def test_flip(self):
trans = transforms.Compose([
transforms.RandomHorizontalFlip(1.0),
transforms.RandomHorizontalFlip(0.0),
transforms.RandomVerticalFlip(0.0),
transforms.RandomVerticalFlip(1.0),
])
self.do_transform(trans)
def test_trans_resize(self):
trans = transforms.Compose([
transforms.Resize(300, [0, 1]),
transforms.RandomResizedCrop((280, 280)),
transforms.Resize(280, [0, 1]),
transforms.Resize((256, 200)),
transforms.Resize((180, 160)),
transforms.CenterCrop(128),
transforms.CenterCrop((128, 128)),
])
self.do_transform(trans)
def test_trans_all(self):
normalize = transforms.Normalize(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375])
trans = transforms.Compose([
transforms.RandomResizedCrop(224), transforms.GaussianNoise(),
transforms.ColorJitter(
brightness=0.4, contrast=0.4, saturation=0.4,
hue=0.4), transforms.RandomHorizontalFlip(),
transforms.Permute(mode='CHW'), normalize
])
self.do_transform(trans)
def __init__(self,
path,
mode='train',
image_size=224,
resize_short_size=256):
super(ImageNetDataset, self).__init__(path)
self.mode = mode
normalize = transforms.Normalize(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375])
if self.mode == 'train':
self.transform = transforms.Compose([
transforms.RandomResizedCrop(image_size),
transforms.RandomHorizontalFlip(),
transforms.Permute(mode='CHW'), normalize
])
else:
self.transform = transforms.Compose([
transforms.Resize(resize_short_size),
transforms.CenterCrop(image_size),
transforms.Permute(mode='CHW'), normalize
])
def load_image(image_path, max_size=400, shape=None):
image = cv2.imread(image_path)
image = image.astype('float32') / 255.0
size = shape if shape is not None else max_size if max(
image.shape[:2]) > max_size else max(image.shape[:2])
transform = transforms.Compose([
transforms.Resize(size),
transforms.Permute(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
image = transform(image)[np.newaxis, :3, :, :]
image = fluid.dygraph.to_variable(image)
return image
def test_exception(self):
trans = transforms.Compose([transforms.Resize(-1)])
trans_batch = transforms.BatchCompose([transforms.Resize(-1)])
with self.assertRaises(Exception):
self.do_transform(trans)
with self.assertRaises(Exception):
self.do_transform(trans_batch)
with self.assertRaises(ValueError):
transforms.ContrastTransform(-1.0)
with self.assertRaises(ValueError):
transforms.SaturationTransform(-1.0),
with self.assertRaises(ValueError):
transforms.HueTransform(-1.0)
with self.assertRaises(ValueError):
transforms.BrightnessTransform(-1.0)
def test_trans_centerCrop(self):
trans = transforms.Compose([
transforms.CenterCropResize(224),
transforms.CenterCropResize(128, 160),
])
self.do_transform(trans)
def test_info(self):
str(transforms.Compose([transforms.Resize((224, 224))]))
str(transforms.BatchCompose([transforms.Resize((224, 224))]))
