mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-08 03:26:47 -04:00
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)
142 lines
5 KiB
Python
142 lines
5 KiB
Python
#!/usr/bin/env python3
|
|
""" Tests for Faceswap Custom Layers.
|
|
|
|
Adapted from Keras tests.
|
|
"""
|
|
|
|
|
|
import pytest
|
|
import numpy as np
|
|
|
|
from numpy.testing import assert_allclose
|
|
|
|
from lib.model import layers
|
|
from lib.utils import get_backend
|
|
from tests.utils import has_arg
|
|
|
|
if get_backend() == "amd":
|
|
from keras import Input, Model, backend as K
|
|
else:
|
|
# Ignore linting errors from Tensorflow's thoroughly broken import system
|
|
from tensorflow.keras import Input, Model, backend as K # pylint:disable=import-error
|
|
|
|
|
|
CONV_SHAPE = (3, 3, 256, 2048)
|
|
CONV_ID = get_backend().upper()
|
|
|
|
|
|
def layer_test(layer_cls, kwargs={}, input_shape=None, input_dtype=None,
|
|
input_data=None, expected_output=None,
|
|
expected_output_dtype=None, fixed_batch_size=False):
|
|
"""Test routine for a layer with a single input tensor
|
|
and single output tensor.
|
|
"""
|
|
# generate input data
|
|
if input_data is None:
|
|
assert input_shape
|
|
if not input_dtype:
|
|
input_dtype = K.floatx()
|
|
input_data_shape = list(input_shape)
|
|
for i, var_e in enumerate(input_data_shape):
|
|
if var_e is None:
|
|
input_data_shape[i] = np.random.randint(1, 4)
|
|
input_data = (10 * np.random.random(input_data_shape))
|
|
input_data = input_data.astype(input_dtype)
|
|
else:
|
|
if input_shape is None:
|
|
input_shape = input_data.shape
|
|
if input_dtype is None:
|
|
input_dtype = input_data.dtype
|
|
if expected_output_dtype is None:
|
|
expected_output_dtype = input_dtype
|
|
|
|
# instantiation
|
|
layer = layer_cls(**kwargs)
|
|
|
|
# test get_weights , set_weights at layer level
|
|
weights = layer.get_weights()
|
|
layer.set_weights(weights)
|
|
|
|
layer.build(input_shape)
|
|
expected_output_shape = layer.compute_output_shape(input_shape)
|
|
|
|
# test in functional API
|
|
if fixed_batch_size:
|
|
inp = Input(batch_shape=input_shape, dtype=input_dtype)
|
|
else:
|
|
inp = Input(shape=input_shape[1:], dtype=input_dtype)
|
|
outp = layer(inp)
|
|
assert K.dtype(outp) == expected_output_dtype
|
|
|
|
# check with the functional API
|
|
model = Model(inp, outp)
|
|
|
|
actual_output = model.predict(input_data)
|
|
actual_output_shape = actual_output.shape
|
|
for expected_dim, actual_dim in zip(expected_output_shape,
|
|
actual_output_shape):
|
|
if expected_dim is not None:
|
|
assert expected_dim == actual_dim
|
|
|
|
if expected_output is not None:
|
|
assert_allclose(actual_output, expected_output, rtol=1e-3)
|
|
|
|
# test serialization, weight setting at model level
|
|
model_config = model.get_config()
|
|
recovered_model = model.__class__.from_config(model_config)
|
|
if model.weights:
|
|
weights = model.get_weights()
|
|
recovered_model.set_weights(weights)
|
|
_output = recovered_model.predict(input_data)
|
|
assert_allclose(_output, actual_output, rtol=1e-3)
|
|
|
|
# test training mode (e.g. useful when the layer has a
|
|
# different behavior at training and testing time).
|
|
if has_arg(layer.call, 'training'):
|
|
model.compile('rmsprop', 'mse')
|
|
model.train_on_batch(input_data, actual_output)
|
|
|
|
# test instantiation from layer config
|
|
layer_config = layer.get_config()
|
|
layer_config['batch_input_shape'] = input_shape
|
|
layer = layer.__class__.from_config(layer_config)
|
|
|
|
# for further checks in the caller function
|
|
return actual_output
|
|
|
|
|
|
@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
|
|
def test_pixel_shuffler(dummy): # pylint:disable=unused-argument
|
|
""" Pixel Shuffler layer test """
|
|
layer_test(layers.PixelShuffler, input_shape=(2, 4, 4, 1024))
|
|
|
|
|
|
@pytest.mark.skipif(get_backend() == "amd", reason="amd does not support this layer")
|
|
@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
|
|
def test_subpixel_upscaling(dummy): # pylint:disable=unused-argument
|
|
""" Sub Pixel up-scaling layer test """
|
|
layer_test(layers.SubPixelUpscaling, input_shape=(2, 4, 4, 1024))
|
|
|
|
|
|
@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
|
|
def test_reflection_padding_2d(dummy): # pylint:disable=unused-argument
|
|
""" Reflection Padding 2D layer test """
|
|
layer_test(layers.ReflectionPadding2D, input_shape=(2, 4, 4, 512))
|
|
|
|
|
|
@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
|
|
def test_global_min_pooling_2d(dummy): # pylint:disable=unused-argument
|
|
""" Global Min Pooling 2D layer test """
|
|
layer_test(layers.GlobalMinPooling2D, input_shape=(2, 4, 4, 1024))
|
|
|
|
|
|
@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
|
|
def test_global_std_pooling_2d(dummy): # pylint:disable=unused-argument
|
|
""" Global Standard Deviation Pooling 2D layer test """
|
|
layer_test(layers.GlobalStdDevPooling2D, input_shape=(2, 4, 4, 1024))
|
|
|
|
|
|
@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
|
|
def test_l2_normalize(dummy): # pylint:disable=unused-argument
|
|
""" L2 Normalize layer test """
|
|
layer_test(layers.L2_normalize, kwargs={"axis": 1}, input_shape=(2, 4, 4, 1024))
|