def make_bar_stimuli(directory='.'):
"""
Creates and saves bar stimulus images. These are used for finding optimal bar
stimuli for units in a CNN, approximating the procedure in:
H. Zhou, H. S. Friedman, and R. von der Heydt, "Coding of border ownership in monkey visual
cortex.," J. Neurosci., vol. 20, no. 17, pp. 6594-6611, 2000.
"""
colours = Colours()
bg_colour_name = 'Light gray (background)'
bg_colour = colours.get_RGB(bg_colour_name, 0)
fg_colour_names = [
key for key in colours.colours.keys() if key != bg_colour_name
]
# TODO: probably need more sizes and angles
lengths = [40, 80]
widths = [4, 8]
angles = np.pi * np.array([0, .25, .5, .75])
parameters = []
for fg_colour_name in fg_colour_names:
n_luminances = colours.get_num_luminances(fg_colour_name)
n_stimuli = len(lengths) * len(widths) * len(angles) * n_luminances
print('Creating {} {} stimuli'.format(n_stimuli, fg_colour_name))
for i in range(n_luminances):
RGB = colours.get_RGB(fg_colour_name, i)
for length in lengths:
for width in widths:
for angle in angles:
parameters.append({
'colour': RGB,
'length': length,
'width': width,
'angle': angle
})
stimulus = get_image((400, 400, 3), bg_colour)
add_rectangle(stimulus, (200, 200), (width, length),
angle, RGB)
filename = 'bar{}.jpg'.format(len(parameters) - 1)
sio.imsave(os.path.join(directory, filename), stimulus)
return parameters
def find_optimal_bars(input, layers, im_shape=(400, 400)):
"""
Finds bar stimuli that optimally activate each of the feature maps in a single layer of a
convolutional network, approximating the procedure in:
H. Zhou, H. S. Friedman, and R. von der Heydt, “Coding of border ownership in monkey visual
cortex.,” J. Neurosci., vol. 20, no. 17, pp. 6594–6611, 2000.
Their description of the procedure is, ""After isolation of a cell, the receptive field was
examined with rectangular bars, and the optimal stimulus parameters were
determined by varying the length, width, color, orientation ..."
We approximate this by applying a variety of bar stimuli, and finding which one most strongly
activates the centre unit in each feature map. Testing the whole layer at once is more
efficient than testing each feature map individually, since the whole network up to that point
must be run whether we record a single unit or all of them.
:param input: Input to TensorFlow model (Placeholder node)
:param layers: Layers of convolutional network to record from
:return: parameters, responses, preferred_stimuli
"""
colours = Colours()
bg_colour_name = 'Light gray (background)'
bg_colour = colours.get_RGB(bg_colour_name, 0)
fg_colour_names = [
key for key in colours.colours.keys() if key != bg_colour_name
]
# TODO: probably need more sizes and angles, also shift bar laterally
lengths = [40, 80]
widths = [4, 8]
angles = np.pi * np.array([0, .25, .5, .75])
parameters = []
responses = {}
preferred_stimuli = {}
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
model_tf = ()
for layer in layers:
model_tf += (sess.graph.get_tensor_by_name(layer), )
responses[layer] = []
for fg_colour_name in fg_colour_names:
input_data = None
n_luminances = colours.get_num_luminances(fg_colour_name)
n_stimuli = len(lengths) * len(widths) * len(angles) * n_luminances
print('Testing {} {} stimuli'.format(n_stimuli, fg_colour_name))
for i in range(n_luminances):
RGB = colours.get_RGB(fg_colour_name, i)
for length in lengths:
for width in widths:
for angle in angles:
parameters.append({
'colour': RGB,
'length': length,
'width': width,
'angle': angle
})
stimulus = get_image((im_shape[0], im_shape[1], 3),
bg_colour)
add_rectangle(stimulus,
(im_shape[0] / 2, im_shape[1] / 2),
(width, length), angle, RGB)
# plt.imshow(stimulus)
# plt.show()
if input_data is None:
input_data = np.expand_dims(stimulus, 0)
else:
input_data = np.concatenate(
(input_data, np.expand_dims(stimulus, 0)),
0)
activities = sess.run(model_tf, feed_dict={input: input_data})
# activities is a tuple with shape stim x h x w x feats
for i, activity in enumerate(activities):
centre = (int(activity.shape[1] / 2),
int(activity.shape[2] / 2))
responses[layers[i]].append(activity[:, centre[0],
centre[1], :])
for layer in layers:
# reshape to layers x stim x feats
responses[layer] = np.concatenate(responses[layer])
preferred_stimuli[layer] = np.argmax(responses[layer], axis=0)
return parameters, responses, preferred_stimuli