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fixes tutorial issue 183 #2031

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Merged
ericspod merged 2 commits into from
Apr 19, 2021
Merged

fixes tutorial issue 183 #2031

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53436c9
fixes docstring
wyli Apr 18, 2021
51bd4f6
update example
wyli Apr 18, 2021
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8 changes: 4 additions & 4 deletions monai/visualize/occlusion_sensitivity.py
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Original file line number Diff line number Diff line change
Expand Up @@ -127,15 +127,15 @@ class OcclusionSensitivity:

model_2d = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3)
occ_sens = OcclusionSensitivity(nn_module=model_2d)
occ_map, most_probable_class = occ_sens(x=torch.rand((1, 1, 48, 64)), class_idx=None, b_box=[-1, -1, 2, 40, 1, 62])
occ_map, most_probable_class = occ_sens(x=torch.rand((1, 1, 48, 64)), b_box=[-1, -1, 2, 40, 1, 62])

# densenet 3d
from monai.networks.nets import DenseNet
from monai.visualize import OcclusionSensitivity

model_3d = DenseNet(spatial_dims=3, in_channels=1, out_channels=3, init_features=2, growth_rate=2, block_config=(6,))
occ_sens = OcclusionSensitivity(nn_module=model_3d, n_batch=10, stride=2)
occ_map, most_probable_class = occ_sens(torch.rand(1, 1, 6, 6, 6), class_idx=1, b_box=[-1, -1, 2, 3, -1, -1, -1, -1])
occ_sens = OcclusionSensitivity(nn_module=model_3d, n_batch=10, stride=3)
occ_map, most_probable_class = occ_sens(torch.rand(1, 1, 6, 6, 6), b_box=[-1, -1, 1, 3, -1, -1, -1, -1])

See Also:

Expand Down Expand Up @@ -187,7 +187,7 @@ def _compute_occlusion_sensitivity(self, x, b_box):
# Get the number of prediction classes
num_classes = self.nn_module(x).numel()

#  If pad val not supplied, get the mean of the image
# If pad val not supplied, get the mean of the image
pad_val = x.mean() if self.pad_val is None else self.pad_val

# List containing a batch of images to be inferred
Expand Down