def random_rectangles_params_gen(batch_size: int, height: int, width: int,
erase_scale_range: Tuple[float, float],
aspect_ratio_range: Tuple[float, float],
value: float = 0., same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
images_area = height * width
target_areas = _adapted_uniform(
(batch_size,), erase_scale_range[0], erase_scale_range[1], same_on_batch) * images_area
if aspect_ratio_range[0] < 1. and aspect_ratio_range[1] > 1.:
aspect_ratios1 = _adapted_uniform((batch_size,), aspect_ratio_range[0], 1, same_on_batch)
aspect_ratios2 = _adapted_uniform((batch_size,), 1, aspect_ratio_range[1], same_on_batch)
rand_idxs = torch.round(torch.rand((batch_size,))).bool()
aspect_ratios = torch.where(rand_idxs, aspect_ratios1, aspect_ratios2)
else:
aspect_ratios = _adapted_uniform((batch_size,), aspect_ratio_range[0], aspect_ratio_range[1], same_on_batch)
# based on target areas and aspect ratios, rectangle params are computed
heights = torch.min(
torch.max(torch.round((target_areas * aspect_ratios) ** (1 / 2)), torch.tensor(1.)),
torch.tensor(float(height))
).int()
widths = torch.min(
torch.max(torch.round((target_areas / aspect_ratios) ** (1 / 2)), torch.tensor(1.)),
torch.tensor(float(width))
).int()
xs = (torch.rand((batch_size,)) * (torch.tensor(width) - widths + 1).float()).int()
ys = (torch.rand((batch_size,)) * (torch.tensor(height) - heights + 1).float()).int()
params: Dict[str, torch.Tensor] = {}
params["widths"] = widths
params["heights"] = heights
params["xs"] = xs
params["ys"] = ys
params["values"] = torch.tensor([value] * batch_size)
return params
def motion_blur_param_generator(
batch_size: int,
kernel_size: Union[int, Tuple[int, int]],
angle: UnionFloat,
direction: UnionFloat,
border_type: Union[int, str, BorderType] = BorderType.CONSTANT.name,
same_on_batch: bool = True) -> Dict[str, torch.Tensor]:
angle_bound: torch.Tensor = _check_and_bound(angle, 'angle', center=0.)
direction_bound: torch.Tensor = _check_and_bound(direction,
'direction',
center=0.,
bounds=(-1, 1))
if isinstance(kernel_size, int):
ksize_factor = torch.tensor([kernel_size] * batch_size)
elif isinstance(kernel_size, tuple):
ksize_x, ksize_y = kernel_size
ksize_factor = _adapted_uniform(
(batch_size, ), ksize_x // 2, ksize_y // 2,
same_on_batch).int() * 2 + 1
else:
raise TypeError(f"Unsupported type: {type(kernel_size)}")
angle_factor = _adapted_uniform((batch_size, ), angle_bound[0],
angle_bound[1], same_on_batch)
direction_factor = _adapted_uniform((batch_size, ), direction_bound[0],
direction_bound[1], same_on_batch)
return dict(ksize_factor=ksize_factor,
angle_factor=angle_factor,
direction_factor=direction_factor,
border_type=torch.tensor(BorderType.get(border_type).value))
def random_crop_generator(
batch_size: int,
input_size: Tuple[int, int],
size: Tuple[int, int],
resize_to: Optional[Tuple[int, int]] = None,
interpolation: Union[str, int, Resample] = Resample.BILINEAR.name,
same_on_batch: bool = False,
align_corners: bool = False) -> Dict[str, torch.Tensor]:
r"""Get parameters for ```crop``` transformation for crop transform.
Args:
batch_size (int): the tensor batch size.
input_size (tuple): Input image shape, like (h, w).
size (tuple): Desired size of the crop operation, like (h, w).
resize_to (tuple): Desired output size of the crop, like (h, w). If None, no resize will be performed.
interpolation (int, str or kornia.Resample): Default: Resample.BILINEAR
same_on_batch (bool): apply the same transformation across the batch. Default: False
align_corners (bool): interpolation flag. Default: False.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
"""
x_diff = input_size[1] - size[1]
y_diff = input_size[0] - size[0]
if x_diff < 0 or y_diff < 0:
raise ValueError(
"input_size %s cannot be smaller than crop size %s in any dimension."
% (str(input_size), str(size)))
x_start = _adapted_uniform((batch_size, ), 0, x_diff + 1,
same_on_batch).long()
y_start = _adapted_uniform((batch_size, ), 0, y_diff + 1,
same_on_batch).long()
crop = torch.tensor([[
[0, 0],
[size[1] - 1, 0],
[size[1] - 1, size[0] - 1],
[0, size[0] - 1],
]]).repeat(batch_size, 1, 1)
crop_src = crop.clone()
crop_src[:, :, 0] += x_start.unsqueeze(dim=0).reshape(batch_size, 1)
crop_src[:, :, 1] += y_start.unsqueeze(dim=0).reshape(batch_size, 1)
if resize_to is None:
crop_dst = crop
else:
crop_dst = torch.tensor([[
[0, 0],
[resize_to[1] - 1, 0],
[resize_to[1] - 1, resize_to[0] - 1],
[0, resize_to[0] - 1],
]]).repeat(batch_size, 1, 1)
return dict(src=crop_src,
dst=crop_dst,
interpolation=torch.tensor(Resample.get(interpolation).value),
align_corners=torch.tensor(align_corners))
def random_crop_gen(batch_size: int, input_size: Tuple[int, int], size: Tuple[int, int],
resize_to: Optional[Tuple[int, int]] = None,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
x_diff = input_size[1] - size[1]
y_diff = input_size[0] - size[0]
if x_diff < 0 or y_diff < 0:
raise ValueError("input_size %s cannot be smaller than crop size %s in any dimension."
