(self, is_ref=True, reverse_indexing=False)
| 31 | """ |
| 32 | |
| 33 | def __init__(self, is_ref=True, reverse_indexing=False): |
| 34 | super().__init__() |
| 35 | self.is_ref = is_ref |
| 36 | self.localization = nn.Sequential( |
| 37 | nn.Conv2d(1, 8, kernel_size=7), |
| 38 | nn.MaxPool2d(2, stride=2), |
| 39 | nn.ReLU(True), |
| 40 | nn.Conv2d(8, 10, kernel_size=5), |
| 41 | nn.MaxPool2d(2, stride=2), |
| 42 | nn.ReLU(True), |
| 43 | ) |
| 44 | # Regressor for the 3 * 2 affine matrix |
| 45 | self.fc_loc = nn.Sequential(nn.Linear(10 * 3 * 3, 32), nn.ReLU(True), nn.Linear(32, 3 * 2)) |
| 46 | # Initialize the weights/bias with identity transformation |
| 47 | self.fc_loc[2].weight.data.zero_() |
| 48 | self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float)) |
| 49 | if not self.is_ref: |
| 50 | self.xform = AffineTransform(align_corners=False, normalized=True, reverse_indexing=reverse_indexing) |
| 51 | |
| 52 | # Spatial transformer network forward function |
| 53 | def stn_ref(self, x): |
nothing calls this directly
no test coverage detected