def create_sequence_dataset_input_fn(dataset_dir, prepare_fn, record_file_name_format, meta_data_file_name_format, bucket_boundaries): prepare_fn(dataset_dir) train_data_file = record_file_name_format.format(dataset_dir, Modes.TRAIN) eval_data_file = record_file_name_format.format(dataset_dir, Modes.EVAL) meta_data_filename = meta_data_file_name_format.format(dataset_dir) train_input_fn = create_input_data_fn( mode=Modes.TRAIN, pipeline_config=PipelineConfig(module='TFRecordSequencePipeline', dynamic_pad=True, bucket_boundaries=bucket_boundaries, params={ 'data_files': train_data_file, 'meta_data_file': meta_data_filename })) eval_input_fn = create_input_data_fn( mode=Modes.EVAL, pipeline_config=PipelineConfig(module='TFRecordSequencePipeline', dynamic_pad=True, bucket_boundaries=bucket_boundaries, params={ 'data_files': eval_data_file, 'meta_data_file': meta_data_filename })) return train_input_fn, eval_input_fn
def create_input_fn(dataset_dir): prepare(dataset_dir) train_data_file = RECORD_FILE_NAME_FORMAT.format(dataset_dir, ModeKeys.TRAIN) eval_data_file = RECORD_FILE_NAME_FORMAT.format(dataset_dir, ModeKeys.EVAL) meta_data_filename = MEAT_DATA_FILENAME_FORMAT.format(dataset_dir) train_input_fn = create_input_data_fn(mode=ModeKeys.TRAIN, pipeline_config=PipelineConfig( name='TFRecordImagePipeline', dynamic_pad=False, params={ 'data_files': train_data_file, 'meta_data_file': meta_data_filename })) eval_input_fn = create_input_data_fn(mode=ModeKeys.EVAL, pipeline_config=PipelineConfig( name='TFRecordImagePipeline', dynamic_pad=False, params={ 'data_files': eval_data_file, 'meta_data_file': meta_data_filename })) return train_input_fn, eval_input_fn
def create_image_dataset_input_fn(dataset_dir, prepare_fn, record_file_name_format, meta_data_file_name_format): prepare_fn(dataset_dir) train_data_file = record_file_name_format.format(dataset_dir, Modes.TRAIN) eval_data_file = record_file_name_format.format(dataset_dir, Modes.EVAL) meta_data_filename = meta_data_file_name_format.format(dataset_dir) 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]]) ]) })) train_input_fn = create_input_data_fn(mode=Modes.TRAIN, pipeline_config=get_pipeline_config( Modes.TRAIN)) eval_input_fn = create_input_data_fn(mode=Modes.EVAL, pipeline_config=get_pipeline_config( Modes.EVAL)) return train_input_fn, eval_input_fn
def create_image_dataset_input_fn(dataset_dir, prepare_fn, record_file_name_format, meta_data_file_name_format): prepare_fn(dataset_dir) train_data_file = record_file_name_format.format(dataset_dir, Modes.TRAIN) eval_data_file = record_file_name_format.format(dataset_dir, Modes.EVAL) meta_data_filename = meta_data_file_name_format.format(dataset_dir) train_input_fn = create_input_data_fn(mode=Modes.TRAIN, pipeline_config=PipelineConfig( module='TFRecordImagePipeline', dynamic_pad=False, params={ 'data_files': train_data_file, 'meta_data_file': meta_data_filename })) eval_input_fn = create_input_data_fn(mode=Modes.EVAL, pipeline_config=PipelineConfig( module='TFRecordImagePipeline', dynamic_pad=False, params={ 'data_files': eval_data_file, 'meta_data_file': meta_data_filename })) return train_input_fn, eval_input_fn
def create_image_dataset_input_fn(dataset_dir, prepare_fn, record_file_name_format, meta_data_file_name_format): prepare_fn(dataset_dir) train_data_file = record_file_name_format.format(dataset_dir, Modes.TRAIN) eval_data_file = record_file_name_format.format(dataset_dir, Modes.EVAL) meta_data_filename = meta_data_file_name_format.format(dataset_dir) train_input_fn = create_input_data_fn( mode=Modes.TRAIN, pipeline_config=PipelineConfig(module='TFRecordImagePipeline', dynamic_pad=False, params={'data_files': train_data_file, 'meta_data_file': meta_data_filename}) ) eval_input_fn = create_input_data_fn( mode=Modes.EVAL, pipeline_config=PipelineConfig(module='TFRecordImagePipeline', dynamic_pad=False, params={'data_files': eval_data_file, 'meta_data_file': meta_data_filename}) ) return train_input_fn, eval_input_fn
