def apply_transform( # type: ignore
self, input: torch.Tensor, label: torch.Tensor, params: Dict[str, torch.Tensor] # type: ignore
) -> Tuple[torch.Tensor, torch.Tensor]:
height, width = input.size(2), input.size(3)
num_mixes = params['mix_pairs'].size(0)
batch_size = params['mix_pairs'].size(1)
_shape_validation(params['mix_pairs'], [num_mixes, batch_size], 'mix_pairs')
_shape_validation(params['crop_src'], [num_mixes, batch_size, 4, 2], 'crop_src')
out_inputs = input.clone()
out_labels = []
for pair, crop in zip(params['mix_pairs'], params['crop_src']):
input_permute = input.index_select(dim=0, index=pair.to(input.device))
labels_permute = label.index_select(dim=0, index=pair.to(label.device))
w, h = infer_box_shape(crop)
lam = w.to(input.dtype) * h.to(input.dtype) / (width * height) # width_beta * height_beta
# compute mask to match input shape
mask = bbox_to_mask(crop, width, height).bool().unsqueeze(dim=1).repeat(1, input.size(1), 1, 1)
out_inputs[mask] = input_permute[mask]
out_labels.append(
torch.stack([label.to(input.dtype), labels_permute.to(input.dtype), lam.to(label.device)], dim=1)
)
return out_inputs, torch.stack(out_labels, dim=0)
def apply_cutmix(
input: torch.Tensor, labels: torch.Tensor,
params: Dict[str, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
r"""Apply cutmix to images in a batch.
CutMix augmentation strategy: patches are cut and pasted among training images where the ground
truth labels are also mixed proportionally to the area of the patches.
Args:
input (torch.Tensor): Tensor to be transformed with shape (H, W), (C, H, W), (B, C, H, W).
labels (torch.Tensor): Label tensor with shape (B,).
params (Dict[str, torch.Tensor]):
- params['mix_pairs']: Mixup indexes with shape (num_mixes, B).
- params['crop_src']: Lambda for the mixup strength (num_mixes, B, 4, 2).
Returns:
Tuple[torch.Tensor, torch.Tensor]:
- Adjusted image, shape of :math:`(B, C, H, W)`.
- Corresponding labels and lambdas for each mix, shape of :math:`(num_mixes, B, 2)`.
Examples:
>>> input = torch.stack([torch.zeros(1, 5, 5), torch.ones(1, 5, 5)], dim=0)
>>> labels = torch.tensor([0, 1])
>>> params = {'mix_pairs': torch.tensor([[1, 0]]), 'crop_src': torch.tensor([[[
... [1., 1.],
... [2., 1.],
... [2., 2.],
... [1., 2.]],
... [[1., 1.],
... [3., 1.],
... [3., 2.],
... [1., 2.]]]])}
>>> apply_cutmix(input, labels, params)
(tensor([[[[0., 0., 0., 0., 0.],
[0., 1., 1., 0., 0.],
[0., 1., 1., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.]]],
<BLANKLINE>
<BLANKLINE>
[[[1., 1., 1., 1., 1.],
[1., 0., 0., 0., 1.],
[1., 0., 0., 0., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]]]]), tensor([[[0.0000, 1.0000, 0.1600],
[1.0000, 0.0000, 0.2400]]]))
"""
height, width = input.size(2), input.size(3)
num_mixes = params['mix_pairs'].size(0)
batch_size = params['mix_pairs'].size(1)
_shape_validation(params['mix_pairs'], [num_mixes, batch_size],
'mix_pairs')
_shape_validation(params['crop_src'], [num_mixes, batch_size, 4, 2],
'crop_src')
out_inputs = input.clone()
out_labels = []
for pair, crop in zip(params['mix_pairs'], params['crop_src']):
input_permute = input.index_select(dim=0, index=pair.to(input.device))
labels_permute = labels.index_select(dim=0,
index=pair.to(labels.device))
w, h = infer_box_shape(crop)
lam = w.to(input.dtype) * h.to(input.dtype) / (
width * height) # width_beta * height_beta
# compute mask to match input shape
mask = bbox_to_mask(crop, width,
height).bool().unsqueeze(dim=1).repeat(
1, input.size(1), 1, 1)
out_inputs[mask] = input_permute[mask]
out_labels.append(
torch.stack([
labels.to(input.dtype),
labels_permute.to(input.dtype),
lam.to(labels.device)
],
dim=1))
return out_inputs, torch.stack(out_labels, dim=0)