def runDiversitywithTransforms(layerName,
layerNeuron,
transforms=None,
imageSize=256,
batch=4,
weight=1e2):
'''
Function to run Lucent neuron diversity optimisation for a given Layer and Neuron (Channel) in a PyTorch CNN.
This function uses image augmentation transforms to help improve the clarity and resolution of the produced neuron maximisations.
'''
if transforms == None:
transforms = [
transform.pad(16),
transform.jitter(8),
transform.random_scale([n / 100. for n in range(80, 120)]),
transform.random_rotate(
list(range(-10, 10)) + list(range(-5, 5)) +
10 * list(range(-2, 2))),
transform.jitter(2),
]
batch_param_f = lambda: param.image(imageSize, batch=batch)
obj = objectives.channel(
layerName, layerNeuron) - weight * objectives.diversity(layerName)
_ = render.render_vis(model_,
obj,
batch_param_f,
transforms=transforms,
show_inline=True)
def runDiversity(layerName, layerNeuron, imageSize=256, batch=4, weight=1e2):
'''
Function to run Lucent neuron diversity optimisation for a given Layer and Neuron (Channel) in a PyTorch CNN.
'''
batch_param_f = lambda: param.image(imageSize, batch=batch)
obj = objectives.channel(
layerName, layerNeuron) - weight * objectives.diversity(layerName)
_ = render.render_vis(model_, obj, batch_param_f, show_inline=True)
def visualize_diversity_fc(model, layer, filter, path, batch_size, device):
model.to(device).eval()
if not os.path.exists(f'{path}/'):
os.makedirs(f'{path}/')
batch_param_f = lambda: param.image(128, batch=batch_size)
layer_name = f'fc_{layer}:{filter}'
obj = objectives.channel(
f"fc_{layer}", filter) - 1e2 * objectives.diversity(f"fc_{layer}")
image_name = f"{path}/{layer_name}_diversity.jpg"
_ = render.render_vis(model,
obj,
batch_param_f,
save_image=True,
image_name=image_name.replace(':', '_'),
show_image=False)
def test_diversity(inceptionv1_model):
objective = objectives.channel("mixed4a", 0) - 100 * objectives.diversity("mixed4a")
assert_gradient_descent(objective, inceptionv1_model)