mirror of
https://github.com/deepfakes/faceswap
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aa39234538
* Remove custom keras importer * first round keras imports fix * launcher.py: Remove KerasFinder references * 2nd round keras imports update (lib and extract) * 3rd round keras imports update (train) * remove KerasFinder from tests * 4th round keras imports update (tests)
116 lines
4.5 KiB
Python
116 lines
4.5 KiB
Python
#!/usr/bin/env python3
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""" Tests for Faceswap Normalization.
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Adapted from Keras tests.
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"""
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from itertools import product
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import numpy as np
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import pytest
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from lib.model import normalization
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from lib.utils import get_backend
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from tests.lib.model.layers_test import layer_test
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if get_backend() == "amd":
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from keras import regularizers, models, layers
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else:
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# Ignore linting errors from Tensorflow's thoroughly broken import system
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from tensorflow.keras import regularizers, models, layers # pylint:disable=import-error
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@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
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def test_instance_normalization(dummy): # pylint:disable=unused-argument
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""" Basic test for instance normalization. """
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layer_test(normalization.InstanceNormalization,
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kwargs={'epsilon': 0.1,
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'gamma_regularizer': regularizers.l2(0.01),
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'beta_regularizer': regularizers.l2(0.01)},
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input_shape=(3, 4, 2))
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layer_test(normalization.InstanceNormalization,
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kwargs={'epsilon': 0.1,
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'axis': 1},
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input_shape=(1, 4, 1))
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layer_test(normalization.InstanceNormalization,
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kwargs={'gamma_initializer': 'ones',
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'beta_initializer': 'ones'},
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input_shape=(3, 4, 2, 4))
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layer_test(normalization.InstanceNormalization,
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kwargs={'epsilon': 0.1,
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'axis': 1,
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'scale': False,
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'center': False},
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input_shape=(3, 4, 2, 4))
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@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
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def test_group_normalization(dummy): # pylint:disable=unused-argument
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""" Basic test for instance normalization. """
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layer_test(normalization.GroupNormalization,
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kwargs={'epsilon': 0.1,
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'gamma_regularizer': regularizers.l2(0.01),
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'beta_regularizer': regularizers.l2(0.01)},
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input_shape=(4, 3, 4, 128))
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layer_test(normalization.GroupNormalization,
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kwargs={'epsilon': 0.1,
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'axis': 1},
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input_shape=(4, 1, 4, 256))
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layer_test(normalization.GroupNormalization,
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kwargs={'gamma_init': 'ones',
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'beta_init': 'ones'},
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input_shape=(4, 64))
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layer_test(normalization.GroupNormalization,
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kwargs={'epsilon': 0.1,
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'axis': 1,
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'group': 16},
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input_shape=(3, 64))
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_PARAMS = ["center", "scale"]
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_VALUES = list(product([True, False], repeat=len(_PARAMS)))
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_IDS = ["{}[{}]".format("|".join([_PARAMS[idx] for idx, b in enumerate(v) if b]),
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get_backend().upper()) for v in _VALUES]
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@pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
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def test_adain_normalization(center, scale):
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""" Basic test for Ada Instance Normalization. """
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norm = normalization.AdaInstanceNormalization(center=center, scale=scale)
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shapes = [(4, 8, 8, 1280), (4, 1, 1, 1280), (4, 1, 1, 1280)]
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norm.build(shapes)
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expected_output_shape = norm.compute_output_shape(shapes)
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inputs = [layers.Input(shape=shapes[0][1:]),
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layers.Input(shape=shapes[1][1:]),
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layers.Input(shape=shapes[2][1:])]
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model = models.Model(inputs, norm(inputs))
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data = [10 * np.random.random(shape) for shape in shapes]
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actual_output = model.predict(data)
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actual_output_shape = actual_output.shape
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for expected_dim, actual_dim in zip(expected_output_shape,
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actual_output_shape):
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if expected_dim is not None:
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assert expected_dim == actual_dim
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@pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
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def test_layer_normalization(center, scale):
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""" Basic test for layer normalization. """
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layer_test(normalization.LayerNormalization,
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kwargs={"center": center, "scale": scale},
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input_shape=(4, 512))
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_PARAMS = ["partial", "bias"]
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_VALUES = [(0.0, False), (0.25, False), (0.5, True), (0.75, False), (1.0, True)]
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_IDS = [f"partial={v[0]}|bias={v[1]}[{get_backend().upper()}]" for v in _VALUES]
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@pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
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def test_rms_normalization(partial, bias): # pylint:disable=unused-argument
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""" Basic test for RMS Layer normalization. """
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layer_test(normalization.RMSNormalization,
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kwargs={"partial": partial, "bias": bias},
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input_shape=(4, 512))
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