901 lines
38 KiB
Python
901 lines
38 KiB
Python
import csv
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import gc
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import io
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import json
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import math
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import os
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import random
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from contextlib import contextmanager
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from threading import Thread
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import albumentations
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import cv2
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import librosa
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import numpy as np
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import torch
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import torchvision.transforms as transforms
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from decord import VideoReader
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from einops import rearrange
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from func_timeout import FunctionTimedOut, func_timeout
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from PIL import Image
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from torch.utils.data import BatchSampler, Sampler
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from torch.utils.data.dataset import Dataset
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from .utils import (VIDEO_READER_TIMEOUT, Camera, VideoReader_contextmanager,
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custom_meshgrid, get_random_mask, get_relative_pose,
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get_video_reader_batch, padding_image, process_pose_file,
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process_pose_params, ray_condition, resize_frame,
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resize_image_with_target_area)
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class WebVid10M(Dataset):
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def __init__(
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self,
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csv_path, video_folder,
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sample_size=256, sample_stride=4, sample_n_frames=16,
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enable_bucket=False, enable_inpaint=False, is_image=False,
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):
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print(f"loading annotations from {csv_path} ...")
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with open(csv_path, 'r') as csvfile:
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self.dataset = list(csv.DictReader(csvfile))
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self.length = len(self.dataset)
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print(f"data scale: {self.length}")
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self.video_folder = video_folder
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self.sample_stride = sample_stride
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self.sample_n_frames = sample_n_frames
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self.enable_bucket = enable_bucket
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self.enable_inpaint = enable_inpaint
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self.is_image = is_image
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sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size)
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self.pixel_transforms = transforms.Compose([
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transforms.Resize(sample_size[0]),
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transforms.CenterCrop(sample_size),
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transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
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])
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def get_batch(self, idx):
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video_dict = self.dataset[idx]
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videoid, name, page_dir = video_dict['videoid'], video_dict['name'], video_dict['page_dir']
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video_dir = os.path.join(self.video_folder, f"{videoid}.mp4")
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video_reader = VideoReader(video_dir)
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video_length = len(video_reader)
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if not self.is_image:
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clip_length = min(video_length, (self.sample_n_frames - 1) * self.sample_stride + 1)
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start_idx = random.randint(0, video_length - clip_length)
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batch_index = np.linspace(start_idx, start_idx + clip_length - 1, self.sample_n_frames, dtype=int)
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else:
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batch_index = [random.randint(0, video_length - 1)]
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if not self.enable_bucket:
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pixel_values = torch.from_numpy(video_reader.get_batch(batch_index).asnumpy()).permute(0, 3, 1, 2).contiguous()
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pixel_values = pixel_values / 255.
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del video_reader
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else:
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pixel_values = video_reader.get_batch(batch_index).asnumpy()
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if self.is_image:
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pixel_values = pixel_values[0]
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return pixel_values, name
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def __len__(self):
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return self.length
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def __getitem__(self, idx):
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while True:
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try:
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pixel_values, name = self.get_batch(idx)
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break
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except Exception as e:
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print("Error info:", e)
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idx = random.randint(0, self.length-1)
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if not self.enable_bucket:
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pixel_values = self.pixel_transforms(pixel_values)
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if self.enable_inpaint:
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mask = get_random_mask(pixel_values.size())
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mask_pixel_values = pixel_values * (1 - mask) + torch.ones_like(pixel_values) * -1 * mask
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sample = dict(pixel_values=pixel_values, mask_pixel_values=mask_pixel_values, mask=mask, text=name)
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else:
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sample = dict(pixel_values=pixel_values, text=name)
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return sample
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class VideoDataset(Dataset):
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def __init__(
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self,
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ann_path, data_root=None,
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sample_size=256, sample_stride=4, sample_n_frames=16,
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enable_bucket=False, enable_inpaint=False
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):
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print(f"loading annotations from {ann_path} ...")
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self.dataset = json.load(open(ann_path, 'r'))
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self.length = len(self.dataset)
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print(f"data scale: {self.length}")
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self.data_root = data_root
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self.sample_stride = sample_stride
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self.sample_n_frames = sample_n_frames
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self.enable_bucket = enable_bucket
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self.enable_inpaint = enable_inpaint
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sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size)
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self.pixel_transforms = transforms.Compose(
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[
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transforms.Resize(sample_size[0]),
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transforms.CenterCrop(sample_size),
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transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
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]
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)
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def get_batch(self, idx):
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video_dict = self.dataset[idx]
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video_id, text = video_dict['file_path'], video_dict['text']
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if self.data_root is None:
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video_dir = video_id
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else:
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video_dir = os.path.join(self.data_root, video_id)
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with VideoReader_contextmanager(video_dir, num_threads=2) as video_reader:
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min_sample_n_frames = min(
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self.video_sample_n_frames,
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int(len(video_reader) * (self.video_length_drop_end - self.video_length_drop_start) // self.video_sample_stride)
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)
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if min_sample_n_frames == 0:
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raise ValueError(f"No Frames in video.")
