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Unverified Commit d367a01a authored by Jirka Borovec's avatar Jirka Borovec Committed by GitHub
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Use f-strings almost everywhere, and other cleanups by applying pyupgrade (#4585) 


Co-authored-by: default avatarNicolas Hug <nicolashug@fb.com>
parent 50dfe207
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Showing with 174 additions and 188 deletions
torchvision/ops/ps_roi_align.py
View file @ d367a01a
......@@ -65,7 +65,7 @@ class PSRoIAlign(nn.Module):
spatial_scale: float,
sampling_ratio: int,
):
super(PSRoIAlign, self).__init__()
super().__init__()
self.output_size = output_size
self.spatial_scale = spatial_scale
self.sampling_ratio = sampling_ratio
......
torchvision/ops/ps_roi_pool.py
View file @ d367a01a
......@@ -52,7 +52,7 @@ class PSRoIPool(nn.Module):
"""
def __init__(self, output_size: int, spatial_scale: float):
super(PSRoIPool, self).__init__()
super().__init__()
self.output_size = output_size
self.spatial_scale = spatial_scale
......
torchvision/ops/roi_align.py
View file @ d367a01a
......@@ -72,7 +72,7 @@ class RoIAlign(nn.Module):
sampling_ratio: int,
aligned: bool = False,
):
super(RoIAlign, self).__init__()
super().__init__()
self.output_size = output_size
self.spatial_scale = spatial_scale
self.sampling_ratio = sampling_ratio
......
torchvision/ops/roi_pool.py
View file @ d367a01a
......@@ -54,7 +54,7 @@ class RoIPool(nn.Module):
"""
def __init__(self, output_size: BroadcastingList2[int], spatial_scale: float):
super(RoIPool, self).__init__()
super().__init__()
self.output_size = output_size
self.spatial_scale = spatial_scale
......
torchvision/ops/stochastic_depth.py
View file @ d367a01a
......@@ -22,9 +22,9 @@ def stochastic_depth(input: Tensor, p: float, mode: str, training: bool = True)
Tensor[N, ...]: The randomly zeroed tensor.
"""
if p < 0.0 or p > 1.0:
raise ValueError("drop probability has to be between 0 and 1, but got {}".format(p))
raise ValueError(f"drop probability has to be between 0 and 1, but got {p}")
if mode not in ["batch", "row"]:
raise ValueError("mode has to be either 'batch' or 'row', but got {}".format(mode))
raise ValueError(f"mode has to be either 'batch' or 'row', but got {mode}")
if not training or p == 0.0:
return input
......
torchvision/prototype/datasets/utils/_dataset.py
View file @ d367a01a
......@@ -71,7 +71,7 @@ class DatasetInfo:
@staticmethod
def read_categories_file(path: pathlib.Path) -> List[List[str]]:
with open(path, "r", newline="") as file:
with open(path, newline="") as file:
return [row for row in csv.reader(file)]
@property
......
torchvision/prototype/datasets/utils/_internal.py
View file @ d367a01a
......@@ -63,7 +63,7 @@ def sequence_to_str(seq: Sequence, separate_last: str = "") -> str:
if len(seq) == 1:
return f"'{seq[0]}'"
return f"""'{"', '".join([str(item) for item in seq[:-1]])}', """ f"""{separate_last}'{seq[-1]}'."""
return f"""'{"', '".join([str(item) for item in seq[:-1]])}', {separate_last}'{seq[-1]}'."""
def add_suggestion(
......
torchvision/prototype/models/_api.py
View file @ d367a01a
......@@ -52,7 +52,7 @@ class Weights(Enum):
obj = cls.from_str(obj)
elif not isinstance(obj, cls) and not isinstance(obj, WeightEntry):
raise TypeError(
f"Invalid Weight class provided; expected {cls.__name__} " f"but received {obj.__class__.__name__}."
f"Invalid Weight class provided; expected {cls.__name__} but received {obj.__class__.__name__}."
)
return obj
......@@ -106,7 +106,7 @@ def get_weight(fn: Callable, weight_name: str) -> Weights:
if weights_class is None:
raise ValueError(
"The weight class for the specific method couldn't be retrieved. Make sure the typing info is " "correct."
"The weight class for the specific method couldn't be retrieved. Make sure the typing info is correct."
)
return weights_class.from_str(weight_name)
torchvision/transforms/_functional_video.py
View file @ d367a01a
......@@ -101,4 +101,4 @@ def hflip(clip):
flipped clip (torch.tensor): Size is (C, T, H, W)
"""
assert _is_tensor_video_clip(clip), "clip should be a 4D torch.tensor"
return clip.flip((-1))
return clip.flip(-1)
torchvision/transforms/_transforms_video.py
View file @ d367a01a
......@@ -44,7 +44,7 @@ class RandomCropVideo(RandomCrop):
return F.crop(clip, i, j, h, w)
def __repr__(self):
return self.__class__.__name__ + "(size={0})".format(self.size)
return self.__class__.__name__ + f"(size={self.size})"
class RandomResizedCropVideo(RandomResizedCrop):
......@@ -77,12 +77,13 @@ class RandomResizedCropVideo(RandomResizedCrop):
return F.resized_crop(clip, i, j, h, w, self.size, self.interpolation_mode)
def __repr__(self):
return self.__class__.__name__ + "(size={0}, interpolation_mode={1}, scale={2}, ratio={3})".format(
self.size, self.interpolation_mode, self.scale, self.ratio
return (
self.__class__.__name__
+ f"(size={self.size}, interpolation_mode={self.interpolation_mode}, scale={self.scale}, ratio={self.ratio})"
)
class CenterCropVideo(object):
class CenterCropVideo:
def __init__(self, crop_size):
if isinstance(crop_size, numbers.Number):
self.crop_size = (int(crop_size), int(crop_size))
......@@ -100,10 +101,10 @@ class CenterCropVideo(object):
return F.center_crop(clip, self.crop_size)
def __repr__(self):
return self.__class__.__name__ + "(crop_size={0})".format(self.crop_size)
return self.__class__.__name__ + f"(crop_size={self.crop_size})"
class NormalizeVideo(object):
class NormalizeVideo:
"""
Normalize the video clip by mean subtraction and division by standard deviation
Args:
......@@ -125,10 +126,10 @@ class NormalizeVideo(object):
return F.normalize(clip, self.mean, self.std, self.inplace)
def __repr__(self):
return self.__class__.__name__ + "(mean={0}, std={1}, inplace={2})".format(self.mean, self.std, self.inplace)
return self.__class__.__name__ + f"(mean={self.mean}, std={self.std}, inplace={self.inplace})"
class ToTensorVideo(object):
class ToTensorVideo:
"""
Convert tensor data type from uint8 to float, divide value by 255.0 and
permute the dimensions of clip tensor
......@@ -150,7 +151,7 @@ class ToTensorVideo(object):
return self.__class__.__name__
class RandomHorizontalFlipVideo(object):
class RandomHorizontalFlipVideo:
"""
Flip the video clip along the horizonal direction with a given probability
Args:
......@@ -172,4 +173,4 @@ class RandomHorizontalFlipVideo(object):
return clip
def __repr__(self):
return self.__class__.__name__ + "(p={0})".format(self.p)
return self.__class__.__name__ + f"(p={self.p})"
torchvision/transforms/autoaugment.py
View file @ d367a01a
......@@ -76,7 +76,7 @@ def _apply_op(
elif op_name == "Identity":
pass
else:
raise ValueError("The provided operator {} is not recognized.".format(op_name))
raise ValueError(f"The provided operator {op_name} is not recognized.")
