def main(unused_argv): assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.' assert FLAGS.eval_dir, '`eval_dir` is missing.' if FLAGS.gpu: os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = FLAGS.gpu tf.gfile.MakeDirs(FLAGS.eval_dir) if FLAGS.pipeline_config_path: configs = config_util.get_configs_from_pipeline_file( FLAGS.pipeline_config_path) tf.gfile.Copy(FLAGS.pipeline_config_path, os.path.join(FLAGS.eval_dir, 'pipeline.config'), overwrite=True) else: configs = config_util.get_configs_from_multiple_files( model_config_path=FLAGS.model_config_path, eval_config_path=FLAGS.eval_config_path, eval_input_config_path=FLAGS.input_config_path) for name, config in [('model.config', FLAGS.model_config_path), ('eval.config', FLAGS.eval_config_path), ('input.config', FLAGS.input_config_path)]: tf.gfile.Copy(config, os.path.join(FLAGS.eval_dir, name), overwrite=True) model_config = configs['model'] eval_config = configs['eval_config'] input_config = configs['eval_input_config'] if FLAGS.eval_training_data: input_config = configs['train_input_config'] model_fn = functools.partial(model_builder.build, model_config=model_config, is_training=False) def get_next(config): return dataset_builder.make_initializable_iterator( dataset_builder.build(config)).get_next() create_input_dict_fn = functools.partial(get_next, input_config) categories = label_map_util.create_categories_from_labelmap( input_config.label_map_path) if FLAGS.run_once: eval_config.max_evals = 1 graph_rewriter_fn = None if 'graph_rewriter_config' in configs: graph_rewriter_fn = graph_rewriter_builder.build( configs['graph_rewriter_config'], is_training=False) evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories, FLAGS.checkpoint_dir, FLAGS.eval_dir, graph_hook_fn=graph_rewriter_fn)
def main(unused_argv): assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.' assert FLAGS.eval_dir, '`eval_dir` is missing.' tf.gfile.MakeDirs(FLAGS.eval_dir) if FLAGS.pipeline_config_path: configs = config_util.get_configs_from_pipeline_file( FLAGS.pipeline_config_path) tf.gfile.Copy(FLAGS.pipeline_config_path, os.path.join(FLAGS.eval_dir, 'pipeline.config'), overwrite=True) else: configs = config_util.get_configs_from_multiple_files( model_config_path=FLAGS.model_config_path, eval_config_path=FLAGS.eval_config_path, eval_input_config_path=FLAGS.input_config_path) for name, config in [('model.config', FLAGS.model_config_path), ('eval.config', FLAGS.eval_config_path), ('input.config', FLAGS.input_config_path)]: tf.gfile.Copy(config, os.path.join(FLAGS.eval_dir, name), overwrite=True) model_config = configs['model'] eval_config = configs['eval_config'] input_config = configs['eval_input_config'] if FLAGS.eval_training_data: input_config = configs['train_input_config'] model_fn = functools.partial( model_builder.build, model_config=model_config, is_training=False) def get_next(config): return dataset_builder.make_initializable_iterator( dataset_builder.build(config)).get_next() create_input_dict_fn = functools.partial(get_next, input_config) label_map = label_map_util.load_labelmap(input_config.label_map_path) max_num_classes = max([item.id for item in label_map.item]) categories = label_map_util.convert_label_map_to_categories( label_map, max_num_classes) if FLAGS.run_once: eval_config.max_evals = 1 graph_rewriter_fn = None if 'graph_rewriter_config' in configs: graph_rewriter_fn = graph_rewriter_builder.build( configs['graph_rewriter_config'], is_training=False) evaluator.evaluate( create_input_dict_fn, model_fn, eval_config, categories, FLAGS.checkpoint_dir, FLAGS.eval_dir, graph_hook_fn=graph_rewriter_fn)
def main(unused_argv): # Use the following lines to potentially restrict the training process to only 30% of the GPU V-RAM #gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3) #sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.' assert FLAGS.eval_dir, '`eval_dir` is missing.' tf.gfile.MakeDirs(FLAGS.eval_dir) if FLAGS.pipeline_config_path: configs = config_util.get_configs_from_pipeline_file( FLAGS.pipeline_config_path) tf.gfile.Copy( FLAGS.pipeline_config_path, os.path.join(FLAGS.eval_dir, 'pipeline.config'), overwrite=True) else: configs = config_util.get_configs_from_multiple_files( model_config_path=FLAGS.model_config_path, eval_config_path=FLAGS.eval_config_path, eval_input_config_path=FLAGS.input_config_path) for name, config in [('model.config', FLAGS.model_config_path), ('eval.config', FLAGS.eval_config_path), ('input.config', FLAGS.input_config_path)]: tf.gfile.Copy(config, os.path.join(FLAGS.eval_dir, name), overwrite=True) model_config = configs['model'] eval_config = configs['eval_config'] input_config = configs['eval_input_config'] if FLAGS.eval_training_data: input_config = configs['train_input_config'] model_fn = functools.partial( model_builder.build, model_config=model_config, is_training=False) def get_next(config): return dataset_builder.make_initializable_iterator( dataset_builder.build(config)).get_next() create_input_dict_fn = functools.partial(get_next, input_config) categories = label_map_util.create_categories_from_labelmap( input_config.label_map_path) if FLAGS.run_once: eval_config.max_evals = 1 graph_rewriter_fn = None if 'graph_rewriter_config' in configs: graph_rewriter_fn = graph_rewriter_builder.build( configs['graph_rewriter_config'], is_training=False) evaluator.evaluate( create_input_dict_fn, model_fn, eval_config, categories, FLAGS.checkpoint_dir, FLAGS.eval_dir, graph_hook_fn=graph_rewriter_fn)
def main(unused_argv): assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.' assert FLAGS.eval_dir, '`eval_dir` is missing.' tf.gfile.MakeDirs(FLAGS.eval_dir) configs = config_util.get_configs_from_pipeline_file( FLAGS.pipeline_config_path) tf.gfile.Copy(FLAGS.pipeline_config_path, os.path.join(FLAGS.eval_dir, 'pipeline.config'), overwrite=True) model_config = configs['model'] eval_config = configs['eval_config'] input_config = configs['eval_input_config'] if FLAGS.eval_training_data: input_config = configs['train_input_config'] model_fn = functools.partial(model_builder.build, model_config=model_config, is_training=False) def get_next(config): return dataset_builder.make_initializable_iterator( dataset_builder.build(config)).get_next() create_input_dict_fn = functools.partial(get_next, input_config) categories = label_map_util.create_categories_from_labelmap( input_config.label_map_path) if FLAGS.run_once: eval_config.max_evals = 1 graph_rewriter_fn = None if 'graph_rewriter_config' in configs: graph_rewriter_fn = graph_rewriter_builder.build( configs['graph_rewriter_config'], is_training=False) evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories, FLAGS.checkpoint_dir, FLAGS.eval_dir, graph_hook_fn=graph_rewriter_fn)
def evaluate(eval_dir, config_dir, checkpoint_dir, eval_training_data=False): ''' Function used to evaluate your trained model. Args: Required: eval_dir: The directory where the tfevent file will be saved. config_dir: The protobuf configuration directory. checkpoint_dir: The directory where the checkpoint you want to evaluate is. Optional: eval_training_data: Is set to True the evaluation will be run on the training dataset. Returns: A dictionnary of metrics ready to be sent to the picsell.ia platform. ''' tf.reset_default_graph() tf.gfile.MakeDirs(eval_dir) configs = config_util.get_configs_from_pipeline_file( os.path.join(config_dir, "pipeline.config")) model_config = configs['model'] eval_config = configs['eval_config'] input_config = configs['eval_input_config'] if eval_training_data: input_config = configs['train_input_config'] model_fn = functools.partial(model_builder.build, model_config=model_config, is_training=False) def get_next(config): return dataset_builder.make_initializable_iterator( dataset_builder.build(config)).get_next() create_input_dict_fn = functools.partial(get_next, input_config) categories = label_map_util.create_categories_from_labelmap( input_config.label_map_path) eval_config.max_evals = 1 graph_rewriter_fn = None if 'graph_rewriter_config' in configs: graph_rewriter_fn = graph_rewriter_builder.build( configs['graph_rewriter_config'], is_training=False) metrics = evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories, checkpoint_dir, eval_dir, graph_hook_fn=graph_rewriter_fn) return {k: str(round(v, 3)) for k, v in metrics.items()}
