def reconstruct_features(model, layer_name, input_var, filename, filter_num):
"""
Reconstruct features in input_img that led to max activation at given layer
:param model:
:param layer_name:
:param input_var:
:param input_img: Ensure 4d with (num_sample, num_filter, H, W)
:return:
"""
orig_img = plt.imread(filename)
prep_orig = scale_image(orig_img)
input_img = prep_orig[None, :, :, :].astype(np.float32)
backprop_func, loss, grads = compute_loss_grads_backprop_func(model, layer_name, input_var, filter_num)
loss, grads = backprop_func(input_img)
print "Original image plotting"
# Visualize img before ascent
plt.figure(1)
plt.imshow(np.transpose(input_img[0], (1, 2, 0)))
plt.show(block=False)
plt.pause(0.01)
print "Input img shape: ", input_img.shape
optimal_img = generate_optimal_img(backprop_func, input_img)
print "Gradient ascent complete"
plt.figure(2)
# Visualize img after ascent
deprocess_test = deprocess_image(optimal_img[0])
plt.imshow(deprocess_test)
plt.show()
def visualize_max_filter_activations(model, layer_name, orig_filename,
adv_filename):
# Original image
orig_img = plt.imread(orig_filename)
prep_orig = scale_image(orig_img)
prep_orig = prep_orig[None, :, :, :].astype(np.float32)
adv_img = plt.imread(adv_filename)
prep_adv = scale_image(adv_img)
prep_adv = prep_adv[None, :, :, :].astype(np.float32)
# Get min/max filter idx
print "Computing max/min filters..."
min_idx, max_idx = get_min_max_filter_idx(model, layer_name, prep_adv,
prep_orig)
print "Min idx {0}, Max idx {1}".format(str(min_idx), str(max_idx))
print "Computing activations..."
activations_orig = get_activations_at_layer(model, layer_name, prep_orig)
activations_adv = get_activations_at_layer(model, layer_name, prep_adv)
print "activations dim: ", activations_adv.shape
print "Converting original image activations to pixel space..."
converted_orig = convert_to_pixel_space(activations_orig, max_idx)
plt.figure(1)
visualize_img(converted_orig)
print "Converting adversarial image activations to pixel space..."
plt.figure(2)
converted_adv = convert_to_pixel_space(activations_adv, max_idx)
visualize_img(converted_adv)
print "Printing image differences..."
plt.figure(3)
image_diff = np.absolute(activations_orig - activations_adv)
converted_diff = convert_to_pixel_space(image_diff, max_idx)
visualize_img(converted_diff)
return min_idx, max_idx
def visualize_max_filter_activations(model, layer_name, orig_filename, adv_filename):
# Original image
orig_img = plt.imread(orig_filename)
prep_orig = scale_image(orig_img)
prep_orig = prep_orig[None, :, :, :].astype(np.float32)
adv_img = plt.imread(adv_filename)
prep_adv = scale_image(adv_img)
prep_adv = prep_adv[None, :, :, :].astype(np.float32)
# Get min/max filter idx
print "Computing max/min filters..."
min_idx, max_idx = get_min_max_filter_idx(model, layer_name, prep_adv, prep_orig)
print "Min idx {0}, Max idx {1}".format(str(min_idx), str(max_idx))
print "Computing activations..."
activations_orig = get_activations_at_layer(model, layer_name, prep_orig)
activations_adv = get_activations_at_layer(model, layer_name, prep_adv)
print "activations dim: ", activations_adv.shape
print "Converting original image activations to pixel space..."
converted_orig = convert_to_pixel_space(activations_orig, max_idx)
plt.figure(1)
visualize_img(converted_orig)
print "Converting adversarial image activations to pixel space..."
plt.figure(2)
converted_adv = convert_to_pixel_space(activations_adv, max_idx)
visualize_img(converted_adv)
print "Printing image differences..."
plt.figure(3)
image_diff = np.absolute(activations_orig - activations_adv)
converted_diff = convert_to_pixel_space(image_diff, max_idx)
visualize_img(converted_diff)
return min_idx, max_idx
def reconstruct_features(model, layer_name, input_var, filename, filter_num):
"""
Reconstruct features in input_img that led to max activation at given layer
:param model:
:param layer_name:
:param input_var:
:param input_img: Ensure 4d with (num_sample, num_filter, H, W)
:return:
"""
orig_img = plt.imread(filename)
prep_orig = scale_image(orig_img)
input_img = prep_orig[None, :, :, :].astype(np.float32)
backprop_func, loss, grads = compute_loss_grads_backprop_func(
model, layer_name, input_var, filter_num)
loss, grads = backprop_func(input_img)
print "Original image plotting"
# Visualize img before ascent
plt.figure(1)
plt.imshow(np.transpose(input_img[0], (1, 2, 0)))
plt.show(block=False)
plt.pause(0.01)
print "Input img shape: ", input_img.shape
optimal_img = generate_optimal_img(backprop_func, input_img)
print "Gradient ascent complete"
plt.figure(2)
# Visualize img after ascent
deprocess_test = deprocess_image(optimal_img[0])
plt.imshow(deprocess_test)
plt.show()