return img
......@@ -208,7 +208,7 @@ class AutoAugment(torch.nn.Module):
(("ShearX", 0.7, 2), ("Invert", 0.1, None)),
]
else:
raise ValueError("The provided policy {} is not recognized.".format(policy))
raise ValueError(f"The provided policy {policy} is not recognized.")
def _augmentation_space(self, num_bins: int, image_size: List[int]) -> Dict[str, Tuple[Tensor, bool]]:
return {
......@@ -270,7 +270,7 @@ class AutoAugment(torch.nn.Module):
return img
def __repr__(self) -> str:
return self.__class__.__name__ + "(policy={}, fill={})".format(self.policy, self.fill)
return self.__class__.__name__ + f"(policy={self.policy}, fill={self.fill})"
class RandAugment(torch.nn.Module):
......
torchvision/transforms/functional.py
View file @ d367a01a
......@@ -111,10 +111,10 @@ def to_tensor(pic):
Tensor: Converted image.
"""
if not (F_pil._is_pil_image(pic) or _is_numpy(pic)):
raise TypeError("pic should be PIL Image or ndarray. Got {}".format(type(pic)))
raise TypeError(f"pic should be PIL Image or ndarray. Got {type(pic)}")
if _is_numpy(pic) and not _is_numpy_image(pic):
raise ValueError("pic should be 2/3 dimensional. Got {} dimensions.".format(pic.ndim))
raise ValueError(f"pic should be 2/3 dimensional. Got {pic.ndim} dimensions.")
default_float_dtype = torch.get_default_dtype()
......@@ -167,7 +167,7 @@ def pil_to_tensor(pic):
Tensor: Converted image.
"""
if not F_pil._is_pil_image(pic):
raise TypeError("pic should be PIL Image. Got {}".format(type(pic)))
raise TypeError(f"pic should be PIL Image. Got {type(pic)}")
if accimage is not None and isinstance(pic, accimage.Image):
# accimage format is always uint8 internally, so always return uint8 here
......@@ -226,11 +226,11 @@ def to_pil_image(pic, mode=None):
PIL Image: Image converted to PIL Image.
"""
if not (isinstance(pic, torch.Tensor) or isinstance(pic, np.ndarray)):
raise TypeError("pic should be Tensor or ndarray. Got {}.".format(type(pic)))
raise TypeError(f"pic should be Tensor or ndarray. Got {type(pic)}.")
elif isinstance(pic, torch.Tensor):
if pic.ndimension() not in {2, 3}:
raise ValueError("pic should be 2/3 dimensional. Got {} dimensions.".format(pic.ndimension()))
raise ValueError(f"pic should be 2/3 dimensional. Got {pic.ndimension()} dimensions.")
elif pic.ndimension() == 2:
# if 2D image, add channel dimension (CHW)
......@@ -238,11 +238,11 @@ def to_pil_image(pic, mode=None):
# check number of channels
if pic.shape[-3] > 4:
raise ValueError("pic should not have > 4 channels. Got {} channels.".format(pic.shape[-3]))
raise ValueError(f"pic should not have > 4 channels. Got {pic.shape[-3]} channels.")
elif isinstance(pic, np.ndarray):
if pic.ndim not in {2, 3}:
raise ValueError("pic should be 2/3 dimensional. Got {} dimensions.".format(pic.ndim))
raise ValueError(f"pic should be 2/3 dimensional. Got {pic.ndim} dimensions.")
elif pic.ndim == 2:
# if 2D image, add channel dimension (HWC)
......@@ -250,7 +250,7 @@ def to_pil_image(pic, mode=None):
# check number of channels
if pic.shape[-1] > 4:
raise ValueError("pic should not have > 4 channels. Got {} channels.".format(pic.shape[-1]))
raise ValueError(f"pic should not have > 4 channels. Got {pic.shape[-1]} channels.")
npimg = pic
if isinstance(pic, torch.Tensor):
......@@ -259,7 +259,7 @@ def to_pil_image(pic, mode=None):
npimg = np.transpose(pic.cpu().numpy(), (1, 2, 0))
if not isinstance(npimg, np.ndarray):
raise TypeError("Input pic must be a torch.Tensor or NumPy ndarray, " + "not {}".format(type(npimg)))
raise TypeError("Input pic must be a torch.Tensor or NumPy ndarray, not {type(npimg)}")
if npimg.shape[2] == 1:
expected_mode = None
......@@ -273,15 +273,13 @@ def to_pil_image(pic, mode=None):
elif npimg.dtype == np.float32:
expected_mode = "F"
if mode is not None and mode != expected_mode:
raise ValueError(
"Incorrect mode ({}) supplied for input type {}. Should be {}".format(mode, np.dtype, expected_mode)
)
raise ValueError(f"Incorrect mode ({mode}) supplied for input type {np.dtype}. Should be {expected_mode}")
mode = expected_mode
elif npimg.shape[2] == 2:
permitted_2_channel_modes = ["LA"]
if mode is not None and mode not in permitted_2_channel_modes:
raise ValueError("Only modes {} are supported for 2D inputs".format(permitted_2_channel_modes))
raise ValueError(f"Only modes {permitted_2_channel_modes} are supported for 2D inputs")
if mode is None and npimg.dtype == np.uint8:
mode = "LA"
......@@ -289,19 +287,19 @@ def to_pil_image(pic, mode=None):
elif npimg.shape[2] == 4:
permitted_4_channel_modes = ["RGBA", "CMYK", "RGBX"]
if mode is not None and mode not in permitted_4_channel_modes:
raise ValueError("Only modes {} are supported for 4D inputs".format(permitted_4_channel_modes))
raise ValueError(f"Only modes {permitted_4_channel_modes} are supported for 4D inputs")
if mode is None and npimg.dtype == np.uint8:
mode = "RGBA"
else:
permitted_3_channel_modes = ["RGB", "YCbCr", "HSV"]
if mode is not None and mode not in permitted_3_channel_modes:
raise ValueError("Only modes {} are supported for 3D inputs".format(permitted_3_channel_modes))
raise ValueError(f"Only modes {permitted_3_channel_modes} are supported for 3D inputs")
if mode is None and npimg.dtype == np.uint8:
mode = "RGB"
if mode is None:
raise TypeError("Input type {} is not supported".format(npimg.dtype))
raise TypeError(f"Input type {npimg.dtype} is not supported")
return Image.fromarray(npimg, mode=mode)
......@@ -325,15 +323,14 @@ def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool
Tensor: Normalized Tensor image.
