def forward(self, batch_shape: torch.Size, same_on_batch: bool = False) -> Dict[str, torch.Tensor]: # type:ignore
batch_size = batch_shape[0]
height = batch_shape[-2]
width = batch_shape[-1]
_device, _dtype = _extract_device_dtype([self.distortion_scale])
_common_param_check(batch_size, same_on_batch)
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=_device, dtype=_dtype
).expand(batch_size, -1, -1)
# generate random offset not larger than half of the image
fx = self._distortion_scale * width / 2
fy = self._distortion_scale * height / 2
factor = torch.stack([fx, fy], dim=0).view(-1, 1, 2).to(device=_device, dtype=_dtype)
# TODO: This line somehow breaks the gradcheck
rand_val: torch.Tensor = _adapted_rsampling(start_points.shape, self.rand_val_sampler, same_on_batch).to(
device=_device, dtype=_dtype
)
if self.sampling_method == "basic":
pts_norm = torch.tensor([[[1, 1], [-1, 1], [-1, -1], [1, -1]]], device=_device, dtype=_dtype)
offset = factor * rand_val * pts_norm
elif self.sampling_method == "area_preserving":
offset = 2 * factor * (rand_val - 0.5)
end_points = start_points + offset
return dict(start_points=start_points, end_points=end_points)
def forward(
self,
batch_shape: torch.Size,
same_on_batch: bool = False) -> Dict[str, Tensor]: # type:ignore
batch_size = batch_shape[0]
_common_param_check(batch_size, same_on_batch)
_device, _dtype = _extract_device_dtype(
[self.brightness, self.contrast, self.hue, self.saturation])
brightness_factor = _adapted_rsampling(
(batch_size, ), self.brightness_sampler, same_on_batch)
contrast_factor = _adapted_rsampling(
(batch_size, ), self.contrast_sampler, same_on_batch)
hue_factor = _adapted_rsampling((batch_size, ), self.hue_sampler,
same_on_batch)
saturation_factor = _adapted_rsampling(
(batch_size, ), self.saturation_sampler, same_on_batch)
return dict(
brightness_factor=brightness_factor.to(device=_device,
dtype=_dtype),
contrast_factor=contrast_factor.to(device=_device, dtype=_dtype),
hue_factor=hue_factor.to(device=_device, dtype=_dtype),
saturation_factor=saturation_factor.to(device=_device,
dtype=_dtype),
order=self.randperm(4).to(device=_device, dtype=_dtype).long(),
)
def random_prob_generator(
batch_size: int,
p: float = 0.5,
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
r"""Generate random probabilities for a batch of inputs.
Args:
batch_size (int): the number of images.
p (float): probability to generate an 1-d binary mask. Default value is 0.5.
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:
torch.Tensor: parameters to be passed for transformation.
- probs (torch.Tensor): element-wise probabilities 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)
if not isinstance(p, (int, float)) or p > 1 or p < 0:
raise TypeError(f"The probability should be a float number within [0, 1]. Got {type(p)}.")
_bernoulli = Bernoulli(torch.tensor(float(p), device=device, dtype=dtype))
probs_mask: torch.Tensor = _adapted_sampling((batch_size,), _bernoulli, same_on_batch).bool()
return probs_mask
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 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_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 forward(self, batch_shape: torch.Size, same_on_batch: bool =
False) -> Dict[str, torch.Tensor]:
batch_size = batch_shape[0]
_common_param_check(batch_size, same_on_batch)
pl_idx = _adapted_rsampling((batch_size,),
self.pl_idx_dist,
same_on_batch)
return dict(idx=pl_idx.long())
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_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 forward(
self,
batch_shape: torch.Size,
same_on_batch: bool = False
) -> Dict[str, torch.Tensor]: # type:ignore
batch_size = batch_shape[0]
depth = batch_shape[-3]
height = batch_shape[-2]
width = batch_shape[-1]
_common_param_check(batch_size, same_on_batch)
_device, _dtype = _extract_device_dtype([self.distortion_scale])
start_points: torch.Tensor = torch.tensor(
[[
[0.0, 0, 0],
[width - 1, 0, 0],
[width - 1, height - 1, 0],
[0, height - 1, 0],
[0.0, 0, depth - 1],
[width - 1, 0, depth - 1],
[width - 1, height - 1, depth - 1],
[0, height - 1, depth - 1],
]],
device=_device,
dtype=_dtype,
).expand(batch_size, -1, -1)
# generate random offset not larger than half of the image
fx = self._distortion_scale * width / 2
fy = self._distortion_scale * height / 2
fz = self._distortion_scale * depth / 2
factor = torch.stack([fx, fy, fz], dim=0).view(-1, 1,
3).to(device=_device,
dtype=_dtype)
rand_val: torch.Tensor = _adapted_rsampling(
start_points.shape, self.rand_sampler,
same_on_batch).to(device=_device, dtype=_dtype)
pts_norm = torch.tensor(
[[[1, 1, 1], [-1, 1, 1], [-1, -1, 1], [1, -1, 1], [1, 1, -1],
[-1, 1, -1], [-1, -1, -1], [1, -1, -1]]],
device=_device,
dtype=_dtype,
)
end_points = start_points + factor * rand_val * pts_norm
return dict(start_points=start_points, end_points=end_points)
def forward(
self,
batch_shape: torch.Size,
same_on_batch: bool = False
) -> Dict[str, torch.Tensor]: # type:ignore
batch_size = batch_shape[0]
