def block_test(layer_func, kwargs={}, input_shape=None):
"""Test routine for faceswap neural network blocks.
Tests are simple and are to ensure that the blocks compile on both tensorflow
and plaidml backends
"""
# generate input data
assert input_shape
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)
expected_output_dtype = input_dtype
# test in functional API
inp = Input(shape=input_shape[1:], dtype=input_dtype)
outp = layer_func(inp, **kwargs)
assert K.dtype(outp) == expected_output_dtype
# check with the functional API
model = Model(inp, outp)
actual_output = model.predict(input_data)
# 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)
# for further checks in the caller function
return actual_output
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