"""
if not isinstance(tensor, torch.Tensor):
raise TypeError("Input tensor should be a torch tensor. Got {}.".format(type(tensor)))
raise TypeError(f"Input tensor should be a torch tensor. Got {type(tensor)}.")
if not tensor.is_floating_point():
raise TypeError("Input tensor should be a float tensor. Got {}.".format(tensor.dtype))
raise TypeError(f"Input tensor should be a float tensor. Got {tensor.dtype}.")
if tensor.ndim < 3:
raise ValueError(
"Expected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = "
"{}.".format(tensor.size())
f"Expected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = {tensor.size()}"
)
if not inplace:
......@@ -343,7 +340,7 @@ def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool
mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)
std = torch.as_tensor(std, dtype=dtype, device=tensor.device)
if (std == 0).any():
raise ValueError("std evaluated to zero after conversion to {}, leading to division by zero.".format(dtype))
raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.")
if mean.ndim == 1:
mean = mean.view(-1, 1, 1)
if std.ndim == 1:
......@@ -425,7 +422,7 @@ def resize(
def scale(*args, **kwargs):
warnings.warn("The use of the transforms.Scale transform is deprecated, " + "please use transforms.Resize instead.")
warnings.warn("The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.")
return resize(*args, **kwargs)
......@@ -923,7 +920,8 @@ def _get_inverse_affine_matrix(
# Thus, the inverse is M^-1 = C * RSS^-1 * C^-1 * T^-1
rot = math.radians(angle)
sx, sy = [math.radians(s) for s in shear]
sx = math.radians(shear[0])
sy = math.radians(shear[1])
cx, cy = center
tx, ty = translate
......@@ -1121,7 +1119,7 @@ def affine(
shear = [shear[0], shear[0]]
if len(shear) != 2:
raise ValueError("Shear should be a sequence containing two values. Got {}".format(shear))
raise ValueError(f"Shear should be a sequence containing two values. Got {shear}")
img_size = get_image_size(img)
if not isinstance(img, torch.Tensor):
......@@ -1201,7 +1199,7 @@ def erase(img: Tensor, i: int, j: int, h: int, w: int, v: Tensor, inplace: bool
Tensor Image: Erased image.
"""
if not isinstance(img, torch.Tensor):
raise TypeError("img should be Tensor Image. Got {}".format(type(img)))
raise TypeError(f"img should be Tensor Image. Got {type(img)}")
if not inplace:
img = img.clone()
......@@ -1237,34 +1235,34 @@ def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: Optional[List[floa
PIL Image or Tensor: Gaussian Blurred version of the image.
"""
if not isinstance(kernel_size, (int, list, tuple)):
raise TypeError("kernel_size should be int or a sequence of integers. Got {}".format(type(kernel_size)))
raise TypeError(f"kernel_size should be int or a sequence of integers. Got {type(kernel_size)}")
if isinstance(kernel_size, int):
kernel_size = [kernel_size, kernel_size]
if len(kernel_size) != 2:
raise ValueError("If kernel_size is a sequence its length should be 2. Got {}".format(len(kernel_size)))
raise ValueError(f"If kernel_size is a sequence its length should be 2. Got {len(kernel_size)}")
for ksize in kernel_size:
if ksize % 2 == 0 or ksize < 0:
raise ValueError("kernel_size should have odd and positive integers. Got {}".format(kernel_size))
raise ValueError(f"kernel_size should have odd and positive integers. Got {kernel_size}")
if sigma is None:
sigma = [ksize * 0.15 + 0.35 for ksize in kernel_size]
if sigma is not None and not isinstance(sigma, (int, float, list, tuple)):
raise TypeError("sigma should be either float or sequence of floats. Got {}".format(type(sigma)))
raise TypeError(f"sigma should be either float or sequence of floats. Got {type(sigma)}")
if isinstance(sigma, (int, float)):
sigma = [float(sigma), float(sigma)]
if isinstance(sigma, (list, tuple)) and len(sigma) == 1:
sigma = [sigma[0], sigma[0]]
if len(sigma) != 2:
raise ValueError("If sigma is a sequence, its length should be 2. Got {}".format(len(sigma)))
raise ValueError(f"If sigma is a sequence, its length should be 2. Got {len(sigma)}")
for s in sigma:
if s <= 0.0:
raise ValueError("sigma should have positive values. Got {}".format(sigma))
raise ValueError(f"sigma should have positive values. Got {sigma}")
t_img = img
if not isinstance(img, torch.Tensor):
if not F_pil._is_pil_image(img):
raise TypeError("img should be PIL Image or Tensor. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image or Tensor. Got {type(img)}")
t_img = to_tensor(img)
......@@ -1307,7 +1305,7 @@ def posterize(img: Tensor, bits: int) -> Tensor:
PIL Image or Tensor: Posterized image.
