Python onnx.save方法代碼示例

# 需要導入模塊: import onnx [as 別名]
# 或者: from onnx import save [as 別名]
def rewrite_onnx_file(model_filename, out_filename, new_input_types):
    xmodel = onnx.load(model_filename)
    xmodel = rewrite_onnx_model(xmodel, new_input_types)
    onnx.save(xmodel, out_filename)
    return xmodel 

# 需要導入模塊: import onnx [as 別名]
# 或者: from onnx import save [as 別名]
def main():
    args = parser.parse_args()
    onnx_model = onnx.load(args.model)
    num_original_nodes, original_graph_str = traverse_graph(onnx_model.graph)

    # Optimizer passes to perform
    passes = [
        #'eliminate_deadend',
        'eliminate_identity',
        'eliminate_nop_dropout',
        'eliminate_nop_pad',
        'eliminate_nop_transpose',
        'eliminate_unused_initializer',
        'extract_constant_to_initializer',
        'fuse_add_bias_into_conv',
        'fuse_bn_into_conv',
        'fuse_consecutive_concats',
        'fuse_consecutive_reduce_unsqueeze',
        'fuse_consecutive_squeezes',
        'fuse_consecutive_transposes',
        #'fuse_matmul_add_bias_into_gemm',
        'fuse_pad_into_conv',
        #'fuse_transpose_into_gemm',
        #'lift_lexical_references',
    ]

    # Apply the optimization on the original serialized model
    optimized_model = optimizer.optimize(onnx_model, passes)

    num_optimized_nodes, optimzied_graph_str = traverse_graph(optimized_model.graph)
    print('==> The model after optimization:\n{}\n'.format(optimzied_graph_str))
    print('==> The optimized model has {} nodes, the original had {}.'.format(num_optimized_nodes, num_original_nodes))

    # Save the ONNX model
    onnx.save(optimized_model, args.output) 

# 需要導入模塊: import onnx [as 別名]
# 或者: from onnx import save [as 別名]
def save_model(MainModel, network_filepath, weight_filepath, dump_filepath):
    model = MainModel.KitModel(weight_filepath)
    onnx.save(model, dump_filepath)
    print('ONNX model file is saved as [{}], generated by [{}.py] and [{}].'.format(
        dump_filepath, network_filepath, weight_filepath)) 

# 需要導入模塊: import onnx [as 別名]
# 或者: from onnx import save [as 別名]
def save(self, filename):
        """Saves the wrapper ONNX ModelProto into a file with given name."""
        onnx.save(self._model_proto, filename) 

# 需要導入模塊: import onnx [as 別名]
# 或者: from onnx import save [as 別名]
def graph_to_onnx(graph, onnx_model_path):
    import onnx
    # to do in the future using onnx ir
    onnx_out = graph.produce_onnx_model()
    onnx.save(onnx_out, onnx_model_path)
    return onnx_out 

# 需要導入模塊: import onnx [as 別名]
# 或者: from onnx import save [as 別名]
def main():
    args = parse_args()

    if not args.out.endswith('.onnx'):
        raise ValueError('The output file must be a onnx file.')

    if len(args.shape) == 1:
        input_shape = (3, args.shape[0], args.shape[0])
    elif len(args.shape) == 2:
        input_shape = (3, ) + tuple(args.shape)
    else:
        raise ValueError('invalid input shape')

    cfg = mmcv.Config.fromfile(args.config)
    cfg.model.pretrained = None

    # build the model and load checkpoint
    model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
    load_checkpoint(model, args.checkpoint, map_location='cpu')
    # Only support CPU mode for now
    model.cpu().eval()
    # Customized ops are not supported, use torchvision ops instead.
    for m in model.modules():
        if isinstance(m, (RoIPool, RoIAlign)):
            # set use_torchvision on-the-fly
            m.use_torchvision = True