_common_param_check(batch_size, same_on_batch)
_device, _dtype = _extract_device_dtype(
[t for t, _, _, _ in self.samplers])
return {
name: _adapted_rsampling((batch_size, ), dist,
same_on_batch).to(device=_device,
dtype=_dtype)
for name, dist in self.sampler_dict.items()
}
def forward(self, batch_shape: torch.Size, same_on_batch: bool = False) -> Dict[str, torch.Tensor]: # type:ignore
batch_size = batch_shape[0]
_common_param_check(batch_size, same_on_batch)
# self.ksize_factor.expand((batch_size, -1))
_device, _dtype = _extract_device_dtype([self.angle, self.direction])
yaw_factor = _adapted_rsampling((batch_size,), self.yaw_sampler, same_on_batch)
pitch_factor = _adapted_rsampling((batch_size,), self.pitch_sampler, same_on_batch)
roll_factor = _adapted_rsampling((batch_size,), self.roll_sampler, same_on_batch)
angle_factor = torch.stack([yaw_factor, pitch_factor, roll_factor], dim=1)
direction_factor = _adapted_rsampling((batch_size,), self.direction_sampler, same_on_batch)
ksize_factor = _adapted_rsampling((batch_size,), self.ksize_sampler, same_on_batch).int() * 2 + 1
return dict(
ksize_factor=ksize_factor.to(device=_device, dtype=torch.int32),
angle_factor=angle_factor.to(device=_device, dtype=_dtype),
direction_factor=direction_factor.to(device=_device, dtype=_dtype),
)
def forward(
self,
batch_shape: torch.Size,
same_on_batch: bool = False
) -> Dict[str, torch.Tensor]: # type:ignore
batch_size = batch_shape[0]
_common_param_check(batch_size, same_on_batch)
_device, _dtype = _extract_device_dtype([self.degrees])
return dict(
yaw=_adapted_rsampling((batch_size, ), self.yaw_sampler,
same_on_batch).to(device=_device,
dtype=_dtype),
pitch=_adapted_rsampling((batch_size, ), self.pitch_sampler,
same_on_batch).to(device=_device,
dtype=_dtype),
roll=_adapted_rsampling((batch_size, ), self.roll_sampler,
same_on_batch).to(device=_device,
dtype=_dtype),
)
def forward(self,
batch_shape: torch.Size,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
batch_size = batch_shape[0]
_common_param_check(batch_size, same_on_batch)
_device, _dtype = _extract_device_dtype([self.lambda_val])
with torch.no_grad():
batch_probs: torch.Tensor = _adapted_sampling(
(batch_size, ), self.prob_sampler, same_on_batch)
mixup_pairs: torch.Tensor = torch.randperm(batch_size,
device=_device,
dtype=_dtype).long()
mixup_lambdas: torch.Tensor = _adapted_rsampling(
(batch_size, ), self.lambda_sampler, 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 forward(
self,
batch_shape: torch.Size,
same_on_batch: bool = False
) -> Dict[str, torch.Tensor]: # type:ignore
batch_size = batch_shape[0]
_common_param_check(batch_size, same_on_batch)
_device, _dtype = _extract_device_dtype(
[self.size if isinstance(self.size, torch.Tensor) else None])
if batch_size == 0:
return dict(
src=torch.zeros([0, 4, 2], device=_device, dtype=_dtype),
dst=torch.zeros([0, 4, 2], device=_device, dtype=_dtype),
)
input_size = (batch_shape[-2], batch_shape[-1])
if not isinstance(self.size, torch.Tensor):
size = torch.tensor(self.size, device=_device,
dtype=_dtype).repeat(batch_size, 1)
else:
size = self.size.to(device=_device, dtype=_dtype)
if size.shape != torch.Size([batch_size, 2]):
raise AssertionError(
"If `size` is a tensor, it must be shaped as (B, 2). "
f"Got {size.shape} while expecting {torch.Size([batch_size, 2])}."
)
if not (input_size[0] > 0 and input_size[1] > 0 and (size > 0).all()):
raise AssertionError(
f"Got non-positive input size or size. {input_size}, {size}.")
size = size.floor()
x_diff = input_size[1] - size[:, 1] + 1
y_diff = input_size[0] - size[:, 0] + 1
# Start point will be 0 if diff < 0
x_diff = x_diff.clamp(0)
y_diff = y_diff.clamp(0)
if same_on_batch:
# If same_on_batch, select the first then repeat.
x_start = (_adapted_rsampling(
(batch_size, ), self.rand_sampler, same_on_batch).to(x_diff) *
x_diff[0]).floor()
y_start = (_adapted_rsampling(
(batch_size, ), self.rand_sampler, same_on_batch).to(y_diff) *
y_diff[0]).floor()
else:
x_start = (_adapted_rsampling(
(batch_size, ), self.rand_sampler, same_on_batch).to(x_diff) *
x_diff).floor()
y_start = (_adapted_rsampling(
(batch_size, ), self.rand_sampler, same_on_batch).to(y_diff) *
y_diff).floor()
crop_src = bbox_generator(
x_start.view(-1).to(device=_device, dtype=_dtype),
y_start.view(-1).to(device=_device, dtype=_dtype),
torch.where(
size[:, 1] == 0,
torch.tensor(input_size[1], device=_device, dtype=_dtype),
size[:, 1]),
torch.where(
size[:, 0] == 0,
torch.tensor(input_size[0], device=_device, dtype=_dtype),
size[:, 0]),
)
if self.resize_to is None:
crop_dst = bbox_generator(
torch.tensor([0] * batch_size, device=_device, dtype=_dtype),
torch.tensor([0] * batch_size, device=_device, dtype=_dtype),
size[:, 1],
size[:, 0],
)
_output_size = size.to(dtype=torch.long)
else:
if not (len(self.resize_to) == 2 and isinstance(
self.resize_to[0],
(int, )) and isinstance(self.resize_to[1], (int, ))
and self.resize_to[0] > 0 and self.resize_to[1] > 0):
raise AssertionError(
f"`resize_to` must be a tuple of 2 positive integers. Got {self.resize_to}."
