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import json
import os
import torch
import random
import torch.utils.data as data
import numpy as np
from glob import glob
from PIL import Image
from torch.utils.data import Dataset
from tqdm import tqdm
from opensora.dataset.transform import center_crop, RandomCropVideo
from opensora.utils.dataset_utils import DecordInit
class T2V_Feature_dataset(Dataset):
def __init__(self, args, temporal_sample):
self.video_folder = args.video_folder
self.num_frames = args.video_length
self.temporal_sample = temporal_sample
print('Building dataset...')
if os.path.exists('samples_430k.json'):
with open('samples_430k.json', 'r') as f:
self.samples = json.load(f)
else:
self.samples = self._make_dataset()
with open('samples_430k.json', 'w') as f:
json.dump(self.samples, f, indent=2)
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 _make_dataset(self):
all_mp4 = list(glob(os.path.join(self.video_folder, '**', '*.mp4'), recursive=True))
# all_mp4 = all_mp4[:1000]
samples = []
for i in tqdm(all_mp4):
video_id = os.path.basename(i).split('.')[0]
ae = os.path.split(i)[0].replace('data_split_tt', 'lb_causalvideovae444_feature')
ae = os.path.join(ae, f'{video_id}_causalvideovae444.npy')
if not os.path.exists(ae):
continue
t5 = os.path.split(i)[0].replace('data_split_tt', 'lb_t5_feature')
cond_list = []
cond_llava = os.path.join(t5, f'{video_id}_t5_llava_fea.npy')
mask_llava = os.path.join(t5, f'{video_id}_t5_llava_mask.npy')
if os.path.exists(cond_llava) and os.path.exists(mask_llava):
llava = dict(cond=cond_llava, mask=mask_llava)
cond_list.append(llava)
cond_sharegpt4v = os.path.join(t5, f'{video_id}_t5_sharegpt4v_fea.npy')
mask_sharegpt4v = os.path.join(t5, f'{video_id}_t5_sharegpt4v_mask.npy')
if os.path.exists(cond_sharegpt4v) and os.path.exists(mask_sharegpt4v):
sharegpt4v = dict(cond=cond_sharegpt4v, mask=mask_sharegpt4v)
cond_list.append(sharegpt4v)
if len(cond_list) > 0:
sample = dict(ae=ae, t5=cond_list)
samples.append(sample)
return samples
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
# try:
sample = self.samples[idx]
ae, t5 = sample['ae'], sample['t5']
t5 = random.choice(t5)
video_origin = np.load(ae)[0] # C T H W
_, total_frames, _, _ = video_origin.shape
# Sampling video frames
start_frame_ind, end_frame_ind = self.temporal_sample(total_frames)
assert end_frame_ind - start_frame_ind >= self.num_frames
select_video_idx = np.linspace(start_frame_ind, end_frame_ind - 1, num=self.num_frames, dtype=int) # start, stop, num=50
# print('select_video_idx', total_frames, select_video_idx)
video = video_origin[:, select_video_idx] # C num_frames H W
video = torch.from_numpy(video)
cond = torch.from_numpy(np.load(t5['cond']))[0] # L
cond_mask = torch.from_numpy(np.load(t5['mask']))[0] # L D
if self.use_image_num != 0 and self.use_img_from_vid:
select_image_idx = np.random.randint(0, total_frames, self.use_image_num)
# print('select_image_idx', total_frames, self.use_image_num, select_image_idx)
images = video_origin[:, select_image_idx] # c, num_img, h, w
images = torch.from_numpy(images)
video = torch.cat([video, images], dim=1) # c, num_frame+num_img, h, w
cond = torch.stack([cond] * (1+self.use_image_num)) # 1+self.use_image_num, l
cond_mask = torch.stack([cond_mask] * (1+self.use_image_num)) # 1+self.use_image_num, l
elif self.use_image_num != 0 and not self.use_img_from_vid:
images, captions = self.img_cap_list[idx]
raise NotImplementedError
else:
pass
return video, cond, cond_mask
# except Exception as e:
# print(f'Error with {e}, {sample}')
# return self.__getitem__(random.randint(0, self.__len__() - 1))
def get_img_cap_list(self):
raise NotImplementedError
class T2V_T5_Feature_dataset(Dataset):
def __init__(self, args, transform, temporal_sample):
self.video_folder = args.video_folder
self.num_frames = args.num_frames
self.transform = transform
self.temporal_sample = temporal_sample
self.v_decoder = DecordInit()
print('Building dataset...')