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video_length = int(self.video_length_drop_end * len(video_reader))
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clip_length = min(video_length, (min_sample_n_frames - 1) * self.video_sample_stride + 1)
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start_idx = random.randint(int(self.video_length_drop_start * video_length), video_length - clip_length) if video_length != clip_length else 0
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batch_index = np.linspace(start_idx, start_idx + clip_length - 1, min_sample_n_frames, dtype=int)
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try:
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sample_args = (video_reader, batch_index)
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pixel_values = func_timeout(
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VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
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)
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except FunctionTimedOut:
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raise ValueError(f"Read {idx} timeout.")
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except Exception as e:
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raise ValueError(f"Failed to extract frames from video. Error is {e}.")
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if not self.enable_bucket:
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pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous()
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pixel_values = pixel_values / 255.
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del video_reader
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else:
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pixel_values = pixel_values
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if not self.enable_bucket:
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pixel_values = self.video_transforms(pixel_values)
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# Random use no text generation
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if random.random() < self.text_drop_ratio:
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text = ''
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return pixel_values, text
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def __len__(self):
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return self.length
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def __getitem__(self, idx):
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while True:
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sample = {}
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try:
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pixel_values, name = self.get_batch(idx)
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sample["pixel_values"] = pixel_values
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sample["text"] = name
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sample["idx"] = idx
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if len(sample) > 0:
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break
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except Exception as e:
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print(e, self.dataset[idx % len(self.dataset)])
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idx = random.randint(0, self.length-1)
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if self.enable_inpaint and not self.enable_bucket:
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mask = get_random_mask(pixel_values.size())
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mask_pixel_values = pixel_values * (1 - mask) + torch.zeros_like(pixel_values) * mask
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sample["mask_pixel_values"] = mask_pixel_values
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sample["mask"] = mask
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clip_pixel_values = sample["pixel_values"][0].permute(1, 2, 0).contiguous()
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clip_pixel_values = (clip_pixel_values * 0.5 + 0.5) * 255
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sample["clip_pixel_values"] = clip_pixel_values
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return sample
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class VideoSpeechDataset(Dataset):
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def __init__(
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self,
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ann_path, data_root=None,
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video_sample_size=512, video_sample_stride=4, video_sample_n_frames=16,
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enable_bucket=False, enable_inpaint=False,
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audio_sr=16000, # 新增：目标音频采样率
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text_drop_ratio=0.1 # 新增：文本丢弃概率
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):
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print(f"loading annotations from {ann_path} ...")
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self.dataset = json.load(open(ann_path, 'r'))
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self.length = len(self.dataset)
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print(f"data scale: {self.length}")
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self.data_root = data_root
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self.video_sample_stride = video_sample_stride
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self.video_sample_n_frames = video_sample_n_frames
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self.enable_bucket = enable_bucket
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self.enable_inpaint = enable_inpaint
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self.audio_sr = audio_sr
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self.text_drop_ratio = text_drop_ratio
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video_sample_size = tuple(video_sample_size) if not isinstance(video_sample_size, int) else (video_sample_size, video_sample_size)
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self.pixel_transforms = transforms.Compose(
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[
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transforms.Resize(video_sample_size[0]),
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transforms.CenterCrop(video_sample_size),
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transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
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]
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)
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def get_batch(self, idx):
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video_dict = self.dataset[idx]
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video_id, text = video_dict['file_path'], video_dict['text']
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audio_id = video_dict['audio_path']
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if self.data_root is None:
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video_path = video_id
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else:
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video_path = os.path.join(self.data_root, video_id)
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if self.data_root is None:
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audio_path = audio_id
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else:
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audio_path = os.path.join(self.data_root, audio_id)
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if not os.path.exists(audio_path):
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raise FileNotFoundError(f"Audio file not found for {video_path}")
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with VideoReader_contextmanager(video_path, num_threads=2) as video_reader:
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total_frames = len(video_reader)
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fps = video_reader.get_avg_fps() # 获取原始视频帧率
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# 计算实际采样的视频帧数（考虑边界）
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max_possible_frames = (total_frames - 1) // self.video_sample_stride + 1
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actual_n_frames = min(self.video_sample_n_frames, max_possible_frames)
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if actual_n_frames <= 0:
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raise ValueError(f"Video too short: {video_path}")
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# 随机选择起始帧
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max_start = total_frames - (actual_n_frames - 1) * self.video_sample_stride - 1
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start_frame = random.randint(0, max_start) if max_start > 0 else 0
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frame_indices = [start_frame + i * self.video_sample_stride for i in range(actual_n_frames)]
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# 读取视频帧
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try:
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sample_args = (video_reader, frame_indices)
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pixel_values = func_timeout(
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VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
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)
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except FunctionTimedOut:
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raise ValueError(f"Read {idx} timeout.")
