Remove comments in 3d_segmentation_demo.py

Author: anzhella-pankratova

demos/3d_segmentation_demo/python/3d_segmentation_demo.py

@@ -245,7 +245,6 @@ def read_image(test_data_path, data_name, sizes=(128, 128, 128), is_series=True,
 def main():
     args = parse_arguments()
 
-    # --------------------------------- 1. Load Plugin for inference engine ---------------------------------
     log.info('OpenVINO Inference Engine')
     log.info('\tbuild: {}'.format(get_version()))
     core = Core()
@@ -262,7 +261,6 @@ def main():
         raise AttributeError("Device {} do not support of 3D convolution. "
                              "Please use CPU, GPU or HETERO:*CPU*, HETERO:*GPU*")
 
-    # --------------------- 2. Read IR Generated by ModelOptimizer (.xml file) ---------------------
     log.info('Reading model {}'.format(args.path_to_model))
     model = core.read_model(args.path_to_model)
 
@@ -279,12 +277,10 @@ def main():
 
     n, c, d, h, w = model.inputs[0].shape
 
-    # ------------------------------------ 3. Loading model to the plugin -------------------------------------
     compiled_model = core.compile_model(model, args.target_device)
     infer_request = compiled_model.create_infer_request()
     log.info('The model {} is loaded to {}'.format(args.path_to_model, args.target_device))
 
-    # --------------------------------------- 4. Preparing input data -----------------------------------------
     start_time = perf_counter()
     if not os.path.exists(args.path_to_input_data):
         raise AttributeError("Path to input data: '{}' does not exist".format(args.path_to_input_data))
@@ -312,9 +308,7 @@ def main():
         original_data = data_crop
         original_size = original_data.shape[-3:]
 
-    # ---------------------------------------------- 5. Do inference --------------------------------------------
     result = infer_request.infer({input_tensor_name: data_crop})
-    # ---------------------------- 6. Processing of the received inference results ------------------------------
     result = next(iter(result.values()))
     batch, channels, out_d, out_h, out_w = result.shape
 
@@ -375,7 +369,6 @@ def main():
     total_latency = (perf_counter() - start_time) * 1e3
     log.info("Metrics report:")
     log.info("\tLatency: {:.1f} ms".format(total_latency))
-    # --------------------------------------------- 7. Save output -----------------------------------------------
     tiff_output_name = os.path.join(args.path_to_output, 'output.tiff')
     Image.new('RGB', (original_data.shape[3], original_data.shape[2])).save(tiff_output_name,
         append_images=list_img, save_all=True)