def test_exception(self):
trans = transforms.Compose([transforms.Resize(-1)])
trans_batch = transforms.Compose([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.Pad([1.0, 2.0, 3.0])
with self.assertRaises(TypeError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, '1')
with self.assertRaises(TypeError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, 1, {})
with self.assertRaises(TypeError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, 1, padding_mode=-1)
with self.assertRaises(ValueError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, [1.0, 2.0, 3.0])
with self.assertRaises(TypeError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, '1')
with self.assertRaises(TypeError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, 1, {})
with self.assertRaises(TypeError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, 1, padding_mode=-1)
with self.assertRaises(ValueError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, [1.0, 2.0, 3.0])
with self.assertRaises(ValueError):
transforms.RandomRotation(-2)
with self.assertRaises(ValueError):
transforms.RandomRotation([1, 2, 3])
with self.assertRaises(ValueError):
trans_gray = transforms.Grayscale(5)
fake_img = self.create_image((100, 120, 3))
trans_gray(fake_img)
with self.assertRaises(TypeError):
transform = transforms.RandomResizedCrop(64)
transform(1)
def test_trans_resize(self):
trans = transforms.Compose([
transforms.Resize(300),
transforms.RandomResizedCrop((280, 280)),
transforms.Resize(280),
transforms.Resize((256, 200)),
transforms.Resize((180, 160)),
transforms.CenterCrop(128),
transforms.CenterCrop((128, 128)),
])
self.do_transform(trans)
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)
with self.assertRaises(ValueError):
transforms.Pad([1.0, 2.0, 3.0])
with self.assertRaises(TypeError):
fake_img = np.random.rand(100, 120, 3).astype('float32')
F.pad(fake_img, '1')
with self.assertRaises(TypeError):
fake_img = np.random.rand(100, 120, 3).astype('float32')
F.pad(fake_img, 1, {})
with self.assertRaises(TypeError):
fake_img = np.random.rand(100, 120, 3).astype('float32')
F.pad(fake_img, 1, padding_mode=-1)
with self.assertRaises(ValueError):
fake_img = np.random.rand(100, 120, 3).astype('float32')
F.pad(fake_img, [1.0, 2.0, 3.0])
with self.assertRaises(ValueError):
transforms.RandomRotate(-2)
with self.assertRaises(ValueError):
transforms.RandomRotate([1, 2, 3])
with self.assertRaises(ValueError):
trans_gray = transforms.Grayscale(5)
fake_img = np.random.rand(100, 120, 3).astype('float32')
trans_gray(fake_img)
def build_model(self):
""" DataLoader """
pad = int(30 * self.img_size // 256)
train_transform = T.Compose([
T.RandomHorizontalFlip(),
T.Resize((self.img_size + pad, self.img_size + pad)),
T.RandomCrop(self.img_size),
T.ToTensor(),
T.Normalize(mean=0.5, std=0.5),
])
test_transform = T.Compose([
T.Resize((self.img_size, self.img_size)),
T.ToTensor(),
T.Normalize(mean=0.5, std=0.5)
])
self.trainA = ImageFolder('dataset/photo2cartoon/trainA', self.img_size, train_transform)
self.trainB = ImageFolder('dataset/photo2cartoon/trainB', self.img_size, train_transform)
self.testA = ImageFolder('dataset/photo2cartoon/testA', self.img_size, test_transform)
self.testB = ImageFolder('dataset/photo2cartoon/testB', self.img_size, test_transform)
self.trainA_loader = DataLoader(self.trainA, batch_size=self.batch_size, shuffle=True)
self.trainB_loader = DataLoader(self.trainB, batch_size=self.batch_size, shuffle=True)
self.testA_loader = DataLoader(self.testA, batch_size=1, shuffle=False)
self.testB_loader = DataLoader(self.testB, batch_size=1, shuffle=False)
""" Define Generator, Discriminator """
self.genA2B = ResnetGenerator(ngf=self.ch, img_size=self.img_size, light=self.light)
self.genB2A = ResnetGenerator(ngf=self.ch, img_size=self.img_size, light=self.light)
self.disGA = Discriminator(input_nc=3, ndf=self.ch, n_layers=7)