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except Exception as e:
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raise ValueError(f"Failed to extract frames from video. Error is {e}.")
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# 视频后处理
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if not self.enable_bucket:
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pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous()
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pixel_values = pixel_values / 255.
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pixel_values = self.pixel_transforms(pixel_values)
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# === 新增：加载并截取对应音频 ===
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# 视频片段的起止时间（秒）
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start_time = start_frame / fps
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end_time = (start_frame + (actual_n_frames - 1) * self.video_sample_stride) / fps
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duration = end_time - start_time
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# 使用 librosa 加载整个音频（或仅加载所需部分，但 librosa.load 不支持精确 seek，所以先加载再切）
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audio_input, sample_rate = librosa.load(audio_path, sr=self.audio_sr) # 重采样到目标 sr
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# 转换为样本索引
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start_sample = int(start_time * self.audio_sr)
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end_sample = int(end_time * self.audio_sr)
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# 安全截取
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if start_sample >= len(audio_input):
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# 音频太短，用零填充或截断
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audio_segment = np.zeros(int(duration * self.audio_sr), dtype=np.float32)
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else:
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audio_segment = audio_input[start_sample:end_sample]
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# 如果太短，补零
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target_len = int(duration * self.audio_sr)
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if len(audio_segment) < target_len:
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audio_segment = np.pad(audio_segment, (0, target_len - len(audio_segment)), mode='constant')
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# === 文本随机丢弃 ===
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if random.random() < self.text_drop_ratio:
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text = ''
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return pixel_values, text, audio_segment, sample_rate
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def __len__(self):
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return self.length
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def __getitem__(self, idx):
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while True:
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sample = {}
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try:
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pixel_values, text, audio, sample_rate = self.get_batch(idx)
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sample["pixel_values"] = pixel_values
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sample["text"] = text
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sample["audio"] = torch.from_numpy(audio).float() # 转为 tensor
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sample["sample_rate"] = sample_rate
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sample["idx"] = idx
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break
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except Exception as e:
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print(f"Error processing {idx}: {e}, retrying with random idx...")
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idx = random.randint(0, self.length - 1)
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if self.enable_inpaint and not self.enable_bucket:
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mask = get_random_mask(pixel_values.size(), image_start_only=True)
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mask_pixel_values = pixel_values * (1 - mask) + torch.zeros_like(pixel_values) * mask
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sample["mask_pixel_values"] = mask_pixel_values
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sample["mask"] = mask
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clip_pixel_values = sample["pixel_values"][0].permute(1, 2, 0).contiguous()
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clip_pixel_values = (clip_pixel_values * 0.5 + 0.5) * 255
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sample["clip_pixel_values"] = clip_pixel_values
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return sample
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class VideoSpeechControlDataset(Dataset):
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def __init__(
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self,
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ann_path, data_root=None,
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video_sample_size=512, video_sample_stride=4, video_sample_n_frames=16,
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enable_bucket=False, enable_inpaint=False,
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audio_sr=16000,
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text_drop_ratio=0.1,
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enable_motion_info=False,
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motion_frames=73,
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):
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print(f"loading annotations from {ann_path} ...")
