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)
93 lines
3.5 KiB
Python
93 lines
3.5 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 numpy.testing import assert_allclose
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from lib.model import optimizers
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from lib.utils import get_backend
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from tests.utils import generate_test_data, to_categorical
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if get_backend() == "amd":
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from keras import optimizers as k_optimizers
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from keras.layers import Dense, Activation
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from keras.models import Sequential
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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 optimizers as k_optimizers # pylint:disable=import-error
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from tensorflow.keras.layers import Dense, Activation # noqa pylint:disable=import-error,no-name-in-module
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from tensorflow.keras.models import Sequential # pylint:disable=import-error,no-name-in-module
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def get_test_data():
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""" Obtain randomized test data for training """
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np.random.seed(1337)
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(x_train, y_train), _ = generate_test_data(num_train=1000,
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num_test=200,
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input_shape=(10,),
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classification=True,
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num_classes=2)
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y_train = to_categorical(y_train)
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return x_train, y_train
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def _test_optimizer(optimizer, target=0.75):
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x_train, y_train = get_test_data()
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model = Sequential()
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model.add(Dense(10, input_shape=(x_train.shape[1],)))
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model.add(Activation("relu"))
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model.add(Dense(y_train.shape[1]))
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model.add(Activation("softmax"))
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model.compile(loss="categorical_crossentropy",
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optimizer=optimizer,
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metrics=["accuracy"])
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history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
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accuracy = "acc" if get_backend() == "amd" else "accuracy"
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assert history.history[accuracy][-1] >= target
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config = k_optimizers.serialize(optimizer)
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optim = k_optimizers.deserialize(config)
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new_config = k_optimizers.serialize(optim)
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config["class_name"] = config["class_name"].lower()
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new_config["class_name"] = new_config["class_name"].lower()
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assert config == new_config
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# Test constraints.
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if get_backend() == "amd":
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# NB: PlaidML does not support constraints, so this test skipped for AMD backends
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return
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model = Sequential()
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dense = Dense(10,
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input_shape=(x_train.shape[1],),
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kernel_constraint=lambda x: 0. * x + 1.,
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bias_constraint=lambda x: 0. * x + 2.,)
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model.add(dense)
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model.add(Activation("relu"))
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model.add(Dense(y_train.shape[1]))
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model.add(Activation("softmax"))
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model.compile(loss="categorical_crossentropy",
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optimizer=optimizer,
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metrics=["accuracy"])
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model.train_on_batch(x_train[:10], y_train[:10])
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kernel, bias = dense.get_weights()
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assert_allclose(kernel, 1.)
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assert_allclose(bias, 2.)
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@pytest.mark.parametrize("dummy", [None], ids=[get_backend().upper()])
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def test_adam(dummy): # pylint:disable=unused-argument
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""" Test for custom Adam optimizer """
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_test_optimizer(k_optimizers.Adam(), target=0.45)
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_test_optimizer(k_optimizers.Adam(decay=1e-3), target=0.45)
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@pytest.mark.parametrize("dummy", [None], ids=[get_backend().upper()])
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def test_adabelief(dummy): # pylint:disable=unused-argument
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""" Test for custom Adam optimizer """
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_test_optimizer(optimizers.AdaBelief(), target=0.45)
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