diff --git a/tools/benchmark/benchmark.py b/tools/benchmark/benchmark.py index 95965c9e653..0764bbf9958 100644 --- a/tools/benchmark/benchmark.py +++ b/tools/benchmark/benchmark.py @@ -66,7 +66,7 @@ class Benchmark: ie_network = self.ie.read_network(xml_filename, bin_filename) - input_info = ie_network.inputs + input_info = ie_network.input_info if not input_info: raise AttributeError('No inputs info is provided') diff --git a/tools/benchmark/main.py b/tools/benchmark/main.py index 8945cc3ee66..df40950c61f 100644 --- a/tools/benchmark/main.py +++ b/tools/benchmark/main.py @@ -175,12 +175,12 @@ def run(args): # --------------------- 5. Resizing network to match image sizes and given batch --------------------------- next_step() - shapes = {k: v.shape.copy() for k, v in ie_network.inputs.items()} + shapes = {k: v.input_data.shape.copy() for k, v in ie_network.input_info.items()} reshape = False if args.shape: - reshape |= update_shapes(shapes, args.shape, ie_network.inputs) + reshape |= update_shapes(shapes, args.shape, ie_network.input_info) if args.batch_size and args.batch_size != ie_network.batch_size: - reshape |= adjust_shapes_batch(shapes, args.batch_size, ie_network.inputs) + reshape |= adjust_shapes_batch(shapes, args.batch_size, ie_network.input_info) if reshape: start_time = datetime.utcnow() @@ -259,7 +259,7 @@ def run(args): if args.paths_to_input: for path in args.paths_to_input: paths_to_input.append(os.path.abspath(*path) if args.paths_to_input else None) - set_inputs(paths_to_input, batch_size, exe_network.inputs, infer_requests) + set_inputs(paths_to_input, batch_size, exe_network.input_info, infer_requests) if statistics: statistics.add_parameters(StatisticsReport.Category.RUNTIME_CONFIG, diff --git a/tools/benchmark/utils/inputs_filling.py b/tools/benchmark/utils/inputs_filling.py index b27f6ebcf56..ae7fa64a089 100644 --- a/tools/benchmark/utils/inputs_filling.py +++ b/tools/benchmark/utils/inputs_filling.py @@ -47,13 +47,13 @@ def set_inputs(paths_to_input, batch_size, input_info, requests): def get_inputs(paths_to_input, batch_size, input_info, requests): input_image_sizes = {} for key in sorted(input_info.keys()): - if is_image(input_info[key]): - input_image_sizes[key] = (input_info[key].shape[2], input_info[key].shape[3]) + if is_image(input_info[key].input_data): + input_image_sizes[key] = (input_info[key].input_data.shape[2], input_info[key].input_data.shape[3]) logger.info("Network input '{}' precision {}, dimensions ({}): {}".format(key, - input_info[key].precision, - input_info[key].layout, + input_info[key].input_data.precision, + input_info[key].input_data.layout, " ".join(str(x) for x in - input_info[key].shape))) + input_info[key].input_data.shape))) images_count = len(input_image_sizes.keys()) binaries_count = len(input_info) - images_count @@ -102,31 +102,31 @@ def get_inputs(paths_to_input, batch_size, input_info, requests): input_data = {} keys = list(sorted(input_info.keys())) for key in keys: - if is_image(input_info[key]): + if is_image(input_info[key].input_data): # input is image if len(image_files) > 0: input_data[key] = fill_blob_with_image(image_files, request_id, batch_size, keys.index(key), - len(keys), input_info[key]) + len(keys), input_info[key].input_data) continue # input is binary if len(binary_files): input_data[key] = fill_blob_with_binary(binary_files, request_id, batch_size, keys.index(key), - len(keys), input_info[key]) + len(keys), input_info[key].input_data) continue # most likely input is image info - if is_image_info(input_info[key]) and len(input_image_sizes) == 1: + if is_image_info(input_info[key].input_data) and len(input_image_sizes) == 1: image_size = input_image_sizes[list(input_image_sizes.keys()).pop()] logger.info("Fill input '" + key + "' with image size " + str(image_size[0]) + "x" + str(image_size[1])) - input_data[key] = fill_blob_with_image_info(image_size, input_info[key]) + input_data[key] = fill_blob_with_image_info(image_size, input_info[key].input_data) continue # fill with random data logger.info("Fill input '{}' with random values ({} is expected)".format(key, "image" if is_image( - input_info[key]) else "some binary data")) - input_data[key] = fill_blob_with_random(input_info[key]) + input_info[key].input_data) else "some binary data")) + input_data[key] = fill_blob_with_random(input_info[key].input_data) requests_input_data.append(input_data) diff --git a/tools/benchmark/utils/utils.py b/tools/benchmark/utils/utils.py index 2cef9f9de5a..16c28bb3828 100644 --- a/tools/benchmark/utils/utils.py +++ b/tools/benchmark/utils/utils.py @@ -62,10 +62,10 @@ def next_step(additional_info='', step_id=0): def config_network_inputs(ie_network: IENetwork): - input_info = ie_network.inputs + input_info = ie_network.input_info for key in input_info.keys(): - if is_image(input_info[key]): + if is_image(input_info[key].input_data): # Set the precision of input data provided by the user # Should be called before load of the network to the plugin input_info[key].precision = 'U8' @@ -261,7 +261,7 @@ def update_shapes(shapes, shapes_string: str, inputs_info): def adjust_shapes_batch(shapes, batch_size: int, inputs_info): updated = False for name, data in inputs_info.items(): - layout = data.layout + layout = data.input_data.layout batch_index = layout.index('N') if 'N' in layout else -1 if batch_index != -1 and shapes[name][batch_index] != batch_size: shapes[name][batch_index] = batch_size