)
crop_dst = torch.tensor(
[[
[0, 0],
[self.resize_to[1] - 1, 0],
[self.resize_to[1] - 1, self.resize_to[0] - 1],
[0, self.resize_to[0] - 1],
]],
device=_device,
dtype=_dtype,
).repeat(batch_size, 1, 1)
_output_size = torch.tensor(self.resize_to,
device=_device,
dtype=torch.long).expand(
batch_size, -1)
_input_size = torch.tensor(input_size,
device=_device,
dtype=torch.long).expand(batch_size, -1)
return dict(src=crop_src,
dst=crop_dst,
input_size=_input_size,
output_size=_output_size)
def forward(
self,
batch_shape: torch.Size,
same_on_batch: bool = False
) -> Dict[str, torch.Tensor]: # type:ignore
batch_size, _, depth, height, width = batch_shape
_common_param_check(batch_size, same_on_batch)
_device, _dtype = _extract_device_dtype(
[self.size if isinstance(self.size, torch.Tensor) else None])
if not isinstance(self.size, torch.Tensor):
size = torch.tensor(self.size, device=_device,
dtype=_dtype).repeat(batch_size, 1)
else:
size = self.size.to(device=_device, dtype=_dtype)
if size.shape != torch.Size([batch_size, 3]):
raise AssertionError(
"If `size` is a tensor, it must be shaped as (B, 3). "
f"Got {size.shape} while expecting {torch.Size([batch_size, 3])}."
)
if not (isinstance(depth, (int, )) and isinstance(height, (int, ))
and isinstance(width, (int, )) and depth > 0 and height > 0
and width > 0):
raise AssertionError(
f"`batch_shape` should not contain negative values. Got {(batch_shape)}."
)
x_diff = width - size[:, 2] + 1
y_diff = height - size[:, 1] + 1
z_diff = depth - size[:, 0] + 1
if (x_diff < 0).any() or (y_diff < 0).any() or (z_diff < 0).any():
raise ValueError(
f"input_size {(depth, height, width)} cannot be smaller than crop size {str(size)} in any dimension."
)
if batch_size == 0:
return dict(
src=torch.zeros([0, 8, 3], device=_device, dtype=_dtype),
dst=torch.zeros([0, 8, 3], device=_device, dtype=_dtype),
)
x_start = _adapted_rsampling((batch_size, ), self.rand_sampler,
same_on_batch).to(device=_device,
dtype=_dtype)
y_start = _adapted_rsampling((batch_size, ), self.rand_sampler,
same_on_batch).to(device=_device,
dtype=_dtype)
z_start = _adapted_rsampling((batch_size, ), self.rand_sampler,
same_on_batch).to(device=_device,
dtype=_dtype)
x_start = (x_start * x_diff).floor()
y_start = (y_start * y_diff).floor()
z_start = (z_start * z_diff).floor()
crop_src = bbox_generator3d(x_start.view(-1), y_start.view(-1),
z_start.view(-1), size[:, 2] - 1,
size[:, 1] - 1, size[:, 0] - 1)
if self.resize_to is None:
crop_dst = bbox_generator3d(
torch.tensor([0] * batch_size, device=_device, dtype=_dtype),
torch.tensor([0] * batch_size, device=_device, dtype=_dtype),
torch.tensor([0] * batch_size, device=_device, dtype=_dtype),
size[:, 2] - 1,
size[:, 1] - 1,
size[:, 0] - 1,
)
else:
if not (len(self.resize_to) == 3 and isinstance(
self.resize_to[0],
(int, )) and isinstance(self.resize_to[1], (int, ))
and isinstance(self.resize_to[2],
(int, )) and self.resize_to[0] > 0
and self.resize_to[1] > 0 and self.resize_to[2] > 0):
raise AssertionError(
f"`resize_to` must be a tuple of 3 positive integers. Got {self.resize_to}."
)
crop_dst = torch.tensor(
[[
[0, 0, 0],
[self.resize_to[-1] - 1, 0, 0],
[self.resize_to[-1] - 1, self.resize_to[-2] - 1, 0],
[0, self.resize_to[-2] - 1, 0],
[0, 0, self.resize_to[-3] - 1],
[self.resize_to[-1] - 1, 0, self.resize_to[-3] - 1],
[
self.resize_to[-1] - 1, self.resize_to[-2] - 1,
self.resize_to[-3] - 1
],
[0, self.resize_to[-2] - 1, self.resize_to[-3] - 1],
]],
device=_device,
dtype=_dtype,
).repeat(batch_size, 1, 1)
return dict(src=crop_src.to(device=_device),
dst=crop_dst.to(device=_device))
def random_rectangles_params_generator(
batch_size: int,
height: int,
width: int,
scale: torch.Tensor,
ratio: torch.Tensor,
value: float = 0.0,
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> 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.
scale (torch.Tensor): range of size of the origin size cropped. Shape (2).
ratio (torch.Tensor): range of aspect ratio of the origin aspect ratio cropped. Shape (2).
value (float): value to be filled in the erased area.
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.
- widths (torch.Tensor): element-wise erasing widths with a shape of (B,).
