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* Remove PlaidML reference from readme files * Remove AMD option from installers * remove amd requirements and update setup.py * remove plaidml test from CI workflow * gpustats: remove plaidml backend * plaid removals: - faceswap.py - python version check - setup.cfg - plaidml typing ignore - lib.keras_utils - All plaid code - lib.launcher.py - All plaidml checks and configuration * remove tf2.2 specific code from GUI event reader * lib.model - remove all plaidml implementations * plugins.extract - remove plaidml code * plugins.train remove plaidml code * lib.convert - remove plaidml code * tools.model: remove plaidml code * Remove plaidML tests from unit tests * remove plaidml_utils and docsting cleanups * Remove plaidML refs from configs * fix keras imports
105 lines
4.0 KiB
Python
105 lines
4.0 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 tensorflow.keras import regularizers, models, layers # noqa:E501 # pylint:disable=import-error
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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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@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 = [f"{'|'.join([_PARAMS[idx] for idx, b in enumerate(v) if b])}[{get_backend().upper()}]"
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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, verbose=0)
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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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_PARAMS = ["partial", "bias"]
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_VALUES = [(0.0, False), (0.25, False), (0.5, True), (0.75, False), (1.0, True)] # type:ignore
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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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