Example #1
0
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()
Example #2
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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
Example #3
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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)
Example #4
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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
Example #7
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def inceptionv1():
    return InceptionV1()
Example #8
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def inceptionv1():
    model = InceptionV1()
    model.load_graphdef()
    return model
Example #9
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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()
Example #10
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def test_InceptionV1_aligned_activations():
    model = InceptionV1()
    activations = get_aligned_activations(model.layers[0])
    assert activations.shape == (100000, 64)