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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 .t2v_datasets import T2V_dataset
from .transform import ToTensorVideo, TemporalRandomCrop, RandomHorizontalFlipVideo, CenterCropResizeVideo, LongSideResizeVideo, SpatialStrideCropVideo
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 == 't2v':
if args.multi_scale:
resize = [
LongSideResizeVideo(args.max_image_size, skip_low_resolution=True),
SpatialStrideCropVideo(args.stride)
]
else:
resize = [CenterCropResizeVideo(args.max_image_size), ]
transform = transforms.Compose([
ToTensorVideo(),
*resize,
# RandomHorizontalFlipVideo(p=0.5), # in case their caption have position decription
norm_fun
])
tokenizer = AutoTokenizer.from_pretrained(args.text_encoder_name, cache_dir=args.cache_dir)
return T2V_dataset(args, transform=transform, temporal_sample=temporal_sample, tokenizer=tokenizer)
raise NotImplementedError(args.dataset)
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