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import json
import os, io, csv, math, random
import numpy as np
import torchvision
from einops import rearrange
from decord import VideoReader
from os.path import join as opj
import gc
import torch
import torchvision.transforms as transforms
from torch.utils.data.dataset import Dataset
from tqdm import tqdm
from PIL import Image
from opensora.utils.dataset_utils import DecordInit
from opensora.utils.utils import text_preprocessing
def random_video_noise(t, c, h, w):
vid = torch.rand(t, c, h, w) * 255.0
vid = vid.to(torch.uint8)
return vid
class T2V_dataset(Dataset):
def __init__(self, args, transform, temporal_sample, tokenizer):
self.image_data = args.image_data
self.video_data = args.video_data
self.num_frames = args.num_frames
self.transform = transform
self.temporal_sample = temporal_sample
self.tokenizer = tokenizer
self.model_max_length = args.model_max_length
self.v_decoder = DecordInit()
self.vid_cap_list = self.get_vid_cap_list()
self.use_image_num = args.use_image_num
self.use_img_from_vid = args.use_img_from_vid
if self.use_image_num != 0 and not self.use_img_from_vid:
self.img_cap_list = self.get_img_cap_list()
def __len__(self):
return len(self.vid_cap_list)
def __getitem__(self, idx):
try:
# import ipdb;ipdb.set_trace()
video_data = self.get_video(idx)
image_data = {}
if self.use_image_num != 0 and self.use_img_from_vid:
image_data = self.get_image_from_video(video_data)
elif self.use_image_num != 0 and not self.use_img_from_vid:
image_data = self.get_image(idx)
else:
raise NotImplementedError
gc.collect()
return dict(video_data=video_data, image_data=image_data)
except Exception as e:
# print(f'Error with {e}, {self.vid_cap_list[idx]}')
if os.path.exists(self.vid_cap_list[idx]['path']) and '_resize_1080p' in self.vid_cap_list[idx]['path']:
os.remove(self.vid_cap_list[idx]['path'])
print('remove:', self.vid_cap_list[idx]['path'])
return self.__getitem__(random.randint(0, self.__len__() - 1))
def get_video(self, idx):
# video = random.choice([random_video_noise(65, 3, 720, 360) * 255, random_video_noise(65, 3, 1024, 1024), random_video_noise(65, 3, 360, 720)])
# # print('random shape', video.shape)
# input_ids = torch.ones(1, 120).to(torch.long).squeeze(0)
# cond_mask = torch.cat([torch.ones(1, 60).to(torch.long), torch.ones(1, 60).to(torch.long)], dim=1).squeeze(0)
video_path = self.vid_cap_list[idx]['path']
frame_idx = self.vid_cap_list[idx]['frame_idx']
#print('before decord')
video = self.decord_read(video_path, frame_idx)
# video = self.tv_read(video_path, frame_idx)
#print('after decord')
video = self.transform(video) # T C H W -> T C H W
# del raw_video
# gc.collect()
# video = torch.rand(65, 3, 512, 512)
#print('after transform')
video = video.transpose(0, 1) # T C H W -> C T H W
text = self.vid_cap_list[idx]['cap']
text = text_preprocessing(text)
text_tokens_and_mask = self.tokenizer(
text,
max_length=self.model_max_length,
padding='max_length',
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors='pt'
)
input_ids = text_tokens_and_mask['input_ids']
cond_mask = text_tokens_and_mask['attention_mask']
return dict(video=video, input_ids=input_ids, cond_mask=cond_mask)
def get_image_from_video(self, video_data):
select_image_idx = np.linspace(0, self.num_frames-1, self.use_image_num, dtype=int)
assert self.num_frames >= self.use_image_num
image = [video_data['video'][:, i:i+1] for i in select_image_idx] # num_img [c, 1, h, w]
input_ids = video_data['input_ids'].repeat(self.use_image_num, 1) # self.use_image_num, l
cond_mask = video_data['cond_mask'].repeat(self.use_image_num, 1) # self.use_image_num, l
return dict(image=image, input_ids=input_ids, cond_mask=cond_mask)
def get_image(self, idx):
idx = idx % len(self.img_cap_list) # out of range
image_data = self.img_cap_list[idx] # [{'path': path, 'cap': cap}, ...]
