Add options for demo scripts to select backend & targets (#43)

* add options for selecting backend & targets

* add eol

demo.py

@@ -19,15 +19,29 @@ def str2bool(v):
     else:
         raise NotImplementedError
 
+backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA]
+targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16]
+help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA"
+help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16"
+try:
+    backends += [cv.dnn.DNN_BACKEND_TIMVX]
+    targets += [cv.dnn.DNN_TARGET_NPU]
+    help_msg_backends += "; {:d}: TIMVX"
+    help_msg_targets += "; {:d}: NPU"
+except:
+    print('This version of OpenCV does not support TIM-VX and NPU. Visit https://gist.github.com/fengyuentau/5a7a5ba36328f2b763aea026c43fa45f for more information.')
+
 parser = argparse.ArgumentParser(description='Deep Residual Learning for Image Recognition (https://arxiv.org/abs/1512.03385, https://github.com/PaddlePaddle/PaddleHub)')
 parser.add_argument('--input', '-i', type=str, help='Path to the input image.')
 parser.add_argument('--model', '-m', type=str, default='image_classification_ppresnet50_2022jan.onnx', help='Path to the model.')
+parser.add_argument('--backend', '-b', type=int, default=backends[0], help=help_msg_backends.format(*backends))
+parser.add_argument('--target', '-t', type=int, default=targets[0], help=help_msg_targets.format(*targets))
 parser.add_argument('--label', '-l', type=str, default='./imagenet_labels.txt', help='Path to the dataset labels.')
 args = parser.parse_args()
 
 if __name__ == '__main__':
     # Instantiate ResNet
-    model = PPResNet(modelPath=args.model, labelPath=args.label)
+    model = PPResNet(modelPath=args.model, labelPath=args.label, backendId=args.backend, targetId=args.target)
 
     # Read image and get a 224x224 crop from a 256x256 resized
     image = cv.imread(args.input)
@@ -40,3 +54,4 @@ if __name__ == '__main__':
 
     # Print result
     print('label: {}'.format(result))
+

ppresnet.py

@@ -9,10 +9,15 @@ import numpy as np
 import cv2 as cv
 
 class PPResNet:
-    def __init__(self, modelPath, labelPath):
+    def __init__(self, modelPath, labelPath, backendId=0, targetId=0):
         self._modelPath = modelPath
-        self._model = cv.dnn.readNet(self._modelPath)
         self._labelPath = labelPath
+        self._backendId = backendId
+        self._targetId = targetId
+
+        self._model = cv.dnn.readNet(self._modelPath)
+        self._model.setPreferableBackend(self._backendId)
+        self._model.setPreferableTarget(self._targetId)
 
         self._inputNames = ''
         self._outputNames = ['save_infer_model/scale_0.tmp_0']
@@ -35,10 +40,12 @@ class PPResNet:
         return self.__class__.__name__
 
     def setBackend(self, backend_id):
-        self._model.setPreferableBackend(backend_id)
+        self._backendId = backend_id
+        self._model.setPreferableBackend(self._backendId)
 
     def setTarget(self, target_id):
-        self._model.setPreferableTarget(target_id)
+        self._targetId = target_id
+        self._model.setPreferableTarget(self._targetId)
 
     def _preprocess(self, image):
         image = image.astype(np.float32, copy=False) / 255.0
@@ -64,4 +71,5 @@ class PPResNet:
 
     def _postprocess(self, outputBlob):
         class_id = np.argmax(outputBlob[0])
-        return self._labels[class_id]
+        return self._labels[class_id]
+