- heights (torch.Tensor): element-wise erasing heights with a shape of (B,).
- xs (torch.Tensor): element-wise erasing x coordinates with a shape of (B,).
- ys (torch.Tensor): element-wise erasing y coordinates with a shape of (B,).
- values (torch.Tensor): element-wise filling values 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)
_device, _dtype = _extract_device_dtype([ratio, 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}.")
if not (isinstance(value, (int, float)) and value >= 0 and value <= 1):
raise AssertionError(
f"'value' must be a number between 0 - 1. Got {value}.")
_joint_range_check(scale, 'scale', bounds=(0, float('inf')))
_joint_range_check(ratio, 'ratio', bounds=(0, float('inf')))
images_area = height * width
target_areas = (_adapted_uniform(
(batch_size, ),
scale[0].to(device=device, dtype=dtype),
scale[1].to(device=device, dtype=dtype),
same_on_batch,
) * images_area)
if ratio[0] < 1.0 and ratio[1] > 1.0:
aspect_ratios1 = _adapted_uniform(
(batch_size, ), ratio[0].to(device=device,
dtype=dtype), 1, same_on_batch)
aspect_ratios2 = _adapted_uniform(
(batch_size, ), 1, ratio[1].to(device=device,
dtype=dtype), same_on_batch)
if same_on_batch:
rand_idxs = (torch.round(
_adapted_uniform(
(1, ),
torch.tensor(0, device=device, dtype=dtype),
torch.tensor(1, device=device, dtype=dtype),
same_on_batch,
)).repeat(batch_size).bool())
else:
rand_idxs = torch.round(
_adapted_uniform(
(batch_size, ),
torch.tensor(0, device=device, dtype=dtype),
torch.tensor(1, device=device, dtype=dtype),
same_on_batch,
)).bool()
aspect_ratios = torch.where(rand_idxs, aspect_ratios1, aspect_ratios2)
else:
aspect_ratios = _adapted_uniform(
(batch_size, ),
ratio[0].to(device=device, dtype=dtype),
ratio[1].to(device=device, dtype=dtype),
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(1.0, device=device, dtype=dtype)),
torch.tensor(height, device=device, dtype=dtype),
)
widths = torch.min(
torch.max(torch.round((target_areas / aspect_ratios)**(1 / 2)),
torch.tensor(1.0, device=device, dtype=dtype)),
torch.tensor(width, device=device, dtype=dtype),
)
xs_ratio = _adapted_uniform(
(batch_size, ),
torch.tensor(0, device=device, dtype=dtype),
torch.tensor(1, device=device, dtype=dtype),
same_on_batch,
)
ys_ratio = _adapted_uniform(
(batch_size, ),
torch.tensor(0, device=device, dtype=dtype),
torch.tensor(1, device=device, dtype=dtype),
same_on_batch,
)
xs = xs_ratio * (torch.tensor(width, device=device, dtype=dtype) - widths +
1)
ys = ys_ratio * (torch.tensor(height, device=device, dtype=dtype) -
heights + 1)
return dict(
widths=widths.floor().to(device=_device, dtype=_dtype),
heights=heights.floor().to(device=_device, dtype=_dtype),
xs=xs.floor().to(device=_device, dtype=_dtype),
ys=ys.floor().to(device=_device, dtype=_dtype),
values=torch.tensor([value] * batch_size, device=_device,
dtype=_dtype),
)
def forward(
self,
batch_shape: torch.Size,
same_on_batch: bool = False
) -> Dict[str, torch.Tensor]: # type:ignore
batch_size = batch_shape[0]
height = batch_shape[-2]
width = batch_shape[-1]
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}."
)
_common_param_check(batch_size, same_on_batch)
_device, _dtype = _extract_device_dtype([self.ratio, self.scale])
images_area = height * width
target_areas = (_adapted_rsampling(
(batch_size, ), self.scale_sampler, same_on_batch).to(
device=_device, dtype=_dtype) * images_area)
if self.ratio[0] < 1.0 and self.ratio[1] > 1.0:
aspect_ratios1 = _adapted_rsampling(
(batch_size, ), self.ratio_sampler1, same_on_batch)
aspect_ratios2 = _adapted_rsampling(
(batch_size, ), self.ratio_sampler2, same_on_batch)
if same_on_batch:
rand_idxs = (torch.round(
_adapted_rsampling(
(1, ), self.index_sampler,
same_on_batch)).repeat(batch_size).bool())
else:
rand_idxs = torch.round(
_adapted_rsampling((batch_size, ), self.index_sampler,
same_on_batch)).bool()
aspect_ratios = torch.where(rand_idxs, aspect_ratios1,
aspect_ratios2)
else:
aspect_ratios = _adapted_rsampling(
(batch_size, ), self.ratio_sampler, same_on_batch)
aspect_ratios = aspect_ratios.to(device=_device, dtype=_dtype)
# 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(1.0, device=_device, dtype=_dtype)),
torch.tensor(height, device=_device, dtype=_dtype),
)
widths = torch.min(
torch.max(torch.round((target_areas / aspect_ratios)**(1 / 2)),
torch.tensor(1.0, device=_device, dtype=_dtype)),
torch.tensor(width, device=_device, dtype=_dtype),
)
xs_ratio = _adapted_rsampling((batch_size, ), self.uniform_sampler,
same_on_batch).to(device=_device,
dtype=_dtype)
ys_ratio = _adapted_rsampling((batch_size, ), self.uniform_sampler,
same_on_batch).to(device=_device,
dtype=_dtype)
xs = xs_ratio * (width - widths + 1)
ys = ys_ratio * (height - heights + 1)
return dict(
widths=widths.floor(),
heights=heights.floor(),
xs=xs.floor(),
ys=ys.floor(),
values=torch.tensor([self.value] * batch_size,
device=_device,
dtype=_dtype),
)
def random_crop_size_generator(
batch_size: int,
size: Tuple[int, int],
scale: torch.Tensor,
ratio: torch.Tensor,
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> Dict[str, torch.Tensor]:
r"""Get cropping heights and widths for ```crop``` transformation for resized crop transform.