self.disGB = Discriminator(input_nc=3, ndf=self.ch, n_layers=7)
self.disLA = Discriminator(input_nc=3, ndf=self.ch, n_layers=5)
self.disLB = Discriminator(input_nc=3, ndf=self.ch, n_layers=5)
""" Define Loss """
self.L1_loss = nn.loss.L1Loss()
self.MSE_loss = nn.loss.MSELoss()
self.BCE_loss = nn.loss.BCEWithLogitsLoss()
self.G_optim = paddle.optimizer.Adam(
learning_rate=self.lr, beta1=0.5, beta2=0.999, weight_decay=0.0001,
parameters=self.genA2B.parameters()+self.genB2A.parameters()
)
self.D_optim = paddle.optimizer.Adam(
learning_rate=self.lr, beta1=0.5, beta2=0.999, weight_decay=0.0001,
parameters=self.disGA.parameters()+self.disGB.parameters()+self.disLA.parameters()+self.disLB.parameters()
)
self.Rho_clipper = RhoClipper(0, self.rho_clipper)
self.W_clipper = WClipper(0, self.w_clipper)
def __init__(self,
path,
mode='train',
image_size=224,
resize_short_size=256):
super(ImageNetDataset, self).__init__(path)
self.mode = mode
self.samples = []
list_file = "train_list.txt" if self.mode == "train" else "val_list.txt"
with open(os.path.join([path, list_file]), 'r') as f:
for line in f:
_image, _label = line.strip().split(" ")
self.samples.append((_image, int(_label)))
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.Transpose(), normalize
])
else:
self.transform = transforms.Compose([
transforms.Resize(resize_short_size),
transforms.CenterCrop(image_size),
transforms.Transpose(), 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 = paddle.to_tensor(image)
return image
def __init__(self,
data_dir,
mode='train',
image_size=224,
resize_short_size=256):
super(ImageNetDataset, self).__init__()
train_file_list = os.path.join(data_dir, 'train_list.txt')
val_file_list = os.path.join(data_dir, 'val_list.txt')
test_file_list = os.path.join(data_dir, 'test_list.txt')
self.data_dir = data_dir
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.Transpose(),
normalize
])
else:
self.transform = transforms.Compose([
transforms.Resize(resize_short_size),
transforms.CenterCrop(image_size), transforms.Transpose(),
normalize
])
if mode == 'train':
with open(train_file_list) as flist:
full_lines = [line.strip() for line in flist]
np.random.shuffle(full_lines)
if os.getenv('PADDLE_TRAINING_ROLE'):
# distributed mode if the env var `PADDLE_TRAINING_ROLE` exits
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
trainer_count = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
per_node_lines = len(full_lines) // trainer_count
lines = full_lines[trainer_id * per_node_lines:(
trainer_id + 1) * per_node_lines]
print(
"read images from %d, length: %d, lines length: %d, total: %d"
% (trainer_id * per_node_lines, per_node_lines,
len(lines), len(full_lines)))
else:
lines = full_lines
self.data = [line.split() for line in lines]
else:
with open(val_file_list) as flist:
lines = [line.strip() for line in flist]
self.data = [line.split() for line in lines]
def load_image(image_path, max_size=400, shape=None):
image = Image.open(image_path).convert('RGB')
size = shape if shape is not None else max_size if max(
image.size) > max_size else max(image.size)
transform = transforms.Compose([
transforms.Resize(size),
transforms.Transpose(),
transforms.Normalize([123.675, 116.28, 103.53],
[58.395, 57.120, 57.375])
])
image = transform(image)[np.newaxis, :3, :, :]
image = paddle.to_tensor(image)
return image
def __init__(self, data_dir, image_size=224, resize_short_size=256):
super(ImageNetValDataset, self).__init__()
val_file_list = os.path.join(data_dir, 'val_list.txt')
test_file_list = os.path.join(data_dir, 'test_list.txt')
self.data_dir = data_dir
normalize = transforms.Normalize(mean=[123.675, 116.28, 103.53],
std=[58.395, 57.120, 57.375])
self.transform = transforms.Compose([
transforms.Resize(resize_short_size),
transforms.CenterCrop(image_size),
transforms.Transpose(), normalize
])
with open(val_file_list) as flist:
lines = [line.strip() for line in flist]