"""
if not (0 <= bits <= 8):
raise ValueError("The number if bits should be between 0 and 8. Got {}".format(bits))
raise ValueError(f"The number if bits should be between 0 and 8. Got {bits}")
if not isinstance(img, torch.Tensor):
return F_pil.posterize(img, bits)
......
torchvision/transforms/functional_pil.py
View file @ d367a01a
......@@ -23,20 +23,20 @@ def _is_pil_image(img: Any) -> bool:
def get_image_size(img: Any) -> List[int]:
if _is_pil_image(img):
return list(img.size)
raise TypeError("Unexpected type {}".format(type(img)))
raise TypeError(f"Unexpected type {type(img)}")
@torch.jit.unused
def get_image_num_channels(img: Any) -> int:
if _is_pil_image(img):
return 1 if img.mode == "L" else 3
raise TypeError("Unexpected type {}".format(type(img)))
raise TypeError(f"Unexpected type {type(img)}")
@torch.jit.unused
def hflip(img: Image.Image) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
return img.transpose(Image.FLIP_LEFT_RIGHT)
......@@ -44,7 +44,7 @@ def hflip(img: Image.Image) -> Image.Image:
@torch.jit.unused
def vflip(img: Image.Image) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
return img.transpose(Image.FLIP_TOP_BOTTOM)
......@@ -52,7 +52,7 @@ def vflip(img: Image.Image) -> Image.Image:
@torch.jit.unused
def adjust_brightness(img: Image.Image, brightness_factor: float) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
enhancer = ImageEnhance.Brightness(img)
img = enhancer.enhance(brightness_factor)
......@@ -62,7 +62,7 @@ def adjust_brightness(img: Image.Image, brightness_factor: float) -> Image.Image
@torch.jit.unused
def adjust_contrast(img: Image.Image, contrast_factor: float) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
enhancer = ImageEnhance.Contrast(img)
img = enhancer.enhance(contrast_factor)
......@@ -72,7 +72,7 @@ def adjust_contrast(img: Image.Image, contrast_factor: float) -> Image.Image:
@torch.jit.unused
def adjust_saturation(img: Image.Image, saturation_factor: float) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
enhancer = ImageEnhance.Color(img)
img = enhancer.enhance(saturation_factor)
......@@ -82,10 +82,10 @@ def adjust_saturation(img: Image.Image, saturation_factor: float) -> Image.Image
@torch.jit.unused
def adjust_hue(img: Image.Image, hue_factor: float) -> Image.Image:
if not (-0.5 <= hue_factor <= 0.5):
raise ValueError("hue_factor ({}) is not in [-0.5, 0.5].".format(hue_factor))
raise ValueError(f"hue_factor ({hue_factor}) is not in [-0.5, 0.5].")
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
input_mode = img.mode
if input_mode in {"L", "1", "I", "F"}:
......@@ -111,7 +111,7 @@ def adjust_gamma(
) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
if gamma < 0:
raise ValueError("Gamma should be a non-negative real number")
......@@ -134,7 +134,7 @@ def pad(
) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
if not isinstance(padding, (numbers.Number, tuple, list)):
raise TypeError("Got inappropriate padding arg")
......@@ -147,9 +147,7 @@ def pad(
padding = tuple(padding)
if isinstance(padding, tuple) and len(padding) not in [1, 2, 4]:
raise ValueError(
"Padding must be an int or a 1, 2, or 4 element tuple, not a " + "{} element tuple".format(len(padding))
)
raise ValueError(f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple")
if isinstance(padding, tuple) and len(padding) == 1:
# Compatibility with `functional_tensor.pad`
......@@ -217,7 +215,7 @@ def crop(
) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
return img.crop((left, top, left + width, top + height))
......@@ -231,9 +229,9 @@ def resize(
) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
if not (isinstance(size, int) or (isinstance(size, Sequence) and len(size) in (1, 2))):
raise TypeError("Got inappropriate size arg: {}".format(size))
raise TypeError(f"Got inappropriate size arg: {size}")
if isinstance(size, Sequence) and len(size) == 1:
size = size[0]
......@@ -281,7 +279,7 @@ def _parse_fill(
fill = tuple([fill] * num_bands)
if isinstance(fill, (list, tuple)):
if len(fill) != num_bands:
msg = "The number of elements in 'fill' does not match the number of " "bands of the image ({} != {})"
msg = "The number of elements in 'fill' does not match the number of bands of the image ({} != {})"
raise ValueError(msg.format(len(fill), num_bands))
fill = tuple(fill)
......@@ -298,7 +296,7 @@ def affine(
) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
output_size = img.size
opts = _parse_fill(fill, img)
......@@ -316,7 +314,7 @@ def rotate(
) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
opts = _parse_fill(fill, img)
return img.rotate(angle, interpolation, expand, center, **opts)
......@@ -331,7 +329,7 @@ def perspective(
) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
opts = _parse_fill(fill, img)
......@@ -341,7 +339,7 @@ def perspective(
@torch.jit.unused
def to_grayscale(img: Image.Image, num_output_channels: int) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
if num_output_channels == 1:
img = img.convert("L")
......@@ -359,28 +357,28 @@ def to_grayscale(img: Image.Image, num_output_channels: int) -> Image.Image:
@torch.jit.unused
def invert(img: Image.Image) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
return ImageOps.invert(img)
@torch.jit.unused
def posterize(img: Image.Image, bits: int) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
return ImageOps.posterize(img, bits)
@torch.jit.unused
def solarize(img: Image.Image, threshold: int) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
return ImageOps.solarize(img, threshold)
@torch.jit.unused
def adjust_sharpness(img: Image.Image, sharpness_factor: float) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
enhancer = ImageEnhance.Sharpness(img)
img = enhancer.enhance(sharpness_factor)
......@@ -390,12 +388,12 @@ def adjust_sharpness(img: Image.Image, sharpness_factor: float) -> Image.Image:
@torch.jit.unused
def autocontrast(img: Image.Image) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
return ImageOps.autocontrast(img)
@torch.jit.unused
def equalize(img: Image.Image) -> Image.Image:
if not _is_pil_image(img):
raise TypeError("img should be PIL Image. Got {}".format(type(img)))
raise TypeError(f"img should be PIL Image. Got {type(img)}")
return ImageOps.equalize(img)
torchvision/transforms/functional_tensor.py
View file @ d367a01a
......@@ -28,7 +28,7 @@ def get_image_num_channels(img: Tensor) -> int:
elif img.ndim > 2:
return img.shape[-3]
raise TypeError("Input ndim should be 2 or more. Got {}".format(img.ndim))
raise TypeError(f"Input ndim should be 2 or more. Got {img.ndim}")
def _max_value(dtype: torch.dtype) -> float:
......@@ -52,7 +52,7 @@ def _max_value(dtype: torch.dtype) -> float:
def _assert_channels(img: Tensor, permitted: List[int]) -> None:
c = get_image_num_channels(img)
if c not in permitted:
raise TypeError("Input image tensor permitted channel values are {}, but found {}".format(permitted, c))
raise TypeError(f"Input image tensor permitted channel values are {permitted}, but found {c}")
def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor:
......@@ -134,7 +134,7 @@ def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor:
def rgb_to_grayscale(img: Tensor, num_output_channels: int = 1) -> Tensor:
if img.ndim < 3:
raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim))
raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}")
_assert_channels(img, [3])
if num_output_channels not in (1, 3):
......@@ -154,7 +154,7 @@ def rgb_to_grayscale(img: Tensor, num_output_channels: int = 1) -> Tensor:
def adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor:
if brightness_factor < 0:
raise ValueError("brightness_factor ({}) is not non-negative.".format(brightness_factor))
raise ValueError(f"brightness_factor ({brightness_factor}) is not non-negative.")