Args:
batch_size (int): the tensor batch size.
size (Tuple[int, int]): expected output size of each edge.
scale (torch.Tensor): range of size of the origin size cropped with (2,) shape.
ratio (torch.Tensor): range of aspect ratio of the origin aspect ratio cropped with (2,) shape.
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.
- size (torch.Tensor): element-wise cropping sizes with a shape of (B, 2).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
Examples:
>>> _ = torch.manual_seed(42)
>>> random_crop_size_generator(3, (30, 30), scale=torch.tensor([.7, 1.3]), ratio=torch.tensor([.9, 1.]))
{'size': tensor([[29., 29.],
[27., 28.],
[26., 29.]])}
"""
_common_param_check(batch_size, same_on_batch)
_joint_range_check(scale, "scale")
_joint_range_check(ratio, "ratio")
if not (len(size) == 2 and type(size[0]) is int and size[1] > 0
and type(size[1]) is int and size[1] > 0):
raise AssertionError(
f"'height' and 'width' must be integers. Got {size}.")
_device, _dtype = _extract_device_dtype([scale, ratio])
if batch_size == 0:
return dict(size=torch.zeros([0, 2], device=_device, dtype=_dtype))
scale = scale.to(device=device, dtype=dtype)
ratio = ratio.to(device=device, dtype=dtype)
# 10 trails for each element
area = _adapted_uniform((batch_size, 10), scale[0] * size[0] * size[1],
scale[1] * size[0] * size[1], same_on_batch)
log_ratio = _adapted_uniform((batch_size, 10), torch.log(ratio[0]),
torch.log(ratio[1]), same_on_batch)
aspect_ratio = torch.exp(log_ratio)
w = torch.sqrt(area * aspect_ratio).round().floor()
h = torch.sqrt(area / aspect_ratio).round().floor()
# Element-wise w, h condition
cond = ((0 < w) * (w < size[0]) * (0 < h) * (h < size[1])).int()
# torch.argmax is not reproducible across devices: https://github.com/pytorch/pytorch/issues/17738
# Here, we will select the first occurrence of the duplicated elements.
cond_bool, argmax_dim1 = ((cond.cumsum(1) == 1) & cond.bool()).max(1)
h_out = w[torch.arange(0, batch_size, device=device, dtype=torch.long),
argmax_dim1]
w_out = h[torch.arange(0, batch_size, device=device, dtype=torch.long),
argmax_dim1]
if not cond_bool.all():
# Fallback to center crop
in_ratio = float(size[0]) / float(size[1])
if in_ratio < ratio.min():
h_ct = torch.tensor(size[0], device=device, dtype=dtype)
w_ct = torch.round(h_ct / ratio.min())
elif in_ratio > ratio.min():
w_ct = torch.tensor(size[1], device=device, dtype=dtype)
h_ct = torch.round(w_ct * ratio.min())
else: # whole image
h_ct = torch.tensor(size[0], device=device, dtype=dtype)
w_ct = torch.tensor(size[1], device=device, dtype=dtype)
h_ct = h_ct.floor()
w_ct = w_ct.floor()
h_out = h_out.where(cond_bool, h_ct)
w_out = w_out.where(cond_bool, w_ct)
return dict(size=torch.stack([h_out, w_out], dim=1).to(device=_device,
dtype=_dtype))
def center_crop_generator(
batch_size: int,
height: int,
width: int,
size: Tuple[int, int],
device: torch.device = torch.device('cpu')
) -> Dict[str, torch.Tensor]:
r"""Get parameters for ```center_crop``` transformation for center crop transform.
Args:
batch_size (int): the tensor batch size.
height (int) : height of the image.
width (int): width of the image.
size (tuple): Desired output size of the crop, like (h, w).
device (torch.device): the device on which the random numbers will be generated. Default: cpu.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- src (torch.Tensor): cropping bounding boxes with a shape of (B, 4, 2).
- dst (torch.Tensor): output bounding boxes with a shape (B, 4, 2).
Note:
No random number will be generated.
"""
_common_param_check(batch_size)
if not isinstance(size, (tuple, list)) and len(size) == 2:
raise ValueError(
f"Input size must be a tuple/list of length 2. Got {size}")
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}.")
if not (height >= size[0] and width >= size[1]):
raise AssertionError(
f"Crop size must be smaller than input size. Got ({height}, {width}) and {size}."
