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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from kornia.geometry.transform import rotate | |
| class LearnableSpatialTransformWrapper(nn.Module): | |
| def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True): | |
| super().__init__() | |
| self.impl = impl | |
| self.angle = torch.rand(1) * angle_init_range | |
| if train_angle: | |
| self.angle = nn.Parameter(self.angle, requires_grad=True) | |
| self.pad_coef = pad_coef | |
| def forward(self, x): | |
| if torch.is_tensor(x): | |
| return self.inverse_transform(self.impl(self.transform(x)), x) | |
| elif isinstance(x, tuple): | |
| x_trans = tuple(self.transform(elem) for elem in x) | |
| y_trans = self.impl(x_trans) | |
| return tuple(self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x)) | |
| else: | |
| raise ValueError(f'Unexpected input type {type(x)}') | |
| def transform(self, x): | |
| height, width = x.shape[2:] | |
| pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) | |
| x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode='reflect') | |
| x_padded_rotated = rotate(x_padded, angle=self.angle.to(x_padded)) | |
| return x_padded_rotated | |
| def inverse_transform(self, y_padded_rotated, orig_x): | |
| height, width = orig_x.shape[2:] | |
| pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) | |
| y_padded = rotate(y_padded_rotated, angle=-self.angle.to(y_padded_rotated)) | |
| y_height, y_width = y_padded.shape[2:] | |
| y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w] | |
| return y | |
| if __name__ == '__main__': | |
| layer = LearnableSpatialTransformWrapper(nn.Identity()) | |
| x = torch.arange(2* 3 * 15 * 15).view(2, 3, 15, 15).float() | |
| y = layer(x) | |
| assert x.shape == y.shape | |
| assert torch.allclose(x[:, :, 1:, 1:][:, :, :-1, :-1], y[:, :, 1:, 1:][:, :, :-1, :-1]) | |
| print('all ok') | |