|
import os, io, csv, math, random |
|
import numpy as np |
|
from einops import rearrange |
|
from decord import VideoReader |
|
|
|
import torch |
|
import torchvision.transforms as transforms |
|
from torch.utils.data.dataset import Dataset |
|
from animatediff.utils.util import zero_rank_print |
|
|
|
|
|
|
|
class WebVid10M(Dataset): |
|
def __init__( |
|
self, |
|
csv_path, video_folder, |
|
sample_size=256, sample_stride=4, sample_n_frames=16, |
|
is_image=False, |
|
): |
|
zero_rank_print(f"loading annotations from {csv_path} ...") |
|
with open(csv_path, 'r') as csvfile: |
|
self.dataset = list(csv.DictReader(csvfile)) |
|
self.length = len(self.dataset) |
|
zero_rank_print(f"data scale: {self.length}") |
|
|
|
self.video_folder = video_folder |
|
self.sample_stride = sample_stride |
|
self.sample_n_frames = sample_n_frames |
|
self.is_image = is_image |
|
|
|
sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size) |
|
self.pixel_transforms = transforms.Compose([ |
|
transforms.RandomHorizontalFlip(), |
|
transforms.Resize(sample_size[0]), |
|
transforms.CenterCrop(sample_size), |
|
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), |
|
]) |
|
|
|
def get_batch(self, idx): |
|
video_dict = self.dataset[idx] |
|
videoid, name, page_dir = video_dict['videoid'], video_dict['name'], video_dict['page_dir'] |
|
|
|
video_dir = os.path.join(self.video_folder, f"{videoid}.mp4") |
|
video_reader = VideoReader(video_dir) |
|
video_length = len(video_reader) |
|
|
|
if not self.is_image: |
|
clip_length = min(video_length, (self.sample_n_frames - 1) * self.sample_stride + 1) |
|
start_idx = random.randint(0, video_length - clip_length) |
|
batch_index = np.linspace(start_idx, start_idx + clip_length - 1, self.sample_n_frames, dtype=int) |
|
else: |
|
batch_index = [random.randint(0, video_length - 1)] |
|
|
|
pixel_values = torch.from_numpy(video_reader.get_batch(batch_index).asnumpy()).permute(0, 3, 1, 2).contiguous() |
|
pixel_values = pixel_values / 255. |
|
del video_reader |
|
|
|
if self.is_image: |
|
pixel_values = pixel_values[0] |
|
|
|
return pixel_values, name |
|
|
|
def __len__(self): |
|
return self.length |
|
|
|
def __getitem__(self, idx): |
|
while True: |
|
try: |
|
pixel_values, name = self.get_batch(idx) |
|
break |
|
|
|
except Exception as e: |
|
idx = random.randint(0, self.length-1) |
|
|
|
pixel_values = self.pixel_transforms(pixel_values) |
|
sample = dict(pixel_values=pixel_values, text=name) |
|
return sample |
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
from animatediff.utils.util import save_videos_grid |
|
|
|
dataset = WebVid10M( |
|
csv_path="/mnt/petrelfs/guoyuwei/projects/datasets/webvid/results_2M_val.csv", |
|
video_folder="/mnt/petrelfs/guoyuwei/projects/datasets/webvid/2M_val", |
|
sample_size=256, |
|
sample_stride=4, sample_n_frames=16, |
|
is_image=True, |
|
) |
|
import pdb |
|
pdb.set_trace() |
|
|
|
dataloader = torch.utils.data.DataLoader(dataset, batch_size=4, num_workers=16,) |
|
for idx, batch in enumerate(dataloader): |
|
print(batch["pixel_values"].shape, len(batch["text"])) |
|
|
|
|
|
|
