[IE Samples] Add api arg to classification sample (#943)

diff --git a/inference-engine/ie_bridges/python/sample/classification_sample_async/classification_sample_async.py b/inference-engine/ie_bridges/python/sample/classification_sample_async/classification_sample_async.py
index ceb13ea..12acabc 100644 (file)
--- a/inference-engine/ie_bridges/python/sample/classification_sample_async/classification_sample_async.py
+++ b/inference-engine/ie_bridges/python/sample/classification_sample_async/classification_sample_async.py
@@ -179,5 +179,6 @@ def main():
         print("\n")
     log.info("This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n")
 
+
 if __name__ == '__main__':
     sys.exit(main() or 0)

diff --git a/inference-engine/ie_bridges/python/sample/style_transfer_sample/README.md b/inference-engine/ie_bridges/python/sample/style_transfer_sample/README.md
index 9671006..3cc1e39 100644 (file)
--- a/inference-engine/ie_bridges/python/sample/style_transfer_sample/README.md
+++ b/inference-engine/ie_bridges/python/sample/style_transfer_sample/README.md
@@ -1,6 +1,6 @@
 # Neural Style Transfer Python* Sample
 
-This topic demonstrates how to run the Neural Style Transfer sample application, which performs 
+This topic demonstrates how to run the Neural Style Transfer sample application, which performs
 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).
@@ -18,7 +18,7 @@ python3 style_transfer_sample.py --help
 The command yields the following usage message:
 ```
 usage: style_transfer_sample.py [-h] -m MODEL -i INPUT [INPUT ...]
-                                [-l CPU_EXTENSION] [-d DEVICE] 
+                                [-l CPU_EXTENSION] [-d DEVICE]
                                 [-nt NUMBER_TOP]
                                 [--mean_val_r MEAN_VAL_R]
                                 [--mean_val_g MEAN_VAL_G]
@@ -27,27 +27,27 @@ usage: style_transfer_sample.py [-h] -m MODEL -i INPUT [INPUT ...]
 Options:
   -h, --help            Show this help message and exit.
   -m MODEL, --model MODEL
-                        Path to an .xml file with a trained model.
+                        Required. Path to an .xml file with a trained model.
   -i INPUT [INPUT ...], --input INPUT [INPUT ...]
-                        Path to a folder with images or path to an image files
+                        Required. Path to a folder with images or path to an image files
   -l CPU_EXTENSION, --cpu_extension CPU_EXTENSION
                         Optional. Required for CPU custom layers. Absolute
                         MKLDNN (CPU)-targeted custom layers. Absolute path to
                         a shared library with the kernels implementations
   -d DEVICE, --device DEVICE
-                        Specify the target device to infer on; CPU, GPU, FPGA,
+                        Optional. Specify the target device to infer on; CPU, GPU, FPGA,
                         HDDL or MYRIAD is acceptable. Sample will look for a
                         suitable plugin for device specified. Default value is CPU
   -nt NUMBER_TOP, --number_top NUMBER_TOP
-                        Number of top results
+                        Optional. Number of top results
   --mean_val_r MEAN_VAL_R, -mean_val_r MEAN_VAL_R
-                        Mean value of red chanel for mean value subtraction in
+                        Optional. Mean value of red chanel for mean value subtraction in
                         postprocessing
   --mean_val_g MEAN_VAL_G, -mean_val_g MEAN_VAL_G
-                        Mean value of green chanel for mean value subtraction
+                        Optional. Mean value of green chanel for mean value subtraction
                         in postprocessing
   --mean_val_b MEAN_VAL_B, -mean_val_b MEAN_VAL_B
-                        Mean value of blue chanel for mean value subtraction
+                        Optional. Mean value of blue chanel for mean value subtraction
                         in postprocessing
 ```
 
@@ -60,9 +60,9 @@ To perform inference of an image using a trained model of NST network on Intel®
 
 ### Demo Output
 
-The application outputs an image (`out1.bmp`) or a sequence of images (`out1.bmp`, ..., `out<N>.bmp`) which are redrawn in style of the style transfer model used for sample. 
+The application outputs an image (`out1.bmp`) or a sequence of images (`out1.bmp`, ..., `out<N>.bmp`) which are redrawn in style of the style transfer model used for sample.
 
-## See Also 
+## See Also
 * [Using Inference Engine Samples](./docs/IE_DG/Samples_Overview.md)
 
 

diff --git a/inference-engine/ie_bridges/python/sample/style_transfer_sample/style_transfer_sample.py b/inference-engine/ie_bridges/python/sample/style_transfer_sample/style_transfer_sample.py
index 6c96bba..7d157db 100644 (file)
--- a/inference-engine/ie_bridges/python/sample/style_transfer_sample/style_transfer_sample.py
+++ b/inference-engine/ie_bridges/python/sample/style_transfer_sample/style_transfer_sample.py
@@ -28,26 +28,26 @@ def build_argparser():
     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="Path to an .xml file with a trained model.", required=True, type=str)
-    args.add_argument("-i", "--input", help="Path to a folder with images or path to an image files", required=True,
+    args.add_argument("-m", "--model", help="Required. Path to an .xml 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",
                       help="Optional. Required for CPU custom layers. "
                            "Absolute MKLDNN (CPU)-targeted custom layers. Absolute path to a shared library with the "
                            "kernels implementations", type=str, default=None)
     args.add_argument("-d", "--device",
-                      help="Specify the target device to infer on; CPU, GPU, FPGA, HDDL or MYRIAD is acceptable. Sample "
+                      help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL or MYRIAD is acceptable. Sample "
                            "will look for a suitable plugin for device specified. Default value is CPU", default="CPU",
                       type=str)
-    args.add_argument("-nt", "--number_top", help="Number of top results", default=10, type=int)
+    args.add_argument("-nt", "--number_top", help="Optional. Number of top results", default=10, type=int)
     args.add_argument("--mean_val_r", "-mean_val_r",
-                      help="Mean value of red chanel for mean value subtraction in postprocessing ", default=0,
+                      help="Optional. Mean value of red chanel for mean value subtraction in postprocessing ", default=0,
                       type=float)
     args.add_argument("--mean_val_g", "-mean_val_g",
-                      help="Mean value of green chanel for mean value subtraction in postprocessing ", default=0,
+                      help="Optional. Mean value of green chanel for mean value subtraction in postprocessing ", default=0,
                       type=float)
     args.add_argument("--mean_val_b", "-mean_val_b",
-                      help="Mean value of blue chanel for mean value subtraction in postprocessing ", default=0,
+                      help="Optional. Mean value of blue chanel for mean value subtraction in postprocessing ", default=0,
                       type=float)
     return parser