_assert_image_tensor(img)
......@@ -165,7 +165,7 @@ def adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor:
def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor:
if contrast_factor < 0:
raise ValueError("contrast_factor ({}) is not non-negative.".format(contrast_factor))
raise ValueError(f"contrast_factor ({contrast_factor}) is not non-negative.")
_assert_image_tensor(img)
......@@ -182,7 +182,7 @@ def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor:
def adjust_hue(img: Tensor, hue_factor: float) -> Tensor:
if not (-0.5 <= hue_factor <= 0.5):
raise ValueError("hue_factor ({}) is not in [-0.5, 0.5].".format(hue_factor))
raise ValueError(f"hue_factor ({hue_factor}) is not in [-0.5, 0.5].")
if not (isinstance(img, torch.Tensor)):
raise TypeError("Input img should be Tensor image")
......@@ -211,7 +211,7 @@ def adjust_hue(img: Tensor, hue_factor: float) -> Tensor:
def adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor:
if saturation_factor < 0:
raise ValueError("saturation_factor ({}) is not non-negative.".format(saturation_factor))
raise ValueError(f"saturation_factor ({saturation_factor}) is not non-negative.")
_assert_image_tensor(img)
......@@ -246,7 +246,7 @@ def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
def center_crop(img: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
"""DEPRECATED"""
warnings.warn(
"This method is deprecated and will be removed in future releases. " "Please, use ``F.center_crop`` instead."
"This method is deprecated and will be removed in future releases. Please, use ``F.center_crop`` instead."
)
_assert_image_tensor(img)
......@@ -268,7 +268,7 @@ def center_crop(img: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
def five_crop(img: Tensor, size: BroadcastingList2[int]) -> List[Tensor]:
"""DEPRECATED"""
warnings.warn(
"This method is deprecated and will be removed in future releases. " "Please, use ``F.five_crop`` instead."
"This method is deprecated and will be removed in future releases. Please, use ``F.five_crop`` instead."
)
_assert_image_tensor(img)
......@@ -293,7 +293,7 @@ def five_crop(img: Tensor, size: BroadcastingList2[int]) -> List[Tensor]:
def ten_crop(img: Tensor, size: BroadcastingList2[int], vertical_flip: bool = False) -> List[Tensor]:
"""DEPRECATED"""
warnings.warn(
"This method is deprecated and will be removed in future releases. " "Please, use ``F.ten_crop`` instead."
"This method is deprecated and will be removed in future releases. Please, use ``F.ten_crop`` instead."
)
_assert_image_tensor(img)
......@@ -382,7 +382,8 @@ def _pad_symmetric(img: Tensor, padding: List[int]) -> Tensor:
# crop if needed
if padding[0] < 0 or padding[1] < 0 or padding[2] < 0 or padding[3] < 0:
crop_left, crop_right, crop_top, crop_bottom = [-min(x, 0) for x in padding]
neg_min_padding = [-min(x, 0) for x in padding]
crop_left, crop_right, crop_top, crop_bottom = neg_min_padding
img = img[..., crop_top : img.shape[-2] - crop_bottom, crop_left : img.shape[-1] - crop_right]
padding = [max(x, 0) for x in padding]
......@@ -421,9 +422,7 @@ def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "con
padding = list(padding)
if isinstance(padding, list) and len(padding) not in [1, 2, 4]:
raise ValueError(
"Padding must be an int or a 1, 2, or 4 element tuple, not a " + "{} element tuple".format(len(padding))
)
raise ValueError(f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple")
if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
raise ValueError("Padding mode should be either constant, edge, reflect or symmetric")
......@@ -501,7 +500,7 @@ def resize(
if isinstance(size, list):
if len(size) not in [1, 2]:
raise ValueError(
"Size must be an int or a 1 or 2 element tuple/list, not a " "{} element tuple/list".format(len(size))
f"Size must be an int or a 1 or 2 element tuple/list, not a {len(size)} element tuple/list"
)
if max_size is not None and len(size) != 1:
raise ValueError(
......@@ -597,7 +596,7 @@ def _assert_grid_transform_inputs(
raise ValueError(msg.format(len(fill), num_channels))
if interpolation not in supported_interpolation_modes:
raise ValueError("Interpolation mode '{}' is unsupported with Tensor input".format(interpolation))
raise ValueError(f"Interpolation mode '{interpolation}' is unsupported with Tensor input")
def _cast_squeeze_in(img: Tensor, req_dtypes: List[torch.dtype]) -> Tuple[Tensor, bool, bool, torch.dtype]:
......@@ -823,7 +822,7 @@ def _get_gaussian_kernel2d(
def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: List[float]) -> Tensor:
if not (isinstance(img, torch.Tensor)):
raise TypeError("img should be Tensor. Got {}".format(type(img)))
raise TypeError(f"img should be Tensor. Got {type(img)}")
_assert_image_tensor(img)
......@@ -852,7 +851,7 @@ def invert(img: Tensor) -> Tensor:
_assert_image_tensor(img)
if img.ndim < 3:
raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim))
raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}")
_assert_channels(img, [1, 3])
......@@ -865,9 +864,9 @@ def posterize(img: Tensor, bits: int) -> Tensor:
_assert_image_tensor(img)
if img.ndim < 3:
raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim))
raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}")
if img.dtype != torch.uint8:
raise TypeError("Only torch.uint8 image tensors are supported, but found {}".format(img.dtype))
raise TypeError(f"Only torch.uint8 image tensors are supported, but found {img.dtype}")
_assert_channels(img, [1, 3])
mask = -int(2 ** (8 - bits)) # JIT-friendly for: ~(2 ** (8 - bits) - 1)
......@@ -879,7 +878,7 @@ def solarize(img: Tensor, threshold: float) -> Tensor:
_assert_image_tensor(img)
if img.ndim < 3:
raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim))
raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}")
_assert_channels(img, [1, 3])
......@@ -912,7 +911,7 @@ def _blurred_degenerate_image(img: Tensor) -> Tensor:
def adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor:
if sharpness_factor < 0:
raise ValueError("sharpness_factor ({}) is not non-negative.".format(sharpness_factor))
raise ValueError(f"sharpness_factor ({sharpness_factor}) is not non-negative.")
