(self, cam_name)
| 31 | |
| 32 | @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) |
| 33 | def test_shape(self, cam_name): |
| 34 | model = DenseNet( |
| 35 | spatial_dims=3, in_channels=1, out_channels=3, init_features=2, growth_rate=2, block_config=(6,) |
| 36 | ) |
| 37 | device = "cuda:0" if torch.cuda.is_available() else "cpu" |
| 38 | model.to(device) |
| 39 | model.eval() |
| 40 | |
| 41 | image = torch.rand((2, 1, 6, 6, 6), device=device) |
| 42 | target_layer = "class_layers.relu" |
| 43 | fc_layer = "class_layers.out" |
| 44 | if cam_name == "CAM": |
| 45 | inferer = SaliencyInferer(cam_name, target_layer, None, fc_layer, upsampler=default_upsampler) |
| 46 | result = inferer(inputs=image, network=model, layer_idx=-1) |
| 47 | else: |
| 48 | inferer = SaliencyInferer(cam_name, target_layer, None, upsampler=default_upsampler) |
| 49 | result = inferer(image, model, -1, retain_graph=False) |
| 50 | |
| 51 | self.assertTupleEqual(result.shape, (2, 1, 6, 6, 6)) |
| 52 | |
| 53 | |
| 54 | if __name__ == "__main__": |
nothing calls this directly
no test coverage detected