def test_register_hooks_to_relu_layers(mocker, available_models):
print()
for name, model_module in available_models:
print(f'Testing model: {name}', end='\r')
stdout.write('\x1b[2K')
model = model_module()
relu_layers = find_relu_layers(model, nn.ReLU)
for layer in relu_layers:
mocker.spy(layer, 'register_forward_hook')
mocker.spy(layer, 'register_backward_hook')
backprop = Backprop(model)
target_class = 5
input_ = torch.zeros([1, 3, 224, 224])
if 'inception' in name:
input_ = torch.zeros([1, 3, 299, 299])
make_mock_output(mocker, model, target_class)
backprop.calculate_gradients(input_, target_class, guided=True)
for layer in relu_layers:
layer.register_forward_hook.assert_called_once()
layer.register_backward_hook.assert_called_once()
def test_checks_input_size_for_inception_model(mocker):
with pytest.raises(ValueError) as error:
model = models.inception_v3()
backprop = Backprop(model)
target_class = 5
input_ = torch.zeros([1, 3, 224, 224])
backprop.calculate_gradients(input_, target_class)
assert 'Image must be 299x299 for Inception models.' in str(error.value)
def test_handle_binary_classifier(mocker, model):
backprop = Backprop(model)
target_class = 0
input_ = torch.zeros([1, 3, 224, 224])
mock_output = torch.tensor([0.8])
mock_output.requires_grad = True
mocker.patch.object(model, 'forward', return_value=mock_output)
backprop.calculate_gradients(input_, target_class)
def test_warn_when_prediction_is_wrong(mocker, model):
backprop = Backprop(model)
top_class = torch.tensor(1)
target_class = 5
input_ = torch.zeros([1, 3, 224, 224])
make_mock_output(mocker, model, top_class)
with pytest.warns(UserWarning):
backprop.calculate_gradients(input_, target_class)
def test_zero_out_gradients(mocker, model):
backprop = Backprop(model)
mocker.spy(model, 'zero_grad')
target_class = 5
input_ = torch.zeros([1, 3, 224, 224])
make_mock_output(mocker, model, target_class)
backprop.calculate_gradients(input_, target_class)
model.zero_grad.assert_called_once()
def test_calc_gradients_of_top_class_if_target_not_provided(mocker, model):
backprop = Backprop(model)
top_class = 5
input_ = torch.zeros([1, 3, 224, 224])
target = make_expected_gradient_target(top_class)
mock_output = make_mock_output(mocker, model, top_class)
backprop.calculate_gradients(input_)
args, kwargs = mock_output.backward.call_args
assert torch.all(kwargs['gradient'].eq(target))
def test_handle_greyscale_input(mocker, model_grayscale):
backprop = Backprop(model_grayscale)
input_ = torch.zeros([1, 1, 224, 224], requires_grad=True)
gradients = backprop.calculate_gradients(input_)
assert gradients.shape == (1, 224, 224)
def test_calc_gradients_of_top_class_if_prediction_is_wrong(mocker, model):
backprop = Backprop(model)
top_class = torch.tensor(5)
target_class = 7
input_ = torch.zeros([1, 3, 224, 224])
target = make_expected_gradient_target(top_class)
mock_output = make_mock_output(mocker, model, top_class)
with pytest.warns(UserWarning):
backprop.calculate_gradients(input_, target_class)
args, kwargs = mock_output.backward.call_args
assert torch.all(kwargs['gradient'].eq(target))
def test_return_max_across_color_channels_if_specified(mocker, model):
backprop = Backprop(model)
target_class = 5
input_ = torch.zeros([1, 3, 224, 224])
make_mock_output(mocker, model, target_class)
gradients = backprop.calculate_gradients(input_,
target_class,
take_max=True)
assert gradients.shape == (1, 224, 224)
def saliency_map(model, img_p):
"""
Return saliency map over the image : shape (255, 255)
"""
# Load and preprocess image.
X = load_and_preprocess_img(img_p) # (3, 255, 255 torch tensor)
X.requires_grad_() # This is critical to actually get gradients.
"""
# Predict y.
ypred = 4 # (for now: set ypred manually)
# Can I get any gradients at all.
with torch.set_grad_enabled(True):
out = model(X)
out = out.reshape((5,))
out.backward(torch.FloatTensor([1., 1., 1., 1., 1.]))
saliency = X.grad
print(saliency)
"""
# Predict grade and get gradient. Use flashtorch library.
# See https://mc.ai/feature-visualisation-in-pytorch%E2%80%8A-%E2%80%8Asaliency-maps/
with torch.set_grad_enabled(True):
backprop = Backprop(model) # flashtorch.saliency Backprop object.
gradients = backprop.calculate_gradients(X,
take_max=True,
guided=False) # (1, 255, 255)
# Cast image, saliency maps to numpy arrays.
X = X.detach() # must 'detach' from gradients before slicing.