if os.path.exists('samples_430k.json'):
with open('samples_430k.json', 'r') as f:
self.samples = json.load(f)
self.samples = [dict(ae=i['ae'].replace('lb_causalvideovae444_feature', 'data_split_1024').replace('_causalvideovae444.npy', '.mp4'), t5=i['t5']) for i in self.samples]
else:
self.samples = self._make_dataset()
with open('samples_430k.json', 'w') as f:
json.dump(self.samples, f, indent=2)
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 _make_dataset(self):
all_mp4 = list(glob(os.path.join(self.video_folder, '**', '*.mp4'), recursive=True))
# all_mp4 = all_mp4[:1000]
samples = []
for i in tqdm(all_mp4):
video_id = os.path.basename(i).split('.')[0]
# ae = os.path.split(i)[0].replace('data_split', 'lb_causalvideovae444_feature')
# ae = os.path.join(ae, f'{video_id}_causalvideovae444.npy')
ae = i
if not os.path.exists(ae):
continue
t5 = os.path.split(i)[0].replace('data_split_1024', 'lb_t5_feature')
cond_list = []
cond_llava = os.path.join(t5, f'{video_id}_t5_llava_fea.npy')
mask_llava = os.path.join(t5, f'{video_id}_t5_llava_mask.npy')
if os.path.exists(cond_llava) and os.path.exists(mask_llava):
llava = dict(cond=cond_llava, mask=mask_llava)
cond_list.append(llava)
cond_sharegpt4v = os.path.join(t5, f'{video_id}_t5_sharegpt4v_fea.npy')
mask_sharegpt4v = os.path.join(t5, f'{video_id}_t5_sharegpt4v_mask.npy')
if os.path.exists(cond_sharegpt4v) and os.path.exists(mask_sharegpt4v):
sharegpt4v = dict(cond=cond_sharegpt4v, mask=mask_sharegpt4v)
cond_list.append(sharegpt4v)
if len(cond_list) > 0:
sample = dict(ae=ae, t5=cond_list)
samples.append(sample)
return samples
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
try:
sample = self.samples[idx]
ae, t5 = sample['ae'], sample['t5']
t5 = random.choice(t5)
video = self.decord_read(ae)
video = self.transform(video) # T C H W -> T C H W
video = video.transpose(0, 1) # T C H W -> C T H W
total_frames = video.shape[1]
cond = torch.from_numpy(np.load(t5['cond']))[0] # L
cond_mask = torch.from_numpy(np.load(t5['mask']))[0] # L D
if self.use_image_num != 0 and self.use_img_from_vid:
select_image_idx = np.random.randint(0, total_frames, self.use_image_num)
# print('select_image_idx', total_frames, self.use_image_num, select_image_idx)
images = video.numpy()[:, select_image_idx] # c, num_img, h, w
images = torch.from_numpy(images)
video = torch.cat([video, images], dim=1) # c, num_frame+num_img, h, w
cond = torch.stack([cond] * (1+self.use_image_num)) # 1+self.use_image_num, l
cond_mask = torch.stack([cond_mask] * (1+self.use_image_num)) # 1+self.use_image_num, l
elif self.use_image_num != 0 and not self.use_img_from_vid:
images, captions = self.img_cap_list[idx]
raise NotImplementedError
else:
pass
return video, cond, cond_mask
except Exception as e:
print(f'Error with {e}, {sample}')
return self.__getitem__(random.randint(0, self.__len__() - 1))
def decord_read(self, path):
decord_vr = self.v_decoder(path)
total_frames = len(decord_vr)
# Sampling video frames
start_frame_ind, end_frame_ind = self.temporal_sample(total_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)
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_img_cap_list(self):
raise NotImplementedError |