Merge pull request #19976 from jiangjiajun:master

* Added PaddlePaddle classification model conversion case

* Modify cv2 import as cv

* Modify documents in dnn_conversion/paddlepaddle

* Modify documents in dnn_conversion/paddlepaddle

samples/dnn/dnn_model_runner/dnn_conversion/paddlepaddle/README.md

@@ -0,0 +1,13 @@
+# Run PaddlePaddle model by OpenCV
+
+This tutorial shows how to run PaddlePaddle model by opencv, run the example code as below,
+
+```shell
+python paddle_resnet50.py
+```
+
+there are 3 part of this execution
+
+- 1. Export PaddlePaddle ResNet50 model to onnx format
+- 2. Use `cv2.dnn.readNetFromONNX` load model file
+- 3. Preprocess image file and do inference

samples/dnn/dnn_model_runner/dnn_conversion/paddlepaddle/paddle_resnet50.py

@@ -0,0 +1,61 @@
+import paddle
+import paddlehub as hub
+import paddlehub.vision.transforms as T
+import cv2 as cv
+import numpy as np
+
+
+def preprocess(image_path):
+    ''' preprocess input image file to np.ndarray
+
+    Args:
+        image_path(str): Path of input image file
+
+    Returns:
+        ProcessedImage(numpy.ndarray): A numpy.ndarray
+                variable which shape is (1, 3, 224, 224)
+    '''
+    transforms = T.Compose([
+            T.Resize((256, 256)),
+            T.CenterCrop(224),
+            T.Normalize(mean=[0.485, 0.456, 0.406],
+                        std=[0.229, 0.224, 0.225])],
+            to_rgb=True)
+    return np.expand_dims(transforms(image_path), axis=0)
+
+
+def export_onnx_mobilenetv2(save_path):
+    ''' export PaddlePaddle model to ONNX format
+
+    Args:
+        save_path(str): Path to save exported ONNX model
+
+    Returns:
+        None
+    '''
+    model = hub.Module(name="resnet50_vd_imagenet_ssld")
+    input_spec = paddle.static.InputSpec(
+            [1, 3, 224, 224], "float32", "image")
+    paddle.onnx.export(model, save_path,
+                       input_spec=[input_spec],
+                       opset_version=10)
+
+
+if __name__ == '__main__':
+    save_path = './resnet50'
+    image_file = './data/cat.jpg'
+    labels = open('./data/labels.txt').read().strip().split('\n')
+    model = export_onnx_mobilenetv2(save_path)
+
+    # load mobilenetv2 use cv.dnn
+    net = cv.dnn.readNetFromONNX(save_path + '.onnx')
+    # read and preprocess image file
+    im = preprocess(image_file)
+    # inference
+    net.setInput(im)
+    result = net.forward(['save_infer_model/scale_0.tmp_0'])
+    # post process
+    class_id = np.argmax(result[0])
+    label = labels[class_id]
+    print("Image: {}".format(image_file))
+    print("Predict Category: {}".format(label))

samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt

@@ -7,3 +7,7 @@ torchvision>=0.6.1
 
 tensorflow>=2.1.0
 tensorflow-gpu>=2.1.0
+
+paddlepaddle>=2.0.0
+paddlepaddle-gpu>=2.0.0
+paddle2onnx>=0.5.1