def test_context_stack(mocker):
"""Check that the context stack is pushed and popped correctly."""
# Default is cpu
assert_that(midnite.get_device()).is_equal_to(torch.device("cpu"))
# Patch out torch
mock_device = mocker.Mock(spec=torch.device)
mocker.patch("torch.device", return_value=mock_device)
# Context
with midnite.device("cuda:0"):
torch.device.assert_called_once_with("cuda:0")
torch.device.reset_mock()
assert_that(midnite.get_device()).is_equal_to(mock_device)
with midnite.device("cuda:1"):
torch.device.assert_called_once_with("cuda:1")
assert_that(midnite.get_device()).is_equal_to(mock_device)
assert_that(midnite.get_device()).is_not_equal_to(mock_device)
#
# The input image is split spatially (`SpatialSplit`), i.e. we _stride_ through the the spatial positions, doing a measurement at every step. The _chunk size_ controls which parts of the image are occluded.
#
# ## Paramters: Chunk Size and Stride
# To be generic for any split, _chunk size_ and _stride_ are three-dimensional tuples for (depth, height, width). In our example, the depth dimension is irrelevant, since our split is spatial.
#
# A small _chunk size_ creates a nosiy, fine-grained heatmap, as only small parts of the image features are occluded. On the other hand, a larger chunk size rather shows which _areas_ are important.
# In[3]:
import midnite
from midnite.visualization.base import *
# Use 'cpu' if you have no GPU available
with midnite.device('cuda:0'):
show_heatmap(Occlusion(
alexnet,
SplitSelector(NeuronSplit(), [283]),
SpatialSplit(),
chunk_size=(1, 3, 3),
stride=(1, 10, 10),
).visualize(example_img))
show_heatmap(Occlusion(
alexnet,
SplitSelector(NeuronSplit(), [283]),
SpatialSplit(),
chunk_size=(1, 15, 15),
stride=(1, 10, 10),
).visualize(example_img))
# ### GradCAM
# Shows which spatial locations had influence on the most important channels for classification.
# In[6]:
gc_heatmap = gradcam(features, classifier, example_img)
show_heatmap(gc_heatmap, scale=1.2)
# ### Occlusion
# Shows which parts of the image are crucial for correct classification. Requires much more computation time since it's not gradient-based, but should be more robust to noise than other methods. Read more about it in the [Occlusion notebook](https://luminovo.gitlab.io/public/midnite/latest/notebooks/details_occlusion.html).
# In[7]:
# does not run in short time without GPU
with midnite.device("cuda:0"):
oc_heatmap = occlusion(net, example_img)
show_heatmap(oc_heatmap, scale=1.2)
# ### Class Visualization
# Generates an image that maximally excites the network for a given class. This is an optimization of the network's gradient in image space and thus requires some computation power.
#
# However, since there is no defined "goal" for visual clarity, it is usually necessary to manually tune the optimization parameters. This can be done by using the [Max Mean Activation](https://luminovo.gitlab.io/public/midnite/latest/notebooks/details_max_mean_activation.html) building block.
# In[8]:
# does not run in short time without GPU
with midnite.device("cuda:0"):
cv_img = class_visualization(net, 283)
def test_context_fail_early():
"""Checks that creating a context with invalid device immediately fails"""
with pytest.raises(RuntimeError):
with midnite.device("does not exist"):
pass
# ### Step 4: Calculate Uncertainties
#
# Correct label for in-distribution image: 283
# In[5]:
import torch
import midnite
import tabulate
# Run without gradients, on cpu (use cuda:0 instead if you have a gpu available)
with torch.no_grad():
with midnite.device("cpu"):
id_pr = alexnet_ensemble(id_example)
ood_pr = alexnet_ensemble(ood_example)
rand_pr = alexnet_ensemble(random_example)
# Print pretty table
table = [
["max prediction", id_pr[0].argmax(),
ood_pr[0].argmax(), rand_pr[0].argmax()],
["max probability", f"{id_pr[0].max():.3f}",
f"{ood_pr[0].max():.3f}", f"{rand_pr[0].max():.3f}"],
["pred. entropy (~total uncert.)", f"{id_pr[1].sum():.3f}",
f"{ood_pr[1].sum():.3f}", f"{rand_pr[1].sum():.3f}"],
["mutual info. (~model uncert.)", f"{id_pr[2].sum():.3f}",
f"{ood_pr[2].sum():.3f}", f"{rand_pr[2].sum():.3f}"]
]