rar.md deleted file mode 100644 index 9cf2d77db2c082ce871bc9a5d46d1ceec1df8260..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Dawn Of War 2 Chaos Rising Trainer Download 2.6.rar.md +++ /dev/null @@ -1,78 +0,0 @@ - dawn of war 2 chaos rising trainer download 2.6.rarDownload File ✯✯✯ https://bytlly.com/2uGy07 - -not being copied to the /res folder. - -Added support for Intel Celeron and Atom CPUs, allowing RivaTuner emulation without a lot of hassle. - -Added Turkish, Swedish, and Romanian support. - -Added German, French, and Italian translations. - -Added support for video mode changes using Xorg autoconfiguration. - -Added support for Enigma2 video emulation. - -Fixed ArtDeco support for missing AudioBuffer Channel array. - -Fixed part of the compiler not working when the /nodefaultlib/ option is set. - -Fixed intel instructions by moving some assembly that was previously in the global/compiler.h file. - -Fixed call to ReadFile() from the main engine. - -Fixed a typo in an include. - -Fixed an error with binding a char* to a double*. - -Fixed some memory leaks. - -Fixed GetPalette() and related bits of code. - -Fixed a bug with SVGA memory virtualization. - -Fixed drivers not starting when the CpuVClkClockFreq parameter is set. - -Changed a macro that was using the wrong variable name, causing problems with the timing of the clock engine. - -Changed the use of the printf function, using the sprintf function instead. - -Changed the local var and multiversecount var types from string to uint16_t and uint8_t respectively. - -Changed the base classes for the driver and the core engine. - -Changed the name of the core engine from "CoreEngine" to "Engine". - -Changed the name of the driver from "Driver" to "DriverCore". - -Changed some compiler warnings to errors. - -Changed the name of the ProjectVCLib. - -Changed the name of the Archive. - -Changed the name of the DbgStr. - -Changed the name of the MessageBox. - -Changed the name of the LogBox. - -Changed the name of the Error. - -Changed the names of the units to be more specific. - -Changed the name of the UpdateBox. - -Changed the name of the SaveBox. - -Changed the name of the Buttons. - -Changed the name of the VCLib to the ProjectVCLib. - -Changed the unit names to be clearer. - -Changed the names of some debug files. - -Changed the name of the console class to better match the rest of the code 4fefd39f24 - - - diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Far Cry 3 Trainer 0101 BEST.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Far Cry 3 Trainer 0101 BEST.md deleted file mode 100644 index 20de98888bffb97dd3b4421b45402152b04dd518..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Far Cry 3 Trainer 0101 BEST.md +++ /dev/null @@ -1,6 +0,0 @@ - Far Cry 3 Trainer 0101Download Zip ⚹ https://bytlly.com/2uGvEn - -... https://www.moddb.com/addons/dungeon-keeper-2-bonus-pack-3 ... https://www.moddb.com/downloads/cathartic-farcry3-mod ... https://www.moddb.com/downloads/praetorians-trainer-for-mod-complex-260 https://www.moddb.com/addons/new-sum-flare ... https://www.moddb.com/downloads/full-pr-deviation-v0101Â ... 