_assert_image_tensor(img)
......@@ -929,7 +928,7 @@ def autocontrast(img: Tensor) -> Tensor:
_assert_image_tensor(img)
if img.ndim < 3:
raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim))
raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}")
_assert_channels(img, [1, 3])
......@@ -976,9 +975,9 @@ def equalize(img: Tensor) -> Tensor:
_assert_image_tensor(img)
if not (3 <= img.ndim <= 4):
raise TypeError("Input image tensor should have 3 or 4 dimensions, but found {}".format(img.ndim))
raise TypeError(f"Input image tensor should have 3 or 4 dimensions, but found {img.ndim}")
if img.dtype != torch.uint8:
raise TypeError("Only torch.uint8 image tensors are supported, but found {}".format(img.dtype))
raise TypeError(f"Only torch.uint8 image tensors are supported, but found {img.dtype}")
_assert_channels(img, [1, 3])
......
torchvision/transforms/transforms.py
View file @ d367a01a
......@@ -98,7 +98,7 @@ class Compose:
format_string = self.__class__.__name__ + "("
for t in self.transforms:
format_string += "\n"
format_string += " {0}".format(t)
format_string += f" {t}"
format_string += "\n)"
return format_string
......@@ -220,7 +220,7 @@ class ToPILImage:
def __repr__(self):
format_string = self.__class__.__name__ + "("
if self.mode is not None:
format_string += "mode={0}".format(self.mode)
format_string += f"mode={self.mode}"
format_string += ")"
return format_string
......@@ -260,7 +260,7 @@ class Normalize(torch.nn.Module):
return F.normalize(tensor, self.mean, self.std, self.inplace)
def __repr__(self):
return self.__class__.__name__ + "(mean={0}, std={1})".format(self.mean, self.std)
return self.__class__.__name__ + f"(mean={self.mean}, std={self.std})"
class Resize(torch.nn.Module):
......@@ -310,7 +310,7 @@ class Resize(torch.nn.Module):
def __init__(self, size, interpolation=InterpolationMode.BILINEAR, max_size=None, antialias=None):
super().__init__()
if not isinstance(size, (int, Sequence)):
raise TypeError("Size should be int or sequence. Got {}".format(type(size)))
raise TypeError(f"Size should be int or sequence. Got {type(size)}")
if isinstance(size, Sequence) and len(size) not in (1, 2):
raise ValueError("If size is a sequence, it should have 1 or 2 values")
self.size = size
......@@ -338,10 +338,8 @@ class Resize(torch.nn.Module):
return F.resize(img, self.size, self.interpolation, self.max_size, self.antialias)
def __repr__(self):
interpolate_str = self.interpolation.value
return self.__class__.__name__ + "(size={0}, interpolation={1}, max_size={2}, antialias={3})".format(
self.size, interpolate_str, self.max_size, self.antialias
)
detail = f"(size={self.size}, interpolation={self.interpolation.value}, max_size={self.max_size}, antialias={self.antialias})"
return self.__class__.__name__ + detail
class Scale(Resize):
......@@ -350,10 +348,8 @@ class Scale(Resize):
"""
def __init__(self, *args, **kwargs):
warnings.warn(
"The use of the transforms.Scale transform is deprecated, " + "please use transforms.Resize instead."
)
super(Scale, self).__init__(*args, **kwargs)
warnings.warn("The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.")
super().__init__(*args, **kwargs)
class CenterCrop(torch.nn.Module):
......@@ -383,7 +379,7 @@ class CenterCrop(torch.nn.Module):
return F.center_crop(img, self.size)
def __repr__(self):
return self.__class__.__name__ + "(size={0})".format(self.size)
return self.__class__.__name__ + f"(size={self.size})"
class Pad(torch.nn.Module):
......@@ -437,7 +433,7 @@ class Pad(torch.nn.Module):
if isinstance(padding, Sequence) and len(padding) not in [1, 2, 4]:
raise ValueError(
"Padding must be an int or a 1, 2, or 4 element tuple, not a " + "{} element tuple".format(len(padding))
f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple"
)
self.padding = padding
......@@ -455,9 +451,7 @@ class Pad(torch.nn.Module):
return F.pad(img, self.padding, self.fill, self.padding_mode)
def __repr__(self):
return self.__class__.__name__ + "(padding={0}, fill={1}, padding_mode={2})".format(
self.padding, self.fill, self.padding_mode
)
return self.__class__.__name__ + f"(padding={self.padding}, fill={self.fill}, padding_mode={self.padding_mode})"
class Lambda:
......@@ -469,7 +463,7 @@ class Lambda:
def __init__(self, lambd):
if not callable(lambd):
raise TypeError("Argument lambd should be callable, got {}".format(repr(type(lambd).__name__)))
raise TypeError(f"Argument lambd should be callable, got {repr(type(lambd).__name__)}")
self.lambd = lambd
def __call__(self, img):
......@@ -498,7 +492,7 @@ class RandomTransforms:
format_string = self.__class__.__name__ + "("
for t in self.transforms:
format_string += "\n"
format_string += " {0}".format(t)
format_string += f" {t}"
format_string += "\n)"
return format_string
......@@ -537,10 +531,10 @@ class RandomApply(torch.nn.Module):
def __repr__(self):
format_string = self.__class__.__name__ + "("
format_string += "\n p={}".format(self.p)
format_string += f"\n p={self.p}"
for t in self.transforms:
format_string += "\n"
format_string += " {0}".format(t)
format_string += f" {t}"
format_string += "\n)"
return format_string
......@@ -571,7 +565,7 @@ class RandomChoice(RandomTransforms):
def __repr__(self):
format_string = super().__repr__()
format_string += "(p={0})".format(self.p)
format_string += f"(p={self.p})"
return format_string
......@@ -634,7 +628,7 @@ class RandomCrop(torch.nn.Module):
th, tw = output_size
if h + 1 < th or w + 1 < tw:
raise ValueError("Required crop size {} is larger then input image size {}".format((th, tw), (h, w)))
raise ValueError(f"Required crop size {(th, tw)} is larger then input image size {(h, w)}")