self.data = [line.split() for line in lines]
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.Transpose(), normalize
])
else:
self.transform = transforms.Compose([
transforms.Resize(resize_short_size),
transforms.CenterCrop(image_size),
transforms.Transpose(), normalize
])
return train_image, 0
def __len__(self):
return len(self.img_names)
if __name__ == '__main__':
from paddle.vision.transforms import transforms as T
from paddle.io import DataLoader
img_size = 256
pad = 30
train_transform = T.Compose([
T.RandomHorizontalFlip(),
T.Resize((img_size + pad, img_size + pad)),
T.RandomCrop(img_size),
T.ToTensor(),
T.Normalize(mean=0.5, std=0.5)
])
dataloader = ImageFolder('dataset/photo2cartoon/trainB',
transform=train_transform)
train_loader = DataLoader(dataloader, batch_size=1, shuffle=True)
print('num: ', len(train_loader))
for i in range(300):
print(i)
try:
real_A, _ = next(trainA_iter)
except:
def test_exception(self):
trans = transforms.Compose([transforms.Resize(-1)])
trans_batch = transforms.Compose([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.Pad([1.0, 2.0, 3.0])
with self.assertRaises(TypeError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, '1')
with self.assertRaises(TypeError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, 1, {})
with self.assertRaises(TypeError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, 1, padding_mode=-1)
with self.assertRaises(ValueError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, [1.0, 2.0, 3.0])
with self.assertRaises(TypeError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, '1')
with self.assertRaises(TypeError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, 1, {})
with self.assertRaises(TypeError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, 1, padding_mode=-1)
with self.assertRaises(ValueError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, [1.0, 2.0, 3.0])
with self.assertRaises(ValueError):
transforms.RandomAffine(-10)
with self.assertRaises(ValueError):
transforms.RandomAffine([-30, 60], translate=[2, 2])
with self.assertRaises(ValueError):
transforms.RandomAffine(10, translate=[0.2, 0.2], scale=[-2, -1]),
with self.assertRaises(ValueError):
transforms.RandomAffine(10, translate=[0.2, 0.2], scale=[1, 2, 3]),
with self.assertRaises(ValueError):
transforms.RandomAffine(10,
translate=[0.2, 0.2],
scale=[0.5, 0.5],
shear=[1, 2, 3]),
with self.assertRaises(ValueError):
transforms.RandomAffine(10,
translate=[0.5, 0.3],
scale=[0.7, 1.3],
shear=[-10, 10, 0, 20, 40])
with self.assertRaises(ValueError):
transforms.RandomRotation(-2)
with self.assertRaises(ValueError):
transforms.RandomRotation([1, 2, 3])
with self.assertRaises(ValueError):
trans_gray = transforms.Grayscale(5)
fake_img = self.create_image((100, 120, 3))
trans_gray(fake_img)
with self.assertRaises(TypeError):
transform = transforms.RandomResizedCrop(64)
transform(1)
def test_info(self):
str(transforms.Compose([transforms.Resize((224, 224))]))
str(transforms.Compose([transforms.Resize((224, 224))]))
def test_exception(self):
trans = transforms.Compose([transforms.Resize(-1)])
trans_batch = transforms.Compose([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)
with self.assertRaises(ValueError):
transforms.Pad([1.0, 2.0, 3.0])
with self.assertRaises(TypeError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, '1')
with self.assertRaises(TypeError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, 1, {})
with self.assertRaises(TypeError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, 1, padding_mode=-1)
with self.assertRaises(ValueError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, [1.0, 2.0, 3.0])
with self.assertRaises(TypeError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, '1')
with self.assertRaises(TypeError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, 1, {})