)
# unpack input sizes
dst_h, dst_w = size
src_h, src_w = height, width
# compute start/end offsets
dst_h_half = dst_h / 2
dst_w_half = dst_w / 2
src_h_half = src_h / 2
src_w_half = src_w / 2
start_x = int(src_w_half - dst_w_half)
start_y = int(src_h_half - dst_h_half)
end_x = start_x + dst_w - 1
end_y = start_y + dst_h - 1
# [y, x] origin
# top-left, top-right, bottom-right, bottom-left
points_src: torch.Tensor = torch.tensor(
[[[start_x, start_y], [end_x, start_y], [end_x, end_y],
[start_x, end_y]]],
device=device,
dtype=torch.long).expand(batch_size, -1, -1)
# [y, x] destination
# top-left, top-right, bottom-right, bottom-left
points_dst: torch.Tensor = torch.tensor(
[[[0, 0], [dst_w - 1, 0], [dst_w - 1, dst_h - 1], [0, dst_h - 1]]],
device=device,
dtype=torch.long).expand(batch_size, -1, -1)
_input_size = torch.tensor((height, width),
device=device,
dtype=torch.long).expand(batch_size, -1)
_output_size = torch.tensor(size, device=device,
dtype=torch.long).expand(batch_size, -1)
return dict(src=points_src,
dst=points_dst,
input_size=_input_size,
output_size=_output_size)
def random_motion_blur_generator(
batch_size: int,
kernel_size: Union[int, Tuple[int, int]],
angle: torch.Tensor,
direction: 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 motion blur.
Args:
batch_size (int): the tensor batch size.
kernel_size (int or (int, int)): motion kernel size (odd and positive) or range.
angle (torch.Tensor): angle of the motion blur in degrees (anti-clockwise rotation).
direction (torch.Tensor): forward/backward direction of the motion blur.
Lower values towards -1.0 will point the motion blur towards the back (with
angle provided via angle), while higher values towards 1.0 will point the motion
blur forward. A value of 0.0 leads to a uniformly (but still angled) motion blur.
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.
- ksize_factor (torch.Tensor): element-wise kernel size factors with a shape of (B,).
- angle_factor (torch.Tensor): element-wise angle factors with a shape of (B,).
- direction_factor (torch.Tensor): element-wise direction 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(angle, 'angle')
_joint_range_check(direction, 'direction', (-1, 1))
_device, _dtype = _extract_device_dtype([angle, direction])
if isinstance(kernel_size, int):
if not (kernel_size >= 3 and kernel_size % 2 == 1):
raise AssertionError(
f"`kernel_size` must be odd and greater than 3. Got {kernel_size}."
)
ksize_factor = torch.tensor([kernel_size] * batch_size,
device=device,
dtype=dtype)
elif isinstance(kernel_size, tuple):
# kernel_size is fixed across the batch
if len(kernel_size) != 2:
raise AssertionError(
f"`kernel_size` must be (2,) if it is a tuple. Got {kernel_size}."
)
ksize_factor = (_adapted_uniform((batch_size, ),
kernel_size[0] // 2,
kernel_size[1] // 2,
same_on_batch=True).int() * 2 + 1)
else:
raise TypeError(f"Unsupported type: {type(kernel_size)}")
angle_factor = _adapted_uniform((batch_size, ), angle[0].to(device=device,
dtype=dtype),
angle[1].to(device=device,
dtype=dtype), same_on_batch)
direction_factor = _adapted_uniform(
(batch_size, ),
direction[0].to(device=device, dtype=dtype),
direction[1].to(device=device, dtype=dtype),
same_on_batch,
)
return dict(
ksize_factor=ksize_factor.to(device=_device, dtype=torch.int32),
angle_factor=angle_factor.to(device=_device, dtype=_dtype),
direction_factor=direction_factor.to(device=_device, dtype=_dtype),
)
def forward(self, batch_shape: torch.Size, same_on_batch: bool = False) -> Dict[str, torch.Tensor]: # type:ignore
batch_size = batch_shape[0]
_common_param_check(batch_size, same_on_batch)
_device, _ = _extract_device_dtype([self.bits if isinstance(self.bits, torch.Tensor) else None])
bits_factor = _adapted_rsampling((batch_size,), self.bit_sampler, same_on_batch)
return dict(bits_factor=bits_factor.to(device=_device, dtype=torch.int32))
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),
)
def random_affine_generator(
batch_size: int,
height: int,
width: int,
degrees: torch.Tensor,
translate: Optional[torch.Tensor] = None,
scale: Optional[torch.Tensor] = None,
shear: 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 ``affine`` for a random affine transform.
Args:
batch_size (int): the tensor batch size.
height (int) : height of the image.
width (int): width of the image.
degrees (torch.Tensor): Range of degrees to select from like (min, max).
translate (tensor, optional): tuple of maximum absolute fraction for horizontal
and vertical translations. For example translate=(a, b), then horizontal shift
is randomly sampled in the range -img_width * a < dx < img_width * a and vertical shift is
randomly sampled in the range -img_height * b < dy < img_height * b. Will not translate by default.
scale (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 (tensor, optional): Range of degrees to select from.
Shear is a 2x2 tensor, a x-axis shear in (shear[0][0], shear[0][1]) and y-axis shear in
(shear[1][0], shear[1][1]) will be applied. Will not apply shear by default.
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.
- translations (torch.Tensor): element-wise translations with a shape of (B, 2).
- center (torch.Tensor): element-wise center with a shape of (B, 2).
- scale (torch.Tensor): element-wise scales with a shape of (B, 2).
- angle (torch.Tensor): element-wise rotation angles with a shape of (B,).
- sx (torch.Tensor): element-wise x-axis shears with a shape of (B,).
- sy (torch.Tensor): element-wise y-axis shears 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")
if not (isinstance(width, (int, )) and isinstance(height, (int, ))
and width > 0 and height > 0):
raise AssertionError(
f"`width` and `height` must be positive integers. Got {width}, {height}."
