{"id":12421,"date":"2023-02-01T19:57:33","date_gmt":"2023-02-01T11:57:33","guid":{"rendered":"http:\/\/139.9.1.231\/?p=12421"},"modified":"2023-02-28T16:06:52","modified_gmt":"2023-02-28T08:06:52","slug":"onnx-pytorch","status":"publish","type":"post","link":"http:\/\/139.9.1.231\/index.php\/2023\/02\/01\/onnx-pytorch\/","title":{"rendered":"ONNX&#8212;-\u6a21\u578b\u90e8\u7f72\u6559\u7a0b\uff081\uff09"},"content":{"rendered":"\n<p class=\"has-text-align-center has-light-pink-background-color has-background\"><strong>\u8f6c\u81ea\uff1a<a rel=\"noreferrer noopener\" href=\"https:\/\/github.com\/open-mmlab\/mmdeploy\/tree\/master\/docs\/zh_cn\/tutorial\" target=\"_blank\">mmdeploy<\/a><\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" width=\"1024\" height=\"173\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2023\/01\/image-103-1024x173.png\" alt=\"\" class=\"wp-image-12487\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2023\/01\/image-103-1024x173.png 1024w, http:\/\/139.9.1.231\/wp-content\/uploads\/2023\/01\/image-103-300x51.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2023\/01\/image-103-768x130.png 768w, http:\/\/139.9.1.231\/wp-content\/uploads\/2023\/01\/image-103.png 1317w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"has-light-pink-background-color has-background\"> <strong>\u6a21\u578b\u8f6c\u6362\u5de5\u5177<\/strong>\uff1a<a rel=\"noreferrer noopener\" href=\"https:\/\/convertmodel.com\/\" target=\"_blank\"> https:\/\/convertmodel.com\/<\/a> <\/p>\n\n\n\n<p class=\"has-text-align-center has-bright-blue-background-color has-background\"><strong>\u5b98\u7f511\uff1a<a href=\"https:\/\/pytorch.org\/docs\/stable\/onnx.html#functions\">https:\/\/pytorch.org\/docs\/stable\/onnx.html#functions<\/a><\/strong><\/p>\n\n\n\n<p class=\"has-text-align-center has-bright-blue-background-color has-background\"><strong>\u5b98\u7f512\uff1a<a href=\"https:\/\/onnxruntime.ai\/docs\/get-started\/\">https:\/\/onnxruntime.ai\/docs\/get-started\/<\/a><\/strong><\/p>\n\n\n\n<h3 id=\"h_477743341_0\">\u524d\u8a00<\/h3>\n\n\n\n<p>OpenMMLab \u7684\u7b97\u6cd5\u5982\u4f55\u90e8\u7f72\uff1f\u662f\u5f88\u591a\u793e\u533a\u7528\u6237\u7684\u56f0\u60d1\u3002\u800c<a href=\"https:\/\/zhuanlan.zhihu.com\/p\/450342651\">\u6a21\u578b\u90e8\u7f72\u5de5\u5177\u7bb1 MMDeploy<\/a>&nbsp;\u7684\u5f00\u6e90\uff0c\u5f3a\u52bf\u6253\u901a\u4e86\u4ece\u7b97\u6cd5\u6a21\u578b\u5230\u5e94\u7528\u7a0b\u5e8f\u8fd9 &#8220;\u6700\u540e\u4e00\u516c\u91cc&#8221;\uff01<\/p>\n\n\n\n<p>\u4eca\u5929\u6211\u4eec\u5c06\u5f00\u542f\u6a21\u578b\u90e8\u7f72\u5165\u95e8\u7cfb\u5217\u6559\u7a0b\uff0c\u5728\u6a21\u578b\u90e8\u7f72\u5f00\u6e90\u5e93 