def make_celeba_dataset(img_paths,
batch_size,
resize=64,
drop_remainder=True,
shuffle=True,
num_workers=4,
pin_memory=False):
crop_size = 108
offset_height = (218 - crop_size) // 2
offset_width = (178 - crop_size) // 2
crop = lambda x: x[:, offset_height:offset_height + crop_size,
offset_width:offset_width + crop_size]
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(crop),
transforms.ToPILImage(),
transforms.Resize(size=(resize, resize)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
dataset = torchlib.DiskImageDataset(img_paths, map_fn=transform)
data_loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
drop_last=drop_remainder,
pin_memory=pin_memory)
img_shape = (resize, resize, 3)
return data_loader, img_shape
def make_custom_datset(img_paths,
batch_size,
resize=64,
drop_remainder=True,
shuffle=True,
num_workers=4,
pin_memory=False):
transform = transforms.Compose([
# ======================================
# = custom =
# ======================================
..., # custom preprocessings
# ======================================
# = custom =
# ======================================
transforms.Resize(size=(resize, resize)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
dataset = torchlib.DiskImageDataset(img_paths, map_fn=transform)
data_loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
drop_last=drop_remainder,
pin_memory=pin_memory)
img_shape = (resize, resize, 3)
return data_loader, img_shape