% (str(input_size), str(size)))
x_start = _adapted_uniform((batch_size,), 0, x_diff + 1, same_on_batch).long()
y_start = _adapted_uniform((batch_size,), 0, y_diff + 1, same_on_batch).long()
crop = torch.tensor([[
[0, 0],
[size[1] - 1, 0],
[size[1] - 1, size[0] - 1],
[0, size[0] - 1],
]]).repeat(batch_size, 1, 1)
crop_src = crop.clone()
crop_src[:, :, 0] += x_start.unsqueeze(dim=0).reshape(batch_size, 1)
crop_src[:, :, 1] += y_start.unsqueeze(dim=0).reshape(batch_size, 1)
if resize_to is None:
crop_dst = crop
else:
crop_dst = torch.tensor([[
[0, 0],
[resize_to[1] - 1, 0],
[resize_to[1] - 1, resize_to[0] - 1],
[0, resize_to[0] - 1],
]]).repeat(batch_size, 1, 1)
return {'src': crop_src, 'dst': crop_dst}
def random_crop_size_gen(size: Tuple[int, int], scale: Tuple[float, float], ratio: Tuple[float, float],
same_on_batch: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
area = _adapted_uniform((10,), scale[0] * size[0] * size[1], scale[1] * size[0] * size[1], same_on_batch)
log_ratio = _adapted_uniform((10,), math.log(ratio[0]), math.log(ratio[1]), same_on_batch)
aspect_ratio = torch.exp(log_ratio)
w = torch.sqrt(area * aspect_ratio).int()
h = torch.sqrt(area / aspect_ratio).int()
# Element-wise w, h condition
cond = ((0 < h) * (h < size[1]) * (0 < w) * (w < size[0])).int()
if torch.sum(cond) > 0:
return (h[torch.argmax(cond)], w[torch.argmax(cond)])
# Fallback to center crop
in_ratio = float(size[0]) / float(size[1])
if (in_ratio < min(ratio)):
w = torch.tensor(size[0])
h = torch.round(w / min(ratio))
elif (in_ratio > max(ratio)):
h = torch.tensor(size[1])
w = torch.round(h * max(ratio))
else: # whole image
w = torch.tensor(size[0])
h = torch.tensor(size[1])
return (h, w)
def random_color_jitter_generator(
batch_size: int,
brightness: FloatUnionType = 0.,
contrast: FloatUnionType = 0.,
saturation: FloatUnionType = 0.,
hue: FloatUnionType = 0.,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Generator random color jiter parameters for a batch of images.
Args:
batch_size (int): the number of images.
brightness (float or tuple): Default value is 0
contrast (float or tuple): Default value is 0
saturation (float or tuple): Default value is 0
hue (float or tuple): Default value is 0
same_on_batch (bool): apply the same transformation across the batch. Default: False
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
"""
brightness_bound: torch.Tensor = _check_and_bound(brightness,
'brightness',
center=1.,
bounds=(0, float('inf')))
contrast_bound: torch.Tensor = _check_and_bound(contrast,
'contrast',
center=1.,
bounds=(0, float('inf')))
saturation_bound: torch.Tensor = _check_and_bound(saturation,
'saturation',
center=1.,
bounds=(0, float('inf')))
hue_bound: torch.Tensor = _check_and_bound(hue, 'hue', bounds=(-0.5, 0.5))
brightness_factor = _adapted_uniform((batch_size, ), brightness_bound[0],
brightness_bound[1], same_on_batch)
contrast_factor = _adapted_uniform((batch_size, ), contrast_bound[0],
contrast_bound[1], same_on_batch)
hue_factor = _adapted_uniform((batch_size, ), hue_bound[0], hue_bound[1],
same_on_batch)
saturation_factor = _adapted_uniform((batch_size, ), saturation_bound[0],
saturation_bound[1], same_on_batch)
return dict(brightness_factor=brightness_factor,
contrast_factor=contrast_factor,
hue_factor=hue_factor,
saturation_factor=saturation_factor,
order=torch.randperm(4))
def random_solarize_generator(
batch_size: int,
thresholds: torch.Tensor = torch.tensor([0.4, 0.6]),
additions: torch.Tensor = torch.tensor([-0.1, 0.1]),
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> Dict[str, torch.Tensor]:
r"""Generate random solarize parameters for a batch of images.
For each pixel in the image less than threshold, we add 'addition' amount to it and then clip the pixel value
to be between 0 and 1.0
Args:
batch_size (int): the number of images.
thresholds (torch.Tensor): Pixels less than threshold will selected. Otherwise, subtract 1.0 from the pixel.
Takes in a range tensor of (0, 1). Default value will be sampled from [0.4, 0.6].
additions (torch.Tensor): The value is between -0.5 and 0.5. Default value will be sampled from [-0.1, 0.1]
same_on_batch (bool): apply the same transformation across the batch. Default: False.
device (torch.device): the device on which the random numbers will be generated. Default: cpu.
dtype (torch.dtype): the data type of the generated random numbers. Default: float32.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- thresholds_factor (torch.Tensor): element-wise thresholds factors with a shape of (B,).
- additions_factor (torch.Tensor): element-wise additions factors with a shape of (B,).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
"""
_common_param_check(batch_size, same_on_batch)
_joint_range_check(thresholds, 'thresholds', (0, 1))
_joint_range_check(additions, 'additions', (-0.5, 0.5))
_device, _dtype = _extract_device_dtype([thresholds, additions])
thresholds_factor = _adapted_uniform(
(batch_size, ),
thresholds[0].to(device=device, dtype=dtype),
thresholds[1].to(device=device, dtype=dtype),
same_on_batch,
)
additions_factor = _adapted_uniform(
(batch_size, ),
additions[0].to(device=device, dtype=dtype),
additions[1].to(device=device, dtype=dtype),
same_on_batch,
)
return dict(
thresholds_factor=thresholds_factor.to(device=_device, dtype=_dtype),
additions_factor=additions_factor.to(device=_device, dtype=_dtype),
)
def random_color_jitter_generator(
batch_size: int,
brightness: FloatUnionType = 0.,
contrast: FloatUnionType = 0.,
saturation: FloatUnionType = 0.,
hue: FloatUnionType = 0.,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Generator random color jiter parameters for a batch of images.
Args:
batch_size (int): the number of images.
brightness (float or tuple): Default value is 0
contrast (float or tuple): Default value is 0
saturation (float or tuple): Default value is 0
hue (float or tuple): Default value is 0
same_on_batch (bool): apply the same transformation across the batch. Default: False
Returns:
dict: generated parameter dictionary.
See :class:`~kornia.augmentation.ColorJitter` for details.
"""
brightness_bound: torch.Tensor = _check_and_bound(brightness,
'brightness',
center=1.,
bounds=(0, 2))
contrast_bound: torch.Tensor = _check_and_bound(contrast,
'contrast',
center=1.)
saturation_bound: torch.Tensor = _check_and_bound(saturation,
'saturation',
center=1.)
hue_bound: torch.Tensor = _check_and_bound(hue, 'hue', bounds=(-0.5, 0.5))
brightness_factor = _adapted_uniform((batch_size, ), brightness_bound[0],
brightness_bound[1], same_on_batch)
contrast_factor = _adapted_uniform((batch_size, ), contrast_bound[0],
contrast_bound[1], same_on_batch)
hue_factor = _adapted_uniform((batch_size, ), hue_bound[0], hue_bound[1],
same_on_batch)
saturation_factor = _adapted_uniform((batch_size, ), saturation_bound[0],
saturation_bound[1], same_on_batch)
return {
"brightness_factor": brightness_factor,
"contrast_factor": contrast_factor,
"hue_factor": hue_factor,
"saturation_factor": saturation_factor,
"order": torch.randperm(4)
}
def random_rotation_gen(batch_size: int, degrees: FloatUnionType,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
if not torch.is_tensor(degrees):
if isinstance(degrees, float):
if degrees < 0:
raise ValueError(f"If Degrees is only one number it must be a positive number. Got{degrees}")
degrees = torch.tensor([-degrees, degrees])
elif isinstance(degrees, (tuple, list)):
degrees = torch.tensor(degrees)
else:
raise TypeError(f"Degrees should be a float number a sequence or a tensor. Got {type(degrees)}")
# https://mypy.readthedocs.io/en/latest/casts.html cast to please mypy gods
degrees = cast(torch.Tensor, degrees)
if degrees.numel() != 2:
raise ValueError("If degrees is a sequence it must be of length 2")
params: Dict[str, torch.Tensor] = {}
params["degrees"] = _adapted_uniform((batch_size,), degrees[0], degrees[1], same_on_batch)
return params
def random_sharpness_generator(
batch_size: int,
sharpness: Union[float, Tuple[float, float], torch.Tensor] = 1.,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Generator random sharpness parameters for a batch of images.
