def __init__(self, root, train=True, test=True, transform=None):
# Data loading code
std_value = 1.0 / 255.0
mean_values = [104 / 255.0, 117 / 255.0, 128 / 255.0]
if transform is None:
transform = [
transforms.Compose([
transforms.CovertBGR(),
transforms.Resize(256),
transforms.RandomResizedCrop(227),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=mean_values,
std=3 * [std_value]),
]),
transforms.Compose([
transforms.CovertBGR(),
transforms.Resize(256),
transforms.CenterCrop(227),
transforms.ToTensor(),
transforms.Normalize(mean=mean_values,
std=3 * [std_value]),
])
]
if root is None:
root = 'DataSet/Products'
traindir = os.path.join(root, 'train')
testdir = os.path.join(root, 'test')
if train:
self.train = datasets.ImageFolder(traindir, transform[0])
if test:
self.test = datasets.ImageFolder(testdir, transform[1])
def __init__(self, root, train=True, test=True, transform=None):
# Data loading code
mean_values = [0.485, 0.456, 0.406]
std_values = [0.229, 0.224, 0.225]
if transform is None:
transform = [
transforms.Compose([
# transforms.CovertBGR(),
transforms.Resize(256),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=mean_values, std=std_values),
]),
transforms.Compose([
# transforms.CovertBGR(),
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=mean_values, std=std_values),
])
]
if root is None:
root = 'DataSet/Products'
traindir = os.path.join(root, 'train')
testdir = os.path.join(root, 'test')
if train:
self.train = datasets.ImageFolder(traindir, transform[0])
if test:
self.test = datasets.ImageFolder(testdir, transform[1])
def Generate_transform_Dict(origin_width=256, width=227, ratio=0.16):
std_value = 1.0 / 255.0
normalize = transforms.Normalize(mean=[104 / 255.0, 117 / 255.0, 128 / 255.0],
std= [1.0/255, 1.0/255, 1.0/255])
transform_dict = {}
transform_dict['rand-crop'] = \
transforms.Compose([
transforms.CovertBGR(),
transforms.Resize((origin_width)),
transforms.RandomResizedCrop(scale=(ratio, 1), size=width),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
transform_dict['center-crop'] = \
transforms.Compose([
transforms.CovertBGR(),
transforms.Resize((origin_width)),
transforms.CenterCrop(width),
transforms.ToTensor(),
normalize,
])
transform_dict['resize'] = \
transforms.Compose([
transforms.CovertBGR(),
transforms.Resize((width)),
transforms.ToTensor(),
normalize,
])
return transform_dict
def __init__(self, root, train=True, test=True, transform=None):
# Data loading code
# std_values = [0.229, 0.224, 0.225]
# mean_values = [104 / 255.0, 117 / 255.0, 128 / 255.0]
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
if transform is None:
transform = [transforms.Compose([
# transforms.CovertBGR(),
transforms.Resize(256),
transforms.RandomResizedCrop(scale=(0.16, 1), size=224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]),
transforms.Compose([
# transforms.CovertBGR(),
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])]
if root is None:
root = '/opt/intern/users/xunwang/DataSet//Products'
traindir = os.path.join(root, 'train')
testdir = os.path.join(root, 'test')
if train:
self.train = datasets.ImageFolder(traindir, transform[0])
if test:
self.test = datasets.ImageFolder(testdir, transform[1])
def Generate_transform_Dict(origin_width=256,
width=227,
ratio=0.16,
RAE='None'):
std_value = 1.0 / 255.0
normalize = transforms.Normalize(
mean=[104 / 255.0, 117 / 255.0, 128 / 255.0],
std=[1.0 / 255, 1.0 / 255, 1.0 / 255])
transform_dict = {}
if RAE == 'None':
print("No RAE")
transform_dict['rand-crop'] = \
transforms.Compose([
transforms.CovertBGR(),
transforms.Resize((origin_width)),
transforms.RandomResizedCrop(scale=(ratio, 1), size=width),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
else:
print("RAE with", RAE, "mode")
transform_dict['rand-crop'] = \
transforms.Compose([
transforms.CovertBGR(),
transforms.Resize((origin_width)),
transforms.RandomResizedCrop(scale=(ratio, 1), size=width),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
transforms.RandomErasing(mode=RAE, device='cpu')
])
transform_dict['center-crop'] = \
transforms.Compose([
transforms.CovertBGR(),
transforms.Resize((origin_width)),
transforms.CenterCrop(width),
transforms.ToTensor(),
normalize,
])
transform_dict['resize'] = \
transforms.Compose([
transforms.CovertBGR(),
transforms.Resize((width)),
transforms.ToTensor(),
normalize,
])
return transform_dict
