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https://github.com/deepfakes/faceswap
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03f5c671bc
* 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
65 lines
2 KiB
Python
65 lines
2 KiB
Python
#!/usr/bin/env python3
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""" Tests for Faceswap Initializers.
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Adapted from Keras tests.
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"""
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import pytest
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import numpy as np
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from tensorflow.keras import backend as K # pylint:disable=import-error
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from tensorflow.keras import initializers as k_initializers # noqa:E501 # pylint:disable=import-error
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from lib.model import initializers
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from lib.utils import get_backend
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CONV_SHAPE = (3, 3, 256, 2048)
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CONV_ID = get_backend().upper()
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def _runner(init, shape, target_mean=None, target_std=None,
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target_max=None, target_min=None):
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variable = K.variable(init(shape))
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output = K.get_value(variable)
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lim = 3e-2
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if target_std is not None:
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assert abs(output.std() - target_std) < lim
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if target_mean is not None:
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assert abs(output.mean() - target_mean) < lim
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if target_max is not None:
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assert abs(output.max() - target_max) < lim
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if target_min is not None:
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assert abs(output.min() - target_min) < lim
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@pytest.mark.parametrize('tensor_shape', [CONV_SHAPE], ids=[CONV_ID])
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def test_icnr(tensor_shape):
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""" ICNR Initialization Test
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Parameters
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----------
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tensor_shape: tuple
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The shape of the tensor to feed to the initializer
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"""
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fan_in, _ = initializers.compute_fans(tensor_shape)
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std = np.sqrt(2. / fan_in)
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_runner(initializers.ICNR(initializer=k_initializers.he_uniform(), # pylint:disable=no-member
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scale=2),
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tensor_shape,
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target_mean=0,
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target_std=std)
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@pytest.mark.parametrize('tensor_shape', [CONV_SHAPE], ids=[CONV_ID])
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def test_convolution_aware(tensor_shape):
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""" Convolution Aware Initialization Test
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Parameters
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----------
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tensor_shape: tuple
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The shape of the tensor to feed to the initializer
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"""
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fan_in, _ = initializers.compute_fans(tensor_shape)
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std = np.sqrt(2. / fan_in)
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_runner(initializers.ConvolutionAware(seed=123), tensor_shape,
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target_mean=0, target_std=std)
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