Args:
batch_size (int): the number of images.
sharpness (float or tuple): Default value is 1. Must be above 0.
same_on_batch (bool): apply the same transformation across the batch. Default: False
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
"""
if not isinstance(sharpness, torch.Tensor):
sharpness = torch.tensor(sharpness)
if len(sharpness.size()) == 0:
lower = torch.tensor(0)
upper = sharpness
elif len(sharpness.size()) == 1 and sharpness.size(0) == 2:
lower = sharpness[0]
upper = sharpness[1]
else:
raise ValueError(f"Expect float or tuple. Got {sharpness}.")
sharpness_factor = _adapted_uniform((batch_size, ), lower.float(),
upper.float(), same_on_batch)
return dict(sharpness_factor=sharpness_factor)
def random_posterize_generator(
batch_size: int,
bits: Union[int, Tuple[int, int], torch.Tensor] = 3,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Generator random posterize parameters for a batch of images.
Args:
batch_size (int): the number of images.
bits (int or tuple): Default value is 3. Integer that ranged from 0 ~ 8.
same_on_batch (bool): apply the same transformation across the batch. Default: False
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
"""
if not isinstance(bits, torch.Tensor):
bits = torch.tensor(bits)
if len(bits.size()) == 0:
lower = bits
upper = torch.tensor(8)
elif len(bits.size()) == 1 and bits.size(0) == 2:
lower = bits[0]
upper = bits[1]
else:
raise ValueError(f"Expect float or tuple. Got {bits}.")
bits_factor = _adapted_uniform((batch_size, ), lower.float(),
upper.float(), same_on_batch).int()
return dict(bits_factor=bits_factor)
def random_rotation_generator(
batch_size: int,
degrees: torch.Tensor,
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> Dict[str, torch.Tensor]:
r"""Get parameters for ``rotate`` for a random rotate transform.
Args:
batch_size (int): the tensor batch size.
degrees (torch.Tensor): range of degrees with shape (2) to select from.
same_on_batch (bool): apply the same transformation across the batch. Default: False.
device (torch.device): the device on which the random numbers will be generated. Default: cpu.
dtype (torch.dtype): the data type of the generated random numbers. Default: float32.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- degrees (torch.Tensor): element-wise rotation degrees with a shape of (B,).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
"""
_common_param_check(batch_size, same_on_batch)
_joint_range_check(degrees, "degrees")
_degrees = _adapted_uniform(
(batch_size, ),
degrees[0].to(device=device, dtype=dtype),
degrees[1].to(device=device, dtype=dtype),
same_on_batch,
)
_degrees = _degrees.to(device=degrees.device, dtype=degrees.dtype)
return dict(degrees=_degrees)
def random_sharpness_generator(
batch_size: int,
sharpness: torch.Tensor = torch.tensor([0, 1.0]),
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> Dict[str, torch.Tensor]:
r"""Generate random sharpness parameters for a batch of images.
Args:
batch_size (int): the number of images.
sharpness (torch.Tensor): Must be above 0. Default value is sampled from (0, 1).
same_on_batch (bool): apply the same transformation across the batch. Default: False.
device (torch.device): the device on which the random numbers will be generated. Default: cpu.
dtype (torch.dtype): the data type of the generated random numbers. Default: float32.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- sharpness_factor (torch.Tensor): element-wise sharpness factors with a shape of (B,).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
"""
_common_param_check(batch_size, same_on_batch)
_joint_range_check(sharpness, 'sharpness', bounds=(0, float('inf')))
sharpness_factor = _adapted_uniform(
(batch_size, ),
sharpness[0].to(device=device, dtype=dtype),
sharpness[1].to(device=device, dtype=dtype),
same_on_batch,
)
return dict(sharpness_factor=sharpness_factor.to(device=sharpness.device,
dtype=sharpness.dtype))
def random_perspective_generator(
batch_size: int,
height: int,
width: int,
distortion_scale: torch.Tensor,
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> Dict[str, torch.Tensor]:
r"""Get parameters for ``perspective`` for a random perspective transform.
Args:
batch_size (int): the tensor batch size.
height (int) : height of the image.
width (int): width of the image.
distortion_scale (torch.Tensor): it controls the degree of distortion and ranges from 0 to 1.
same_on_batch (bool): apply the same transformation across the batch. Default: False.
device (torch.device): the device on which the random numbers will be generated. Default: cpu.
dtype (torch.dtype): the data type of the generated random numbers. Default: float32.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- start_points (torch.Tensor): element-wise perspective source areas with a shape of (B, 4, 2).
- end_points (torch.Tensor): element-wise perspective target areas with a shape of (B, 4, 2).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
"""
_common_param_check(batch_size, same_on_batch)
if not (distortion_scale.dim() == 0 and 0 <= distortion_scale <= 1):
raise AssertionError(f"'distortion_scale' must be a scalar within [0, 1]. Got {distortion_scale}.")
if not (type(height) is int and height > 0 and type(width) is int and width > 0):
raise AssertionError(f"'height' and 'width' must be integers. Got {height}, {width}.")
