OpenVINO resnet_50_v1 python example with VNNI

pyzoo/zoo/examples/vnni/openvino/inference.py

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+import sys
+from optparse import OptionParser
+import numpy as np
+
+from zoo.pipeline.inference import InferenceModel
+from zoo.common.nncontext import init_nncontext
+from zoo.feature.image import *
+from zoo.pipeline.nnframes import *
+
+
+
+def predict(model_path, img_path, partition_num):
+    model = InferenceModel()
+    model.load_openvino(model_path,
+                        weight_path=model_path[:model_path.rindex(".")] + ".bin")
+    sc = init_nncontext("OpenVINO Object Detection Inference Example")
+    infer_transformer = ChainedPreprocessing([ImageBytesToMat(),
+                                             ImageResize(256,256),
+                                             ImageCenterCrop(224,224),
+                                             ImageMatToTensor(format="NHWC", to_RGB=True)
+                                             ])
+    image_set = ImageSet.read(img_path, sc, partition_num).transform(infer_transformer).get_image().collect()
+    image_set = np.expand_dims(image_set, axis=1)
+
+    predictions = model.predict(image_set)
+
+    result = np.swapaxes(predictions,0,1)[0]
+
+    for r in result:
+        output = {}
+        max_index = np.argmax(r)
+        output["Top-1"] = str(max_index)
+        print("* Predict result " + str(output))
+
+
+if __name__ == "__main__":
+    parser = OptionParser()
+    parser.add_option("--image", type=str, dest="img_path",
+                      help="The path where the images are stored, "
+                           "can be either a folder or an image path")
+    parser.add_option("--model", type=str, dest="model_path",
+                      help="Zoo Model Path")
+    parser.add_option("--partition_num", type=int, dest="partition_num", default=4,
+                      help="The number of partitions")
+
+    (options, args) = parser.parse_args(sys.argv)
+
+    predict(options.model_path, options.img_path, options.partition_num)