img_np = X.numpy()[0] # (3, 255, 255)
img_np = img_np.swapaxes(0, 1) # (255, 3, 255)
img_np = img_np.swapaxes(1, 2) # (255, 255, 3)
saliency_map_np = gradients.numpy()[0] # (255, 255)
print(max(np.max(saliency_map_np, axis=0)))
print(saliency_map_np)
print(img_np.shape)
print(saliency_map_np.shape)
# Smooth heatmap.
saliency_map_np = gaussian_filter(saliency_map_np, sigma=10)
# Plot image and overlay saliency map.
heatmap = sns.heatmap(saliency_map_np, alpha=0.5)
heatmap.imshow(img_np, cmap="YlGnBu")
plt.show()
return saliency_map_np
def test_register_hooks_to_relu_layers(mocker, name, model_module):
model = model_module()
relu_layers = find_relu_layers(model, nn.ReLU)
for layer in relu_layers:
mocker.spy(layer, 'register_forward_hook')
mocker.spy(layer, 'register_backward_hook')
backprop = Backprop(model)
target_class = 5
input_ = torch.zeros([1, 3, 224, 224])
if 'inception' in name:
input_ = torch.zeros([1, 3, 299, 299])
make_mock_output(mocker, model, target_class)
backprop.calculate_gradients(input_, target_class, guided=True)
for layer in relu_layers:
layer.register_forward_hook.assert_called_once()
layer.register_backward_hook.assert_called_once()
def get_saliency_map(model, img_p, ypred):
"""
Return saliency map over the image : shape (255, 255)
Parameters
----------
model (PyTorch model object)
img_p (str) path to image
ypred (int) 0-4. Used to control saliency map
Returns
-----------
img_np : img as (255, 255, 3) numpy array
saliency_map_np : saliency map as (255, 255) numpy array
TODO:
Examine this code more closely. At the moment, saliency maps
don't change much across classes. I think a different saliency
mapping technique is needed. The Guided=True flag may be possiblw
but at the moment causes NaN errors.
"""
# Load and preprocess image: (1, 3, 255, 255) torch tensor.
X = load_and_preprocess_img(img_p)
# Require gradient.
X.requires_grad_() # This is critical to actually get gradients.
# Get gradient using flashtorch.
with torch.set_grad_enabled(True):
backprop = Backprop(model)
gradients = backprop.calculate_gradients(input_=X,
target_class=ypred,
take_max=True,
guided=False) # (1, 255, 255)
# Cast image and saliency maps to numpy arrays.
X = X.detach()
img_np = X.numpy()[0] # (3, 255, 255)
img_np = img_np.transpose(1, 2, 0) # (255, 255, 3)
saliency_map_np = gradients[0].numpy()
#saliency_map_np = np.absolute(saliency_map_np) # absolute value
# Smooth heatmap.
saliency_map_np = gaussian_filter(saliency_map_np, sigma=10)
return img_np, saliency_map_np
def test_calculate_gradients_for_all_models(mocker, name, model_module):
model = model_module()
backprop = Backprop(model)
target_class = 5
input_ = torch.zeros([1, 3, 224, 224])
if 'inception' in name:
input_ = torch.zeros([1, 3, 299, 299])
make_mock_output(mocker, model, target_class)
gradients = backprop.calculate_gradients(input_,
target_class,
use_gpu=True)
assert gradients.shape == input_.size()[1:]
def test_calculate_gradients_for_all_models(mocker, available_models):
print()
for name, model in available_models:
print(f'Testing model: {name}', end='\r')
stdout.write('\x1b[2K')
model = model()
backprop = Backprop(model)
target_class = 5
input_ = torch.zeros([1, 3, 224, 224])
if 'inception' in name:
input_ = torch.zeros([1, 3, 299, 299])
make_mock_output(mocker, model, target_class)
gradients = backprop.calculate_gradients(input_, target_class)
assert gradients.shape == input_.size()[1:]
plt.axis('off')
# next is needed, otherwise no image pops up
plt.waitforbuttonpress()
model = models.alexnet(pretrained=True)
backprop = Backprop(model)
imagenet = ImageNetIndex()
target_class = imagenet['tabby cat']
input_ = apply_transforms(image)
# Calculate the gradients of each pixel w.r.t. the input image
gradients = backprop.calculate_gradients(input_, target_class)
# Or, take the maximum of the gradients for each pixel across colour channels.
max_gradients = backprop.calculate_gradients(input_,
target_class,
take_max=True)
print('Shape of the gradients:', gradients.shape)
print('Shape of the max gradients:', max_gradients.shape)
visualize(input_, gradients, max_gradients)
plt.waitforbuttonpress()
backprop = Backprop(model)