start_points: torch.Tensor = torch.tensor(
[[[0.0, 0], [width - 1, 0], [width - 1, height - 1], [0, height - 1]]],
device=distortion_scale.device,
dtype=distortion_scale.dtype,
).expand(batch_size, -1, -1)
# generate random offset not larger than half of the image
fx = distortion_scale * width / 2
fy = distortion_scale * height / 2
factor = torch.stack([fx, fy], dim=0).view(-1, 1, 2)
# TODO: This line somehow breaks the gradcheck
rand_val: torch.Tensor = _adapted_uniform(
start_points.shape,
torch.tensor(0, device=device, dtype=dtype),
torch.tensor(1, device=device, dtype=dtype),
same_on_batch,
).to(device=distortion_scale.device, dtype=distortion_scale.dtype)
pts_norm = torch.tensor(
[[[1, 1], [-1, 1], [-1, -1], [1, -1]]], device=distortion_scale.device, dtype=distortion_scale.dtype
)
end_points = start_points + factor * rand_val * pts_norm
return dict(start_points=start_points, end_points=end_points)
def _get_perspective_params(
batch_size: int,
width: int,
height: int,
distortion_scale: float,
same_on_batch: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
start_points: torch.Tensor = torch.tensor([[
[0., 0],
[width - 1, 0],
[width - 1, height - 1],
[0, height - 1],
]]).expand(batch_size, -1, -1)
# generate random offset not larger than half of the image
fx: float = distortion_scale * width / 2
fy: float = distortion_scale * height / 2
factor = torch.tensor([fx, fy]).view(-1, 1, 2)
rand_val: torch.Tensor = _adapted_uniform((batch_size, 4, 2), 0, 1,
same_on_batch)
pts_norm = torch.tensor([[[1, 1], [-1, 1], [-1, -1], [1, -1]]])
end_points = start_points + factor * rand_val * pts_norm
return start_points, end_points
def random_prob_generator(
batch_size: int,
p: float = 0.5,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Generator random probabilities for a batch of inputs.
Args:
batch_size (int): the number of images.
p (float): probability of the image being flipped or grayscaled. Default value is 0.5
same_on_batch (bool): apply the same transformation across the batch. Default: False
Returns:
dict: generated parameter dictionary.
See :class:`~kornia.augmentation.RandomGrayscale` for details.
See :class:`~kornia.augmentation.RandomHorizontalFlip` for details.
See :class:`~kornia.augmentation.RandomVerticalFlip` for details.
"""
if not isinstance(p, float):
raise TypeError(
f"The probability should be a float number. Got {type(p)}")
probs: torch.Tensor = _adapted_uniform((batch_size, ), 0, 1, same_on_batch)
batch_prob: torch.Tensor = probs < p
return {'batch_prob': batch_prob}
def random_posterize_generator(
batch_size: int,
bits: torch.Tensor = torch.tensor([3, 5]),
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> Dict[str, torch.Tensor]:
r"""Generate random posterize parameters for a batch of images.
Args:
batch_size (int): the number of images.
bits (int or tuple): Takes in an integer tuple tensor that ranged from 0 ~ 8. Default value is [3, 5].
same_on_batch (bool): apply the same transformation across the batch. Default: False.
device (torch.device): the device on which the random numbers will be generated. Default: cpu.
dtype (torch.dtype): the data type of the generated random numbers. Default: float32.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- bits_factor (torch.Tensor): element-wise bit factors with a shape of (B,).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
"""
_common_param_check(batch_size, same_on_batch)
_joint_range_check(bits, 'bits', (0, 8))
bits_factor = _adapted_uniform(
(batch_size,), bits[0].to(device=device, dtype=dtype), bits[1].to(device=device, dtype=dtype), same_on_batch
).int()
return dict(bits_factor=bits_factor.to(device=bits.device, dtype=torch.int32))
def random_mixup_generator(
batch_size: int,
p: float = 0.5,
lambda_val: Optional[torch.Tensor] = None,
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> Dict[str, torch.Tensor]:
r"""Generate mixup indexes and lambdas for a batch of inputs.
Args:
batch_size (int): the number of images. If batchsize == 1, the output will be as same as the input.
p (flot): probability of applying mixup.
lambda_val (torch.Tensor, optional): min-max strength for mixup images, ranged from [0., 1.].
If None, it will be set to tensor([0., 1.]), which means no restrictions.
same_on_batch (bool): apply the same transformation across the batch. Default: False.
device (torch.device): the device on which the random numbers will be generated. Default: cpu.
dtype (torch.dtype): the data type of the generated random numbers. Default: float32.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- mix_pairs (torch.Tensor): element-wise probabilities with a shape of (B,).
- mixup_lambdas (torch.Tensor): element-wise probabilities with a shape of (B,).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
Examples:
>>> rng = torch.manual_seed(0)
>>> random_mixup_generator(5, 0.7)
{'mixup_pairs': tensor([4, 0, 3, 1, 2]), 'mixup_lambdas': tensor([0.6323, 0.0000, 0.4017, 0.0223, 0.1689])}
"""
_common_param_check(batch_size, same_on_batch)
_device, _dtype = _extract_device_dtype([lambda_val])
lambda_val = torch.as_tensor(
[0.0, 1.0] if lambda_val is None else lambda_val,
device=device,
dtype=dtype)
_joint_range_check(lambda_val, 'lambda_val', bounds=(0, 1))
batch_probs: torch.Tensor = random_prob_generator(
batch_size, p, same_on_batch=same_on_batch, device=device, dtype=dtype)
mixup_pairs: torch.Tensor = torch.randperm(batch_size,
device=device,
dtype=dtype).long()
mixup_lambdas: torch.Tensor = _adapted_uniform((batch_size, ),
lambda_val[0],
lambda_val[1],
same_on_batch=same_on_batch)
mixup_lambdas = mixup_lambdas * batch_probs
return dict(
mixup_pairs=mixup_pairs.to(device=_device, dtype=torch.long),
mixup_lambdas=mixup_lambdas.to(device=_device, dtype=_dtype),
)
def random_crop_size_generator(
size: Tuple[int, int],
scale: Tuple[float, float],
ratio: Tuple[float, float],
same_on_batch: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
r"""Get cropping heights and widths for ```crop``` transformation for resized crop transform.
Args:
size (Tuple[int, int]): expected output size of each edge
scale: range of size of the origin size cropped
ratio: range of aspect ratio of the origin aspect ratio cropped
same_on_batch (bool): apply the same transformation across the batch. Default: False
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
"""
area = _adapted_uniform((10, ), scale[0] * size[0] * size[1],
scale[1] * size[0] * size[1], same_on_batch)
log_ratio = _adapted_uniform((10, ), math.log(ratio[0]),
math.log(ratio[1]), same_on_batch)
aspect_ratio = torch.exp(log_ratio)
w = torch.sqrt(area * aspect_ratio).int()
h = torch.sqrt(area / aspect_ratio).int()
# Element-wise w, h condition
cond = ((0 < h) * (h < size[1]) * (0 < w) * (w < size[0])).int()
if torch.sum(cond) > 0:
return (h[torch.argmax(cond)], w[torch.argmax(cond)])
# Fallback to center crop
in_ratio = float(size[0]) / float(size[1])
if (in_ratio < min(ratio)):
w = torch.tensor(size[0])
h = torch.round(w / min(ratio))
elif (in_ratio > max(ratio)):
h = torch.tensor(size[1])
w = torch.round(h * max(ratio))
else: # whole image
w = torch.tensor(size[0])
h = torch.tensor(size[1])
return (h, w)
def random_rectangles_params_generator(
batch_size: int,
height: int,
width: int,
p: float,
scale: Tuple[float, float],
ratio: Tuple[float, float],
value: float = 0.,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Get parameters for ```erasing``` transformation for erasing transform
Args:
batch_size (int): the tensor batch size.
height (int) : height of the image.
width (int): width of the image.
p (float): probability of applying random earaing.
scale ([int, int]): range of size of the origin size cropped.
ratio ([int, int]): range of aspect ratio of the origin aspect ratio cropped.
value (float): value to be filled in the erased area.
same_on_batch (bool): apply the same transformation across the batch. Default: False.
