(self, X)
| 1118 | ) |
| 1119 | |
| 1120 | def forward(self, X): |
| 1121 | # (N, H, W, C) -> (N, C, H, W) |
| 1122 | self.X = np.moveaxis(X, [0, 1, 2, 3], [0, -2, -1, -3]) |
| 1123 | if not isinstance(self.X, torch.Tensor): |
| 1124 | self.X = torchify(self.X) |
| 1125 | |
| 1126 | self.X.retain_grad() |
| 1127 | self.Y = self.layer1(self.X) |
| 1128 | self.Y.retain_grad() |
| 1129 | return self.Y |
| 1130 | |
| 1131 | def extract_grads(self, X): |
| 1132 | self.forward(X) |
no test coverage detected