import os
import sys
sys.path.append(os.path.abspath('.'))
import argparse
import datetime
import numpy as np
import time
import torch
import io
import json
import jsonlines
import cv2
import math
import random
from pathlib import Path
from tqdm import tqdm
from concurrent import futures
from torch.utils.data import Dataset, DataLoader, DistributedSampler
from collections import OrderedDict
from torchvision import transforms as pth_transforms
from torchvision.transforms.functional import InterpolationMode
from PIL import Image
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from trainer_misc import init_distributed_mode
from video_vae import CausalVideoVAELossWrapper
def get_transform(width, height, new_width=None, new_height=None, resize=False,):
transform_list = []
if resize:
# rescale according to the largest ratio
scale = max(new_width / width, new_height / height)
resized_width = round(width * scale)
resized_height = round(height * scale)
transform_list.append(pth_transforms.Resize((resized_height, resized_width), InterpolationMode.BICUBIC, antialias=True))
transform_list.append(pth_transforms.CenterCrop((new_height, new_width)))
transform_list.extend([
pth_transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
transform_list = pth_transforms.Compose(transform_list)
return transform_list
def load_video_and_transform(video_path, frame_indexs, frame_number, new_width=None, new_height=None, resize=False):
video_capture = None
frame_indexs_set = set(frame_indexs)
try:
video_capture = cv2.VideoCapture(video_path)
frames = []
frame_index = 0
while True:
flag, frame = video_capture.read()
if not flag:
break
if frame_index > frame_indexs[-1]:
break
if frame_index in frame_indexs_set:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = torch.from_numpy(frame)
frame = frame.permute(2, 0, 1)
frames.append(frame)
frame_index += 1
video_capture.release()
if len(frames) == 0:
print(f"Empty video {video_path}")
return None
frames = frames[:frame_number]
duration = ((len(frames) - 1) // 8) * 8 + 1 # make sure the frames match: f * 8 + 1
frames = frames[:duration]
frames = torch.stack(frames).float() / 255
video_transform = get_transform(frames.shape[-1], frames.shape[-2], new_width, new_height, resize=resize)
frames = video_transform(frames).permute(1, 0, 2, 3)
return frames
except Exception as e:
print(f"Loading video: {video_path} exception {e}")
if video_capture is not None:
video_capture.release()
return None
class VideoDataset(Dataset):
def __init__(self, anno_file, width, height, num_frames):
super().__init__()
self.annotation = []
self.width = width
self.height = height
self.num_frames = num_frames
with jsonlines.open(anno_file, 'r') as reader:
for item in tqdm(reader):
self.annotation.append(item)
tot_len = len(self.annotation)
print(f"Totally {len(self.annotation)} videos")
def process_one_video(self, video_item):
videos_per_task = []
video_path = video_item['video']
output_latent_path = video_item['latent']
# The sampled frame indexs of a video, if not specified, load frames: [0, num_frames)
frame_indexs = video_item['frames'] if 'frames' in video_item else list(range(self.num_frames))
try:
video_frames_tensors = load_video_and_transform(
video_path,
frame_indexs,
frame_number=self.num_frames, # The num_frames to encode
new_width=self.width,
new_height=self.height,
resize=True
)
if video_frames_tensors is None:
return videos_per_task
video_frames_tensors = video_frames_tensors.unsqueeze(0)
videos_per_task.append({'video': video_path, 'input': video_frames_tensors, 'output': output_latent_path})
except Exception as e:
print(f"Load video tensor ERROR: {e}")
return videos_per_task
def __getitem__(self, index):
try:
video_item = self.annotation[index]
videos_per_task = self.process_one_video(video_item)
except Exception as e:
print(f'Error with {e}')
videos_per_task = []
return videos_per_task
def __len__(self):
return len(self.annotation)
def get_args():
parser = argparse.ArgumentParser('Pytorch Multi-process Training script', add_help=False)
parser.add_argument('--batch_size', default=4, type=int)
parser.add_argument('--model_path', default='', type=str, help='The pre-trained weight path')
parser.add_argument('--model_dtype', default='bf16', type=str, help="The Model Dtype: bf16 or df16")
parser.add_argument('--anno_file', type=str, default='', help="The video annotation file")
parser.add_argument('--width', type=int, default=640, help="The video width")
parser.add_argument('--height', type=int, default=384, help="The video height")
parser.add_argument('--num_frames', type=int, default=121, help="The frame number to encode")
parser.add_argument('--save_memory', action='store_true', help="Open the VAE tiling")
return parser.parse_args()
def build_model(args):
model_path = args.model_path
model_dtype = args.model_dtype
model = CausalVideoVAELossWrapper(model_path, model_dtype=model_dtype, interpolate=False, add_discriminator=False)
model = model.eval()
return model
def build_data_loader(args):
def collate_fn(batch):
return_batch = {'input' : [], 'output': []}
for videos_ in batch:
for video_input in videos_:
return_batch['input'].append(video_input['input'])
return_batch['output'].append(video_input['output'])
return return_batch
dataset = VideoDataset(args.anno_file, args.width, args.height, args.num_frames)
sampler = DistributedSampler(dataset, num_replicas=args.world_size, rank=args.rank, shuffle=False)
loader = DataLoader(
dataset, batch_size=args.batch_size, num_workers=6, pin_memory=True,
sampler=sampler, shuffle=False, collate_fn=collate_fn, drop_last=False, prefetch_factor=2,
)
return loader
def save_tensor(tensor, output_path):
try:
torch.save(tensor.clone(), output_path)
except Exception as e:
pass
def main():
args = get_args()
init_distributed_mode(args)
device = torch.device('cuda')
rank = args.rank
model = build_model(args)
model.to(device)
if args.model_dtype == "bf16":
torch_dtype = torch.bfloat16
elif args.model_dtype == "fp16":
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
data_loader = build_data_loader(args)
torch.distributed.barrier()
window_size = 16
temporal_chunk = True
task_queue = []
if args.save_memory:
# Open the tiling, to reduce gpu memory cost
model.vae.enable_tiling()
with futures.ThreadPoolExecutor(max_workers=16) as executor:
for sample in tqdm(data_loader):
input_video_list = sample['input']
output_path_list = sample['output']
with torch.no_grad(), torch.cuda.amp.autocast(enabled=True, dtype=torch_dtype):
for video_input, output_path in zip(input_video_list, output_path_list):
video_latent = model.encode_latent(video_input.to(device), sample=True, window_size=window_size, temporal_chunk=temporal_chunk, tile_sample_min_size=256)
video_latent = video_latent.to(torch_dtype).cpu()
task_queue.append(executor.submit(save_tensor, video_latent, output_path))
for future in futures.as_completed(task_queue):
res = future.result()
torch.distributed.barrier()
if __name__ == "__main__":
main()