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self.dataset = json.load(open(ann_path, 'r'))
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self.length = len(self.dataset)
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print(f"data scale: {self.length}")
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self.data_root = data_root
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self.video_sample_stride = video_sample_stride
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self.video_sample_n_frames = video_sample_n_frames
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self.enable_bucket = enable_bucket
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self.enable_inpaint = enable_inpaint
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self.audio_sr = audio_sr
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self.text_drop_ratio = text_drop_ratio
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self.enable_motion_info = enable_motion_info
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self.motion_frames = motion_frames
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video_sample_size = tuple(video_sample_size) if not isinstance(video_sample_size, int) else (video_sample_size, video_sample_size)
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self.pixel_transforms = transforms.Compose(
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[
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transforms.Resize(video_sample_size[0]),
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transforms.CenterCrop(video_sample_size),
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transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
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]
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)
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self.video_sample_size = video_sample_size
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def get_batch(self, idx):
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video_dict = self.dataset[idx]
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video_id, text = video_dict['file_path'], video_dict['text']
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audio_id = video_dict['audio_path']
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control_video_id = video_dict['control_file_path']
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if self.data_root is None:
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video_path = video_id
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else:
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video_path = os.path.join(self.data_root, video_id)
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if self.data_root is None:
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audio_path = audio_id
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else:
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audio_path = os.path.join(self.data_root, audio_id)
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if self.data_root is None:
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control_video_id = control_video_id
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else:
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control_video_id = os.path.join(self.data_root, control_video_id)
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if not os.path.exists(audio_path):
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raise FileNotFoundError(f"Audio file not found for {video_path}")
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# Video information
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with VideoReader_contextmanager(video_path, num_threads=2) as video_reader:
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total_frames = len(video_reader)
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fps = video_reader.get_avg_fps()
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if fps <= 0:
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raise ValueError(f"Video has negative fps: {video_path}")
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local_video_sample_stride = self.video_sample_stride
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new_fps = int(fps // local_video_sample_stride)
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while new_fps > 30:
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local_video_sample_stride = local_video_sample_stride + 1
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new_fps = int(fps // local_video_sample_stride)
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max_possible_frames = (total_frames - 1) // local_video_sample_stride + 1
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actual_n_frames = min(self.video_sample_n_frames, max_possible_frames)
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if actual_n_frames <= 0:
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raise ValueError(f"Video too short: {video_path}")
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max_start = total_frames - (actual_n_frames - 1) * local_video_sample_stride - 1
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start_frame = random.randint(0, max_start) if max_start > 0 else 0
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frame_indices = [start_frame + i * local_video_sample_stride for i in range(actual_n_frames)]
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try:
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sample_args = (video_reader, frame_indices)
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pixel_values = func_timeout(
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VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
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)
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except FunctionTimedOut:
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raise ValueError(f"Read {idx} timeout.")
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except Exception as e:
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raise ValueError(f"Failed to extract frames from video. Error is {e}.")
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_, height, width, channel = np.shape(pixel_values)
|
|
if self.enable_motion_info:
|
|
motion_pixel_values = np.ones([self.motion_frames, height, width, channel]) * 127.5
|
|
if start_frame > 0:
|
|
motion_max_possible_frames = (start_frame - 1) // local_video_sample_stride + 1
|
|
motion_frame_indices = [0 + i * local_video_sample_stride for i in range(motion_max_possible_frames)]
|
|
motion_frame_indices = motion_frame_indices[-self.motion_frames:]
|
|
|
|
_motion_sample_args = (video_reader, motion_frame_indices)
|
|
_motion_pixel_values = func_timeout(
|
|
VIDEO_READER_TIMEOUT, get_video_reader_batch, args=_motion_sample_args
|
|
)
|
|
motion_pixel_values[-len(motion_frame_indices):] = _motion_pixel_values
|
|
|
|
if not self.enable_bucket:
|
|
motion_pixel_values = torch.from_numpy(motion_pixel_values).permute(0, 3, 1, 2).contiguous()
|
|
motion_pixel_values = motion_pixel_values / 255.
|
|
motion_pixel_values = self.pixel_transforms(motion_pixel_values)
|
|
else:
|
|
motion_pixel_values = None
|
|
|
|
if not self.enable_bucket:
|
|
pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous()
|
|
pixel_values = pixel_values / 255.