def main(unused_argv): assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.' assert FLAGS.eval_dir, '`eval_dir` is missing.' tf.gfile.MakeDirs(FLAGS.eval_dir) if FLAGS.pipeline_config_path: configs = config_util.get_configs_from_pipeline_file( FLAGS.pipeline_config_path) tf.gfile.Copy(FLAGS.pipeline_config_path, os.path.join(FLAGS.eval_dir, 'pipeline.config'), overwrite=True) else: configs = config_util.get_configs_from_multiple_files( model_config_path=FLAGS.model_config_path, eval_config_path=FLAGS.eval_config_path, eval_input_config_path=FLAGS.input_config_path) for name, config in [('model.config', FLAGS.model_config_path), ('eval.config', FLAGS.eval_config_path), ('input.config', FLAGS.input_config_path)]: tf.gfile.Copy(config, os.path.join(FLAGS.eval_dir, name), overwrite=True) model_config = configs['model'] eval_config = configs['eval_config'] input_config = configs['eval_input_config'] if FLAGS.eval_training_data: input_config = configs['train_input_config'] model_fn = functools.partial(model_builder.build, model_config=model_config, is_training=False) def get_next(config): return dataset_builder.make_initializable_iterator( dataset_builder.build(config)).get_next() #return dataset_util.make_initializable_iterator(dataset_builder.build(config)).get_next() create_input_dict_fn = functools.partial(get_next, input_config) label_map = label_map_util.load_labelmap(input_config.label_map_path) max_num_classes = max([item.id for item in label_map.item]) categories = label_map_util.convert_label_map_to_categories( label_map, max_num_classes) if FLAGS.run_once: eval_config.max_evals = 1 metrics = evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories, FLAGS.checkpoint_dir, FLAGS.eval_dir) print(metrics)
def main(unused_argv): assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.' assert FLAGS.eval_dir, '`eval_dir` is missing.' if FLAGS.pipeline_config_path: model_config, eval_config, input_config = get_configs_from_pipeline_file( ) else: model_config, eval_config, input_config = get_configs_from_multiple_files( ) model_fn = functools.partial(model_builder.build, model_config=model_config, is_training=False) create_input_dict_fn = functools.partial(input_reader_builder.build, input_config) label_map = label_map_util.load_labelmap(input_config.label_map_path) max_num_classes = max([item.id for item in label_map.item]) categories = label_map_util.convert_label_map_to_categories( label_map, max_num_classes) evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories, FLAGS.checkpoint_dir, FLAGS.eval_dir)
def main(unused_argv): assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.' assert FLAGS.eval_dir, '`eval_dir` is missing.' tf.gfile.MakeDirs(FLAGS.eval_dir) if FLAGS.pipeline_config_path: configs = config_util.get_configs_from_pipeline_file( FLAGS.pipeline_config_path) tf.gfile.Copy(FLAGS.pipeline_config_path, os.path.join(FLAGS.eval_dir, 'pipeline.config'), overwrite=True) else: configs = config_util.get_configs_from_multiple_files( model_config_path=FLAGS.model_config_path, eval_config_path=FLAGS.eval_config_path, eval_input_config_path=FLAGS.input_config_path) for name, config in [('model.config', FLAGS.model_config_path), ('eval.config', FLAGS.eval_config_path), ('input.config', FLAGS.input_config_path)]: tf.gfile.Copy(config, os.path.join(FLAGS.eval_dir, name), overwrite=True) model_config = configs['model'] eval_config = configs['eval_config'] input_config = configs['eval_input_config'] if FLAGS.eval_training_data: input_config = configs['train_input_config'] model_fn = functools.partial(model_builder.build, model_config=model_config, is_training=False) def get_next(config): return dataset_builder.make_initializable_iterator( dataset_builder.build(config)).get_next() create_input_dict_fn = functools.partial(get_next, input_config) categories = label_map_util.create_categories_from_labelmap( input_config.label_map_path) if FLAGS.run_once: eval_config.max_evals = 1 graph_rewriter_fn = None if 'graph_rewriter_config' in configs: graph_rewriter_fn = graph_rewriter_builder.build( configs['graph_rewriter_config'], is_training=False) metrics_dict = evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories, FLAGS.checkpoint_dir, FLAGS.eval_dir, graph_hook_fn=graph_rewriter_fn) with open(FLAGS.output_json_path, 'w') as op_json_file: temp_dict = {} for key, value in metrics_dict.items(): temp_dict[key] = str(value) json.dump(temp_dict, op_json_file)