)
_device, _dtype = _extract_device_dtype([degrees, translate, scale, shear])
degrees = degrees.to(device=device, dtype=dtype)
angle = _adapted_uniform((batch_size, ), degrees[0], degrees[1],
same_on_batch)
angle = angle.to(device=_device, dtype=_dtype)
# compute tensor ranges
if scale is not None:
scale = scale.to(device=device, dtype=dtype)
if not (len(scale.shape) == 1 and len(scale) in (2, 4)):
raise AssertionError(
f"`scale` shall have 2 or 4 elements. Got {scale}.")
_joint_range_check(cast(torch.Tensor, scale[:2]), "scale")
_scale = _adapted_uniform((batch_size, ), scale[0], scale[1],
same_on_batch).unsqueeze(1).repeat(1, 2)
if len(scale) == 4:
_joint_range_check(cast(torch.Tensor, scale[2:]), "scale_y")
_scale[:, 1] = _adapted_uniform((batch_size, ), scale[2], scale[3],
same_on_batch)
_scale = _scale.to(device=_device, dtype=_dtype)
else:
_scale = torch.ones((batch_size, 2), device=_device, dtype=_dtype)
if translate is not None:
translate = translate.to(device=device, dtype=dtype)
if not (0.0 <= translate[0] <= 1.0 and 0.0 <= translate[1] <= 1.0
and translate.shape == torch.Size([2])):
raise AssertionError(
f"Expect translate contains two elements and ranges are in [0, 1]. Got {translate}."
)
max_dx: torch.Tensor = translate[0] * width
max_dy: torch.Tensor = 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,
)
translations = translations.to(device=_device, dtype=_dtype)
else:
translations = torch.zeros((batch_size, 2),
device=_device,
dtype=_dtype)
center: torch.Tensor = torch.tensor(
[width, height], device=_device, dtype=_dtype).view(1, 2) / 2.0 - 0.5
center = center.expand(batch_size, -1)
if shear is not None:
shear = shear.to(device=device, dtype=dtype)
_joint_range_check(cast(torch.Tensor, shear)[0], "shear")
_joint_range_check(cast(torch.Tensor, shear)[1], "shear")
sx = _adapted_uniform((batch_size, ), shear[0][0], shear[0][1],
same_on_batch)
sy = _adapted_uniform((batch_size, ), shear[1][0], shear[1][1],
same_on_batch)
sx = sx.to(device=_device, dtype=_dtype)
sy = sy.to(device=_device, dtype=_dtype)
else:
sx = sy = torch.tensor([0] * batch_size, device=_device, dtype=_dtype)
return dict(translations=translations,
center=center,
scale=_scale,
angle=angle,
sx=sx,
sy=sy)
def random_crop_generator(
batch_size: int,
input_size: Tuple[int, int],
size: Union[Tuple[int, int], torch.Tensor],
resize_to: Optional[Tuple[int, int]] = 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 ```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).
If tensor, it must be (B, 2).
resize_to (tuple): Desired output size of the crop, like (h, w). If None, no resize will be performed.
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.
- src (torch.Tensor): cropping bounding boxes with a shape of (B, 4, 2).
- dst (torch.Tensor): output bounding boxes with a shape (B, 4, 2).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
Example:
>>> _ = torch.manual_seed(0)
>>> crop_size = torch.tensor([[25, 28], [27, 29], [26, 28]])
>>> random_crop_generator(3, (30, 30), size=crop_size, same_on_batch=False)
{'src': tensor([[[ 1., 0.],
[28., 0.],
[28., 24.],
[ 1., 24.]],
<BLANKLINE>
[[ 1., 1.],
[29., 1.],
[29., 27.],
[ 1., 27.]],
<BLANKLINE>
[[ 0., 3.],
[27., 3.],
[27., 28.],
[ 0., 28.]]]), 'dst': tensor([[[ 0., 0.],
[27., 0.],
[27., 24.],
[ 0., 24.]],
<BLANKLINE>
[[ 0., 0.],
[28., 0.],
[28., 26.],
[ 0., 26.]],
<BLANKLINE>
[[ 0., 0.],
[27., 0.],
[27., 25.],
[ 0., 25.]]]), 'input_size': tensor([[30, 30],
[30, 30],
[30, 30]])}
"""
_common_param_check(batch_size, same_on_batch)
_device, _dtype = _extract_device_dtype(
[size if isinstance(size, torch.Tensor) else None])
# Use float point instead
_dtype = _dtype if _dtype in [torch.float16, torch.float32, torch.float64
] else dtype
if not isinstance(size, torch.Tensor):
size = torch.tensor(size, device=_device,
dtype=_dtype).repeat(batch_size, 1)
else:
size = size.to(device=_device, dtype=_dtype)
if size.shape != torch.Size([batch_size, 2]):
raise AssertionError(
"If `size` is a tensor, it must be shaped as (B, 2). "
f"Got {size.shape} while expecting {torch.Size([batch_size, 2])}.")
if not (input_size[0] > 0 and input_size[1] > 0 and (size > 0).all()):
raise AssertionError(
f"Got non-positive input size or size. {input_size}, {size}.")