MMDeploy \u7684\u8f85\u52a9\u4e0b\uff0c\u4ecb\u7ecd\u4ee5\u4e0b\u5185\u5bb9\uff1a<\/p>\n\n\n\n<ul><li>\u4e2d\u95f4\u8868\u793a <strong>ONNX <\/strong>\u7684\u5b9a\u4e49\u6807\u51c6<\/li><li>PyTorch \u6a21\u578b\u8f6c\u6362\u5230 <strong>ONNX <\/strong>\u6a21\u578b\u7684\u65b9\u6cd5<\/li><li>\u63a8\u7406\u5f15\u64ce ONNX Runtime\u3001<strong>TensorRT<\/strong> \u7684\u4f7f\u7528\u65b9\u6cd5<\/li><li>\u90e8\u7f72\u6d41\u6c34\u7ebf PyTorch &#8211; ONNX &#8211; ONNX Runtime\/TensorRT \u7684\u793a\u4f8b\u53ca\u5e38\u89c1\u90e8\u7f72\u95ee\u9898\u7684\u89e3\u51b3\u65b9\u6cd5<\/li><li>MMDeploy C\/C++ \u63a8\u7406 SDK<\/li><\/ul>\n\n\n\n<p>\u5e0c\u671b\u901a\u8fc7\u672c\u7cfb\u5217\u6559\u7a0b\uff0c\u5e26\u9886\u5927\u5bb6\u5b66\u4f1a\u5982\u4f55\u628a\u81ea\u5df1\u7684 PyTorch \u6a21\u578b\u90e8\u7f72\u5230 ONNX Runtime\/TensorRT \u4e0a\uff0c\u5e76\u5b66\u4f1a\u5982\u4f55\u628a OpenMMLab \u5f00\u6e90\u4f53\u7cfb\u4e2d\u5404\u4e2a\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u7684\u6a21\u578b\u7528&nbsp;<a href=\"https:\/\/github.com\/open-mmlab\/mmdeploy\" target=\"_blank\" rel=\"noreferrer noopener\">MMDeploy<\/a>&nbsp;\u90e8\u7f72\u5230\u5404\u4e2a\u63a8\u7406\u5f15\u64ce\u4e0a\u3002<\/p>\n\n\n\n<h3 id=\"h_477743341_1\"><strong>\u521d\u8bc6\u6a21\u578b\u90e8\u7f72<\/strong><\/h3>\n\n\n\n<p>       \u5728\u8f6f\u4ef6\u5de5\u7a0b\u4e2d\uff0c\u90e8\u7f72\u6307\u628a\u5f00\u53d1\u5b8c\u6bd5\u7684\u8f6f\u4ef6\u6295\u5165\u4f7f\u7528\u7684\u8fc7\u7a0b\uff0c\u5305\u62ec\u73af\u5883\u914d\u7f6e\u3001\u8f6f\u4ef6\u5b89\u88c5\u7b49\u6b65\u9aa4\u3002\u7c7b\u4f3c\u5730\uff0c\u5bf9\u4e8e\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u6765\u8bf4\uff0c\u6a21\u578b\u90e8\u7f72\u6307\u8ba9\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5728\u7279\u5b9a\u73af\u5883\u4e2d\u8fd0\u884c\u7684\u8fc7\u7a0b\u3002\u76f8\u6bd4\u4e8e\u8f6f\u4ef6\u90e8\u7f72\uff0c\u6a21\u578b\u90e8\u7f72\u4f1a\u9762\u4e34\u66f4\u591a\u7684\u96be\u9898\uff1a<\/p>\n\n\n\n<p>1\uff09\u8fd0\u884c\u6a21\u578b\u6240\u9700\u7684\u73af\u5883\u96be\u4ee5\u914d\u7f6e\u3002\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u901a\u5e38\u662f\u7531\u4e00\u4e9b\u6846\u67b6\u7f16\u5199\uff0c\u6bd4\u5982 