"""
batch_prob = random_prob_generator(batch_size, p,
same_on_batch)['batch_prob']
zeros = torch.zeros((batch_size, ))
images_area = height * width
target_areas = _adapted_uniform(
(batch_size, ), scale[0], scale[1], same_on_batch) * images_area
if ratio[0] < 1. and ratio[1] > 1.:
aspect_ratios1 = _adapted_uniform((batch_size, ), ratio[0], 1,
same_on_batch)
aspect_ratios2 = _adapted_uniform((batch_size, ), 1, ratio[1],
same_on_batch)
rand_idxs = torch.round(torch.rand((batch_size, ))).bool()
aspect_ratios = torch.where(rand_idxs, aspect_ratios1, aspect_ratios2)
else:
aspect_ratios = _adapted_uniform((batch_size, ), ratio[0], ratio[1],
same_on_batch)
# based on target areas and aspect ratios, rectangle params are computed
heights = torch.min(
torch.max(torch.round((target_areas * aspect_ratios)**(1 / 2)),
torch.tensor(1.)), torch.tensor(float(height))).int()
widths = torch.min(
torch.max(torch.round((target_areas / aspect_ratios)**(1 / 2)),
torch.tensor(1.)), torch.tensor(float(width))).int()
xs = (_adapted_uniform((batch_size, ), 0, 1, same_on_batch) *
(torch.tensor(width) - widths + 1).float()).int()
ys = (_adapted_uniform((batch_size, ), 0, 1, same_on_batch) *
(torch.tensor(height) - heights + 1).float()).int()
params: Dict[str, torch.Tensor] = {}
params["widths"] = torch.where(batch_prob, widths, zeros.to(widths.dtype))
params["heights"] = torch.where(batch_prob, heights,
zeros.to(widths.dtype))
params["xs"] = xs
params["ys"] = ys
params["values"] = torch.tensor([value] * batch_size)
return params
def random_rotation_generator3d(
batch_size: int,
degrees: torch.Tensor,
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> Dict[str, torch.Tensor]:
r"""Get parameters for ``rotate`` for a random rotate transform.
Args:
batch_size (int): the tensor batch size.
degrees (torch.Tensor): Ranges of degrees (3, 2) for yaw, pitch and roll.
same_on_batch (bool): apply the same transformation across the batch. Default: False.
device (torch.device): the device on which the random numbers will be generated. Default: cpu.
dtype (torch.dtype): the data type of the generated random numbers. Default: float32.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- yaw (torch.Tensor): element-wise rotation yaws with a shape of (B,).
- pitch (torch.Tensor): element-wise rotation pitches with a shape of (B,).
- roll (torch.Tensor): element-wise rotation rolls with a shape of (B,).
"""
if degrees.shape != torch.Size([3, 2]):
raise AssertionError(
f"'degrees' must be the shape of (3, 2). Got {degrees.shape}.")
_device, _dtype = _extract_device_dtype([degrees])
degrees = degrees.to(device=device, dtype=dtype)
yaw = _adapted_uniform((batch_size, ), degrees[0][0], degrees[0][1],
same_on_batch)
pitch = _adapted_uniform((batch_size, ), degrees[1][0], degrees[1][1],
same_on_batch)
roll = _adapted_uniform((batch_size, ), degrees[2][0], degrees[2][1],
same_on_batch)
return dict(
yaw=yaw.to(device=_device, dtype=_dtype),
pitch=pitch.to(device=_device, dtype=_dtype),
roll=roll.to(device=_device, dtype=_dtype),
)
def _get_random_affine_params(
batch_size: int,
height: int,
width: int,
degrees: TupleFloat,
translate: Optional[TupleFloat],
scales: Optional[TupleFloat],
shears: Optional[TupleFloat],
resample: Union[str, int, Resample] = Resample.BILINEAR.name,
same_on_batch: bool = False,
align_corners: bool = False) -> Dict[str, torch.Tensor]:
r"""Get parameters for ```affine``` transformation random affine transform.
The returned matrix is Bx3x3.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
"""
angle = _adapted_uniform((batch_size, ), degrees[0], degrees[1],
same_on_batch)
# compute tensor ranges
if scales is not None:
scale = _adapted_uniform((batch_size, ), scales[0], scales[1],
same_on_batch)
else:
scale = torch.ones(batch_size)
if translate is not None:
max_dx: float = translate[0] * width
max_dy: float = translate[1] * height
translations = torch.stack([
_adapted_uniform((batch_size, ), -max_dx, max_dx, same_on_batch),
_adapted_uniform((batch_size, ), -max_dy, max_dy, same_on_batch)
],
dim=-1)
else:
translations = torch.zeros(batch_size, 2)
center: torch.Tensor = torch.tensor(
[width, height], dtype=torch.float32).view(1, 2) / 2. - 0.5
center = center.expand(batch_size, -1)
if shears is not None:
sx = _adapted_uniform((batch_size, ), shears[0], shears[1],
same_on_batch)
sy = _adapted_uniform((batch_size, ), shears[0], shears[1],
same_on_batch)
else:
sx = sy = torch.tensor([0] * batch_size)
return dict(translations=translations,
center=center,
scale=scale,
angle=angle,
sx=sx,
sy=sy,
resample=torch.tensor(Resample.get(resample).value),
align_corners=torch.tensor(align_corners))
def random_solarize_generator(
batch_size: int,
thresholds: FloatUnionType = 0.1,
additions: FloatUnionType = 0.1,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Generator random solarize parameters for a batch of images. For each pixel in the image less than threshold,
we add 'addition' amount to it and then clip the pixel value to be between 0 and 1.0
Args:
batch_size (int): the number of images.
thresholds (float or tuple): Pixels less than threshold will selected. Otherwise, subtract 1.0 from the pixel.
Default value is 0.1
additions (float or tuple): The value is between -0.5 and 0.5. Default value is 0.1
same_on_batch (bool): apply the same transformation across the batch. Default: False
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
"""
thresholds_bound: torch.Tensor = _check_and_bound(thresholds,
'thresholds',
center=0.5,
bounds=(0., 1.))
additions_bound: torch.Tensor = _check_and_bound(additions,
'additions',
bounds=(-0.5, 0.5))
thresholds_factor = _adapted_uniform(
(batch_size, ), thresholds_bound[0].float(),
thresholds_bound[1].float(), same_on_batch)
additions_factor = _adapted_uniform(
(batch_size, ), additions_bound[0].float(), additions_bound[1].float(),
same_on_batch)
return dict(thresholds_factor=thresholds_factor,
additions_factor=additions_factor)
def random_rotation_generator(
batch_size: int,
degrees: FloatUnionType,
interpolation: Union[str, int, Resample] = Resample.BILINEAR.name,
same_on_batch: bool = False,
align_corners: bool = False) -> Dict[str, torch.Tensor]:
r"""Get parameters for ``rotate`` for a random rotate transform.
