mirror of
https://github.com/deepfakes/faceswap
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c1512fd41d
* Update maximum tf version in setup + requirements * - bump max version of tf version in launcher - standardise tf version check * update keras get_custom_objects for tf>2.6 * bugfix: force black text in GUI file dialogs (linux) * dssim loss - Move to stock tf.ssim function * Update optimizer imports for compatibility * fix logging for tf2.8 * Fix GUI graphing for TF2.8 * update tests * bump requirements.txt versions * Remove limit on nvidia-ml-py * Graphing bugfixes - Prevent live graph from displaying if data not yet available * bugfix: Live graph. Collect loss labels correctly * fix: live graph - swallow inconsistent loss errors * Bugfix: Prevent live graph from clearing during training * Fix graphing for AMD
86 lines
3 KiB
Python
86 lines
3 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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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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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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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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