size = size.floor()
x_diff = input_size[1] - size[:, 1] + 1
y_diff = input_size[0] - size[:, 0] + 1
# Start point will be 0 if diff < 0
x_diff = x_diff.clamp(0)
y_diff = y_diff.clamp(0)
if batch_size == 0:
return dict(
src=torch.zeros([0, 4, 2], device=_device, dtype=_dtype),
dst=torch.zeros([0, 4, 2], device=_device, dtype=_dtype),
)
if same_on_batch:
# If same_on_batch, select the first then repeat.
x_start = _adapted_uniform((batch_size, ), 0,
x_diff[0].to(device=device, dtype=dtype),
same_on_batch).floor()
y_start = _adapted_uniform((batch_size, ), 0,
y_diff[0].to(device=device, dtype=dtype),
same_on_batch).floor()
else:
x_start = _adapted_uniform((1, ), 0,
x_diff.to(device=device, dtype=dtype),
same_on_batch).floor()
y_start = _adapted_uniform((1, ), 0,
y_diff.to(device=device, dtype=dtype),
same_on_batch).floor()
crop_src = bbox_generator(
x_start.view(-1).to(device=_device, dtype=_dtype),
y_start.view(-1).to(device=_device, dtype=_dtype),
torch.where(size[:, 1] == 0,
torch.tensor(input_size[1], device=_device, dtype=_dtype),
size[:, 1]),
torch.where(size[:, 0] == 0,
torch.tensor(input_size[0], device=_device, dtype=_dtype),
size[:, 0]),
)
if resize_to is None:
crop_dst = bbox_generator(
torch.tensor([0] * batch_size, device=_device, dtype=_dtype),
torch.tensor([0] * batch_size, device=_device, dtype=_dtype),
size[:, 1],
size[:, 0],
)
else:
if not (len(resize_to) == 2 and isinstance(resize_to[0], (int, )) and
isinstance(resize_to[1],
(int, )) and resize_to[0] > 0 and resize_to[1] > 0):
raise AssertionError(
f"`resize_to` must be a tuple of 2 positive integers. Got {resize_to}."
)
crop_dst = torch.tensor(
[[[0, 0], [resize_to[1] - 1, 0],
[resize_to[1] - 1, resize_to[0] - 1], [0, resize_to[0] - 1]]],
device=_device,
dtype=_dtype,
).repeat(batch_size, 1, 1)
_input_size = torch.tensor(input_size, device=_device,
dtype=torch.long).expand(batch_size, -1)
return dict(src=crop_src, dst=crop_dst, input_size=_input_size)
def forward(self,
batch_shape: torch.Size,
same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
batch_size = batch_shape[0]
height = batch_shape[-2]
width = batch_shape[-1]
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}."
)
_device, _dtype = _extract_device_dtype([self.beta, self.cut_size])
_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),
)
with torch.no_grad():
batch_probs: torch.Tensor = _adapted_sampling(
(batch_size * self.num_mix, ), self.prob_sampler,
same_on_batch)
mix_pairs: torch.Tensor = torch.rand(self.num_mix,
batch_size,
device=_device,
dtype=_dtype).argsort(dim=1)
cutmix_betas: torch.Tensor = _adapted_rsampling(
(batch_size * self.num_mix, ), self.beta_sampler, 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, self._cut_size[0]),
self._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: torch.Tensor = _adapted_rsampling(
_gen_shape, self.rand_sampler,
same_on_batch) * (width - cut_width - 1)
y_start: torch.Tensor = _adapted_rsampling(
_gen_shape, self.rand_sampler,
same_on_batch) * (height - cut_height - 1)
x_start = x_start.floor().to(device=_device, dtype=_dtype)
y_start = y_start.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(self.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),
)
def forward(
self,
batch_shape: torch.Size,
same_on_batch: bool = False
) -> Dict[str, torch.Tensor]: # type: ignore
batch_size = batch_shape[0]
height = batch_shape[-2]
width = batch_shape[-1]
_device, _dtype = _extract_device_dtype(
[self.degrees, self.translate, self.scale, self.shear])
_common_param_check(batch_size, same_on_batch)
if not (isinstance(width, (int, )) and isinstance(height, (int, ))
and width > 0 and height > 0):
raise AssertionError(
f"`width` and `height` must be positive integers. Got {width}, {height}."
)
angle = _adapted_rsampling((batch_size, ), self.degree_sampler,
same_on_batch).to(device=_device,
dtype=_dtype)
# compute tensor ranges
if self.scale_2_sampler is not None:
_scale = _adapted_rsampling(
(batch_size, ), self.scale_2_sampler,
same_on_batch).unsqueeze(1).repeat(1, 2)
if self.scale_4_sampler is not None:
_scale[:, 1] = _adapted_rsampling(
(batch_size, ), self.scale_4_sampler, same_on_batch)
_scale = _scale.to(device=_device, dtype=_dtype)
else:
_scale = torch.ones((batch_size, 2), device=_device, dtype=_dtype)
if self.translate_x_sampler is not None and self.translate_y_sampler is not None:
translations = torch.stack(
[
_adapted_rsampling(
(batch_size, ), self.translate_x_sampler,
same_on_batch) * width,
_adapted_rsampling(
(batch_size, ), self.translate_y_sampler,
same_on_batch) * height,
],
dim=-1,
)
translations = translations.to(device=_device, dtype=_dtype)
else:
translations = torch.zeros((batch_size, 2),
device=_device,
dtype=_dtype)
center: torch.Tensor = torch.tensor(
[width, height], device=_device, dtype=_dtype).view(1,
2) / 2.0 - 0.5
center = center.expand(batch_size, -1)
if self.shear_x_sampler is not None and self.shear_y_sampler is not None:
sx = _adapted_rsampling((batch_size, ), self.shear_x_sampler,
same_on_batch)
sy = _adapted_rsampling((batch_size, ), self.shear_y_sampler,
same_on_batch)
sx = sx.to(device=_device, dtype=_dtype)
sy = sy.to(device=_device, dtype=_dtype)
else:
sx = sy = torch.tensor([0] * batch_size,
device=_device,
dtype=_dtype)
return dict(translations=translations,
center=center,
scale=_scale,
angle=angle,
sx=sx,
sy=sy)