PyTorch\u3001TensorFlow\u3002\u7531\u4e8e\u6846\u67b6\u89c4\u6a21\u3001\u4f9d\u8d56\u73af\u5883\u7684\u9650\u5236\uff0c\u8fd9\u4e9b\u6846\u67b6\u4e0d\u9002\u5408\u5728\u624b\u673a\u3001\u5f00\u53d1\u677f\u7b49\u751f\u4ea7\u73af\u5883\u4e2d\u5b89\u88c5\u3002<\/p>\n\n\n\n<p>2\uff09\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u7ed3\u6784\u901a\u5e38\u6bd4\u8f83\u5e9e\u5927\uff0c\u9700\u8981\u5927\u91cf\u7684\u7b97\u529b\u624d\u80fd\u6ee1\u8db3\u5b9e\u65f6\u8fd0\u884c\u7684\u9700\u6c42\u3002\u6a21\u578b\u7684\u8fd0\u884c\u6548\u7387\u9700\u8981\u4f18\u5316\u3002<\/p>\n\n\n\n<p>\u56e0\u4e3a\u8fd9\u4e9b\u96be\u9898\u7684\u5b58\u5728\uff0c\u6a21\u578b\u90e8\u7f72\u4e0d\u80fd\u9760\u7b80\u5355\u7684\u73af\u5883\u914d\u7f6e\u4e0e\u5b89\u88c5\u5b8c\u6210\u3002\u7ecf\u8fc7\u5de5\u4e1a\u754c\u548c\u5b66\u672f\u754c\u6570\u5e74\u7684\u63a2\u7d22\uff0c\u6a21\u578b\u90e8\u7f72\u6709\u4e86\u4e00\u6761\u6d41\u884c\u7684\u6d41\u6c34\u7ebf\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/pic2.zhimg.com\/v2-59b12a53cb37514ca9fa0126612c6d25_r.jpg\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-light-pink-background-color has-background\"><strong>\u4e3a\u4e86\u8ba9\u6a21\u578b\u6700\u7ec8\u80fd\u591f\u90e8\u7f72\u5230\u67d0\u4e00\u73af\u5883\u4e0a\uff0c\u5f00\u53d1\u8005\u4eec\u53ef\u4ee5\u4f7f\u7528\u4efb\u610f\u4e00\u79cd\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u6765\u5b9a\u4e49\u7f51\u7edc\u7ed3\u6784\uff0c\u5e76\u901a\u8fc7\u8bad\u7ec3\u786e\u5b9a\u7f51\u7edc\u4e2d\u7684\u53c2\u6570\u3002\u4e4b\u540e\uff0c\u6a21\u578b\u7684\u7ed3\u6784\u548c\u53c2\u6570\u4f1a\u88ab\u8f6c\u6362\u6210\u4e00\u79cd\u53ea\u63cf\u8ff0\u7f51\u7edc\u7ed3\u6784\u7684\u4e2d\u95f4\u8868\u793a\uff0c\u4e00\u4e9b\u9488\u5bf9\u7f51\u7edc\u7ed3\u6784\u7684\u4f18\u5316\u4f1a\u5728\u4e2d\u95f4\u8868\u793a\u4e0a\u8fdb\u884c\u3002\u6700\u540e\uff0c\u7528\u9762\u5411\u786c\u4ef6\u7684\u9ad8\u6027\u80fd\u7f16\u7a0b\u6846\u67b6(\u5982 CUDA\uff0cOpenCL\uff09\u7f16\u5199\uff0c\u80fd\u9ad8\u6548\u6267\u884c\u6df1\u5ea6\u5b66\u4e60\u7f51\u7edc\u4e2d\u7b97\u5b50\u7684\u63a8\u7406\u5f15\u64ce\u4f1a\u628a\u4e2d\u95f4\u8868\u793a\u8f6c\u6362\u6210\u7279\u5b9a\u7684\u6587\u4ef6\u683c\u5f0f\uff0c\u5e76\u5728\u5bf9\u5e94\u786c\u4ef6\u5e73\u53f0\u4e0a\u9ad8\u6548\u8fd0\u884c\u6a21\u578b\u3002<\/strong><\/p>\n\n\n\n<p>\u8fd9\u4e00\u6761\u6d41\u6c34\u7ebf\u89e3\u51b3\u4e86\u6a21\u578b\u90e8\u7f72\u4e2d\u7684\u4e24\u5927\u95ee\u9898\uff1a\u4f7f\u7528\u5bf9\u63a5\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u548c\u63a8\u7406\u5f15\u64ce\u7684\u4e2d\u95f4\u8868\u793a\uff0c\u5f00\u53d1\u8005\u4e0d\u5fc5\u62c5\u5fc3\u5982\u4f55\u5728\u65b0\u73af\u5883\u4e2d\u8fd0\u884c\u5404\u4e2a\u590d\u6742\u7684\u6846\u67b6\uff1b\u901a\u8fc7\u4e2d\u95f4\u8868\u793a\u7684\u7f51\u7edc\u7ed3\u6784\u4f18\u5316\u548c\u63a8\u7406\u5f15\u64ce\u5bf9\u8fd0\u7b97\u7684\u5e95\u5c42\u4f18\u5316\uff0c\u6a21\u578b\u7684\u8fd0\u7b97\u6548\u7387\u5927\u5e45\u63d0\u5347\u3002<\/p>\n\n\n\n<h3 