def test_mul_div_raises():
with pytest.raises(Exception) as excinfo:
objective = objectives.channel("mixed4a", 0) / objectives.channel("mixed4a", 0)
assert str(excinfo.value) == "Can only divide by int or float. Received type <class 'lucent.optvis.objectives.Objective'>"
with pytest.raises(Exception) as excinfo:
objective = objectives.channel("mixed4a", 0) * objectives.channel("mixed4a", 0)
assert str(excinfo.value) == "Can only multiply by int or float. Received type <class 'lucent.optvis.objectives.Objective'>"
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_multiple_neurons(model, neuron_names, save_path, device):
model.to(device).eval()
param_f = lambda: param.image(512, batch=1)
neuron1 = neuron_names[0]
print(neuron1)
obj = objectives.channel(neuron_names[0][0], neuron_names[0][1])
for i in range(1, len(neuron_names)):
obj += objectives.channel(neuron_names[i][0], neuron_names[i][1])
_ = render.render_vis(model,
obj,
param_f,
save_image=True,
image_name=f'{save_path}_placeholder.jpg',
show_image=False)
def gen_objective(targetid, model, params, neuron=False):
print('generating feature_viz objective string for %s' % targetid)
if '-' in targetid:
within_id = edgename_2_singlenum(model, targetid, params)
layer_name = nodeid_2_perlayerid(targetid.split('-')[1], params)[2]
if not neuron:
#return layer_name+'_preadd_conv:'+str(within_id)
return objectives.channel(layer_name + '_preadd_conv',
int(within_id))
else:
return objectives.neuron(layer_name + '_preadd_conv',
int(within_id))
else:
layer, within_id, layer_name = nodeid_2_perlayerid(targetid, params)
if not neuron:
#return layer_name+':'+str(within_id)
return objectives.channel(layer_name, int(within_id))
else:
return objectives.neuron(layer_name, int(within_id))
def test_gan_deep_optim(inceptionv1_model):
""" Test if GAN generated image could be optimized """
G = upconvGAN("fc6").to(device)
def GANparam(batch=1, sd=1):
code = (torch.randn(
(batch, G.codelen)) * sd).to(device).requires_grad_(True)
imagef = lambda: G.visualize(code)
return [code], imagef
objective = objectives.channel("mixed3a_1x1_pre_relu_conv", 0)
assert_gan_gradient_descent(GANparam, objective, inceptionv1_model)
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_linear_transform(inceptionv1_model):
objective = 1 + 1 * -objectives.channel("mixed4a", 0) / 1 - 1
assert_gradient_descent(objective, inceptionv1_model)
def test_sum(inceptionv1_model):
channel = lambda n: objectives.channel("mixed4a_pool_reduce_pre_relu_conv", n)
objective = objectives.Objective.sum([channel(21), channel(32)])
assert_gradient_descent(objective, inceptionv1_model)
def test_channel(inceptionv1_model):
objective = objectives.channel("mixed3a_1x1_pre_relu_conv", 0)
assert_gradient_descent(objective, inceptionv1_model)
def test_diversity(inceptionv1_model):
objective = objectives.channel("mixed4a", 0) - 100 * objectives.diversity("mixed4a")
assert_gradient_descent(objective, inceptionv1_model)
def fetch_deepviz_img_for_subgraph(model, layer_name, within_id, targetid,
viz_folder, params):
model = set_across_model(model, 'target_node', None)
objective_str = layer_name + ':' + str(targetid)
neuron = params['deepviz_neuron']
#generate objective
#objective = gen_objective(targetid,model,params,neuron=neuron)
print('generating feature_viz objective string for %s' % targetid)
if not params['deepviz_neuron']:
#return layer_name+':'+str(within_id)
objective = objectives.channel(layer_name, int(within_id))
else:
objective = objectives.neuron(layer_name, int(within_id))
file_path = viz_folder + '/images.csv'
parametrizer = params['deepviz_param']
optimizer = params['deepviz_optim']
transforms = params['deepviz_transforms']
image_size = params['deepviz_image_size']
param_str = object_2_str(parametrizer, "params['deepviz_param']=")
optimizer_str = object_2_str(optimizer, "params['deepviz_optim']=")
transforms_str = object_2_str(transforms, "params['deepviz_transforms']=")
df = pd.read_csv(file_path, dtype=str)
df_sel = df.loc[(df['targetid'] == str(targetid))
& (df['objective'] == objective_str) &
(df['parametrizer'] == param_str) &
(df['optimizer'] == optimizer_str) &
(df['transforms'] == transforms_str) &
(df['neuron'] == str(neuron))]
if len(df_sel) == 0:
print('deepviz image not found for %s, generating . . .' % targetid)
#image_name = 'deepviz_'+str(targetid)+'_'+objective+'_'+str(time.time())+'.jpg'
image_name = str(targetid) + '_' + objective_str + '_' + str(
time.time()) + '.jpg'
#gen_visualization(model,image_name,objective,parametrizer,optimizer,transforms,image_size,neuron,params)
if neuron:
full_image_path = viz_folder + '/neuron/' + image_name
else:
full_image_path = viz_folder + '/channel/' + image_name
if parametrizer is None:
parametrizer = lambda: param.image(image_size)
print('generating featviz with objective: %s' % str(objective_str))
_ = render.render_vis(model,
objective,
parametrizer,
optimizer,
transforms=transforms,
save_image=True,
image_name=full_image_path,
show_inline=True)
with open(file_path, 'a') as csv:
csv.write(','.join([
image_name,
str(targetid), objective_str, param_str, optimizer_str,
transforms_str,
str(neuron)
]) + '\n')
else:
print('found pre-generated image')
image_name = df_sel.iloc[0]['image_name']
if neuron:
return 'neuron/' + image_name
else:
return 'channel/' + image_name