with self.assertRaises(TypeError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, 1, padding_mode=-1)
with self.assertRaises(ValueError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, [1.0, 2.0, 3.0])
with self.assertRaises(ValueError):
transforms.RandomAffine(-10)
with self.assertRaises(ValueError):
transforms.RandomAffine([-30, 60], translate=[2, 2])
with self.assertRaises(ValueError):
transforms.RandomAffine(10, translate=[0.2, 0.2], scale=[1, 2, 3]),
with self.assertRaises(ValueError):
transforms.RandomAffine(10,
translate=[0.2, 0.2],
scale=[0.5, 0.5],
shear=[1, 2, 3]),
with self.assertRaises(ValueError):
transforms.RandomAffine(10,
translate=[0.5, 0.3],
scale=[0.7, 1.3],
shear=[-10, 10, 0, 20, 40])
with self.assertRaises(ValueError):
transforms.RandomAffine(10,
translate=[0.5, 0.3],
scale=[0.7, 1.3],
shear=[-10, 10, 20, 40],
fill=114,
center=(1, 2, 3))
with self.assertRaises(ValueError):
transforms.RandomRotation(-2)
with self.assertRaises(ValueError):
transforms.RandomRotation([1, 2, 3])
with self.assertRaises(ValueError):
trans_gray = transforms.Grayscale(5)
fake_img = self.create_image((100, 120, 3))
trans_gray(fake_img)
with self.assertRaises(TypeError):
transform = transforms.RandomResizedCrop(64)
transform(1)
with self.assertRaises(ValueError):
transform = transforms.BrightnessTransform([-0.1, -0.2])
with self.assertRaises(TypeError):
transform = transforms.BrightnessTransform('0.1')
with self.assertRaises(ValueError):
transform = transforms.BrightnessTransform('0.1', keys=1)
with self.assertRaises(NotImplementedError):
transform = transforms.BrightnessTransform('0.1', keys='a')
with self.assertRaises(Exception):
transform = transforms.RandomErasing(scale=0.5)
with self.assertRaises(Exception):
transform = transforms.RandomErasing(ratio=0.8)
with self.assertRaises(Exception):
transform = transforms.RandomErasing(scale=(10, 0.4))
with self.assertRaises(Exception):
transform = transforms.RandomErasing(ratio=(3.3, 0.3))
with self.assertRaises(Exception):
transform = transforms.RandomErasing(prob=1.5)
with self.assertRaises(Exception):
transform = transforms.RandomErasing(value="0")
def test_exception(self):
trans = transforms.Compose([transforms.Resize(-1)])
trans_batch = transforms.Compose([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)
with self.assertRaises(ValueError):
transforms.Pad([1.0, 2.0, 3.0])
with self.assertRaises(TypeError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, '1')
with self.assertRaises(TypeError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, 1, {})
with self.assertRaises(TypeError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, 1, padding_mode=-1)
with self.assertRaises(ValueError):
fake_img = self.create_image((100, 120, 3))
F.pad(fake_img, [1.0, 2.0, 3.0])
with self.assertRaises(TypeError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, '1')
with self.assertRaises(TypeError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, 1, {})
with self.assertRaises(TypeError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, 1, padding_mode=-1)
with self.assertRaises(ValueError):
tensor_img = paddle.rand((3, 100, 100))
F.pad(tensor_img, [1.0, 2.0, 3.0])
with self.assertRaises(ValueError):
transforms.RandomRotation(-2)
with self.assertRaises(ValueError):
transforms.RandomRotation([1, 2, 3])
with self.assertRaises(ValueError):
trans_gray = transforms.Grayscale(5)
fake_img = self.create_image((100, 120, 3))
trans_gray(fake_img)
with self.assertRaises(TypeError):
transform = transforms.RandomResizedCrop(64)
transform(1)
with self.assertRaises(ValueError):
transform = transforms.BrightnessTransform([-0.1, -0.2])
with self.assertRaises(TypeError):
transform = transforms.BrightnessTransform('0.1')
with self.assertRaises(ValueError):
transform = transforms.BrightnessTransform('0.1', keys=1)
with self.assertRaises(NotImplementedError):
transform = transforms.BrightnessTransform('0.1', keys='a')