1fdad05405 - - - diff --git a/spaces/longlian/llm-grounded-diffusion/shared.py b/spaces/longlian/llm-grounded-diffusion/shared.py deleted file mode 100644 index 021bf54db00cbd5dbf6922cf6173a189ff65d528..0000000000000000000000000000000000000000 --- a/spaces/longlian/llm-grounded-diffusion/shared.py +++ /dev/null @@ -1,15 +0,0 @@ -from models import load_sd, sam - - -DEFAULT_SO_NEGATIVE_PROMPT = "artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate, two, many, group, occlusion, occluded, side, border, collate" -DEFAULT_OVERALL_NEGATIVE_PROMPT = "artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate" - - -use_fp16 = False - -sd_key = "gligen/diffusers-generation-text-box" - -print(f"Using SD: {sd_key}") -model_dict = load_sd(key=sd_key, use_fp16=use_fp16, load_inverse_scheduler=False) - -sam_model_dict = sam.load_sam() diff --git a/spaces/luckybender/ChatGPT4/README.md b/spaces/luckybender/ChatGPT4/README.md deleted file mode 100644 index 7938de14e5355209aaae713f289ca469181bbb17..0000000000000000000000000000000000000000 --- a/spaces/luckybender/ChatGPT4/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Chat-with-GPT4 -emoji: 🚀 -colorFrom: red -colorTo: indigo -sdk: gradio -sdk_version: 3.21.0 -app_file: app.py -pinned: false -license: mit -duplicated_from: ysharma/ChatGPT4 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/m3hrdadfi/zabanshenas/libs/normalizer.py b/spaces/m3hrdadfi/zabanshenas/libs/normalizer.py deleted file mode 100644 index f4d79804580a634a74b39107a3742f9fed8cdc1e..0000000000000000000000000000000000000000 --- a/spaces/m3hrdadfi/zabanshenas/libs/normalizer.py +++ /dev/null @@ -1,86 +0,0 @@ -import re -import regex -import sys -import textwrap -from typing import Any, Dict, Optional - -punctuations = [ - '!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '.', - '/', ':', ';', '<', '=', '>', '?', '@', '[', '\\', ']', '^', '_', - '`', '{', '|', '}', '~', '»', '«', '“', '”', "-", -] - - -class Normalizer: - """A general normalizer for every language""" - - _whitelist = r"[" + "\p{N}\p{L}\p{M}" + re.escape("".join(punctuations)) + "]+" - _dictionary = {} - - def __init__( - self, - whitelist: str = None, - dictionary: Dict[str, str] = None, - ) -> None: - self.whitelist = whitelist if whitelist and isinstance(whitelist, str) else self._whitelist - self.dictionary = dictionary if dictionary and isinstance(dictionary, dict) else self._dictionary - - def chars_to_map(self, sentence: str) -> str: - """Maps every character, words, and phrase into a proper one. - - Args: - sentence (str): A piece of text. - """ - if not len(self.dictionary) > 0: - return sentence - - pattern = "|".join(map(re.escape, self.dictionary.keys())) - return re.sub(pattern, lambda m: self.dictionary[m.group()], str(sentence)) - - def chars_to_preserve( - self, - sentence: str, - ) -> str: - """Keeps specified characters from sentence - - Args: - sentence (str): A piece of text. - """ - try: - tokenized = regex.findall(self.whitelist, sentence) - return " ".join(tokenized) - except Exception as error: - print( - textwrap.dedent( - f""" - Bad characters range {self.whitelist}, - {error} - """ - ) - ) - raise - - def text_level_normalizer(self, text: str) -> str: - """A text level of normalization""" - - text = regex.sub(r"([" + re.escape("".join(punctuations)) + "])", r" \1 ", text) - text = text.strip() - - return text - - def __call__( - self, - text: str, - do_lowercase: Optional[bool] = False - ) -> Any: - """Normalization caller""" - - text = self.chars_to_map(text) - text = self.chars_to_preserve(text) - text = self.text_level_normalizer(text) - text = re.sub(r"\s+", " ", text) - - if do_lowercase: - text = text.lower() - - return text diff --git a/spaces/ma-xu/LIVE/pybind11/include/pybind11/buffer_info.h b/spaces/ma-xu/LIVE/pybind11/include/pybind11/buffer_info.h deleted file mode 100644 index 8349a46b8b92f87e9f641b30b7b86617b7f85d50..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/pybind11/include/pybind11/buffer_info.h +++ /dev/null @@ -1,116 +0,0 @@ -/* - pybind11/buffer_info.h: Python buffer object interface - - Copyright (c) 2016 Wenzel Jakob |