    # TODO: a better way to override forward function
    if hasattr(model, 'forward_dummy'):
        model.forward = model.forward_dummy
    else:
        raise NotImplementedError(
            'ONNX conversion is currently not currently supported with '
            f'{model.__class__.__name__}')

    input_data = torch.empty((1, *input_shape),
                             dtype=next(model.parameters()).dtype,
                             device=next(model.parameters()).device)

    onnx_model = export_onnx_model(model, (input_data, ), args.passes)
    # Print a human readable representation of the graph
    onnx.helper.printable_graph(onnx_model.graph)
    print(f'saving model in {args.out}')
    onnx.save(onnx_model, args.out) 

# 需要導入模塊: import onnx [as 別名]
# 或者: from onnx import save [as 別名]
def main():
    """Run the DarkNet-to-ONNX conversion for YOLOv3-608."""
    # Have to use python 2 due to hashlib compatibility
    if sys.version_info[0] > 2:
        raise Exception("This is script is only compatible with python2, please re-run this script \
    with python2. The rest of this sample can be run with either version of python")

    # Download the config for YOLOv3 if not present yet, and analyze the checksum:
    cfg_file_path = download_file(
        'yolov3.cfg',
        'https://raw.githubusercontent.com/pjreddie/darknet/f86901f6177dfc6116360a13cc06ab680e0c86b0/cfg/yolov3.cfg',
        'b969a43a848bbf26901643b833cfb96c')

    # These are the only layers DarkNetParser will extract parameters from. The three layers of
    # type 'yolo' are not parsed in detail because they are included in the post-processing later:
    supported_layers = ['net', 'convolutional', 'shortcut',
                        'route', 'upsample']

    # Create a DarkNetParser object, and the use it to generate an OrderedDict with all
    # layer's configs from the cfg file:
    parser = DarkNetParser(supported_layers)
    layer_configs = parser.parse_cfg_file(cfg_file_path)
    # We do not need the parser anymore after we got layer_configs:
    del parser

    # In above layer_config, there are three outputs that we need to know the output
    # shape of (in CHW format):
    output_tensor_dims = OrderedDict()
    output_tensor_dims['082_convolutional'] = [255, 19, 19]
    output_tensor_dims['094_convolutional'] = [255, 38, 38]
    output_tensor_dims['106_convolutional'] = [255, 76, 76]

    # Create a GraphBuilderONNX object with the known output tensor dimensions:
    builder = GraphBuilderONNX(output_tensor_dims)

    # We want to populate our network with weights later, that's why we download those from
    # the official mirror (and verify the checksum):
    weights_file_path = download_file(
        'yolov3.weights',
        'https://pjreddie.com/media/files/yolov3.weights',
        'c84e5b99d0e52cd466ae710cadf6d84c')

    # Now generate an ONNX graph with weights from the previously parsed layer configurations
    # and the weights file:
    yolov3_model_def = builder.build_onnx_graph(
        layer_configs=layer_configs,
        weights_file_path=weights_file_path,
        verbose=True)
    # Once we have the model definition, we do not need the builder anymore:
    del builder

    # Perform a sanity check on the ONNX model definition:
    onnx.checker.check_model(yolov3_model_def)

    # Serialize the generated ONNX graph to this file:
    output_file_path = 'yolov3.onnx'
    onnx.save(yolov3_model_def, output_file_path) 

# 需要導入模塊: import onnx [as 別名]
# 或者: from onnx import save [as 別名]
def main():
    """Run the DarkNet-to-ONNX conversion for YOLOv3-608."""
    # Have to use python 2 due to hashlib compatibility
    if sys.version_info[0] > 2:
        raise Exception("This script is only compatible with python2, please re-run this script with python2. The rest of this sample can be run with either version of python.")

