def visualize_top_images(layer, f, constrast):
"""
Visualize the activating pixels of the 9 images that maximally activate a given filter in a layer
"""
conv_base_model = AlexNet().model
get_from_folder = 'Layer{}_Strongest_max_IMG'.format(layer)
save_to_folder = 'Layer{}_Projections_and_Images'.format(layer)
if not os.path.exists(save_to_folder):
os.makedirs(save_to_folder)
for t in range(1, 10):
file_name = '/Layer{}_Filter{}_Top{}.JPEG'.format(layer, f, t)
projection = Deconvolution(conv_base_model).project_down(
get_from_folder + file_name, layer, f)
original_image = preprocess_image_batch(get_from_folder + file_name)
activation_filename = save_to_folder + '/Layer{}_Filter{}_Top{}_Activations.JPEG'.format(
layer, f, t)
if os.path.exists(activation_filename):
os.remove(activation_filename)
DeconvOutput(projection,
constrast).save_as(filename=activation_filename)
original_filename = save_to_folder + '/Layer{}_Filter{}_Top{}.JPEG'.format(
layer, f, t)
if os.path.exists(original_filename):
os.remove(original_filename)
DeconvOutput(original_image).save_as(filename=original_filename)
def project_top_layer_filters(img_id=None, deconv_base_model=None):
if img_id is None:
img_id = randint(1, 50000)
if deconv_base_model is None:
deconv_base_model = Deconvolution(AlexNet().model)
path = get_path_from_id(img_id)
save_to_folder = 'TopFilters'
projections = []
box_borders = []
layer = 5
for max_filter in get_strongest_filters(img_id, layer, top=3):
projection = deconv_base_model.project_down(path, layer, max_filter)
# Increase Contrast
percentile = 99
max_val = np.percentile(projection, percentile)
projection *= (20 / max_val)
box_borders.append(get_bounding_box_coordinates(projection))
projections.append(projection)
superposed_projections = np.maximum.reduce(projections)
# superposed_projections = sum(projections)
assert superposed_projections.shape == projections[0].shape
DeconvOutput(superposed_projections).save_as(
save_to_folder, '{}_activations.JPEG'.format(img_id))
original_image = preprocess_image_batch(path)
original_image = draw_bounding_box(original_image, box_borders)
DeconvOutput(original_image).save_as(save_to_folder,
'{}.JPEG'.format(img_id))
def project_complete_image(layer, file_name='Example_JPG/Elephant.jpg'):
conv_base_model = AlexNet().model
projection = Deconvolution(conv_base_model).project_down(file_name,
layer=layer,
use_bias=True)
original_image = preprocess_image_batch(file_name)
activation_filename = 'test.JPEG'
DeconvOutput(projection).save_as(filename=activation_filename)
original_filename = 'test_original.JPEG'
DeconvOutput(original_image).save_as(filename=original_filename)
def project_multiple_layer_filters(img_id=None, deconv_base_model=None):
if img_id is None:
img_id = randint(1, 50000)
if deconv_base_model is None:
deconv_base_model = Deconvolution(AlexNet().model)
path = get_path_from_id(img_id)
save_to_folder = 'MultipleLayers'
projections = []
box_borders = []
contrast = [None, 1, 3, 7, 13, 22]
for layer in (5, ): # 1, 2, 3, 4,
assert get_strongest_filter(img_id,
layer) == get_strongest_filters(img_id,
layer,
top=1)
max_filter = get_strongest_filter(img_id, layer)
if layer == 1: print(img_id, ': ', max_filter)
projection = deconv_base_model.project_down(
path, layer, max_filter) # 某一层的特征图解卷积后的重构图
if layer != 1:
# 提升对比度
percentile = 99
# max_val = np.nanargmax(unarranged_array)
max_val = np.percentile(
projection, percentile
) # 百分位数： 具体参见 stackflow.com/questions/2374640/how-do-i-calculate-percentiles-with-python-numpy
projection *= (contrast[layer] / max_val)
else:
projection *= 0.3
box_borders.append(get_bounding_box_coordinates(projection))
# x_diff[layer].append(box_borders[-1][1] - box_borders[-1][0])
# y_diff[layer].append(box_borders[-1][3] - box_borders[-1][2])
projections.append(projection)
superposed_projections = np.maximum.reduce(projections)
# superposed_projections = sum(projections)
assert superposed_projections.shape == projections[0].shape
DeconvOutput(superposed_projections).save_as(
save_to_folder, '{}_activations.JPEG'.format(img_id))
original_image = preprocess_image_batch(path)
original_image = draw_bounding_box(original_image, box_borders)
DeconvOutput(original_image).save_as(save_to_folder,
'{}.JPEG'.format(img_id))