def trainloader_dct_subset(args):
traindir = os.path.join(args.data, 'train')
train_dataset = ImageFolderDCT(traindir, transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.TransformDCT(),
transforms.ToTensorDCT(),
transforms.SubsetDCT(args.subset_channels),
transforms.NormalizeDCT(
train_y_mean, train_y_std,
train_cb_mean, train_cb_std,
train_cr_mean, train_cr_std),
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
train_loader_len = len(train_loader)
return train_loader, train_sampler, train_loader_len
def trainloader_dct_resized(args):
traindir = os.path.join(args.data, 'train')
train_dataset = ImageFolderDCT(traindir, transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.TransformDCT(), # 28x28x192
transforms.DCTFlatten2D(),
transforms.UpsampleDCT(upscale_ratio_h=4, upscale_ratio_w=4, debug=False),
transforms.ToTensorDCT(),
transforms.SubsetDCT(channels=args.subset),
transforms.Aggregate(),
transforms.NormalizeDCT(
train_dct_subset_mean,
train_dct_subset_std,
channels=args.subset
)
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
train_loader_len = len(train_loader)
return train_loader, train_sampler, train_loader_len
def cvt_transform(self, img):
return cvtransforms.Compose([
cvtransforms.RandomResizedCrop(self.img_size),
# cvtransforms.RandomHorizontalFlip(),
cvtransforms.Upscale(upscale_factor=2),
cvtransforms.TransformUpscaledDCT(),
cvtransforms.ToTensorDCT(),
cvtransforms.SubsetDCT(channels=192),
cvtransforms.Aggregate(),
cvtransforms.NormalizeDCT(train_upscaled_static_mean,
train_upscaled_static_std,
channels=192)
])(img)
def trainloader_upscaled_static(args, model='mobilenet'):
traindir = os.path.join(args.data, 'train')
if model == 'mobilenet':
input_size = 896
elif model == 'resnet':
input_size = 448
else:
raise NotImplementedError
transform = transforms.Compose([
transforms.RandomResizedCrop(input_size),
transforms.RandomHorizontalFlip(),
transforms.Upscale(upscale_factor=2),
transforms.TransformUpscaledDCT(),
transforms.ToTensorDCT(),
transforms.SubsetDCT(channels=args.subset, pattern=args.pattern),
transforms.Aggregate(),
transforms.NormalizeDCT(
train_upscaled_static_mean,
train_upscaled_static_std,
channels=args.subset,
pattern=args.pattern
)
])
train_dataset = ImageFolderDCT(traindir, transform)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
train_loader_len = len(train_loader)
return train_loader, train_sampler, train_loader_len
def get_composed_transform_dct(self, aug=False, filter_size=8):
# print("aug: ", aug)
# print("filter size,", filter_size)
if aug == False:
transform = transforms_dct.Compose([ #transform_funcs,
transforms_dct.Resize(int(filter_size * 56 * 1.15)),
transforms_dct.CenterCrop(filter_size * 56),
transforms_dct.GetDCT(filter_size),
transforms_dct.UpScaleDCT(size=56),
transforms_dct.ToTensorDCT(),
transforms_dct.SubsetDCT(channels=24),
transforms_dct.Aggregate(),
transforms_dct.NormalizeDCT(
# train_y_mean_resized, train_y_std_resized,
# train_cb_mean_resized, train_cb_std_resized,
# train_cr_mean_resized, train_cr_std_resized),
train_upscaled_static_mean,
train_upscaled_static_std,
channels=24)
#transforms_dct.Aggregate()
])
else:
transform = transforms_dct.Compose([ #transform_funcs,
transforms_dct.RandomResizedCrop(filter_size * 56),
transforms_dct.ImageJitter(self.jitter_param),
transforms_dct.RandomHorizontalFlip(),
transforms_dct.GetDCT(filter_size),
transforms_dct.UpScaleDCT(size=56),
transforms_dct.ToTensorDCT(),
transforms_dct.SubsetDCT(channels=24),
transforms_dct.Aggregate(),
transforms_dct.NormalizeDCT(
# train_y_mean_resized, train_y_std_resized,
# train_cb_mean_resized, train_cb_std_resized,
# train_cr_mean_resized, train_cr_std_resized),
train_upscaled_static_mean,
train_upscaled_static_std,
channels=24)
])
return transform
train_y_std_resized,
train_cb_mean_resized,
train_cb_std_resized,
train_cr_mean_resized,
train_cr_std_resized),
])),
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=False)
# train_dataset = ImageFolderDCT('/mnt/ssd/kai.x/dataset/ILSVRC2012/train', transforms.Compose([
train_dataset = ImageFolderDCT(
'/storage-t1/user/kaixu/datasets/ILSVRC2012/train',
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToYCrCb(),
transforms.ChromaSubsample(),
transforms.UpsampleDCT(size=224, T=896, debug=False),
transforms.ToTensorDCT(),
transforms.NormalizeDCT(train_y_mean_resized,
train_y_std_resized,
train_cb_mean_resized,
train_cb_std_resized,
train_cr_mean_resized,
train_cr_std_resized),
]))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=1,
# transforms.NormalizeDCT(
# train_y_mean_resized, train_y_std_resized,
# train_cb_mean_resized, train_cb_std_resized,
# train_cr_mean_resized, train_cr_std_resized),
# ])
# transform3 =transforms.Compose([
# transforms.RandomResizedCrop(224),
# transforms.RandomHorizontalFlip(),
# transforms.ResizedTransformDCT(),
# transforms.ToTensorDCT(),
# transforms.SubsetDCT(32),
# ])
transform4 = transforms.Compose([
transforms.RandomResizedCrop(896),
transforms.RandomHorizontalFlip(),
transforms.Upscale(upscale_factor=2),
transforms.TransformUpscaledDCT(),
transforms.ToTensorDCT(),
transforms.SubsetDCT(channels='24'),
transforms.Aggregate(),
transforms.NormalizeDCT(
train_upscaled_static_mean,
train_upscaled_static_std,
channels='24'
)
])
transform5 = transforms.Compose([
transforms.DCTFlatten2D(),