【PaddlePaddle+OpenVINO】用PaddeHub的ONNX模型

!pip install -q  paddle2onnx
!pip install -q  openvino
!pip install -q onnx
!pip install -q onnxruntime

import osimport timeimport paddle# 从模型代码中导入模型from paddle.vision.models import mobilenet_v2# 实例化模型net = mobilenet_v2()print(net)# 将模型设置为推理状态net.eval()

# 定义输入数据input_spec = paddle.static.InputSpec(shape=[None, 3, 224, 224], dtype='float32', name='image')# ONNX模型导出# enable_onnx_checker设置为True，表示使用官方ONNX工具包来check模型的正确性，需要安装ONNX（pip install onnx）paddle.onnx.export(net, 'mobilenet_v2', input_spec=[input_spec], opset_version=12, enable_onnx_checker=True)

# 动态图导出的ONNX模型测试import timeimport numpy as npfrom onnxruntime import InferenceSession# 加载ONNX模型sess = InferenceSession('mobilenet_v2.onnx')# 准备输入x = np.random.random((1, 3, 224, 224)).astype('float32')# 模型预测start = time.time()
ort_outs = sess.run(output_names=None, input_feed={'image': x})
end = time.time()print("Exported model has been predicted by ONNXRuntime!")print('ONNXRuntime predict time: %.04f s' % (end - start))# 对比ONNXRuntime和Paddle预测的结果paddle_outs = net(paddle.to_tensor(x))

diff = ort_outs[0] - paddle_outs.numpy()
max_abs_diff = np.fabs(diff).max()if max_abs_diff < 1e-05:    print("The difference of results between ONNXRuntime and Paddle looks good!")else:
    relative_diff = max_abs_diff / np.fabs(paddle_outs.numpy()).max()    if relative_diff < 1e-05:        print("The difference of results between ONNXRuntime and Paddle looks good!")    else:        print("The difference of results between ONNXRuntime and Paddle looks bad!")    print('relative_diff: ', relative_diff)print('max_abs_diff: ', max_abs_diff)

import paddleimport paddlehub as hub# 定义输入数据input_spec = paddle.static.InputSpec(shape=[None, 3, 224, 224], dtype='float32', name='image')# ONNX模型导出#paddle.onnx.export(model, 'stylepro', input_spec=[input_spec], opset_version=12)#Source: https://www.paddlepaddle.org.cn/hublistmodel = hub.Module(name="mobilenet_v3_large_imagenet_ssld")
model.save_inference_model(dirname="mobilenet_v3_large_imagenet_ssld/", 
                           model_filename="mobilenet_v3_large_imagenet_ssld/inference.pdmodel", 
                           params_filename="mobilenet_v3_large_imagenet_ssld/inference.pdiparams")#paddle.onnx.export(model, 'mobilenet', input_spec=[input_spec], opset_version=11)

import paddleimport paddlehub as hub# ONNX模型导出#paddle.onnx.export(model, 'stylepro', input_spec=[input_spec], opset_version=12)#Source: https://www.paddlepaddle.org.cn/hublistmodel = hub.Module(name="stylepro_artistic")
model.save_inference_model(dirname="stylepro_artistic/", 
                           model_filename="stylepro_artistic/inference.pdmodel", 
                           params_filename="stylepro_artistic/inference.pdiparams")#paddle.onnx.export(model, 'mobilenet', input_spec=[input_spec], opset_version=11)

# stylepro_artistic 模型的encoder 与decoder!paddle2onnx \
    --model_dir stylepro_artistic/encoder \
    --model_filename inference.pdmodel \
    --params_filename inference.pdiparams \
    --save_file stylepro_artistic_encoder.onnx \
    --opset_version 12!paddle2onnx \
    --model_dir stylepro_artistic/decoder \
    --model_filename inference.pdmodel \
    --params_filename inference.pdiparams \
    --save_file stylepro_artistic_decoder.onnx \
    --opset_version 12