image = [Image.open(i['path']).convert('RGB') for i in image_data] # num_img [h, w, c]
image = [torch.from_numpy(np.array(i)) for i in image] # num_img [h, w, c]
image = [rearrange(i, 'h w c -> c h w').unsqueeze(0) for i in image] # num_img [1 c h w]
image = [self.transform(i) for i in image] # num_img [1 C H W] -> num_img [1 C H W]
image = [i.transpose(0, 1) for i in image] # num_img [1 C H W] -> num_img [C 1 H W]
caps = [i['cap'] for i in image_data]
text = [text_preprocessing(cap) for cap in caps]
input_ids, cond_mask = [], []
for t in text:
text_tokens_and_mask = self.tokenizer(
t,
max_length=self.model_max_length,
padding='max_length',
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors='pt'
)
input_ids.append(text_tokens_and_mask['input_ids'])
cond_mask.append(text_tokens_and_mask['attention_mask'])
input_ids = torch.cat(input_ids) # self.use_image_num, l
cond_mask = torch.cat(cond_mask) # self.use_image_num, l
return dict(image=image, input_ids=input_ids, cond_mask=cond_mask)
def tv_read(self, path, frame_idx=None):
vframes, aframes, info = torchvision.io.read_video(filename=path, pts_unit='sec', output_format='TCHW')
total_frames = len(vframes)
if frame_idx is None:
start_frame_ind, end_frame_ind = self.temporal_sample(total_frames)
else:
start_frame_ind, end_frame_ind = frame_idx.split(':')
start_frame_ind, end_frame_ind = int(start_frame_ind), int(end_frame_ind)
# assert end_frame_ind - start_frame_ind >= self.num_frames
frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, self.num_frames, dtype=int)
# frame_indice = np.linspace(0, 63, self.num_frames, dtype=int)
video = vframes[frame_indice] # (T, C, H, W)
return video
def decord_read(self, path, frame_idx=None):
decord_vr = self.v_decoder(path)
total_frames = len(decord_vr)
# Sampling video frames
if frame_idx is None:
start_frame_ind, end_frame_ind = self.temporal_sample(total_frames)
else:
start_frame_ind, end_frame_ind = frame_idx.split(':')
start_frame_ind, end_frame_ind = int(start_frame_ind), int(end_frame_ind)
start_frame_ind, end_frame_ind = int(start_frame_ind), int(start_frame_ind) + self.num_frames
# assert end_frame_ind - start_frame_ind >= self.num_frames
frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, self.num_frames, dtype=int)
# frame_indice = np.linspace(0, 63, self.num_frames, dtype=int)
video_data = decord_vr.get_batch(frame_indice).asnumpy()
video_data = torch.from_numpy(video_data)
video_data = video_data.permute(0, 3, 1, 2) # (T, H, W, C) -> (T C H W)
return video_data
def get_vid_cap_list(self):
vid_cap_lists = []
with open(self.video_data, 'r') as f:
folder_anno = [i.strip().split(',') for i in f.readlines() if len(i.strip()) > 0]
# print(folder_anno)
for folder, anno in folder_anno:
with open(anno, 'r') as f:
vid_cap_list = json.load(f)
print(f'Building {anno}...')
for i in tqdm(range(len(vid_cap_list))):
path = opj(folder, vid_cap_list[i]['path'])
if os.path.exists(path.replace('.mp4', '_resize_1080p.mp4')):
path = path.replace('.mp4', '_resize_1080p.mp4')
vid_cap_list[i]['path'] = path
vid_cap_lists += vid_cap_list
return vid_cap_lists
def get_img_cap_list(self):
img_cap_lists = []
with open(self.image_data, 'r') as f:
folder_anno = [i.strip().split(',') for i in f.readlines() if len(i.strip()) > 0]
for folder, anno in folder_anno:
with open(anno, 'r') as f:
img_cap_list = json.load(f)
print(f'Building {anno}...')
for i in tqdm(range(len(img_cap_list))):
img_cap_list[i]['path'] = opj(folder, img_cap_list[i]['path'])
img_cap_lists += img_cap_list
img_cap_lists = [img_cap_lists[i: i+self.use_image_num] for i in range(0, len(img_cap_lists), self.use_image_num)]
return img_cap_lists[:-1] # drop last to avoid error length
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