def create_sequence_dataset_input_fn(dataset_dir, prepare_fn, record_file_name_format, meta_data_file_name_format, bucket_boundaries): prepare_fn(dataset_dir) train_data_file = record_file_name_format.format(dataset_dir, Modes.TRAIN) eval_data_file = record_file_name_format.format(dataset_dir, Modes.EVAL) meta_data_filename = meta_data_file_name_format.format(dataset_dir) train_input_fn = create_input_data_fn( mode=Modes.TRAIN, pipeline_config=PipelineConfig(module='TFRecordSequencePipeline', dynamic_pad=True, bucket_boundaries=bucket_boundaries, params={'data_files': train_data_file, 'meta_data_file': meta_data_filename}) ) eval_input_fn = create_input_data_fn( mode=Modes.EVAL, pipeline_config=PipelineConfig(module='TFRecordSequencePipeline', dynamic_pad=True, bucket_boundaries=bucket_boundaries, params={'data_files': eval_data_file, 'meta_data_file': meta_data_filename}) ) return train_input_fn, eval_input_fn
def create_dataset_predict_input_fn(dataset_dir, prepare_fn, record_file_name_format, meta_data_file_name_format): prepare_fn(dataset_dir) test_data_file = record_file_name_format.format(dataset_dir, Modes.PREDICT) meta_data_filename = meta_data_file_name_format.format(dataset_dir) test_input_fn = create_input_data_fn( mode=Modes.PREDICT, pipeline_config=PipelineConfig(module='TFRecordImagePipeline', dynamic_pad=False, num_epochs=1, params={'data_files': test_data_file, 'meta_data_file': meta_data_filename}) ) return test_input_fn
def create_image_dataset_predict_input_fn(dataset_dir, prepare_fn, record_file_name_format, meta_data_file_name_format): prepare_fn(dataset_dir) test_data_file = record_file_name_format.format(dataset_dir, Modes.PREDICT) meta_data_filename = meta_data_file_name_format.format(dataset_dir) test_input_fn = create_input_data_fn( mode=Modes.PREDICT, pipeline_config=PipelineConfig(module='TFRecordImagePipeline', dynamic_pad=False, num_epochs=1, params={'data_files': test_data_file, 'meta_data_file': meta_data_filename}) ) return test_input_fn
def create_image_dataset_predict_input_fn(dataset_dir, prepare_fn, record_file_name_format, meta_data_file_name_format): prepare_fn(dataset_dir) test_data_file = record_file_name_format.format(dataset_dir, Modes.PREDICT) meta_data_filename = meta_data_file_name_format.format(dataset_dir) test_input_fn = create_input_data_fn( mode=Modes.PREDICT, pipeline_config=TFRecordImagePipelineConfig( dynamic_pad=False, num_epochs=1, data_files=test_data_file, meta_data_file=meta_data_filename)) return test_input_fn
def create_sequence_dataset_predict_input_fn(dataset_dir, prepare_fn, record_file_name_format, meta_data_file_name_format, bucket_boundaries): prepare_fn(dataset_dir) test_data_file = record_file_name_format.format(dataset_dir, Modes.PREDICT) meta_data_filename = meta_data_file_name_format.format(dataset_dir) test_input_fn = create_input_data_fn( mode=Modes.PREDICT, pipeline_config=PipelineConfig(module='TFRecordSequencePipeline', dynamic_pad=True, num_epochs=1, batch_size=4, min_after_dequeue=0, bucket_boundaries=bucket_boundaries, params={'data_files': test_data_file, 'meta_data_file': meta_data_filename}) ) return test_input_fn
def create_sequence_dataset_predict_input_fn(dataset_dir, prepare_fn, record_file_name_format, meta_data_file_name_format, bucket_boundaries): prepare_fn(dataset_dir) test_data_file = record_file_name_format.format(dataset_dir, Modes.PREDICT) meta_data_filename = meta_data_file_name_format.format(dataset_dir) test_input_fn = create_input_data_fn( mode=Modes.PREDICT, pipeline_config=TFRecordSequencePipelineConfig( dynamic_pad=True, num_epochs=1, batch_size=4, min_after_dequeue=0, bucket_boundaries=bucket_boundaries, data_files=test_data_file, meta_data_file=meta_data_filename)) return test_input_fn