|
|
pixel_values = self.pixel_transforms(pixel_values)
|
|
|
|
# Audio information
|
|
start_time = start_frame / fps
|
|
end_time = (start_frame + (actual_n_frames - 1) * local_video_sample_stride) / fps
|
|
duration = end_time - start_time
|
|
|
|
audio_input, sample_rate = librosa.load(audio_path, sr=self.audio_sr)
|
|
start_sample = int(start_time * self.audio_sr)
|
|
end_sample = int(end_time * self.audio_sr)
|
|
|
|
if start_sample >= len(audio_input):
|
|
raise ValueError(f"Audio file too short: {audio_path}")
|
|
else:
|
|
audio_segment = audio_input[start_sample:end_sample]
|
|
target_len = int(duration * self.audio_sr)
|
|
if len(audio_segment) < target_len:
|
|
raise ValueError(f"Audio file too short: {audio_path}")
|
|
|
|
# Control information
|
|
with VideoReader_contextmanager(control_video_id, num_threads=2) as control_video_reader:
|
|
try:
|
|
sample_args = (control_video_reader, frame_indices)
|
|
control_pixel_values = func_timeout(
|
|
VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
|
|
)
|
|
resized_frames = []
|
|
for i in range(len(control_pixel_values)):
|
|
frame = control_pixel_values[i]
|
|
resized_frame = resize_frame(frame, max(self.video_sample_size))
|
|
resized_frames.append(resized_frame)
|
|
control_pixel_values = np.array(control_pixel_values)
|
|
except FunctionTimedOut:
|
|
raise ValueError(f"Read {idx} timeout.")
|
|
except Exception as e:
|
|
raise ValueError(f"Failed to extract frames from video. Error is {e}.")
|
|
|
|
if not self.enable_bucket:
|
|
control_pixel_values = torch.from_numpy(control_pixel_values).permute(0, 3, 1, 2).contiguous()
|
|
control_pixel_values = control_pixel_values / 255.
|
|
del control_video_reader
|
|
else:
|
|
control_pixel_values = control_pixel_values
|
|
|
|
if not self.enable_bucket:
|
|
control_pixel_values = self.video_transforms(control_pixel_values)
|
|
|
|
if random.random() < self.text_drop_ratio:
|
|
text = ''
|
|
|
|
return pixel_values, motion_pixel_values, control_pixel_values, text, audio_segment, sample_rate, new_fps
|
|
|
|
def __len__(self):
|
|
return self.length
|
|
|
|
def __getitem__(self, idx):
|
|
while True:
|
|
sample = {}
|
|
try:
|
|
pixel_values, motion_pixel_values, control_pixel_values, text, audio, sample_rate, new_fps = self.get_batch(idx)
|
|
sample["pixel_values"] = pixel_values
|
|
sample["motion_pixel_values"] = motion_pixel_values
|
|
sample["control_pixel_values"] = control_pixel_values
|
|
sample["text"] = text
|
|
sample["audio"] = torch.from_numpy(audio).float() # 转为 tensor
|
|
sample["sample_rate"] = sample_rate
|
|
sample["fps"] = new_fps
|
|
sample["idx"] = idx
|
|
break
|
|
except Exception as e:
|
|
print(f"Error processing {idx}: {e}, retrying with random idx...")
|
|
idx = random.randint(0, self.length - 1)
|
|
|
|
if self.enable_inpaint and not self.enable_bucket:
|
|
mask = get_random_mask(pixel_values.size(), image_start_only=True)
|
|
mask_pixel_values = pixel_values * (1 - mask) + torch.zeros_like(pixel_values) * mask
|
|
sample["mask_pixel_values"] = mask_pixel_values
|
|
sample["mask"] = mask
|
|
|
|
clip_pixel_values = sample["pixel_values"][0].permute(1, 2, 0).contiguous()
|
|
clip_pixel_values = (clip_pixel_values * 0.5 + 0.5) * 255
|
|
sample["clip_pixel_values"] = clip_pixel_values
|
|
|
|
return sample
|
|
|
|
|
|
class VideoAnimateDataset(Dataset):
|
|
def __init__(
|
|
self,
|
|
ann_path, data_root=None,
|
|
video_sample_size=512,
|
|
video_sample_stride=4,
|
|
video_sample_n_frames=16,
|
|
video_repeat=0,
|
|
text_drop_ratio=0.1,
|
|
enable_bucket=False,
|
|
video_length_drop_start=0.1,
|
|
video_length_drop_end=0.9,
|
|
return_file_name=False,
|
|
):
|
|
# Loading annotations from files
|
|
print(f"loading annotations from {ann_path} ...")