Args:
batch_size (int): the tensor batch size.
degrees (sequence or float or tensor): range of degrees to select from. If degrees is a number the
range of degrees to select from will be (-degrees, +degrees)
interpolation (int, str or kornia.Resample): Default: Resample.BILINEAR
same_on_batch (bool): apply the same transformation across the batch. Default: False
align_corners (bool): interpolation flag. Default: False.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
"""
if not torch.is_tensor(degrees):
if isinstance(degrees, (float, int)):
if degrees < 0:
raise ValueError(
f"If Degrees is only one number it must be a positive number. Got{degrees}"
)
degrees = torch.tensor([-degrees, degrees]).to(torch.float32)
elif isinstance(degrees, (tuple, list)):
degrees = torch.tensor(degrees).to(torch.float32)
else:
raise TypeError(
f"Degrees should be a float number a sequence or a tensor. Got {type(degrees)}"
)
# https://mypy.readthedocs.io/en/latest/casts.html cast to please mypy gods
degrees = cast(torch.Tensor, degrees)
if degrees.numel() != 2:
raise ValueError("If degrees is a sequence it must be of length 2")
degrees = _adapted_uniform((batch_size, ), degrees[0], degrees[1],
same_on_batch)
return dict(degrees=degrees,
interpolation=torch.tensor(Resample.get(interpolation).value),
align_corners=torch.tensor(align_corners))
def _get_random_affine_params(
batch_size: int, height: int, width: int,
degrees: TupleFloat, translate: Optional[TupleFloat],
scales: Optional[TupleFloat], shears: Optional[TupleFloat],
same_on_batch: bool = False
) -> torch.Tensor:
r"""Get parameters for affine transformation random generation.
The returned matrix is Bx3x3.
Returns:
torch.Tensor: params to be passed to the affine transformation.
"""
angle = _adapted_uniform((batch_size,), degrees[0], degrees[1], same_on_batch)
# compute tensor ranges
if scales is not None:
scale = _adapted_uniform((batch_size,), scales[0], scales[1], same_on_batch)
else:
scale = torch.ones(batch_size)
if translate is not None:
max_dx: float = translate[0] * width
max_dy: float = translate[1] * height
translations = torch.stack([
_adapted_uniform((batch_size,), -max_dx, max_dx, same_on_batch),
_adapted_uniform((batch_size,), -max_dy, max_dy, same_on_batch)
], dim=-1)
else:
translations = torch.zeros(batch_size, 2)
center: torch.Tensor = torch.tensor(
[width, height], dtype=torch.float32).view(1, 2) / 2. - 0.5
center = center.expand(batch_size, -1)
if shears is not None:
shears = math.radians(shears[0]), math.radians(shears[1])
sx = _adapted_uniform((batch_size,), shears[0], shears[1], same_on_batch)
sy = _adapted_uniform((batch_size,), shears[0], shears[1], same_on_batch)
ones = torch.ones_like(sx)
else:
sx = sy = None
# concatenate transforms
transform_h = _compose_affine_matrix_3x3(translations,
center,
scale,
angle,
sx,
sy)
return transform_h
def _get_perspective_params(batch_size: int, width: int, height: int, distortion_scale: float,
same_on_batch: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
r"""Get parameters for ``perspective`` for a random perspective transform.
Args:
batch_size (int): the tensor batch size.
width (int): width of the image.
height (int) : height of the image.
distortion_scale (float): it controls the degree of distortion and ranges from 0 to 1. Default value is 0.5.
Returns:
List containing [top-left, top-right, bottom-right, bottom-left] of the original image,
List containing [top-left, top-right, bottom-right, bottom-left] of the transformed image.
The points are in -x order.
"""
start_points: torch.Tensor = torch.tensor([[
[0., 0],
[width - 1, 0],
[width - 1, height - 1],
[0, height - 1],
]]).expand(batch_size, -1, -1)
# generate random offset not larger than half of the image
fx: float = distortion_scale * width / 2
fy: float = distortion_scale * height / 2
factor = torch.tensor([fx, fy]).view(-1, 1, 2)
rand_val: torch.Tensor = _adapted_uniform((batch_size, 4, 2), 0, 1, same_on_batch)
pts_norm = torch.tensor([[
[1, 1],
[-1, 1],
[-1, -1],
[1, -1]
]])
end_points = start_points + factor * rand_val * pts_norm
return start_points, end_points
def random_prob_generator(
batch_size: int,
p: float = 0.5,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Generator random probabilities for a batch of inputs.
Args:
batch_size (int): the number of images.
p (float): probability of the image being flipped or grayscaled. Default value is 0.5
same_on_batch (bool): apply the same transformation across the batch. Default: False
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
"""
if not isinstance(p, float):
raise TypeError(
f"The probability should be a float number. Got {type(p)}")
probs: torch.Tensor = _adapted_uniform((batch_size, ), 0, 1, same_on_batch)
batch_prob: torch.Tensor = (probs < p)
return dict(batch_prob=batch_prob)
def random_color_jitter_gen(batch_size: int, brightness: FloatUnionType = 0.,
contrast: FloatUnionType = 0., saturation: FloatUnionType = 0.,
hue: FloatUnionType = 0., same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
r"""Generator random color jiter parameters for a batch of images.
Args:
batch_size (int): the number of images.
brightness (float or tuple): Default value is 0
contrast (float or tuple): Default value is 0
saturation (float or tuple): Default value is 0
hue (float or tuple): Default value is 0
Returns:
dict: generated parameter dictionary.
See :class:`~kornia.augmentation.ColorJitter` for details.
