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, 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 setUp(self):
self.original_urls = se_mod.SE_NET_MODELS.copy()
replace_url = test_is_quick()
if not replace_url:
try:
SEResNet50(pretrained=True, spatial_dims=2, in_channels=3, num_classes=2)
except OSError as rt_e:
print(rt_e)
if "certificate" in str(rt_e): # [SSL: CERTIFICATE_VERIFY_FAILED]
replace_url = True
if replace_url:
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data")
testing_data_urls = {
"senet154": {
"url": testing_data_config("models", "senet154-c7b49a05", "url"),
"filename": "senet154-c7b49a05.pth",
},
"se_resnet50": {
"url": testing_data_config("models", "se_resnet50-ce0d4300", "url"),
"filename": "se_resnet50-ce0d4300.pth",
},
"se_resnet101": {
"url": testing_data_config("models", "se_resnet101-7e38fcc6", "url"),
"filename": "se_resnet101-7e38fcc6.pth",
},
"se_resnet152": {
"url": testing_data_config("models", "se_resnet152-d17c99b7", "url"),
"filename": "se_resnet152-d17c99b7.pth",
},
"se_resnext50_32x4d": {
"url": testing_data_config("models", "se_resnext50_32x4d-a260b3a4", "url"),
"filename": "se_resnext50_32x4d-a260b3a4.pth",
},
"se_resnext101_32x4d": {
"url": testing_data_config("models", "se_resnext101_32x4d-3b2fe3d8", "url"),
"filename": "se_resnext101_32x4d-3b2fe3d8.pth",
},
}
for item in testing_data_urls:
testing_data_urls[item]["filename"] = os.path.join(testing_dir, testing_data_urls[item]["filename"])
se_mod.SE_NET_MODELS = testing_data_urls
import unittest
import torch
from parameterized import parameterized
from monai.networks.nets import DenseNet, DenseNet121, SEResNet50
from monai.visualize import GuidedBackpropGrad, GuidedBackpropSmoothGrad, SmoothGrad, VanillaGrad
DENSENET2D = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3)
DENSENET3D = DenseNet(spatial_dims=3,
in_channels=1,
out_channels=3,
init_features=2,
growth_rate=2,
block_config=(6, ))
SENET2D = SEResNet50(spatial_dims=2, in_channels=3, num_classes=4)
SENET3D = SEResNet50(spatial_dims=3, in_channels=3, num_classes=4)
TESTS = []
for type in (VanillaGrad, SmoothGrad, GuidedBackpropGrad,
GuidedBackpropSmoothGrad):
# 2D densenet
TESTS.append([type, DENSENET2D, (1, 1, 48, 64), (1, 1, 48, 64)])
# 3D densenet
TESTS.append([type, DENSENET3D, (1, 1, 6, 6, 6), (1, 1, 6, 6, 6)])
# 2D senet
TESTS.append([type, SENET2D, (1, 3, 64, 64), (1, 1, 64, 64)])
# 3D senet
TESTS.append([type, SENET3D, (1, 3, 8, 8, 48), (1, 1, 8, 8, 48)])