|
|
if ann_path.endswith('.csv'):
|
|
with open(ann_path, 'r') as csvfile:
|
|
dataset = list(csv.DictReader(csvfile))
|
|
elif ann_path.endswith('.json'):
|
|
dataset = json.load(open(ann_path))
|
|
|
|
self.data_root = data_root
|
|
|
|
# It's used to balance num of images and videos.
|
|
if video_repeat > 0:
|
|
self.dataset = []
|
|
for data in dataset:
|
|
if data.get('type', 'image') != 'video':
|
|
self.dataset.append(data)
|
|
|
|
for _ in range(video_repeat):
|
|
for data in dataset:
|
|
if data.get('type', 'image') == 'video':
|
|
self.dataset.append(data)
|
|
else:
|
|
self.dataset = dataset
|
|
del dataset
|
|
|
|
self.length = len(self.dataset)
|
|
print(f"data scale: {self.length}")
|
|
# TODO: enable bucket training
|
|
self.enable_bucket = enable_bucket
|
|
self.text_drop_ratio = text_drop_ratio
|
|
|
|
self.video_length_drop_start = video_length_drop_start
|
|
self.video_length_drop_end = video_length_drop_end
|
|
|
|
# Video params
|
|
self.video_sample_stride = video_sample_stride
|
|
self.video_sample_n_frames = video_sample_n_frames
|
|
self.video_sample_size = tuple(video_sample_size) if not isinstance(video_sample_size, int) else (video_sample_size, video_sample_size)
|
|
self.video_transforms = transforms.Compose(
|
|
[
|
|
transforms.Resize(min(self.video_sample_size)),
|
|
transforms.CenterCrop(self.video_sample_size),
|
|
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
|
|
]
|
|
)
|
|
|
|
self.larger_side_of_image_and_video = min(self.video_sample_size)
|
|
|
|
def get_batch(self, idx):
|
|
data_info = self.dataset[idx % len(self.dataset)]
|
|
video_id, text = data_info['file_path'], data_info['text']
|
|
|
|
if self.data_root is None:
|
|
video_dir = video_id
|
|
else:
|
|
video_dir = os.path.join(self.data_root, video_id)
|
|
|
|
with VideoReader_contextmanager(video_dir, num_threads=2) as video_reader:
|
|
min_sample_n_frames = min(
|
|
self.video_sample_n_frames,
|
|
int(len(video_reader) * (self.video_length_drop_end - self.video_length_drop_start) // self.video_sample_stride)
|
|
)
|
|
if min_sample_n_frames == 0:
|
|
raise ValueError(f"No Frames in video.")
|
|
|
|
video_length = int(self.video_length_drop_end * len(video_reader))
|
|
clip_length = min(video_length, (min_sample_n_frames - 1) * self.video_sample_stride + 1)
|
|
start_idx = random.randint(int(self.video_length_drop_start * video_length), video_length - clip_length) if video_length != clip_length else 0
|
|
batch_index = np.linspace(start_idx, start_idx + clip_length - 1, min_sample_n_frames, dtype=int)
|
|
|
|
try:
|
|
sample_args = (video_reader, batch_index)
|
|
pixel_values = func_timeout(
|
|
VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
|
|
)
|
|
resized_frames = []
|
|
for i in range(len(pixel_values)):
|
|
frame = pixel_values[i]
|
|
resized_frame = resize_frame(frame, self.larger_side_of_image_and_video)
|
|
resized_frames.append(resized_frame)
|
|
pixel_values = np.array(resized_frames)
|
|
except FunctionTimedOut:
|
|
raise ValueError(f"Read {idx} timeout.")
|
|
except Exception as e:
|
|
raise ValueError(f"Failed to extract frames from video. Error is {e}.")
|
|
|
|
if not self.enable_bucket:
|
|
pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous()
|
|
pixel_values = pixel_values / 255.
|
|
del video_reader
|
|
else:
|
|
pixel_values = pixel_values
|
|
|
|
if not self.enable_bucket:
|
|
pixel_values = self.video_transforms(pixel_values)
|
|
|
|
# Random use no text generation
|
|
if random.random() < self.text_drop_ratio:
|
|
text = ''
|
|
|
|
control_video_id = data_info['control_file_path']
|
|
|
|
if control_video_id is not None:
|
|
if self.data_root is None:
|
|
control_video_id = control_video_id
|
|
else:
|
|
control_video_id = os.path.join(self.data_root, control_video_id)
|
|
|
|
if control_video_id is not None:
|
|
with VideoReader_contextmanager(control_video_id, num_threads=2) as control_video_reader:
|
|
try:
|
|
sample_args = (control_video_reader, batch_index)
|
|
control_pixel_values = func_timeout(
|
|
VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
|
|
)
|
|
resized_frames = []
|
|
for i in range(len(control_pixel_values)):
|
|
frame = control_pixel_values[i]
|
|
resized_frame = resize_frame(frame, self.larger_side_of_image_and_video)
|
|
resized_frames.append(resized_frame)
|
|
control_pixel_values = np.array(resized_frames)
|
|
except FunctionTimedOut:
|
|
raise ValueError(f"Read {idx} timeout.")
