pylissom.utils

Submodules

pylissom.utils.helpers module

pylissom.utils.helpers.save_model(model, optimizer=None, fname='best_model.pth.tar')

pylissom.utils.helpers.load_model(fname='model.pth.tar')

pylissom.utils.helpers.debug()

Calls a debugger that works with jupyter notebooks

class pylissom.utils.helpers.TimeIt

Bases: object

At instantiation starts a timer and prints value when end() is called

end()

pylissom.utils.orientation_maps module

Provides some helpers to calculate Orientation Preferences of a Lissom Network

class pylissom.utils.orientation_maps.OrientationMap(model, inputs, use_tqdm_notebook=True)

Bases: object

maximum_activations(model, inputs)

calculate_keys_activations(model, inputs)

get_orientation_map

static orientation_hist(orientation_map)

get_orientation_hist

pylissom.utils.orientation_maps.plot_orientation_map(orientation_map)

pylissom.utils.orientation_maps.plot_orientation_hist(orientation_hist)

pylissom.utils.orientation_maps.metrics_orientation_hist(orientation_hist)

pylissom.utils.orientation_maps.get_oriented_lines(size, orientations=180)

pylissom.utils.orientation_maps.numpy_dict_to_tensors(d)

pylissom.utils.stimuli module

Provides several functions that create and manipulate matrices representing different stimuli, mainly guassians disks TODO: only use cv2 or scikit-image, not both

pylissom.utils.stimuli.translate(bar, step=1, over_x=True)

pylissom.utils.stimuli.random_translation(img, x_offset=5, y_offset=5)

pylissom.utils.stimuli.generate_horizontal_bar(size)

pylissom.utils.stimuli.generate_gaussian(shape, mu_x=0.0, mu_y=0.0, sigma_x=1.0, sigma_y=None)

pylissom.utils.stimuli.rotations(img, num=13)

Returns: Returns a dictionary of len 180 / num, representing {rotation_degrees: rotated_img}

pylissom.utils.stimuli.gaussian_generator(size, mu_x, mu_y, sigma_x, sigma_y, orientation)

Parameters:orientation – It’s actually redundant because orientation is a function of the sigmas, but make it easier to use

Returns: A numpy matrix representing a gaussian of shape = (size, size)

pylissom.utils.stimuli.generate_random_gaussian(size)

Returns: A numpy matrix with a random gaussian of shape = (size, size)

pylissom.utils.stimuli.random_gaussians_generator(size, gaussians=1)

Parameters:

• size – img will have shape = (size, size)
• gaussians – How many gaussians per matrix

Returns: Yields a squared numpy matrix with gaussians disks

pylissom.utils.stimuli.generate_random_faces(size)

pylissom.utils.stimuli.generate_three_dots(size, mu_x, mu_y, sigma_x, orientation)

Returns: A numpy matrix with 3 gaussian disks representing a face

pylissom.utils.stimuli.faces_generator(size, num=1)

Parameters:

• size – img will have shape = (size, size)
• gaussians – How many faces per matrix

Returns: Yields a squared numpy matrix with 3-gaussians faces

pylissom.utils.stimuli.combine_matrices(*matrices)

Returns: Merges matrices in one matrix using the maximum values in each pixel

pylissom.utils.stimuli.sine_grating(size, freq, phase)