MLECO-1253 Adding ASR sample application using the PyArmNN api

Change-Id: I450b23800ca316a5bfd4608c8559cf4f11271c21
Signed-off-by: Éanna Ó Catháin <eanna.ocathain@arm.com>

1 files changed, 46 insertions, 45 deletions

diff --git a/python/pyarmnn/examples/image_classification/onnx_mobilenetv2.py b/python/pyarmnn/examples/image_classification/onnx_mobilenetv2.py
index 9e95f76dcc..be28b585ba 100644
--- a/python/pyarmnn/examples/image_classification/onnx_mobilenetv2.py
+++ b/python/pyarmnn/examples/image_classification/onnx_mobilenetv2.py
@@ -44,48 +44,49 @@ def preprocess_onnx(img: Image, width: int, height: int, data_type, scale: float
     return img
 
 
-args = eu.parse_command_line()
-
-model_filename = 'mobilenetv2-1.0.onnx'
-labels_filename = 'synset.txt'
-archive_filename = 'mobilenetv2-1.0.zip'
-labels_url = 'https://s3.amazonaws.com/onnx-model-zoo/' + labels_filename
-model_url = 'https://s3.amazonaws.com/onnx-model-zoo/mobilenet/mobilenetv2-1.0/' + model_filename
-
-# Download resources
-image_filenames = eu.get_images(args.data_dir)
-
-model_filename, labels_filename = eu.get_model_and_labels(args.model_dir, model_filename, labels_filename,
-                                                          archive_filename,
-                                                          [model_url, labels_url])
-
-# all 3 resources must exist to proceed further
-assert os.path.exists(labels_filename)
-assert os.path.exists(model_filename)
-assert image_filenames
-for im in image_filenames:
-    assert (os.path.exists(im))
-
-# Create a network from a model file
-net_id, parser, runtime = eu.create_onnx_network(model_filename)
-
-# Load input information from the model and create input tensors
-input_binding_info = parser.GetNetworkInputBindingInfo("data")
-
-# Load output information from the model and create output tensors
-output_binding_info = parser.GetNetworkOutputBindingInfo("mobilenetv20_output_flatten0_reshape0")
-output_tensors = ann.make_output_tensors([output_binding_info])
-
-# Load labels
-labels = eu.load_labels(labels_filename)
-
-# Load images and resize to expected size
-images = eu.load_images(image_filenames,
-                        224, 224,
-                        np.float32,
-                        255.0,
-                        [0.485, 0.456, 0.406],
-                        [0.229, 0.224, 0.225],
-                        preprocess_onnx)
-
-eu.run_inference(runtime, net_id, images, labels, input_binding_info, output_binding_info)
+if __name__ == "__main__":
+    args = eu.parse_command_line()
+
+    model_filename = 'mobilenetv2-1.0.onnx'
+    labels_filename = 'synset.txt'
+    archive_filename = 'mobilenetv2-1.0.zip'
+    labels_url = 'https://s3.amazonaws.com/onnx-model-zoo/' + labels_filename
+    model_url = 'https://s3.amazonaws.com/onnx-model-zoo/mobilenet/mobilenetv2-1.0/' + model_filename
+
+    # Download resources
+    image_filenames = eu.get_images(args.data_dir)
+
+    model_filename, labels_filename = eu.get_model_and_labels(args.model_dir, model_filename, labels_filename,
+                                                              archive_filename,
+                                                              [model_url, labels_url])
+
+    # all 3 resources must exist to proceed further
+    assert os.path.exists(labels_filename)
+    assert os.path.exists(model_filename)
+    assert image_filenames
+    for im in image_filenames:
+        assert (os.path.exists(im))
+
+    # Create a network from a model file
+    net_id, parser, runtime = eu.create_onnx_network(model_filename)
+
+    # Load input information from the model and create input tensors
+    input_binding_info = parser.GetNetworkInputBindingInfo("data")
+
+    # Load output information from the model and create output tensors
+    output_binding_info = parser.GetNetworkOutputBindingInfo("mobilenetv20_output_flatten0_reshape0")
+    output_tensors = ann.make_output_tensors([output_binding_info])
+
+    # Load labels
+    labels = eu.load_labels(labels_filename)
+
+    # Load images and resize to expected size
+    images = eu.load_images(image_filenames,
+                            224, 224,
+                            np.float32,
+                            255.0,
+                            [0.485, 0.456, 0.406],
+                            [0.229, 0.224, 0.225],
+                            preprocess_onnx)
+
+    eu.run_inference(runtime, net_id, images, labels, input_binding_info, output_binding_info)