if w == tw and h == th:
return 0, 0, h, w
......@@ -679,7 +673,7 @@ class RandomCrop(torch.nn.Module):
return F.crop(img, i, j, h, w)
def __repr__(self):
return self.__class__.__name__ + "(size={0}, padding={1})".format(self.size, self.padding)
return self.__class__.__name__ + f"(size={self.size}, padding={self.padding})"
class RandomHorizontalFlip(torch.nn.Module):
......@@ -709,7 +703,7 @@ class RandomHorizontalFlip(torch.nn.Module):
return img
def __repr__(self):
return self.__class__.__name__ + "(p={})".format(self.p)
return self.__class__.__name__ + f"(p={self.p})"
class RandomVerticalFlip(torch.nn.Module):
......@@ -739,7 +733,7 @@ class RandomVerticalFlip(torch.nn.Module):
return img
def __repr__(self):
return self.__class__.__name__ + "(p={})".format(self.p)
return self.__class__.__name__ + f"(p={self.p})"
class RandomPerspective(torch.nn.Module):
......@@ -839,7 +833,7 @@ class RandomPerspective(torch.nn.Module):
return startpoints, endpoints
def __repr__(self):
return self.__class__.__name__ + "(p={})".format(self.p)
return self.__class__.__name__ + f"(p={self.p})"
class RandomResizedCrop(torch.nn.Module):
......@@ -951,10 +945,10 @@ class RandomResizedCrop(torch.nn.Module):
def __repr__(self):
interpolate_str = self.interpolation.value
format_string = self.__class__.__name__ + "(size={0}".format(self.size)
format_string += ", scale={0}".format(tuple(round(s, 4) for s in self.scale))
format_string += ", ratio={0}".format(tuple(round(r, 4) for r in self.ratio))
format_string += ", interpolation={0})".format(interpolate_str)
format_string = self.__class__.__name__ + f"(size={self.size}"
format_string += f", scale={tuple(round(s, 4) for s in self.scale)}"
format_string += f", ratio={tuple(round(r, 4) for r in self.ratio)}"
format_string += f", interpolation={interpolate_str})"
return format_string
......@@ -968,7 +962,7 @@ class RandomSizedCrop(RandomResizedCrop):
"The use of the transforms.RandomSizedCrop transform is deprecated, "
+ "please use transforms.RandomResizedCrop instead."
)
super(RandomSizedCrop, self).__init__(*args, **kwargs)
super().__init__(*args, **kwargs)
class FiveCrop(torch.nn.Module):
......@@ -1014,7 +1008,7 @@ class FiveCrop(torch.nn.Module):
return F.five_crop(img, self.size)
def __repr__(self):
return self.__class__.__name__ + "(size={0})".format(self.size)
return self.__class__.__name__ + f"(size={self.size})"
class TenCrop(torch.nn.Module):
......@@ -1063,7 +1057,7 @@ class TenCrop(torch.nn.Module):
return F.ten_crop(img, self.size, self.vertical_flip)
def __repr__(self):
return self.__class__.__name__ + "(size={0}, vertical_flip={1})".format(self.size, self.vertical_flip)
return self.__class__.__name__ + f"(size={self.size}, vertical_flip={self.vertical_flip})"
class LinearTransformation(torch.nn.Module):
......@@ -1090,22 +1084,18 @@ class LinearTransformation(torch.nn.Module):
if transformation_matrix.size(0) != transformation_matrix.size(1):
raise ValueError(
"transformation_matrix should be square. Got "
+ "[{} x {}] rectangular matrix.".format(*transformation_matrix.size())
f"{tuple(transformation_matrix.size())} rectangular matrix."
)
if mean_vector.size(0) != transformation_matrix.size(0):
raise ValueError(
"mean_vector should have the same length {}".format(mean_vector.size(0))
+ " as any one of the dimensions of the transformation_matrix [{}]".format(
tuple(transformation_matrix.size())
)
f"mean_vector should have the same length {mean_vector.size(0)}"
f" as any one of the dimensions of the transformation_matrix [{tuple(transformation_matrix.size())}]"
)
if transformation_matrix.device != mean_vector.device:
raise ValueError(
"Input tensors should be on the same device. Got {} and {}".format(
transformation_matrix.device, mean_vector.device
)
f"Input tensors should be on the same device. Got {transformation_matrix.device} and {mean_vector.device}"
)
self.transformation_matrix = transformation_matrix
......@@ -1124,14 +1114,14 @@ class LinearTransformation(torch.nn.Module):
if n != self.transformation_matrix.shape[0]:
raise ValueError(
"Input tensor and transformation matrix have incompatible shape."
+ "[{} x {} x {}] != ".format(shape[-3], shape[-2], shape[-1])
+ "{}".format(self.transformation_matrix.shape[0])
+ f"[{shape[-3]} x {shape[-2]} x {shape[-1]}] != "
+ f"{self.transformation_matrix.shape[0]}"
)
if tensor.device.type != self.mean_vector.device.type:
raise ValueError(
"Input tensor should be on the same device as transformation matrix and mean vector. "
"Got {} vs {}".format(tensor.device, self.mean_vector.device)
f"Got {tensor.device} vs {self.mean_vector.device}"
)
flat_tensor = tensor.view(-1, n) - self.mean_vector
......@@ -1178,15 +1168,15 @@ class ColorJitter(torch.nn.Module):
def _check_input(self, value, name, center=1, bound=(0, float("inf")), clip_first_on_zero=True):
if isinstance(value, numbers.Number):
if value < 0:
raise ValueError("If {} is a single number, it must be non negative.".format(name))
raise ValueError(f"If {name} is a single number, it must be non negative.")
value = [center - float(value), center + float(value)]
if clip_first_on_zero:
value[0] = max(value[0], 0.0)
elif isinstance(value, (tuple, list)) and len(value) == 2:
if not bound[0] <= value[0] <= value[1] <= bound[1]:
raise ValueError("{} values should be between {}".format(name, bound))
raise ValueError(f"{name} values should be between {bound}")
else:
raise TypeError("{} should be a single number or a list/tuple with length 2.".format(name))
raise TypeError(f"{name} should be a single number or a list/tuple with length 2.")
