def test_integration(decorrelate, fft):
inceptionv1 = InceptionV1()
obj = objectives.neuron("mixed3a_pre_relu", 0)
param_f = lambda: param.image(16, decorrelate=decorrelate, fft=fft)
rendering = render.render_vis(
inceptionv1,
obj,
param_f=param_f,
thresholds=(1, 2),
verbose=False,
transforms=[],
)
start_image = rendering[0]
end_image = rendering[-1]
objective_f = objectives.neuron("mixed3a", 177)
param_f = lambda: param.image(64, decorrelate=decorrelate, fft=fft)
rendering = render.render_vis(
inceptionv1,
objective_f,
param_f,
verbose=False,
thresholds=(0, 64),
use_fixed_seed=True,
)
start_image, end_image = rendering
assert (start_image != end_image).any()
def test_InceptionV1_graph_import():
model = InceptionV1()
model.import_graph()
nodes = tf.get_default_graph().as_graph_def().node
node_names = set(node.name for node in nodes)
for layer_name in important_layer_names:
assert "import/" + layer_name + "_pre_relu" in node_names
def test_aligned_activation_atlas():
model1 = AlexNet()
layer1 = model1.layers[1]
model2 = InceptionV1()
layer2 = model2.layers[8] # mixed4d
atlasses = aligned_activation_atlas(model1,
layer1,
model2,
layer2,
number_activations=subset)
path = "tests/recipes/results/activation_atlas/aligned_atlas-{}-of-{}.jpg".format(
index, len(atlasses))
for index, atlas in enumerate(atlasses):
save(atlas, path)
def test_integration_any_channels():
inceptionv1 = InceptionV1()
objectives_f = [
objectives.deepdream("mixed4a_pre_relu"),
objectives.channel("mixed4a_pre_relu", 360),
objectives.neuron("mixed3a", 177)
]
params_f = [
lambda: param.grayscale_image_rgb(128),
lambda: arbitrary_channels_to_rgb(128, channels=10)
]
for objective_f in objectives_f:
for param_f in params_f:
rendering = render.render_vis(
inceptionv1,
objective_f,
param_f,
verbose=False,
thresholds=(0, 64),
use_fixed_seed=True,
)
start_image, end_image = rendering
assert (start_image != end_image).any()
def test_InceptionV1_labels():
model = InceptionV1()
assert model.labels is not None
assert model.labels[0] == "dummy"
def test_InceptionV1_model_download():
model = InceptionV1()
model.load_graphdef()
assert model.graph_def is not None
def inceptionv1():
return InceptionV1()
def inceptionv1():
model = InceptionV1()
model.load_graphdef()
return model
from __future__ import absolute_import, division, print_function
import pytest
import tensorflow as tf
from lucid.modelzoo.vision_models import InceptionV1
from lucid.optvis import objectives, param, render, transform
model = InceptionV1()
model.load_graphdef()
@pytest.mark.parametrize("decorrelate", [True, False])
@pytest.mark.parametrize("fft", [True, False])
def test_integration(decorrelate, fft):
obj = objectives.neuron("mixed3a_pre_relu", 0)
param_f = lambda: param.image(16, decorrelate=decorrelate, fft=fft)
rendering = render.render_vis(model,
obj,
param_f=param_f,
thresholds=(1, 2),
verbose=False,
transforms=[])
start_image = rendering[0]
end_image = rendering[-1]
assert (start_image != end_image).any()
def test_InceptionV1_aligned_activations():
model = InceptionV1()
activations = get_aligned_activations(model.layers[0])
assert activations.shape == (100000, 64)
