100 lines
4.1 KiB
Python
100 lines
4.1 KiB
Python
from torchvision.transforms import Compose
|
|
from transformers import AutoTokenizer
|
|
|
|
from .feature_datasets import T2V_Feature_dataset, T2V_T5_Feature_dataset
|
|
from torchvision import transforms
|
|
from torchvision.transforms import Lambda
|
|
|
|
from .landscope import Landscope
|
|
from .t2v_datasets import T2V_dataset
|
|
from .transform import ToTensorVideo, TemporalRandomCrop, RandomHorizontalFlipVideo, CenterCropResizeVideo
|
|
from .ucf101 import UCF101
|
|
from .sky_datasets import Sky
|
|
|
|
ae_norm = {
|
|
'CausalVAEModel_4x8x8': Lambda(lambda x: 2. * x - 1.),
|
|
'CausalVQVAEModel_4x4x4': Lambda(lambda x: x - 0.5),
|
|
'CausalVQVAEModel_4x8x8': Lambda(lambda x: x - 0.5),
|
|
'VQVAEModel_4x4x4': Lambda(lambda x: x - 0.5),
|
|
'VQVAEModel_4x8x8': Lambda(lambda x: x - 0.5),
|
|
"bair_stride4x2x2": Lambda(lambda x: x - 0.5),
|
|
"ucf101_stride4x4x4": Lambda(lambda x: x - 0.5),
|
|
"kinetics_stride4x4x4": Lambda(lambda x: x - 0.5),
|
|
"kinetics_stride2x4x4": Lambda(lambda x: x - 0.5),
|
|
'stabilityai/sd-vae-ft-mse': transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
|
|
'stabilityai/sd-vae-ft-ema': transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
|
|
'vqgan_imagenet_f16_1024': Lambda(lambda x: 2. * x - 1.),
|
|
'vqgan_imagenet_f16_16384': Lambda(lambda x: 2. * x - 1.),
|
|
'vqgan_gumbel_f8': Lambda(lambda x: 2. * x - 1.),
|
|
|
|
}
|
|
ae_denorm = {
|
|
'CausalVAEModel_4x8x8': lambda x: (x + 1.) / 2.,
|
|
'CausalVQVAEModel_4x4x4': lambda x: x + 0.5,
|
|
'CausalVQVAEModel_4x8x8': lambda x: x + 0.5,
|
|
'VQVAEModel_4x4x4': lambda x: x + 0.5,
|
|
'VQVAEModel_4x8x8': lambda x: x + 0.5,
|
|
"bair_stride4x2x2": lambda x: x + 0.5,
|
|
"ucf101_stride4x4x4": lambda x: x + 0.5,
|
|
"kinetics_stride4x4x4": lambda x: x + 0.5,
|
|
"kinetics_stride2x4x4": lambda x: x + 0.5,
|
|
'stabilityai/sd-vae-ft-mse': lambda x: 0.5 * x + 0.5,
|
|
'stabilityai/sd-vae-ft-ema': lambda x: 0.5 * x + 0.5,
|
|
'vqgan_imagenet_f16_1024': lambda x: (x + 1.) / 2.,
|
|
'vqgan_imagenet_f16_16384': lambda x: (x + 1.) / 2.,
|
|
'vqgan_gumbel_f8': lambda x: (x + 1.) / 2.,
|
|
}
|
|
|
|
def getdataset(args):
|
|
temporal_sample = TemporalRandomCrop(args.num_frames * args.sample_rate) # 16 x
|
|
norm_fun = ae_norm[args.ae]
|
|
if args.dataset == 'ucf101':
|
|
transform = Compose(
|
|
[
|
|
ToTensorVideo(), # TCHW
|
|
CenterCropResizeVideo(size=args.max_image_size),
|
|
RandomHorizontalFlipVideo(p=0.5),
|
|
norm_fun,
|
|
]
|
|
)
|
|
return UCF101(args, transform=transform, temporal_sample=temporal_sample)
|
|
if args.dataset == 'landscope':
|
|
transform = Compose(
|
|
[
|
|
ToTensorVideo(), # TCHW
|
|
CenterCropResizeVideo(size=args.max_image_size),
|
|
RandomHorizontalFlipVideo(p=0.5),
|
|
norm_fun,
|
|
]
|
|
)
|
|
return Landscope(args, transform=transform, temporal_sample=temporal_sample)
|
|
elif args.dataset == 'sky':
|
|
transform = transforms.Compose([
|
|
ToTensorVideo(),
|
|
CenterCropResizeVideo(args.max_image_size),
|
|
RandomHorizontalFlipVideo(p=0.5),
|
|
norm_fun
|
|
])
|
|
return Sky(args, transform=transform, temporal_sample=temporal_sample)
|
|
elif args.dataset == 't2v':
|
|
transform = transforms.Compose([
|
|
ToTensorVideo(),
|
|
CenterCropResizeVideo(args.max_image_size),
|
|
RandomHorizontalFlipVideo(p=0.5),
|
|
norm_fun
|
|
])
|
|
tokenizer = AutoTokenizer.from_pretrained(args.text_encoder_name, cache_dir='./cache_dir')
|
|
return T2V_dataset(args, transform=transform, temporal_sample=temporal_sample, tokenizer=tokenizer)
|
|
elif args.dataset == 't2v_feature':
|
|
return T2V_Feature_dataset(args, temporal_sample)
|
|
elif args.dataset == 't2v_t5_feature':
|
|
transform = transforms.Compose([
|
|
ToTensorVideo(),
|
|
CenterCropResizeVideo(args.max_image_size),
|
|
RandomHorizontalFlipVideo(p=0.5),
|
|
norm_fun
|
|
])
|
|
return T2V_T5_Feature_dataset(args, transform, temporal_sample)
|
|
else:
|
|
raise NotImplementedError(args.dataset)
|