- **Image Classification Sample Async** – Inference of image classification networks like AlexNet and GoogLeNet using Asynchronous Inference Request API (the sample supports only images as inputs).
- [Image Classification C++ Sample Async](../../inference-engine/samples/classification_sample_async/README.md)
- [Image Classification Python* Sample Async](../../inference-engine/ie_bridges/python/sample/classification_sample_async/README.md)
-- **[Image Classification Python* Sample](../../inference-engine/ie_bridges/python/sample/classification_sample/README.md)** – Inference of image classification networks like AlexNet and GoogLeNet using Synchronous Inference Request API (the sample supports only images as inputs).
+- **[Image Classification Python* Sample](../../inference-engine/ie_bridges/python/sample/hello_classification/README.md)** – Inference of image classification networks like AlexNet and GoogLeNet using Synchronous Inference Request API (the sample supports only images as inputs).
- **Neural Style Transfer Sample** – Style Transfer sample (the sample supports only images as inputs).
- [Neural Style Transfer C++ Sample](../../inference-engine/samples/style_transfer_sample/README.md)
- [Neural Style Transfer Python* Sample](../../inference-engine/ie_bridges/python/sample/style_transfer_sample/README.md)
To run the sample, you can use AlexNet and GoogLeNet or other image classification models. You can download the pre-trained models with the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or from [https://download.01.org/opencv/](https://download.01.org/opencv/).
> **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
-
+>
+> The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
You can do inference of an image using a trained AlexNet network on FPGA with fallback to CPU using the following command:
```
parser = ArgumentParser(add_help=False)
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("-m", "--model", help="Required. Path to an .xml file with a trained model.",
+ 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="+")
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
args = build_argparser().parse_args()
- model_xml = args.model
- model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
log.info("Creating Inference Engine")
ie = IECore()
if args.cpu_extension and 'CPU' in args.device:
ie.add_extension(args.cpu_extension, "CPU")
- # Read IR
- log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
- net = ie.read_network(model=model_xml, weights=model_bin)
+
+ # Read a model in OpenVINO Intermediate Representation (.xml and .bin files) or ONNX (.onnx file) format
+ model = args.model
+ model_bin = None
+ model_name, model_ext = os.path.splitext(model)
+ log.info(f"Loading network files:\n\t{model}")
+ if model_ext == ".xml":
+ # Read .bin weights for IR format only
+ model_bin = model_name + ".bin"
+ log.info(f"\n\t{model_bin}")
+ net = ie.read_network(model=model, weights=model_bin)
assert len(net.input_info.keys()) == 1, "Sample supports only single input topologies"
assert len(net.outputs) == 1, "Sample supports only single output topologies"
To run the sample, you can use AlexNet and GoogLeNet or other image classification models. You can download the pre-trained models with the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or from [https://download.01.org/opencv/](https://download.01.org/opencv/).
> **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
+>
+> The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
For example, to perform inference of an AlexNet model (previously converted to the Inference Engine format) on CPU, use the following command:
-#!/usr/bin/env python
+#!/usr/bin/env python3
"""
Copyright (C) 2018-2020 Intel Corporation
parser = ArgumentParser(add_help=False)
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("-m", "--model", help="Required. Path to an .xml file with a trained model.", required=True,
+ 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,
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
args = build_argparser().parse_args()
- model_xml = args.model
- model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
log.info("Creating Inference Engine")
ie = IECore()
if args.cpu_extension and 'CPU' in args.device:
ie.add_extension(args.cpu_extension, "CPU")
- # Read IR
- log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
- net = ie.read_network(model=model_xml, weights=model_bin)
+
+ # Read a model in OpenVINO Intermediate Representation (.xml and .bin files) or ONNX (.onnx file) format
+ model = args.model
+ model_bin = None
+ model_name, model_ext = os.path.splitext(model)
+ log.info(f"Loading network files:\n\t{model}")
+ if model_ext == ".xml":
+ # Read .bin weights for IR format only
+ model_bin = model_name + ".bin"
+ log.info(f"\n\t{model_bin}")
+ net = ie.read_network(model=model, weights=model_bin)
assert len(net.input_info.keys()) == 1, "Sample supports only single input topologies"
assert len(net.outputs) == 1, "Sample supports only single output topologies"
To run the sample, you can use RMNet_SSD or other object-detection models. You can download the pre-trained models with the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or from [https://download.01.org/opencv/](https://download.01.org/opencv/).
> **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
-
+>
+> The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
You can do inference of an image using a trained RMNet_SSD network on FPGA with fallback to CPU using the following command:
```
parser = ArgumentParser(add_help=False)
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("-m", "--model", help="Required. Path to an .xml file with a trained model.",
+ 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 = build_argparser().parse_args()
log.info("Loading Inference Engine")
ie = IECore()
- # --------------------------- 1. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------
- model_xml = args.model
- model_bin = os.path.splitext(model_xml)[0] + ".bin"
- log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
- net = ie.read_network(model=model_xml, weights=model_bin)
+
+ # ---1. Read a model in OpenVINO Intermediate Representation (.xml and .bin files) or ONNX (.onnx file) format ---
+ model = args.model
+ model_bin = None
+ model_name, model_ext = os.path.splitext(model)
+ log.info(f"Loading network files:\n\t{model}")
+ if model_ext == ".xml":
+ # Read .bin weights for IR format only
+ model_bin = model_name + ".bin"
+ log.info(f"\n\t{model_bin}")
+ net = ie.read_network(model=model, weights=model_bin)
func = ng.function_from_cnn(net)
ops = func.get_ordered_ops()
# -----------------------------------------------------------------------------------------------------
inference of style transfer models.
> **NOTE**: The OpenVINO™ toolkit does not include a pre-trained model to run the Neural Style Transfer sample. A public model from the [Zhaw's Neural Style Transfer repository](https://github.com/zhaw/neural_style) can be used. Read the [Converting a Style Transfer Model from MXNet*](../../../../../docs/MO_DG/prepare_model/convert_model/mxnet_specific/Convert_Style_Transfer_From_MXNet.md) topic from the [Model Optimizer Developer Guide](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) to learn about how to get the trained model and how to convert it to the Inference Engine format (\*.xml + \*.bin).
+>
+> The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
## How It Works
parser = ArgumentParser(add_help=False)
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("-m", "--model", help="Required. Path to an .xml file with a trained model.", required=True, type=str)
+ 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("-l", "--cpu_extension",
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
args = build_argparser().parse_args()
- model_xml = args.model
- model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
log.info("Creating Inference Engine")
ie = IECore()
if args.cpu_extension and 'CPU' in args.device:
ie.add_extension(args.cpu_extension, "CPU")
- # Read IR
- log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
- net = ie.read_network(model=model_xml, weights=model_bin)
+
+ # Read a model in OpenVINO Intermediate Representation (.xml and .bin files) or ONNX (.onnx file) format
+ model = args.model
+ model_bin = None
+ model_name, model_ext = os.path.splitext(model)
+ log.info(f"Loading network files:\n\t{model}")
+ if model_ext == ".xml":
+ # Read .bin weights for IR format only
+ model_bin = model_name + ".bin"
+ log.info(f"\n\t{model_bin}")
+ net = ie.read_network(model=model, weights=model_bin)
assert len(net.input_info.keys()) == 1, "Sample supports only single input topologies"
assert len(net.outputs) == 1, "Sample supports only single output topologies"
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