def loader_from_list(root,
file_list,
batch_size,
new_size=None,
height=64,
width=128,
crop=True,
num_workers=4,
shuffle=True,
center_crop=True,
return_paths=False,
drop_last=True):
# transform_list = [transforms.ToTensor(),
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
# if center_crop:
# transform_list = [transforms.CenterCrop((height, width))] + \
# transform_list if crop else transform_list
# else:
# transform_list = [transforms.RandomCrop((height, width))] + \
# transform_list if crop else transform_list
# transform_list = [transforms.Resize(new_size)] + transform_list \
# if new_size is not None else transform_list
# if not center_crop:
# transform_list = [transforms.RandomHorizontalFlip()] + transform_list
transform_list = []
transform_list += [
transforms.Resize(new_size),
transforms.CenterCrop(height),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
]
transform = transforms.Compose(transform_list)
dataset = ImageLabelFilelist(root,
file_list,
transform,
return_paths=return_paths)
loader = DataLoader(dataset,
batch_size,
shuffle=shuffle,
drop_last=drop_last,
num_workers=num_workers)
return loader
def loader_from_list(
root,
file_list,
batch_size,
new_size=None,
height=128,
width=128,
crop=True,
num_workers=4,
shuffle=True,
center_crop=False,
return_paths=False,
drop_last=True):
transform_list = [transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
if center_crop:
transform_list = [transforms.CenterCrop((height, width))] + \
transform_list if crop else transform_list
else:
transform_list = [transforms.RandomCrop((height, width))] + \
transform_list if crop else transform_list
# 先将图片转换为140*140，再随机挑出128*128
transform_list = [transforms.Resize((new_size,new_size))] + transform_list \
if new_size is not None else transform_list
if not center_crop: # 以一定概率水平翻转
transform_list = [transforms.RandomHorizontalFlip()] + transform_list
transform = transforms.Compose(transform_list) # 得到transform组合体
dataset = ImageLabelFilelist(root,
file_list,
transform,
return_paths=return_paths)
loader = DataLoader(dataset,
batch_size,
shuffle=shuffle,
drop_last=drop_last,
num_workers=num_workers)
return loader