id=\"h_477743341_4\"><strong>\u4e2d\u95f4\u8868\u793a &#8211; ONNX<\/strong><\/h3>\n\n\n\n<p>       \u5728\u4ecb\u7ecd ONNX \u4e4b\u524d\uff0c\u6211\u4eec\u5148\u4ece\u672c\u8d28\u4e0a\u6765\u8ba4\u8bc6\u4e00\u4e0b\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u6784\u3002\u795e\u7ecf\u7f51\u7edc\u5b9e\u9645\u4e0a\u53ea\u662f\u63cf\u8ff0\u4e86\u6570\u636e\u8ba1\u7b97\u7684\u8fc7\u7a0b\uff0c\u5176\u7ed3\u6784\u53ef\u4ee5\u7528\u8ba1\u7b97\u56fe\u8868\u793a\u3002\u6bd4\u5982 a+b \u53ef\u4ee5\u7528\u4e0b\u9762\u7684\u8ba1\u7b97\u56fe\u6765\u8868\u793a\uff1a<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img src=\"https:\/\/pic1.zhimg.com\/80\/v2-7dba402f6c35d2e6adbb577c21bafa08_1440w.webp\" alt=\"\"\/><\/figure><\/div>\n\n\n\n<p>    \u4e3a\u4e86\u52a0\u901f\u8ba1\u7b97\uff0c\u4e00\u4e9b\u6846\u67b6\u4f1a\u4f7f\u7528\u5bf9\u795e\u7ecf\u7f51\u7edc\u201c\u5148\u7f16\u8bd1\uff0c\u540e\u6267\u884c\u201d\u7684\u9759\u6001\u56fe\u6765\u63cf\u8ff0\u7f51\u7edc\u3002\u9759\u6001\u56fe\u7684\u7f3a\u70b9\u662f\u96be\u4ee5\u63cf\u8ff0\u63a7\u5236\u6d41\uff08\u6bd4\u5982 if-else \u5206\u652f\u8bed\u53e5\u548c for \u5faa\u73af\u8bed\u53e5\uff09\uff0c\u76f4\u63a5\u5bf9\u5176\u5f15\u5165\u63a7\u5236\u8bed\u53e5\u4f1a\u5bfc\u81f4\u4ea7\u751f\u4e0d\u540c\u7684\u8ba1\u7b97\u56fe\u3002\u6bd4\u5982\u5faa\u73af\u6267\u884c n \u6b21 a=a+b\uff0c\u5bf9\u4e8e\u4e0d\u540c\u7684 n\uff0c\u4f1a\u751f\u6210\u4e0d\u540c\u7684\u8ba1\u7b97\u56fe\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/pic3.zhimg.com\/v2-208d1ebafa6948c344fafad6fa145612_r.jpg\" alt=\"\"\/><\/figure>\n\n\n\n<p>       ONNX \uff08Open Neural Network Exchange\uff09\u662f Facebook \u548c\u5fae\u8f6f\u57282017\u5e74\u5171\u540c\u53d1\u5e03\u7684\uff0c\u7528\u4e8e<strong>\u6807\u51c6\u63cf\u8ff0\u8ba1\u7b97\u56fe<\/strong>\u7684\u4e00\u79cd\u683c\u5f0f\u3002\u76ee\u524d\uff0c\u5728\u6570\u5bb6\u673a\u6784\u7684\u5171\u540c\u7ef4\u62a4\u4e0b\uff0cONNX \u5df2\u7ecf\u5bf9\u63a5\u4e86\u591a\u79cd\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u548c\u591a\u79cd\u63a8\u7406\u5f15\u64ce\u3002\u56e0\u6b64\uff0cONNX \u88ab\u5f53\u6210\u4e86\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u5230\u63a8\u7406\u5f15\u64ce\u7684\u6865\u6881\uff0c\u5c31\u50cf\u7f16\u8bd1\u5668\u7684\u4e2d\u95f4\u8bed\u8a00\u4e00\u6837\u3002\u7531\u4e8e\u5404\u6846\u67b6\u517c\u5bb9\u6027\u4e0d\u4e00\uff0c\u6211\u4eec\u901a\u5e38\u53ea\u7528 ONNX \u8868\u793a\u66f4\u5bb9\u6613\u90e8\u7f72\u7684\u9759\u6001\u56fe\u3002<\/p>\n\n\n\n<h3 id=\"h_477743341_3\"><strong>\u521b\u5efa PyTorch \u6a21\u578b<\/strong><\/h3>\n\n\n\n<p>\u8ba9\u6211\u4eec\u7528 PyTorch \u5b9e\u73b0\u4e00\u4e2a\u8d85\u5206\u8fa8\u7387\u6a21\u578b\uff0c\u5e76\u628a\u6a21\u578b\u90e8\u7f72\u5230 ONNX Runtime \u8fd9\u4e2a\u63a8\u7406\u5f15\u64ce\u4e0a\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code><em># \u5b89\u88c5 ONNX Runtime, ONNX, OpenCV <\/em>\npip install onnxruntime onnx opencv-python\n<\/code><\/pre>\n\n\n\n<p>\u5728\u4e00\u5207\u90fd\u914d\u7f6e\u5b8c\u6bd5\u540e\uff0c\u7528\u4e0b\u9762\u7684\u4ee3\u7801\u6765\u521b\u5efa\u4e00\u4e2a\u7ecf\u5178\u7684\u8d85\u5206\u8fa8\u7387\u6a21\u578b SRCNN\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import os \n \nimport cv2 \nimport numpy as np \nimport requests \nimport torch \nimport torch.onnx \nfrom torch import nn \n \nclass SuperResolutionNet(nn.Module): \n    def __init__(self, upscale_factor): \n        super().__init__() \n        self.upscale_factor = upscale_factor \n        self.img_upsampler = nn.Upsample( \n            scale_factor=self.upscale_factor, \n            mode='bicubic', \n            align_corners=False) \n \n        self.conv1 = nn.Conv2d(3,64,kernel_size=9,padding=4) \n        self.conv2 = nn.Conv2d(64,32,kernel_size=1,padding=0) \n        self.conv3 = nn.Conv2d(32,3,kernel_size=5,padding=2) \n \n        self.relu = nn.ReLU() \n \n    def forward(self, x): \n        x = self.img_upsampler(x) \n        out = self.relu(self.conv1(x)) \n        out = self.relu(self.conv2(out)) \n        out = self.conv3(out) \n        return out \n \n<em># Download checkpoint and test image <\/em>\nurls = &#91;'https:\/\/download.openmmlab.com\/mmediting\/restorers\/srcnn\/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth', \n    'https:\/\/raw.githubusercontent.com\/open-mmlab\/mmediting\/master\/tests\/data\/face\/000001.png'] \nnames = &#91;'srcnn.pth', 'face.png'] \nfor url, name in zip(urls, names): \n    if not os.path.exists(name): \n        open(name, 'wb').write(requests.get(url).content) \n \ndef init_torch_model(): \n    torch_model = SuperResolutionNet(upscale_factor=3) \n \n    state_dict = torch.load('srcnn.pth')&#91;'state_dict'] \n \n    <em># Adapt the checkpoint <\/em>\n    for old_key in list(state_dict.keys()): \n        new_key = '.'.join(old_key.split('.')