|
|
except Exception as e:
|
|
raise ValueError(f"Failed to extract frames from video. Error is {e}.")
|
|
|
|
if not self.enable_bucket:
|
|
control_pixel_values = torch.from_numpy(control_pixel_values).permute(0, 3, 1, 2).contiguous()
|
|
control_pixel_values = control_pixel_values / 255.
|
|
del control_video_reader
|
|
else:
|
|
control_pixel_values = control_pixel_values
|
|
|
|
if not self.enable_bucket:
|
|
control_pixel_values = self.video_transforms(control_pixel_values)
|
|
else:
|
|
if not self.enable_bucket:
|
|
control_pixel_values = torch.zeros_like(pixel_values)
|
|
else:
|
|
control_pixel_values = np.zeros_like(pixel_values)
|
|
|
|
face_video_id = data_info['face_file_path']
|
|
|
|
if face_video_id is not None:
|
|
if self.data_root is None:
|
|
face_video_id = face_video_id
|
|
else:
|
|
face_video_id = os.path.join(self.data_root, face_video_id)
|
|
|
|
if face_video_id is not None:
|
|
with VideoReader_contextmanager(face_video_id, num_threads=2) as face_video_reader:
|
|
try:
|
|
sample_args = (face_video_reader, batch_index)
|
|
face_pixel_values = func_timeout(
|
|
VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
|
|
)
|
|
resized_frames = []
|
|
for i in range(len(face_pixel_values)):
|
|
frame = face_pixel_values[i]
|
|
resized_frame = resize_frame(frame, self.larger_side_of_image_and_video)
|
|
resized_frames.append(resized_frame)
|
|
face_pixel_values = np.array(resized_frames)
|
|
except FunctionTimedOut:
|
|
raise ValueError(f"Read {idx} timeout.")
|
|
except Exception as e:
|
|
raise ValueError(f"Failed to extract frames from video. Error is {e}.")
|
|
|
|
if not self.enable_bucket:
|
|
face_pixel_values = torch.from_numpy(face_pixel_values).permute(0, 3, 1, 2).contiguous()
|
|
face_pixel_values = face_pixel_values / 255.
|
|
del face_video_reader
|
|
else:
|
|
face_pixel_values = face_pixel_values
|
|
|
|
if not self.enable_bucket:
|
|
face_pixel_values = self.video_transforms(face_pixel_values)
|
|
else:
|
|
if not self.enable_bucket:
|
|
face_pixel_values = torch.zeros_like(pixel_values)
|
|
else:
|
|
face_pixel_values = np.zeros_like(pixel_values)
|
|
|
|
background_video_id = data_info.get('background_file_path', None)
|
|
|
|
if background_video_id is not None:
|
|
if self.data_root is None:
|
|
background_video_id = background_video_id
|
|
else:
|
|
background_video_id = os.path.join(self.data_root, background_video_id)
|
|
|
|
if background_video_id is not None:
|
|
with VideoReader_contextmanager(background_video_id, num_threads=2) as background_video_reader:
|
|
try:
|
|
sample_args = (background_video_reader, batch_index)
|
|
background_pixel_values = func_timeout(
|
|
VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
|
|
)
|
|
resized_frames = []
|
|
for i in range(len(background_pixel_values)):
|
|
frame = background_pixel_values[i]
|
|
resized_frame = resize_frame(frame, self.larger_side_of_image_and_video)
|
|
resized_frames.append(resized_frame)
|
|
background_pixel_values = np.array(resized_frames)
|
|
except FunctionTimedOut:
|
|
raise ValueError(f"Read {idx} timeout.")
|
|
except Exception as e:
|
|
raise ValueError(f"Failed to extract frames from video. Error is {e}.")
|
|
|
|
if not self.enable_bucket:
|
|
background_pixel_values = torch.from_numpy(background_pixel_values).permute(0, 3, 1, 2).contiguous()
|
|
background_pixel_values = background_pixel_values / 255.
