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Reduced usage of batch in python samples (#3178)

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* Reduced usage of batch in python samples

And corrected input arguments
This commit is contained in:
Mikhail Ryzhov 2020-11-18 20:43:59 +03:00 committed by GitHub
parent 123b9ab66e
commit 39ec38383a
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 7 additions and 12 deletions
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9
inference-engine/ie_bridges/python/sample/hello_classification/hello_classification.py
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@ -30,9 +30,8 @@ def build_argparser():
args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
args.add_argument("-m", "--model", help="Required. Path to an .xml or .onnx file with a trained model.", required=True,
type=str)
args.add_argument("-i", "--input", help="Required. Path to a folder with images or path to an image files",
required=True,
type=str, nargs="+")
args.add_argument("-i", "--input", help="Required. Path to an image file.",
required=True, type=str)
args.add_argument("-l", "--cpu_extension",
help="Optional. Required for CPU custom layers. "
"MKLDNN (CPU)-targeted custom layers. Absolute path to a shared library with the"
@ -69,19 +68,17 @@ def main():
log.info("Preparing input blobs")
input_blob = next(iter(net.input_info))
out_blob = next(iter(net.outputs))
net.batch_size = len(args.input)
# Read and pre-process input images
n, c, h, w = net.input_info[input_blob].input_data.shape
images = np.ndarray(shape=(n, c, h, w))
for i in range(n):
image = cv2.imread(args.input[i])
image = cv2.imread(args.input)
if image.shape[:-1] != (h, w):
log.warning("Image {} is resized from {} to {}".format(args.input[i], image.shape[:-1], (h, w)))
image = cv2.resize(image, (w, h))
image = image.transpose((2, 0, 1)) # Change data layout from HWC to CHW
images[i] = image
log.info("Batch size is {}".format(n))
# Loading model to the plugin
log.info("Loading model to the plugin")

3
inference-engine/ie_bridges/python/sample/ngraph_function_creation_sample/ngraph_function_creation_sample.py
View File

@ -33,8 +33,7 @@ def build_argparser() -> ArgumentParser:
args = parser.add_argument_group('Options')
args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
args.add_argument('-i', '--input', help='Required. Path to a folder with images or path to an image files',
required=True,
type=str, nargs="+")
required=True, type=str, nargs="+")
args.add_argument('-m', '--model', help='Required. Path to file where weights for the network are located')
args.add_argument('-d', '--device',
help='Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL, MYRIAD or HETERO: '

7
inference-engine/ie_bridges/python/sample/object_detection_sample_ssd/object_detection_sample_ssd.py
View File

@ -31,8 +31,8 @@ def build_argparser():
args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
args.add_argument("-m", "--model", help="Required. Path to an .xml or .onnx file with a trained model.",
required=True, type=str)
args.add_argument("-i", "--input", help="Required. Path to image file.",
required=True, type=str, nargs="+")
args.add_argument("-i", "--input", help="Required. Path to an image file.",
required=True, type=str)
args.add_argument("-l", "--cpu_extension",
help="Optional. Required for CPU custom layers. "
"Absolute path to a shared library with the kernels implementations.",
@ -88,7 +88,7 @@ def main():
images = np.ndarray(shape=(n, c, h, w))
images_hw = []
for i in range(n):
image = cv2.imread(args.input[i])
image = cv2.imread(args.input)
ih, iw = image.shape[:-1]
images_hw.append((ih, iw))
log.info("File was added: ")
@ -112,7 +112,6 @@ def main():
for input_key in net.input_info:
if len(net.input_info[input_key].layout) == 4:
input_name = input_key
log.info("Batch size is {}".format(net.batch_size))
net.input_info[input_key].precision = 'U8'
elif len(net.input_info[input_key].layout) == 2:
input_info_name = input_key