&#91;1:]) \n        state_dict&#91;new_key] = state_dict.pop(old_key) \n \n    torch_model.load_state_dict(state_dict) \n    torch_model.eval() \n    return torch_model \n \nmodel = init_torch_model() \ninput_img = cv2.imread('face.png').astype(np.float32) \n \n<em># HWC to NCHW <\/em>\ninput_img = np.transpose(input_img, &#91;2, 0, 1]) \ninput_img = np.expand_dims(input_img, 0) \n \n<em># Inference <\/em>\ntorch_output = model(torch.from_numpy(input_img)).detach().numpy() \n \n<em># NCHW to HWC <\/em>\ntorch_output = np.squeeze(torch_output, 0) \ntorch_output = np.clip(torch_output, 0, 255) \ntorch_output = np.transpose(torch_output, &#91;1, 2, 0]).astype(np.uint8) \n \n<em># Show image <\/em>\ncv2.imwrite(\"face_torch.png\", torch_output)<\/code><\/pre>\n\n\n\n<p>SRCNN \u5148\u628a\u56fe\u50cf\u4e0a\u91c7\u6837\u5230\u5bf9\u5e94\u5206\u8fa8\u7387\uff0c\u518d\u7528 3 \u4e2a\u5377\u79ef\u5c42\u5904\u7406\u56fe\u50cf\u3002\u4e3a\u4e86\u65b9\u4fbf\u8d77\u89c1\uff0c\u6211\u4eec\u8df3\u8fc7\u8bad\u7ec3\u7f51\u7edc\u7684\u6b65\u9aa4\uff0c\u76f4\u63a5\u4e0b\u8f7d\u6a21\u578b\u6743\u91cd\uff08\u7531\u4e8e MMEditing \u4e2d SRCNN \u7684\u6743\u91cd\u7ed3\u6784\u548c\u6211\u4eec\u5b9a\u4e49\u7684\u6a21\u578b\u4e0d\u592a\u4e00\u6837\uff0c\u6211\u4eec\u4fee\u6539\u4e86\u6743\u91cd\u5b57\u5178\u7684 key \u6765\u9002\u914d\u6211\u4eec\u5b9a\u4e49\u7684\u6a21\u578b\uff09\uff0c\u540c\u65f6\u4e0b\u8f7d\u597d\u8f93\u5165\u56fe\u7247\u3002\u4e3a\u4e86\u8ba9\u6a21\u578b\u8f93\u51fa\u6210\u6b63\u786e\u7684\u56fe\u7247\u683c\u5f0f\uff0c\u6211\u4eec\u628a\u6a21\u578b\u7684\u8f93\u51fa\u8f6c\u6362\u6210 HWC \u683c\u5f0f\uff0c\u5e76\u4fdd\u8bc1\u6bcf\u4e00\u901a\u9053\u7684\u989c\u8272\u503c\u90fd\u5728 0~255 \u4e4b\u95f4\u3002\u5982\u679c\u811a\u672c\u6b63\u5e38\u8fd0\u884c\u7684\u8bdd\uff0c\u4e00\u5e45\u8d85\u5206\u8fa8\u7387\u7684\u4eba\u8138\u7167\u7247\u4f1a\u4fdd\u5b58\u5728 \u201cface_torch.png\u201d \u4e2d\u3002<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img src=\"https:\/\/pic1.zhimg.com\/80\/v2-79d8f4d00614311aabd2a10684539cb8_1440w.webp\" alt=\"\"\/><\/figure><\/div>\n\n\n\n<p>\u5728 PyTorch \u6a21\u578b\u6d4b\u8bd5\u6b63\u786e\u540e\uff0c\u6211\u4eec\u6765\u6b63\u5f0f\u5f00\u59cb\u90e8\u7f72\u8fd9\u4e2a\u6a21\u578b\u3002\u6211\u4eec\u4e0b\u4e00\u6b65\u7684\u4efb\u52a1\u662f\u628a PyTorch \u6a21\u578b\u8f6c\u6362\u6210\u7528\u4e2d\u95f4\u8868\u793a ONNX \u63cf\u8ff0\u7684\u6a21\u578b\u3002<\/p>\n\n\n\n<p>\u8ba9\u6211\u4eec\u7528\u4e0b\u9762\u7684\u4ee3\u7801\u6765\u628a PyTorch \u7684\u6a21\u578b\u8f6c\u6362\u6210 ONNX \u683c\u5f0f\u7684\u6a21\u578b\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>x = torch.randn(1, 3, 256, 256) \n \nwith torch.no_grad(): \n    torch.onnx.export( \n        model, \n        x, \n        \"srcnn.onnx\", \n        opset_version=11, \n        input_names=&#91;'input'], \n        output_names=&#91;'output'])<\/code><\/pre>\n\n\n\n<p>\u5176\u4e2d\uff0c<strong>torch.onnx.export<\/strong>&nbsp;\u662f