Reduced usage of batch in python samples (#3104)
* Reduced usage of batch in python sampes Excluded from hello_classification and object_detection samples
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@ -30,9 +30,8 @@ def build_argparser():
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args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
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args.add_argument("-m", "--model", help="Required. Path to an .xml or .onnx file with a trained model.", required=True,
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type=str)
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args.add_argument("-i", "--input", help="Required. Path to a folder with images or path to an image files",
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required=True,
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type=str, nargs="+")
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args.add_argument("-i", "--input", help="Required. Path to image file.",
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required=True, type=str)
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args.add_argument("-l", "--cpu_extension",
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help="Optional. Required for CPU custom layers. "
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"MKLDNN (CPU)-targeted custom layers. Absolute path to a shared library with the"
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@ -69,7 +68,6 @@ def main():
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log.info("Preparing input blobs")
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input_blob = next(iter(net.input_info))
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out_blob = next(iter(net.outputs))
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net.batch_size = len(args.input)
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# Read and pre-process input images
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n, c, h, w = net.input_info[input_blob].input_data.shape
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@ -81,7 +79,6 @@ def main():
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image = cv2.resize(image, (w, h))
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image = image.transpose((2, 0, 1)) # Change data layout from HWC to CHW
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images[i] = image
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log.info("Batch size is {}".format(n))
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# Loading model to the plugin
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log.info("Loading model to the plugin")
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@ -33,8 +33,7 @@ def build_argparser() -> ArgumentParser:
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args = parser.add_argument_group('Options')
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args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
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args.add_argument('-i', '--input', help='Required. Path to a folder with images or path to an image files',
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required=True,
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type=str, nargs="+")
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required=True, type=str, nargs="+")
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args.add_argument('-m', '--model', help='Required. Path to file where weights for the network are located')
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args.add_argument('-d', '--device',
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help='Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL, MYRIAD or HETERO: '
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@ -112,7 +112,6 @@ def main():
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for input_key in net.input_info:
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if len(net.input_info[input_key].layout) == 4:
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input_name = input_key
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log.info("Batch size is {}".format(net.batch_size))
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net.input_info[input_key].precision = 'U8'
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elif len(net.input_info[input_key].layout) == 2:
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input_info_name = input_key
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