"""
def _check_and_bound(factor: FloatUnionType, name: str, center: float = 0.,
bounds: Tuple[float, float] = (0, float('inf'))) -> torch.Tensor:
r"""Check inputs and compute the corresponding factor bounds
"""
if isinstance(factor, float):
if factor < 0:
raise ValueError(f"If {name} is a single number number, it must be non negative. Got {factor}")
factor_bound = torch.tensor([center - factor, center + factor], dtype=torch.float32)
factor_bound = torch.clamp(factor_bound, bounds[0], bounds[1])
elif (isinstance(factor, torch.Tensor) and factor.dim() == 0):
if factor < 0:
raise ValueError(f"If {name} is a single number number, it must be non negative. Got {factor}")
factor_bound = torch.tensor(
[torch.tensor(center) - factor, torch.tensor(center) + factor], dtype=torch.float32)
factor_bound = torch.clamp(factor_bound, bounds[0], bounds[1])
elif isinstance(factor, (tuple, list)) and len(factor) == 2:
if not bounds[0] <= factor[0] <= factor[1] <= bounds[1]:
raise ValueError(f"{name}[0] should be smaller than {name}[1] got {factor}")
factor_bound = torch.tensor(factor, dtype=torch.float32)
elif isinstance(factor, torch.Tensor) and factor.shape[0] == 2 and factor.dim() == 1:
if not bounds[0] <= factor[0] <= factor[1] <= bounds[1]:
raise ValueError(f"{name}[0] should be smaller than {name}[1] got {factor}")
factor_bound = factor
else:
raise TypeError(
f"The {name} should be a float number or a tuple with length 2 whose values move between {bounds}.")
return factor_bound
brightness_bound: torch.Tensor = _check_and_bound(
brightness, 'brightness', center=1., bounds=(0, 2))
contrast_bound: torch.Tensor = _check_and_bound(contrast, 'contrast', center=1.)
saturation_bound: torch.Tensor = _check_and_bound(saturation, 'saturation', center=1.)
hue_bound: torch.Tensor = _check_and_bound(hue, 'hue', bounds=(-0.5, 0.5))
brightness_factor = _adapted_uniform((batch_size,), brightness_bound[0], brightness_bound[1], same_on_batch)
contrast_factor = _adapted_uniform((batch_size,), contrast_bound[0], contrast_bound[1], same_on_batch)
hue_factor = _adapted_uniform((batch_size,), hue_bound[0], hue_bound[1], same_on_batch)
saturation_factor = _adapted_uniform((batch_size,), saturation_bound[0], saturation_bound[1], same_on_batch)
return {
"brightness_factor": brightness_factor,
"contrast_factor": contrast_factor,
"hue_factor": hue_factor,
"saturation_factor": saturation_factor,
"order": torch.randperm(4)
}
def random_affine_generator3d(
batch_size: int,
depth: int,
height: int,
width: int,
degrees: torch.Tensor,
translate: Optional[torch.Tensor] = None,
scale: Optional[torch.Tensor] = None,
shears: Optional[torch.Tensor] = None,
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> Dict[str, torch.Tensor]:
r"""Get parameters for ```3d affine``` transformation random affine transform.
Args:
batch_size (int): the tensor batch size.
depth (int) : depth of the image.
height (int) : height of the image.
width (int): width of the image.
degrees (torch.Tensor): Ranges of degrees with shape (3, 2) for yaw, pitch and roll.
translate (torch.Tensor, optional): maximum absolute fraction with shape (3,) for horizontal, vertical
and depthical translations (dx,dy,dz). Will not translate by default.
scale (torch.Tensor, optional): scaling factor interval, e.g (a, b), then scale is
randomly sampled from the range a <= scale <= b. Will keep original scale by default.
shear (sequence or float, optional): Range of degrees to select from.
Shaped as (6, 2) for 6 facet (xy, xz, yx, yz, zx, zy).
The shear to the i-th facet in the range (-shear[i, 0], shear[i, 1]) will be applied.
same_on_batch (bool): apply the same transformation across the batch. Default: False
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- translations (torch.Tensor): element-wise translations with a shape of (B, 3).
- center (torch.Tensor): element-wise center with a shape of (B, 3).
- scale (torch.Tensor): element-wise scales with a shape of (B, 3).
- angle (torch.Tensor): element-wise rotation angles with a shape of (B, 3).
- sxy (torch.Tensor): element-wise x-y-facet shears with a shape of (B,).
- sxz (torch.Tensor): element-wise x-z-facet shears with a shape of (B,).
- syx (torch.Tensor): element-wise y-x-facet shears with a shape of (B,).
- syz (torch.Tensor): element-wise y-z-facet shears with a shape of (B,).
- szx (torch.Tensor): element-wise z-x-facet shears with a shape of (B,).
- szy (torch.Tensor): element-wise z-y-facet shears with a shape of (B,).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
"""
if not (type(depth) is int and depth > 0 and type(height) is int
and height > 0 and type(width) is int and width > 0):
raise AssertionError(
f"'depth', 'height' and 'width' must be integers. Got {depth}, {height}, {width}."
)
_device, _dtype = _extract_device_dtype(
[degrees, translate, scale, shears])
if degrees.shape != torch.Size([3, 2]):
raise AssertionError(
f"'degrees' must be the shape of (3, 2). Got {degrees.shape}.")
degrees = degrees.to(device=device, dtype=dtype)
yaw = _adapted_uniform((batch_size, ), degrees[0][0], degrees[0][1],
same_on_batch)
pitch = _adapted_uniform((batch_size, ), degrees[1][0], degrees[1][1],
same_on_batch)
roll = _adapted_uniform((batch_size, ), degrees[2][0], degrees[2][1],
same_on_batch)
angles = torch.stack([yaw, pitch, roll], dim=1)
# compute tensor ranges
if scale is not None:
if scale.shape != torch.Size([3, 2]):
raise AssertionError(
f"'scale' must be the shape of (3, 2). Got {scale.shape}.")
scale = scale.to(device=device, dtype=dtype)
scale = torch.stack(
[
_adapted_uniform(
(batch_size, ), scale[0, 0], scale[0, 1], same_on_batch),
_adapted_uniform(
(batch_size, ), scale[1, 0], scale[1, 1], same_on_batch),
_adapted_uniform(
(batch_size, ), scale[2, 0], scale[2, 1], same_on_batch),
],
dim=1,
)
else:
scale = torch.ones(batch_size, device=device,
dtype=dtype).reshape(batch_size, 1).repeat(1, 3)
if translate is not None:
if translate.shape != torch.Size([3]):
raise AssertionError(
f"'translate' must be the shape of (2). Got {translate.shape}."
)
translate = translate.to(device=device, dtype=dtype)
max_dx: torch.Tensor = translate[0] * width
max_dy: torch.Tensor = translate[1] * height
max_dz: torch.Tensor = translate[2] * depth
# translations should be in x,y,z
translations = torch.stack(
[
_adapted_uniform(
(batch_size, ), -max_dx, max_dx, same_on_batch),
_adapted_uniform(
(batch_size, ), -max_dy, max_dy, same_on_batch),
_adapted_uniform(
(batch_size, ), -max_dz, max_dz, same_on_batch),
],
dim=1,
)
else:
translations = torch.zeros((batch_size, 3), device=device, dtype=dtype)
# center should be in x,y,z
center: torch.Tensor = torch.tensor([width, height, depth],
device=device,
dtype=dtype).view(1, 3) / 2.0 - 0.5
center = center.expand(batch_size, -1)
if shears is not None:
if shears.shape != torch.Size([6, 2]):
raise AssertionError(
f"'shears' must be the shape of (6, 2). Got {shears.shape}.")