# mobilenet模型!paddle2onnx \
    --model_dir mobilenet_v3_large_imagenet_ssld \
    --model_filename inference.pdmodel \
    --params_filename inference.pdiparams \
    --save_file mobilenet_v3_large_imagenet_ssld.onnx \
    --opset_version 12

import os, os.pathimport sysimport jsonimport urllib.requestimport cv2import numpy as npfrom imagenet_class_index_json import imagenet_classes #print(imagenet_classes['12']) imagenet类别index对应表from openvino.inference_engine import IENetwork, IECore, ExecutableNetworkfrom IPython.display import Imagedef image_preprocess_mobilenetv3(img_path):
    img = cv2.imread(img_path)
    img = cv2.resize(img, (224,224))
    img = np.transpose(img, [2,0,1]) / 255
    img = np.expand_dims(img, 0)
    img_mean = np.array([0.485, 0.456,0.406]).reshape((3,1,1))
    img_std = np.array([0.229, 0.224, 0.225]).reshape((3,1,1))
    img -= img_mean
    img /= img_std    return img.astype(np.float32)def top_k(result, topk=5):
    #imagenet_classes = json.loads(open("utils/imagenet_class_index.json").read())
    indices = np.argsort(-result[0])    for i in range(topk):        print("Class name:","'"+imagenet_classes[str(indices[0][i])][1]+"'",              ", probability:", result[0][0][indices[0][i]])
              
ie = IECore()

onnx_model='mobilenet_v3_large_imagenet_ssld.onnx'# 直接使用ONNX格式加载net = ie.read_network(model=onnx_model)

filename = "/root/tmp/test1.jpg"test_image = image_preprocess_mobilenetv3(filename) 

# pdmodel might be dynamic shape, this will reshape based on the inputinput_key = list(net.input_info.items())[0][0] # 'inputs'net.reshape({input_key: test_image.shape})#load the network on CPUexec_net = ie.load_network(net, 'CPU') 
assert isinstance(exec_net, ExecutableNetwork)#perform the inference stepoutput = exec_net.infer({input_key: test_image})
result_ie = list(output.values())print(result_ie)#Image(filename=filename)

	输出的图片out

import os, os.pathimport sysimport jsonimport urllib.requestimport cv2import numpy as npfrom processor import *from openvino.inference_engine import IENetwork, IECore, ExecutableNetworkfrom IPython.display import Image

modelPath=""picPath='work/'def decodeImg(input_data,onnx_model=modelPath+'stylepro_artistic_decoder.onnx'):    
    #onnx_model=rootPath+'stylepro_artistic_encoder.onnx'
    ie = IECore()    #
    net = ie.read_network(model=onnx_model)    # pdmodel might be dynamic shape, this will reshape based on the input
    input_key = list(net.input_info.items())[0][0] # 'inputs'
    net.reshape({input_key: input_data.shape})    #load the network on CPU
    exec_net = ie.load_network(net, 'CPU')    assert isinstance(exec_net, ExecutableNetwork)    #perform the inference step
    output = exec_net.infer({input_key: input_data})
    result_ie =list(output.values())[0]
    result_ie=np.array(result_ie)    #filter and print the top 5 results 
    print(result_ie.shape)    # ´(1, 1, 512, 28, 28)
    return result_iedef encodeFeat(picname,onnx_model=modelPath+'stylepro_artistic_encoder.onnx'):
    
    #onnx_model=rootPath+'stylepro_artistic_encoder.onnx'
    ie = IECore()    # 
    net = ie.read_network(model=onnx_model)
    test_image = image_preprocess(picname) 
    # pdmodel might be dynamic shape, this will reshape based on the input
    input_key = list(net.input_info.items())[0][0] # 'inputs'
    net.reshape({input_key: test_image.shape})    #load the network on CPU
    exec_net = ie.load_network(net, 'CPU')    assert isinstance(exec_net, ExecutableNetwork)    #perform the inference step
    output = exec_net.infer({input_key: test_image})
    result_ie =list(output.values())
    result_ie=np.array(result_ie)    #filter and print the top 5 results 
    print(result_ie.shape)    # output is :(1, 1, 512, 28, 28)
    return result_ie


content =cv2.imread(picPath+ "jj6.jpg")

pattern =cv2.imread(picPath+ "shahua.jpg")## get feature of pattern picture and content picturefea1=outputFeat(content)
fea2=outputFeat(pattern)##fr_feats = fr(fea1,fea2,1)print('fr_feats',fr_feats.shape)

output=decodeImg(fr_feats)
output=np.squeeze(output)#print('output',output.shape)#out=post_process(output,content.shape)print('out,',out['data'].shape)
cv2.imwrite(picPath+'out.jpg',out['data'])