test_graph_builder

Complexity

Total Complexity

4

Size/Duplication

Total Lines	64
Duplicated Lines	0 %

Importance

Changes

0

2 Functions

Rating	Name	Duplication	Size	Complexity
B	test_building_layers()	0	54	3
A	graph_builder()	0	4	1

import pytest


@pytest.fixture
def graph_builder():
    from artificial_artwork.style_model.graph_builder import GraphBuilder
    return GraphBuilder()


def test_building_layers(graph_builder):
    import tensorflow as tf
    tf.compat.v1.reset_default_graph()
    tf.compat.v1.enable_eager_execution()
    import numpy as np
    height = 2
    width = 6
    channels = 2
    expected_input_shape = (1, height, width, channels)

    graph_builder.input(type('ImageSpecs', (), {
        'width': width,
        'height': height,
        'color_channels': channels
    })())
    # assert previous layer is the 'input' layer we just added/created
    assert tuple(graph_builder._prev_layer.shape) == expected_input_shape
    assert (graph_builder._prev_layer.numpy() - graph_builder.graph['input'].numpy()).all() == 0
    assert graph_builder.graph['input'].numpy().all() == 0

    # create relu(convolution) layer
    W = np.array(np.random.rand(*expected_input_shape[1:], channels), dtype=np.float32)

    b_weight = 6.0
    b = np.array([b_weight], dtype=np.float32)
    graph_builder.relu_conv_2d('convo1', (W, b))

    # assert the previous layer is the relu(convolution) layer we just added
    assert tuple(graph_builder._prev_layer.shape) == expected_input_shape
    assert (graph_builder.graph['convo1'].numpy() - graph_builder._prev_layer.numpy()).all() == 0
    # We expect that the tensor values are equal to the weight because the algorithm initializes input with tf.zeros
    assert (graph_builder.graph['convo1'].numpy() - b).all() == 0


    # create Average Pooling layer
    layer_id = 'avgpool1'
    graph_builder.avg_pool(layer_id)

    # assert previous layer is the layer we just added/created
    expected_avg_pool_shape = (1, 1, 3, 2)
    expected_avg_output = np.array(
        [[[[b_weight, b_weight, b_weight],
            [b_weight, b_weight, b_weight],
            [b_weight, b_weight, b_weight]
        ]]]
    ,dtype=np.float32)
    assert graph_builder.graph[layer_id].numpy().shape == expected_avg_pool_shape
    assert (graph_builder.graph[layer_id].numpy() - graph_builder._prev_layer.numpy()).all() == 0
    assert (graph_builder.graph[layer_id].numpy() - np.array([b_weight])).all() == 0

    for i in range(2):
        for c in range(2):
            assert graph_builder._prev_layer[0][0][i][c] == graph_builder.graph[layer_id][0][0][i][c]
            assert graph_builder._prev_layer[0][0][i][c] == expected_avg_output[0][0][i][c]