def test_shape(self, input_data, expected_shape):
if input_data["model"] == "densenet2d":
model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3)
if input_data["model"] == "densenet3d":
model = DenseNet(spatial_dims=3,
in_channels=1,
out_channels=3,
init_features=2,
growth_rate=2,
block_config=(6, ))
if input_data["model"] == "senet2d":
model = SEResNet50(spatial_dims=2, in_channels=3, num_classes=4)
if input_data["model"] == "senet3d":
model = SEResNet50(spatial_dims=3, in_channels=3, num_classes=4)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()
cam = GradCAM(nn_module=model,
target_layers=input_data["target_layers"])
image = torch.rand(input_data["shape"], device=device)
result = cam(x=image, layer_idx=-1)
np.testing.assert_array_equal(cam.nn_module.class_idx.cpu(),
model(image).max(1)[-1].cpu())
fea_shape = cam.feature_map_size(input_data["shape"], device=device)
self.assertTupleEqual(fea_shape, input_data["feature_shape"])
self.assertTupleEqual(result.shape, expected_shape)
# check result is same whether class_idx=None is used or not
result2 = cam(x=image,
layer_idx=-1,
class_idx=model(image).max(1)[-1].cpu())
np.testing.assert_array_almost_equal(result, result2)
def test_shape(self, input_data, expected_shape):
if input_data["model"] == "densenet2d":
model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3)
if input_data["model"] == "densenet3d":
model = DenseNet(spatial_dims=3,
in_channels=1,
out_channels=3,
init_features=2,
growth_rate=2,
block_config=(6, ))
if input_data["model"] == "senet2d":
model = SEResNet50(spatial_dims=2, in_channels=3, num_classes=4)
if input_data["model"] == "senet3d":
model = SEResNet50(spatial_dims=3, in_channels=3, num_classes=4)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()
cam = CAM(nn_module=model,
target_layers=input_data["target_layers"],
fc_layers=input_data["fc_layers"])
image = torch.rand(input_data["shape"], device=device)
result = cam(x=image, layer_idx=-1)
fea_shape = cam.feature_map_size(input_data["shape"], device=device)
self.assertTupleEqual(fea_shape, input_data["feature_shape"])
self.assertTupleEqual(result.shape, expected_shape)
def test_shape(self, cam_name):
model = DenseNet(spatial_dims=3,
in_channels=1,
out_channels=3,
init_features=2,
growth_rate=2,
block_config=(6, ))
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()
image = torch.rand((2, 1, 6, 6, 6), device=device)
target_layer = "class_layers.relu"
fc_layer = "class_layers.out"
if cam_name == "CAM":
inferer = SaliencyInferer(cam_name,
target_layer,
None,
fc_layer,
upsampler=default_upsampler)
result = inferer(inputs=image, network=model, layer_idx=-1)
else:
inferer = SaliencyInferer(cam_name,
target_layer,
None,
upsampler=default_upsampler)
result = inferer(image, model, -1, retain_graph=False)
self.assertTupleEqual(result.shape, (2, 1, 6, 6, 6))
out_channels_3d = 3
model_2d = DenseNet121(spatial_dims=2,
in_channels=1,
out_channels=out_channels_2d).to(device)
model_2d_2c = DenseNet121(spatial_dims=2,
in_channels=2,
out_channels=out_channels_2d).to(device)
model_3d = DenseNet(spatial_dims=3,
in_channels=1,
out_channels=out_channels_3d,
init_features=2,
growth_rate=2,
block_config=(6, )).to(device)
model_2d.eval()
model_2d_2c.eval()
model_3d.eval()
# 2D w/ bounding box
TEST_CASE_0 = [
{
"nn_module": model_2d
},
{
"x": torch.rand(1, 1, 48, 64).to(device),
"b_box": [-1, -1, 2, 40, 1, 62]
},
(1, 1, 39, 62, out_channels_2d),
(1, 1, 39, 62),
]
# 3D w/ bounding box and stride
TEST_CASE_1 = [