[MO] remove deprecated mean_file option (#14137)
Signed-off-by: andrei.kochin <andrei.kochin@intel.com> Signed-off-by: andrei.kochin <andrei.kochin@intel.com>
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Andrei Kochin
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1a4117b855
@ -17,19 +17,6 @@ Caffe-specific parameters:
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Path to python Caffe parser generated from caffe.proto
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-k K Path to CustomLayersMapping.xml to register custom
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layers
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--mean_file MEAN_FILE, -mf MEAN_FILE
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[DEPRECATED] Mean image to be used for the input. Should be a
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binaryproto file
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--mean_file_offsets MEAN_FILE_OFFSETS, -mo MEAN_FILE_OFFSETS
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[DEPRECATED] Mean image offsets to be used for the input
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binaryproto file. When the mean image is bigger than
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the expected input, it is cropped. By default, centers
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of the input image and the mean image are the same and
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the mean image is cropped by dimensions of the input
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image. The format to pass this option is the
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following: "-mo (x,y)". In this case, the mean file is
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cropped by dimensions of the input image with offset
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(x,y) from the upper left corner of the mean image
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--disable_omitting_optional
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Disable omitting optional attributes to be used for
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custom layers. Use this option if you want to transfer
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@ -237,18 +237,6 @@ def arguments_post_parsing(argv: argparse.Namespace):
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if is_tf and argv.tensorflow_use_custom_operations_config is not None:
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argv.transformations_config = argv.tensorflow_use_custom_operations_config
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if is_caffe and argv.mean_file and argv.mean_values:
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raise Error('Both --mean_file and mean_values are specified. Specify either mean file or mean values. ' +
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refer_to_faq_msg(17))
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elif is_caffe and argv.mean_file and argv.mean_file_offsets:
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values = get_tuple_values(argv.mean_file_offsets, t=int, num_exp_values=2)
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mean_file_offsets = mo_array([int(x) for x in values[0].split(',')])
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if not all([offset >= 0 for offset in mean_file_offsets]):
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raise Error("Negative value specified for --mean_file_offsets option. "
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"Please specify positive integer values in format '(x,y)'. " +
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refer_to_faq_msg(18))
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argv.mean_file_offsets = mean_file_offsets
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if argv.scale and argv.scale_values:
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raise Error(
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'Both --scale and --scale_values are defined. Specify either scale factor or scale values per input ' +
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@ -26,59 +26,6 @@ def import_caffe_pb2(caffe_parser_path: str):
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return caffe_pb2
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def parse_mean(file_path: str, in_shape: np.ndarray, mean_file_offsets: [tuple, None], caffe_pb2):
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blob = caffe_pb2.BlobProto()
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with open(file_path, 'rb') as file:
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data = file.read()
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if not data:
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raise Error('Mean file "{}" is empty.' + refer_to_faq_msg(5),
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file_path)
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try:
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blob.ParseFromString(data)
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data = mo_array(blob.data) # pylint: disable=no-member
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if blob.HasField('channels') or blob.HasField('height') or blob.HasField('width'):
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data = data.reshape(blob.channels, blob.height, blob.width) # pylint: disable=no-member
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else:
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data = data.reshape(blob.shape.dim) # pylint: disable=no-member
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# crop mean image according to input size
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if in_shape[2] > data.shape[1] or in_shape[3] > data.shape[2]:
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raise Error(
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'Input image of shape {} is larger than mean image {} from file "{}". ' +
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refer_to_faq_msg(4),
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in_shape,
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data.shape,
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file_path
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)
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if mean_file_offsets is not None and len(mean_file_offsets) == 2:
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offset_x = mean_file_offsets[0]
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offset_y = mean_file_offsets[1]
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else:
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offset_x = int((data.shape[1] - in_shape[2]) / 2)
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offset_y = int((data.shape[2] - in_shape[3]) / 2)
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mean = []
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for i in range(in_shape[1]):
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data_channel = np.zeros(in_shape[2] * in_shape[3], dtype=np.float32)
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for x in range(in_shape[2]):
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for y in range(in_shape[3]):
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data_channel[x * in_shape[3] + y] = data[i, x + offset_x, y + offset_y]
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mean.append(data_channel)
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return mean
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except Exception as err:
