def get_input_fn(mode, data_files, meta_data_file): return plx.processing.create_input_data_fn( mode=mode, pipeline_config=TFRecordImagePipelineConfig( shuffle=plx.Modes.is_train(mode), dynamic_pad=False, batch_size=64 if plx.Modes.is_train(mode) else 32, data_files=data_files, meta_data_file=meta_data_file, feature_processors=FeatureProcessorsConfig.from_dict( {'image': { 'input_layers': [['image', 0, 0]], 'output_layers': [['std', 0, 0]], 'layers': [ {'Cast': { 'name': 'cast', 'dtype': 'float32', 'inbound_nodes': [['image', 0, 0]] }}, {'Standardization': { 'name': 'std', 'inbound_nodes': [['cast', 0, 0]] }}, ] }}) ) )
def get_pipeline_config(mode): return TFRecordImagePipelineConfig( dynamic_pad=False, data_files=train_data_file if Modes.is_train(mode) else eval_data_file, meta_data_file=meta_data_filename, feature_processors=FeatureProcessorsConfig({ 'image': GraphConfig(input_layers=[['image', 0, 0]], output_layers=[['image_out', 0, 0]], layers=[ CastConfig(dtype='float32', name='image_out', inbound_nodes=[['image', 0, 0]]) ]) }))
def test_feature_processors(self): config_dict = { 'image1': { 'input_layers': ['image'], 'output_layers': ['reshap_0'], 'layers': [{ 'Resize': { 'height': 28, 'width': 28 } }, { 'Reshape': { 'target_shape': [784] } }] }, 'image2': { 'input_layers': ['image'], 'output_layers': ['reshap_0'], 'layers': [{ 'Standardization': {} }, { 'Resize': { 'height': 28, 'width': 28 } }, { 'Reshape': { 'target_shape': [784] } }] } } config = FeatureProcessorsConfig.from_dict(config_dict) config_to_dict = config.to_dict() assert_equal_feature_processors(config_to_dict, config_dict)