|
|
del background_video_reader
|
|
else:
|
|
background_pixel_values = background_pixel_values
|
|
|
|
if not self.enable_bucket:
|
|
background_pixel_values = self.video_transforms(background_pixel_values)
|
|
else:
|
|
if not self.enable_bucket:
|
|
background_pixel_values = torch.ones_like(pixel_values) * 127.5
|
|
else:
|
|
background_pixel_values = np.ones_like(pixel_values) * 127.5
|
|
|
|
mask_video_id = data_info.get('mask_file_path', None)
|
|
|
|
if mask_video_id is not None:
|
|
if self.data_root is None:
|
|
mask_video_id = mask_video_id
|
|
else:
|
|
mask_video_id = os.path.join(self.data_root, mask_video_id)
|
|
|
|
if mask_video_id is not None:
|
|
with VideoReader_contextmanager(mask_video_id, num_threads=2) as mask_video_reader:
|
|
try:
|
|
sample_args = (mask_video_reader, batch_index)
|
|
mask = func_timeout(
|
|
VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
|
|
)
|
|
resized_frames = []
|
|
for i in range(len(mask)):
|
|
frame = mask[i]
|
|
resized_frame = resize_frame(frame, self.larger_side_of_image_and_video)
|
|
resized_frames.append(resized_frame)
|
|
mask = np.array(resized_frames)
|
|
except FunctionTimedOut:
|
|
raise ValueError(f"Read {idx} timeout.")
|
|
except Exception as e:
|
|
raise ValueError(f"Failed to extract frames from video. Error is {e}.")
|
|
|
|
if not self.enable_bucket:
|
|
mask = torch.from_numpy(mask).permute(0, 3, 1, 2).contiguous()
|
|
mask = mask / 255.
|
|
del mask_video_reader
|
|
else:
|
|
mask = mask
|
|
else:
|
|
if not self.enable_bucket:
|
|
mask = torch.ones_like(pixel_values)
|
|
else:
|
|
mask = np.ones_like(pixel_values) * 255
|
|
mask = mask[:, :, :, :1]
|
|
|
|
ref_pixel_values_path = data_info.get('ref_file_path', [])
|
|
if self.data_root is not None:
|
|
ref_pixel_values_path = os.path.join(self.data_root, ref_pixel_values_path)
|
|
ref_pixel_values = Image.open(ref_pixel_values_path).convert('RGB')
|
|
|
|
if not self.enable_bucket:
|
|
raise ValueError("Not enable_bucket is not supported now. ")
|
|
else:
|
|
ref_pixel_values = np.array(ref_pixel_values)
|
|
|
|
return pixel_values, control_pixel_values, face_pixel_values, background_pixel_values, mask, ref_pixel_values, text, "video"
|
|
|
|
def __len__(self):
|
|
return self.length
|
|
|
|
def __getitem__(self, idx):
|
|
data_info = self.dataset[idx % len(self.dataset)]
|
|
data_type = data_info.get('type', 'image')
|
|
while True:
|
|
sample = {}
|
|
try:
|
|
data_info_local = self.dataset[idx % len(self.dataset)]
|
|
data_type_local = data_info_local.get('type', 'image')
|
|
if data_type_local != data_type:
|
|
raise ValueError("data_type_local != data_type")
|
|
|
|
pixel_values, control_pixel_values, face_pixel_values, background_pixel_values, mask, ref_pixel_values, name, data_type = \
|
|
self.get_batch(idx)
|
|
|
|
sample["pixel_values"] = pixel_values
|
|
sample["control_pixel_values"] = control_pixel_values
|
|
sample["face_pixel_values"] = face_pixel_values
|
|
sample["background_pixel_values"] = background_pixel_values
|
|
sample["mask"] = mask
|
|
sample["ref_pixel_values"] = ref_pixel_values
|
|
sample["clip_pixel_values"] = ref_pixel_values
|
|
sample["text"] = name
|
|
sample["data_type"] = data_type
|
|
sample["idx"] = idx
|
|
|
|
if len(sample) > 0:
|
|
break
|
|
except Exception as e:
|
|
print(e, self.dataset[idx % len(self.dataset)])
|
|
idx = random.randint(0, self.length-1)
|
|
|
|
return sample
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if 1:
|
|
dataset = VideoDataset(
|
|
json_path="./webvidval/results_2M_val.json",
|
|
sample_size=256,
|
|
sample_stride=4, sample_n_frames=16,
|
|
)
|
|
|
|
if 0:
|
|
dataset = WebVid10M(
|
|
csv_path="./webvid/results_2M_val.csv",
|
|
video_folder="./webvid/2M_val",
|
|
sample_size=256,
|
|
sample_stride=4, sample_n_frames=16,
|
|
is_image=False,
|
|
)
|
|
|
|
dataloader = torch.utils.data.DataLoader(dataset, batch_size=4, num_workers=0,)
|
|
for idx, batch in enumerate(dataloader):
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print(batch["pixel_values"].shape, len(batch["text"])) |