test_image_transform = T.Compose([ Resize(config.resize_size), center_crop, SplitInSites(), T.Lambda(lambda xs: torch.stack([to_tensor(x) for x in xs], 0)), ]) train_transform = T.Compose([ ApplyTo(['image'], T.Compose([ RandomSite(), Resize(config.resize_size), random_crop, RandomFlip(), RandomTranspose(), to_tensor, ChannelReweight(config.aug.channel_reweight), ])), normalize, Extract(['image', 'exp', 'label', 'id']), ]) eval_transform = T.Compose([ ApplyTo(['image'], infer_image_transform), normalize, Extract(['image', 'exp', 'label', 'id']), ]) unsup_transform = T.Compose([ ApplyTo(['image'], T.Compose([ Resize(config.resize_size), random_crop, RandomFlip(),
random_crop = Resetable(RandomCrop) center_crop = Resetable(CenterCrop) train_transform = T.Compose([ ApplyTo( ['image'], T.Compose([ RandomSite(), Resize(config.resize_size), random_crop, RandomFlip(), RandomTranspose(), RandomRotation(180), # FIXME: ToTensor(), ChannelReweight(config.aug.channel_weight), ])), # NormalizeByRefStats(), Extract(['image', 'feat', 'label', 'id']), ]) eval_transform = T.Compose([ ApplyTo( ['image'], T.Compose([ RandomSite(), # FIXME: Resize(config.resize_size), center_crop, ToTensor(), ])), # NormalizeByRefStats(), Extract(['image', 'feat', 'label', 'id']),