Reduced usage of batch in python samples (#3178)

* Reduced usage of batch in python samples

And corrected input arguments

inference-engine/ie_bridges/python/sample/hello_classification/hello_classification.py

@@ -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",
+    args.add_argument("-i", "--input", help="Required. Path to an image file.",
-                      required=True,
+                      required=True, type=str)
-                      type=str, nargs="+")
     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")

inference-engine/ie_bridges/python/sample/ngraph_function_creation_sample/ngraph_function_creation_sample.py

@@ -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,
+                      required=True, type=str, nargs="+")
-                      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: '

inference-engine/ie_bridges/python/sample/object_detection_sample_ssd/object_detection_sample_ssd.py

@@ -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.",
+    args.add_argument("-i", "--input", help="Required. Path to an image file.",
-                      required=True, type=str, nargs="+")
+                      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