# if value is 0 or (1., 1.) for brightness/contrast/saturation
# or (0., 0.) for hue, do nothing
......@@ -1252,10 +1242,10 @@ class ColorJitter(torch.nn.Module):
def __repr__(self):
format_string = self.__class__.__name__ + "("
format_string += "brightness={0}".format(self.brightness)
format_string += ", contrast={0}".format(self.contrast)
format_string += ", saturation={0}".format(self.saturation)
format_string += ", hue={0})".format(self.hue)
format_string += f"brightness={self.brightness}"
format_string += f", contrast={self.contrast}"
format_string += f", saturation={self.saturation}"
format_string += f", hue={self.hue})"
return format_string
......@@ -1352,13 +1342,13 @@ class RandomRotation(torch.nn.Module):
def __repr__(self):
interpolate_str = self.interpolation.value
format_string = self.__class__.__name__ + "(degrees={0}".format(self.degrees)
format_string += ", interpolation={0}".format(interpolate_str)
format_string += ", expand={0}".format(self.expand)
format_string = self.__class__.__name__ + f"(degrees={self.degrees}"
format_string += f", interpolation={interpolate_str}"
format_string += f", expand={self.expand}"
if self.center is not None:
format_string += ", center={0}".format(self.center)
format_string += f", center={self.center}"
if self.fill is not None:
format_string += ", fill={0}".format(self.fill)
format_string += f", fill={self.fill}"
format_string += ")"
return format_string
......@@ -1568,7 +1558,7 @@ class Grayscale(torch.nn.Module):
return F.rgb_to_grayscale(img, num_output_channels=self.num_output_channels)
def __repr__(self):
return self.__class__.__name__ + "(num_output_channels={0})".format(self.num_output_channels)
return self.__class__.__name__ + f"(num_output_channels={self.num_output_channels})"
class RandomGrayscale(torch.nn.Module):
......@@ -1605,7 +1595,7 @@ class RandomGrayscale(torch.nn.Module):
return img
def __repr__(self):
return self.__class__.__name__ + "(p={0})".format(self.p)
return self.__class__.__name__ + f"(p={self.p})"
class RandomErasing(torch.nn.Module):
......@@ -1726,7 +1716,7 @@ class RandomErasing(torch.nn.Module):
if value is not None and not (len(value) in (1, img.shape[-3])):
raise ValueError(
"If value is a sequence, it should have either a single value or "
"{} (number of input channels)".format(img.shape[-3])
f"{img.shape[-3]} (number of input channels)"
)
x, y, h, w, v = self.get_params(img, scale=self.scale, ratio=self.ratio, value=value)
......@@ -1734,11 +1724,11 @@ class RandomErasing(torch.nn.Module):
return img
def __repr__(self):
s = "(p={}, ".format(self.p)
s += "scale={}, ".format(self.scale)
s += "ratio={}, ".format(self.ratio)
s += "value={}, ".format(self.value)
s += "inplace={})".format(self.inplace)
s = f"(p={self.p}, "
s += f"scale={self.scale}, "
s += f"ratio={self.ratio}, "
s += f"value={self.value}, "
s += f"inplace={self.inplace})"
return self.__class__.__name__ + s
......@@ -1803,8 +1793,8 @@ class GaussianBlur(torch.nn.Module):
return F.gaussian_blur(img, self.kernel_size, [sigma, sigma])
def __repr__(self):
s = "(kernel_size={}, ".format(self.kernel_size)
s += "sigma={})".format(self.sigma)
s = f"(kernel_size={self.kernel_size}, "
s += f"sigma={self.sigma})"
return self.__class__.__name__ + s
......@@ -1824,15 +1814,15 @@ def _setup_size(size, error_msg):
def _check_sequence_input(x, name, req_sizes):
msg = req_sizes[0] if len(req_sizes) < 2 else " or ".join([str(s) for s in req_sizes])
if not isinstance(x, Sequence):
raise TypeError("{} should be a sequence of length {}.".format(name, msg))
raise TypeError(f"{name} should be a sequence of length {msg}.")
if len(x) not in req_sizes:
raise ValueError("{} should be sequence of length {}.".format(name, msg))
raise ValueError(f"{name} should be sequence of length {msg}.")
def _setup_angle(x, name, req_sizes=(2,)):
if isinstance(x, numbers.Number):
if x < 0:
raise ValueError("If {} is a single number, it must be positive.".format(name))
raise ValueError(f"If {name} is a single number, it must be positive.")
x = [-x, x]
else:
_check_sequence_input(x, name, req_sizes)
......@@ -1867,7 +1857,7 @@ class RandomInvert(torch.nn.Module):
return img
def __repr__(self):
return self.__class__.__name__ + "(p={})".format(self.p)
return self.__class__.__name__ + f"(p={self.p})"
class RandomPosterize(torch.nn.Module):
......@@ -1899,7 +1889,7 @@ class RandomPosterize(torch.nn.Module):
return img
def __repr__(self):
return self.__class__.__name__ + "(bits={},p={})".format(self.bits, self.p)
return self.__class__.__name__ + f"(bits={self.bits},p={self.p})"
class RandomSolarize(torch.nn.Module):
......@@ -1931,7 +1921,7 @@ class RandomSolarize(torch.nn.Module):
return img
def __repr__(self):
return self.__class__.__name__ + "(threshold={},p={})".format(self.threshold, self.p)
return self.__class__.__name__ + f"(threshold={self.threshold},p={self.p})"
class RandomAdjustSharpness(torch.nn.Module):
......@@ -1963,7 +1953,7 @@ class RandomAdjustSharpness(torch.nn.Module):
return img
def __repr__(self):
return self.__class__.__name__ + "(sharpness_factor={},p={})".format(self.sharpness_factor, self.p)
return self.__class__.__name__ + f"(sharpness_factor={self.sharpness_factor},p={self.p})"
class RandomAutocontrast(torch.nn.Module):
......@@ -1993,7 +1983,7 @@ class RandomAutocontrast(torch.nn.Module):
return img
def __repr__(self):
return self.__class__.__name__ + "(p={})".format(self.p)
return self.__class__.__name__ + f"(p={self.p})"
class RandomEqualize(torch.nn.Module):
......@@ -2023,4 +2013,4 @@ class RandomEqualize(torch.nn.Module):
return img
def __repr__(self):
return self.__class__.__name__ + "(p={})".format(self.p)
return self.__class__.__name__ + f"(p={self.p})"
torchvision/utils.py
View file @ d367a01a
import math
import pathlib
import warnings
from typing import Union, Optional, List, Tuple, Text, BinaryIO
from typing import Union, Optional, List, Tuple, BinaryIO
import numpy as np
import torch
......@@ -114,7 +114,7 @@ def make_grid(
@torch.no_grad()
def save_image(
tensor: Union[torch.Tensor, List[torch.Tensor]],
fp: Union[Text, pathlib.Path, BinaryIO],
fp: Union[str, pathlib.Path, BinaryIO],
format: Optional[str] = None,
**kwargs,
) -> None:
......
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