shears = shears.to(device=device, dtype=dtype)
sxy = _adapted_uniform((batch_size, ), shears[0, 0], shears[0, 1],
same_on_batch)
sxz = _adapted_uniform((batch_size, ), shears[1, 0], shears[1, 1],
same_on_batch)
syx = _adapted_uniform((batch_size, ), shears[2, 0], shears[2, 1],
same_on_batch)
syz = _adapted_uniform((batch_size, ), shears[3, 0], shears[3, 1],
same_on_batch)
szx = _adapted_uniform((batch_size, ), shears[4, 0], shears[4, 1],
same_on_batch)
szy = _adapted_uniform((batch_size, ), shears[5, 0], shears[5, 1],
same_on_batch)
else:
sxy = sxz = syx = syz = szx = szy = torch.tensor([0] * batch_size,
device=device,
dtype=dtype)
return dict(
translations=translations.to(device=_device, dtype=_dtype),
center=center.to(device=_device, dtype=_dtype),
scale=scale.to(device=_device, dtype=_dtype),
angles=angles.to(device=_device, dtype=_dtype),
sxy=sxy.to(device=_device, dtype=_dtype),
sxz=sxz.to(device=_device, dtype=_dtype),
syx=syx.to(device=_device, dtype=_dtype),
syz=syz.to(device=_device, dtype=_dtype),
szx=szx.to(device=_device, dtype=_dtype),
szy=szy.to(device=_device, dtype=_dtype),
)
def random_cutmix_generator(
batch_size: int,
width: int,
height: int,
p: float = 0.5,
num_mix: int = 1,
beta: Optional[torch.Tensor] = None,
cut_size: Optional[torch.Tensor] = None,
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> Dict[str, torch.Tensor]:
r"""Generate cutmix indexes and lambdas for a batch of inputs.
Args:
batch_size (int): the number of images. If batchsize == 1, the output will be as same as the input.
width (int): image width.
height (int): image height.
p (float): probability of applying cutmix.
num_mix (int): number of images to mix with. Default is 1.
beta (torch.Tensor, optional): hyperparameter for generating cut size from beta distribution.
If None, it will be set to 1.
cut_size (torch.Tensor, optional): controlling the minimum and maximum cut ratio from [0, 1].
If None, it will be set to [0, 1], which means no restriction.
same_on_batch (bool): apply the same transformation across the batch. Default: False.
device (torch.device): the device on which the random numbers will be generated. Default: cpu.
dtype (torch.dtype): the data type of the generated random numbers. Default: float32.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- mix_pairs (torch.Tensor): element-wise probabilities with a shape of (num_mix, B).
- crop_src (torch.Tensor): element-wise probabilities with a shape of (num_mix, B, 4, 2).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
Examples:
>>> rng = torch.manual_seed(0)
>>> random_cutmix_generator(3, 224, 224, p=0.5, num_mix=2)
{'mix_pairs': tensor([[2, 0, 1],
[1, 2, 0]]), 'crop_src': tensor([[[[ 35., 25.],
[208., 25.],
[208., 198.],
[ 35., 198.]],
<BLANKLINE>
[[156., 137.],
[155., 137.],
[155., 136.],
[156., 136.]],
<BLANKLINE>
[[ 3., 12.],
[210., 12.],
[210., 219.],
[ 3., 219.]]],
<BLANKLINE>
<BLANKLINE>
[[[ 83., 125.],
[177., 125.],
[177., 219.],
[ 83., 219.]],
<BLANKLINE>
[[ 54., 8.],
[205., 8.],
[205., 159.],
[ 54., 159.]],
<BLANKLINE>
[[ 97., 70.],
[ 96., 70.],
[ 96., 69.],
[ 97., 69.]]]])}
"""
_device, _dtype = _extract_device_dtype([beta, cut_size])
beta = torch.as_tensor(1.0 if beta is None else beta,
device=device,
dtype=dtype)
cut_size = torch.as_tensor([0.0, 1.0] if cut_size is None else cut_size,
device=device,
dtype=dtype)
if not (num_mix >= 1 and isinstance(num_mix, (int, ))):
raise AssertionError(
f"`num_mix` must be an integer greater than 1. Got {num_mix}.")
if not (type(height) is int and height > 0 and type(width) is int
and width > 0):
raise AssertionError(
f"'height' and 'width' must be integers. Got {height}, {width}.")
_joint_range_check(cut_size, 'cut_size', bounds=(0, 1))
_common_param_check(batch_size, same_on_batch)
if batch_size == 0:
return dict(
mix_pairs=torch.zeros([0, 3], device=_device, dtype=torch.long),
crop_src=torch.zeros([0, 4, 2], device=_device, dtype=torch.long),
)
batch_probs: torch.Tensor = random_prob_generator(batch_size * num_mix,
p,
same_on_batch,
device=device,
dtype=dtype)
mix_pairs: torch.Tensor = torch.rand(num_mix,
batch_size,
device=device,
dtype=dtype).argsort(dim=1)
cutmix_betas: torch.Tensor = _adapted_beta((batch_size * num_mix, ),
beta,
beta,
same_on_batch=same_on_batch)
# Note: torch.clamp does not accept tensor, cutmix_betas.clamp(cut_size[0], cut_size[1]) throws:
# Argument 1 to "clamp" of "_TensorBase" has incompatible type "Tensor"; expected "float"
cutmix_betas = torch.min(torch.max(cutmix_betas, cut_size[0]), cut_size[1])
cutmix_rate = torch.sqrt(1.0 - cutmix_betas) * batch_probs
cut_height = (cutmix_rate * height).floor().to(device=device, dtype=_dtype)
cut_width = (cutmix_rate * width).floor().to(device=device, dtype=_dtype)
_gen_shape = (1, )
if same_on_batch:
_gen_shape = (cut_height.size(0), )
cut_height = cut_height[0]
cut_width = cut_width[0]
# Reserve at least 1 pixel for cropping.
x_start = (_adapted_uniform(
_gen_shape,
torch.zeros_like(cut_width, device=device, dtype=dtype),
(width - cut_width - 1).to(device=device, dtype=dtype),
same_on_batch,
).floor().to(device=device, dtype=_dtype))
y_start = (_adapted_uniform(
_gen_shape,
torch.zeros_like(cut_height, device=device, dtype=dtype),
(height - cut_height - 1).to(device=device, dtype=dtype),
same_on_batch,
).floor().to(device=device, dtype=_dtype))
crop_src = bbox_generator(x_start.squeeze(), y_start.squeeze(), cut_width,
cut_height)
# (B * num_mix, 4, 2) => (num_mix, batch_size, 4, 2)
crop_src = crop_src.view(num_mix, batch_size, 4, 2)
return dict(
mix_pairs=mix_pairs.to(device=_device, dtype=torch.long),
crop_src=crop_src.floor().to(device=_device, dtype=_dtype),
)
