mirror of
https://github.com/zebrajr/faceswap.git
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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
73 lines
2.9 KiB
Python
73 lines
2.9 KiB
Python
#!/usr/bin/env python3
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""" Tests for Faceswap Losses.
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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 keras import backend as K
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from keras import losses as k_losses
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from keras.layers import Conv2D
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from keras.models import Sequential
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from keras.optimizers import Adam
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from lib.model import losses
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from lib.utils import get_backend
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_PARAMS = [(losses.GeneralizedLoss(), (2, 16, 16)),
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(losses.GradientLoss(), (2, 16, 16)),
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# TODO Make sure these output dimensions are correct
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(losses.GMSDLoss(), (2, 1, 1)),
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# TODO Make sure these output dimensions are correct
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(losses.LInfNorm(), (2, 1, 1))]
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_IDS = ["GeneralizedLoss", "GradientLoss", "GMSDLoss", "LInfNorm"]
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_IDS = ["{}[{}]".format(loss, get_backend().upper()) for loss in _IDS]
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@pytest.mark.parametrize(["loss_func", "output_shape"], _PARAMS, ids=_IDS)
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def test_loss_output(loss_func, output_shape):
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""" Basic shape tests for loss functions. """
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if get_backend() == "amd" and isinstance(loss_func, losses.GMSDLoss):
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pytest.skip("GMSD Loss is not currently compatible with PlaidML")
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y_a = K.variable(np.random.random((2, 16, 16, 3)))
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y_b = K.variable(np.random.random((2, 16, 16, 3)))
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objective_output = loss_func(y_a, y_b)
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if get_backend() == "amd":
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assert K.eval(objective_output).shape == output_shape
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else:
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output = objective_output.numpy()
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assert output.dtype == "float32" and not np.isnan(output)
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_LWPARAMS = [losses.GeneralizedLoss(), losses.GradientLoss(), losses.GMSDLoss(),
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losses.LInfNorm(), k_losses.mean_absolute_error, k_losses.mean_squared_error,
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k_losses.logcosh, losses.DSSIMObjective()]
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_LWIDS = ["GeneralizedLoss", "GradientLoss", "GMSDLoss", "LInfNorm", "mae", "mse", "logcosh",
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"DSSIMObjective"]
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_LWIDS = ["{}[{}]".format(loss, get_backend().upper()) for loss in _LWIDS]
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@pytest.mark.parametrize("loss_func", _LWPARAMS, ids=_LWIDS)
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def test_loss_wrapper(loss_func):
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""" Test penalized loss wrapper works as expected """
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if get_backend() == "amd":
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if isinstance(loss_func, losses.GMSDLoss):
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pytest.skip("GMSD Loss is not currently compatible with PlaidML")
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if hasattr(loss_func, "__name__") and loss_func.__name__ == "logcosh":
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pytest.skip("LogCosh Loss is not currently compatible with PlaidML")
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y_a = K.variable(np.random.random((2, 16, 16, 4)))
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y_b = K.variable(np.random.random((2, 16, 16, 3)))
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p_loss = losses.LossWrapper()
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p_loss.add_loss(loss_func, 1.0, -1)
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p_loss.add_loss(k_losses.mean_squared_error, 2.0, 3)
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output = p_loss(y_a, y_b)
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if get_backend() == "amd":
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assert K.dtype(output) == "float32" and K.eval(output).shape == (2, )
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else:
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output = output.numpy()
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assert output.dtype == "float32" and not np.isnan(output)
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