    # Download the config for YOLOv3 if not present yet, and analyze the checksum:
    cfg_file_path = download_file(
        'yolov3.cfg',
        'https://raw.githubusercontent.com/pjreddie/darknet/f86901f6177dfc6116360a13cc06ab680e0c86b0/cfg/yolov3.cfg',
        'b969a43a848bbf26901643b833cfb96c')

    # These are the only layers DarkNetParser will extract parameters from. The three layers of
    # type 'yolo' are not parsed in detail because they are included in the post-processing later:
    supported_layers = ['net', 'convolutional', 'shortcut',
                        'route', 'upsample']

    # Create a DarkNetParser object, and the use it to generate an OrderedDict with all
    # layer's configs from the cfg file:
    parser = DarkNetParser(supported_layers)
    layer_configs = parser.parse_cfg_file(cfg_file_path)
    # We do not need the parser anymore after we got layer_configs:
    del parser

    # In above layer_config, there are three outputs that we need to know the output
    # shape of (in CHW format):
    output_tensor_dims = OrderedDict()
    output_tensor_dims['082_convolutional'] = [255, 19, 19]
    output_tensor_dims['094_convolutional'] = [255, 38, 38]
    output_tensor_dims['106_convolutional'] = [255, 76, 76]

    # Create a GraphBuilderONNX object with the known output tensor dimensions:
    builder = GraphBuilderONNX(output_tensor_dims)

    # We want to populate our network with weights later, that's why we download those from
    # the official mirror (and verify the checksum):
    weights_file_path = download_file(
        'yolov3.weights',
        'https://pjreddie.com/media/files/yolov3.weights',
        'c84e5b99d0e52cd466ae710cadf6d84c')

    # Now generate an ONNX graph with weights from the previously parsed layer configurations
    # and the weights file:
    yolov3_model_def = builder.build_onnx_graph(
        layer_configs=layer_configs,
        weights_file_path=weights_file_path,
        verbose=True)
    # Once we have the model definition, we do not need the builder anymore:
    del builder

    # Perform a sanity check on the ONNX model definition:
    onnx.checker.check_model(yolov3_model_def)

    # Serialize the generated ONNX graph to this file:
    output_file_path = 'yolov3.onnx'
    onnx.save(yolov3_model_def, output_file_path) 

# 需要導入模塊: import onnx [as 別名]
# 或者: from onnx import save [as 別名]
def main():
    parser = argparse.ArgumentParser(
        description='Quantize model with specified parameters')
    parser.add_argument('--no_per_channel', '-t',
                        action='store_true', default=False)
    parser.add_argument('--nbits', type=int, default=8)
    parser.add_argument('--quantization_mode', default='Integer',
                        choices=('Integer', 'QLinear'))
    parser.add_argument('--static', '-s', action='store_true', default=False)
    parser.add_argument('--asymmetric_input_types',
                        action='store_true', default=False)
    parser.add_argument('--input_quantization_params', default='')
    parser.add_argument('--output_quantization_params', default='')
    parser.add_argument('model')
    parser.add_argument('output')
    args = parser.parse_args()

    args.per_channel = not args.no_per_channel
    del args.no_per_channel

    if args.quantization_mode == 'QLinear':
        args.quantization_mode = quantize.QuantizationMode.QLinearOps
    else:
        args.quantization_mode = quantize.QuantizationMode.IntegerOps

    if len(args.input_quantization_params) != 0:
        args.input_quantization_params = json.loads(
            args.input_quantization_params)
    else:
        args.input_quantization_params = None

    if len(args.output_quantization_params) != 0:
        args.output_quantization_params = json.loads(
            args.output_quantization_params)
    else:
        args.output_quantization_params = None

    # Load the onnx model
    model_file = args.model
    model = onnx.load(model_file)
    del args.model

    output_file = args.output
    del args.output

    # Quantize
    print('Quantize config: {}'.format(vars(args)))
    quantized_model = quantize.quantize(model, **vars(args))

    print('Saving "{}" to "{}"'.format(model_file, output_file))

    # Save the quantized model
    onnx.save(quantized_model, output_file)