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 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_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.RandomHorizontalFlip(),
normalize,
])
self.do_transform(trans)
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_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,
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 __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
])
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_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')