增加migraphx后端io_bind方式推理

Python/Classifier_io_binding.py

+# -*- coding: utf-8 -*-
+"""
+分类器示例
+"""
+import cv2
+import argparse
+import numpy as np
+import onnxruntime as ort
+
+def Preprocessing(pathOfImage):
+    # 读取图像
+    image = cv2.imread(pathOfImage, cv2.IMREAD_COLOR)             
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
+    # 调整大小，使短边为256，保持长宽比
+    ratio = float(256) / min(image.shape[0], image.shape[1])
+    if image.shape[0] > image.shape[1]:
+        new_size = [int(round(ratio * image.shape[0])), 256]
+    else:
+        new_size = [256, int(round(ratio * image.shape[1]))]
+    image = np.array(cv2.resize(image, (new_size[1],new_size[0])))
+    
+    # 裁剪中心窗口为224*224
+    h, w, c = image.shape
+    start_x = w//2 - 224//2
+    start_y = h//2 - 224//2
+    image = image[start_y:start_y+224, start_x:start_x+224, :]
+    
+    # transpose
+    image = image.transpose(2, 0, 1)
+    
+    # 将输入数据转换为float32
+    img_data = image.astype('float32')
+    
+    # normalize
+    mean_vec = np.array([123.675, 116.28, 103.53])
+    stddev_vec = np.array([58.395, 57.12, 57.375])
+    norm_img_data = np.zeros(img_data.shape).astype('float32')
+    for i in range(img_data.shape[0]):
+        norm_img_data[i,:,:] = (img_data[i,:,:] - mean_vec[i]) / stddev_vec[i]
+    
+    # 调整尺寸
+    norm_img_data = norm_img_data.reshape(1, 3, 224, 224).astype('float32')
+    return norm_img_data
+
+def postprocess(scores,pathOfImage):
+    '''
+    Postprocessing with mxnet gluon
+    The function takes scores generated by the network and returns the class IDs in decreasing order
+    of probability
+    '''
+    with open('../Resource/synset.txt', 'r') as f:
+        labels = [l.rstrip() for l in f]
+    preds = np.squeeze(scores)
+    a = np.argsort(preds)[::-1]
+    print('class=%s ; probability=%f' %(labels[a[0]],preds[a[0]]))
+
+    text = 'class=%s ' % (labels[a[0]])
+    saveimage(pathOfImage,text)
+
+def ort_seg_dcu(model_path,image):
+    
+    provider_options=[{'device_id':'0','migraphx_fp16_enable':'true','dynamic_model':'false'}]
+    dcu_session = ort.InferenceSession(model_path, providers=['MIGraphXExecutionProvider'], provider_options=provider_options)
+    output_data = np.empty(dcu_session.get_outputs()[0].shape).astype(np.float32)
+
+    inputData={}
+    outputData={}
+    inputData[dcu_session.get_inputs()[0].name]=ort.OrtValue.ortvalue_from_numpy(image, device_type='cuda')
+    outputData[dcu_session.get_outputs()[0].name]=ort.OrtValue.ortvalue_from_numpy(output_data, device_type='cuda')
+
+    io_binding = dcu_session.io_binding()
+    io_binding.bind_input(
+        name=dcu_session.get_inputs()[0].name,
+        device_type=inputData[dcu_session.get_inputs()[0].name].device_name(),
+        device_id=0,
+        element_type=np.float32,
+        shape=inputData[dcu_session.get_inputs()[0].name].shape(),
+        buffer_ptr=inputData[dcu_session.get_inputs()[0].name].data_ptr())
+
+    io_binding.bind_output(
+        name=dcu_session.get_outputs()[0].name,
+        device_type=outputData[dcu_session.get_outputs()[0].name].device_name(),
+        device_id=0,
+        element_type=np.float32,
+        shape=outputData[dcu_session.get_outputs()[0].name].shape(),
+        buffer_ptr=outputData[dcu_session.get_outputs()[0].name].data_ptr())
+
+    dcu_session.run_with_iobinding(io_binding)
+    scores=np.array(io_binding.copy_outputs_to_cpu()[0])
+    print("ort result.shape:",scores.shape)
+
+    return scores
+
+def saveimage(pathOfImage,text):
+    iimage = cv2.imread(pathOfImage, cv2.IMREAD_COLOR)
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    font_scale = 0.5
+    font_color = (0, 0, 255)  
+    font_thickness = 1
+    text_position = (5, 20)
+    cv2.putText(iimage, text, text_position, font, font_scale, font_color, font_thickness)
+    cv2.imwrite("./output_image.jpg", iimage)
+    cv2.destroyAllWindows()
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--imgPath', type=str, default='../Resource/Images/ImageNet_01.jpg', help="image path")
+    parser.add_argument('--staticModelPath', type=str, default='../Resource/Models/resnet50_static.onnx', help="static onnx filepath")
+    args = parser.parse_args()
+
+    # 数据预处理
+    image = Preprocessing(args.imgPath)
+
+    # 静态推理
+    result = ort_seg_dcu(args.staticModelPath,image)
+    
+    # 解析分类结果
+    postprocess(result,args.imgPath)
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