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raise Error(
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'While processing mean file "{}": {}. Probably mean file has incorrect format. ' +
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refer_to_faq_msg(6),
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file_path,
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str(err)) from err
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def load_caffe_proto_model(caffe_pb2, proto_path: str, model_path: [str, None] = None):
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# 1. python protobuf is used
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if api_implementation._implementation_type == 'python':
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@ -28,17 +28,4 @@ class CaffeMeanFileProcessing(MiddleReplacementPattern):
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caffe_pb2 = graph.graph['caffe_pb2']
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del graph.graph['caffe_pb2']
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input_names = find_inputs(graph)
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mf = []
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try:
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if argv.mean_file and len(original_shapes) == 1:
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mf = loader.parse_mean(argv.mean_file, original_shapes[input_names[0]],
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argv.mean_file_offsets, caffe_pb2)
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elif argv.mean_file:
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raise Error('Mean file for topologies with multiple inputs is not supported. ' +
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refer_to_faq_msg(9))
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except ValueError as e:
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raise Error('Cannot load or process mean file: value error {}. ' +
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refer_to_faq_msg(10), str(e)) from e
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graph.graph['mf'] = mf
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graph.graph['input_names'] = input_names
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@ -823,8 +823,6 @@ class DeprecatedCanonicalizePathCheckExistenceAction(CanonicalizePathCheckExiste
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option_string)
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if 'tensorflow_use_custom_operations_config' in option_string:
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dep_msg += 'Please use --transformations_config cli option instead'
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if 'mean_file' in option_string or 'mean_offset' in option_string:
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dep_msg += 'Please use --mean_values cli option instead.'
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log.error(dep_msg, extra={'is_warning': True})
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super().__call__(parser, namespace, values, option_string)
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@ -1127,8 +1125,6 @@ def get_caffe_cli_options():
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d = {
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'input_proto': ['- Path to the Input prototxt', lambda x: x],
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'caffe_parser_path': ['- Path to Python Caffe* parser generated from caffe.proto', lambda x: x],
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'mean_file': ['- Path to a mean file', lambda x: x if x else 'Not specified'],
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'mean_file_offsets': ['- Offsets for a mean file', lambda x: x if x else 'Not specified'],
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'k': '- Path to CustomLayersMapping.xml',
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'disable_resnet_optimization': ['- Enable resnet optimization', lambda x: not x],
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}
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@ -1181,8 +1177,8 @@ def get_onnx_cli_options():
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def get_params_with_paths_list():
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return ['input_model', 'output_dir', 'caffe_parser_path', 'extensions', 'k', 'output_dir',
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'input_checkpoint', 'input_meta_graph', 'input_proto', 'input_symbol', 'mean_file',
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'mean_file_offsets', 'pretrained_model_name', 'saved_model_dir', 'tensorboard_logdir',
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'input_checkpoint', 'input_meta_graph', 'input_proto', 'input_symbol',
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'pretrained_model_name', 'saved_model_dir', 'tensorboard_logdir',
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'tensorflow_custom_layer_libraries', 'tensorflow_custom_operations_config_update',
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'tensorflow_object_detection_api_pipeline_config', 'tensorflow_use_custom_operations_config',
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'transformations_config']
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@ -1219,20 +1215,6 @@ def get_caffe_cli_parser(parser: argparse.ArgumentParser = None):
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'front', 'caffe',
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'CustomLayersMapping.xml'),
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action=CanonicalizePathCheckExistenceAction)
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caffe_group.add_argument('--mean_file', '-mf',
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help='[DEPRECATED] ' +
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'Mean image to be used for the input. Should be a binaryproto file',
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default=None,
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action=DeprecatedCanonicalizePathCheckExistenceAction)
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caffe_group.add_argument('--mean_file_offsets', '-mo',
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help='[DEPRECATED] ' +
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'Mean image offsets to be used for the input binaryproto file. ' +
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'When the mean image is bigger than the expected input, it is cropped. By default, centers ' +
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'of the input image and the mean image are the same and the mean image is cropped by ' +
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'dimensions of the input image. The format to pass this option is the following: "-mo (x,y)". In this ' +
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'case, the mean file is cropped by dimensions of the input image with offset (x,y) ' +
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'from the upper left corner of the mean image',
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default=None)
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caffe_group.add_argument('--disable_omitting_optional',
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help=mo_convert_params_caffe['disable_omitting_optional'].description,
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action='store_true',
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