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