PyTorch \u81ea\u5e26\u7684\u628a\u6a21\u578b\u8f6c\u6362\u6210 ONNX \u683c\u5f0f\u7684\u51fd\u6570\u3002\u8ba9\u6211\u4eec\u5148\u770b\u4e00\u4e0b\u524d\u4e09\u4e2a\u5fc5\u9009\u53c2\u6570\uff1a\u524d\u4e09\u4e2a\u53c2\u6570\u5206\u522b\u662f\u8981\u8f6c\u6362\u7684\u6a21\u578b\u3001\u6a21\u578b\u7684\u4efb\u610f\u4e00\u7ec4\u8f93\u5165\u3001\u5bfc\u51fa\u7684 ONNX \u6587\u4ef6\u7684\u6587\u4ef6\u540d\u3002\u8f6c\u6362\u6a21\u578b\u65f6\uff0c\u9700\u8981\u539f\u6a21\u578b\u548c\u8f93\u51fa\u6587\u4ef6\u540d\u662f\u5f88\u5bb9\u6613\u7406\u89e3\u7684\uff0c\u4f46\u4e3a\u4ec0\u4e48\u9700\u8981\u4e3a\u6a21\u578b\u63d0\u4f9b\u4e00\u7ec4\u8f93\u5165\u5462\uff1f\u8fd9\u5c31\u6d89\u53ca\u5230 ONNX \u8f6c\u6362\u7684\u539f\u7406\u4e86\u3002\u4ece PyTorch \u7684\u6a21\u578b\u5230 ONNX \u7684\u6a21\u578b\uff0c\u672c\u8d28\u4e0a\u662f\u4e00\u79cd\u8bed\u8a00\u4e0a\u7684\u7ffb\u8bd1\u3002\u76f4\u89c9\u4e0a\u7684\u60f3\u6cd5\u662f\u50cf\u7f16\u8bd1\u5668\u4e00\u6837\u5f7b\u5e95\u89e3\u6790\u539f\u6a21\u578b\u7684\u4ee3\u7801\uff0c\u8bb0\u5f55\u6240\u6709\u63a7\u5236\u6d41\u3002\u4f46\u524d\u9762\u4e5f\u8bb2\u5230\uff0c\u6211\u4eec\u901a\u5e38\u53ea\u7528 ONNX \u8bb0\u5f55\u4e0d\u8003\u8651\u63a7\u5236\u6d41\u7684\u9759\u6001\u56fe\u3002\u56e0\u6b64\uff0cPyTorch \u63d0\u4f9b\u4e86\u4e00\u79cd\u53eb\u505a\u8ffd\u8e2a\uff08trace\uff09\u7684\u6a21\u578b\u8f6c\u6362\u65b9\u6cd5\uff1a\u7ed9\u5b9a\u4e00\u7ec4\u8f93\u5165\uff0c\u518d\u5b9e\u9645\u6267\u884c\u4e00\u904d\u6a21\u578b\uff0c\u5373\u628a\u8fd9\u7ec4\u8f93\u5165\u5bf9\u5e94\u7684\u8ba1\u7b97\u56fe\u8bb0\u5f55\u4e0b\u6765\uff0c\u4fdd\u5b58\u4e3a ONNX \u683c\u5f0f\u3002export \u51fd\u6570\u7528\u7684\u5c31\u662f\u8ffd\u8e2a\u5bfc\u51fa\u65b9\u6cd5\uff0c\u9700\u8981\u7ed9\u4efb\u610f\u4e00\u7ec4\u8f93\u5165\uff0c\u8ba9\u6a21\u578b\u8dd1\u8d77\u6765\u3002\u6211\u4eec\u7684\u6d4b\u8bd5\u56fe\u7247\u662f\u4e09\u901a\u9053\uff0c256&#215;256\u5927\u5c0f\u7684\uff0c\u8fd9\u91cc\u4e5f\u6784\u9020\u4e00\u4e2a\u540c\u6837\u5f62\u72b6\u7684\u968f\u673a\u5f20\u91cf\u3002<\/p>\n\n\n\n<p>\u5269\u4e0b\u7684\u53c2\u6570\u4e2d\uff0copset_version \u8868\u793a ONNX \u7b97\u5b50\u96c6\u7684\u7248\u672c\u3002\u6df1\u5ea6\u5b66\u4e60\u7684\u53d1\u5c55\u4f1a\u4e0d\u65ad\u8bde\u751f\u65b0\u7b97\u5b50\uff0c\u4e3a\u4e86\u652f\u6301\u8fd9\u4e9b\u65b0\u589e\u7684\u7b97\u5b50\uff0cONNX\u4f1a\u7ecf\u5e38\u53d1\u5e03\u65b0\u7684\u7b97\u5b50\u96c6\uff0c\u76ee\u524d\u5df2\u7ecf\u66f4\u65b015\u4e2a\u7248\u672c\u3002\u6211\u4eec\u4ee4 opset_version = 11\uff0c\u5373\u4f7f\u7528\u7b2c11\u4e2a ONNX \u7b97\u5b50\u96c6\uff0c\u662f\u56e0\u4e3a SRCNN \u4e2d\u7684 bicubic \uff08\u53cc\u4e09\u6b21\u63d2\u503c\uff09\u5728 opset11 \u4e2d\u624d\u5f97\u5230\u652f\u6301\u3002\u5269\u4e0b\u7684\u4e24\u4e2a\u53c2\u6570 input_names, output_names \u662f\u8f93\u5165\u3001\u8f93\u51fa tensor \u7684\u540d\u79f0\uff0c\u6211\u4eec\u7a0d\u540e\u4f1a\u7528\u5230\u8fd9\u4e9b\u540d\u79f0\u3002<\/p>\n\n\n\n<p>\u5982\u679c\u4e0a\u8ff0\u4ee3\u7801\u8fd0\u884c\u6210\u529f\uff0c\u76ee\u5f55\u4e0b\u4f1a\u65b0\u589e\u4e00\u4e2a&#8221;srcnn.onnx&#8221;\u7684 ONNX \u6a21\u578b\u6587\u4ef6\u3002\u6211\u4eec\u53ef\u4ee5\u7528\u4e0b\u9762\u7684\u811a\u672c\u6765\u9a8c\u8bc1\u4e00\u4e0b\u6a21\u578b\u6587\u4ef6\u662f\u5426\u6b63\u786e\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import onnx \n \nonnx_model = onnx.load(\"srcnn.onnx\") \ntry: \n    onnx.checker.check_model(onnx_model) \nexcept Exception: \n    print(\"Model incorrect\") \nelse: \n    print(\"Model correct\")<\/code><\/pre>\n\n\n\n<p>\u5176\u4e2d\uff0c<strong>onnx.load<\/strong>&nbsp;\u51fd\u6570\u7528\u4e8e\u8bfb\u53d6\u4e00\u4e2a ONNX \u6a21\u578b\u3002<strong>onnx.checker.check_model<\/strong>&nbsp;\u7528\u4e8e\u68c0\u67e5\u6a21\u578b\u683c\u5f0f\u662f\u5426\u6b63\u786e\uff0c\u5982\u679c\u6709\u9519\u8bef\u7684\u8bdd\u8be5\u51fd\u6570\u4f1a\u76f4\u63a5\u62a5\u9519\u3002\u6211\u4eec\u7684\u6a21\u578b\u662f\u6b63\u786e\u7684\uff0c\u63a7\u5236\u53f0\u4e2d\u5e94\u8be5\u4f1a\u6253\u5370\u51fa&#8221;Model correct&#8221;\u3002<\/p>\n\n\n\n<p>\u63a5\u4e0b\u6765\uff0c\u8ba9\u6211\u4eec\u6765\u770b\u4e00\u770b ONNX \u6a21\u578b\u5177\u4f53\u7684\u7ed3\u6784\u662f\u600e\u4e48\u6837\u7684\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528&nbsp;<strong>Netron<\/strong>&nbsp;\uff08\u5f00\u6e90\u7684\u6a21\u578b\u53ef\u89c6\u5316\u5de5\u5177\uff09\u6765\u53ef\u89c6\u5316 ONNX \u6a21\u578b\u3002\u628a srcnn.onnx \u6587\u4ef6\u4ece\u672c\u5730\u7684\u6587\u4ef6\u7cfb\u7edf\u62d6\u5165\u7f51\u7ad9\uff0c\u5373\u53ef\u770b\u5230\u5982\u4e0b\u7684\u53ef\u89c6\u5316\u7ed3\u679c\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/pic1.zhimg.com\/80\/v2-3922c56ffd867bd7d391dfe4e34c2d54_1440w.webp\" alt=\"\"\/><\/figure>\n\n\n\n<p>\u70b9\u51fb input \u6216\u8005 output\uff0c\u53ef\u4ee5\u67e5\u770b ONNX \u6a21\u578b\u7684\u57fa\u672c\u4fe1\u606f\uff0c\u5305\u62ec\u6a21\u578b\u7684\u7248\u672c\u4fe1\u606f\uff0c\u4ee5\u53ca\u6a21\u578b\u8f93\u5165\u3001\u8f93\u51fa\u7684\u540d\u79f0\u548c\u6570\u636e\u7c7b\u578b\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/pic4.zhimg.com\/v2-bdcbbd114371cd059ff423cfb6b620f7_r.jpg\" alt=\"\"\/><\/figure>\n\n\n\n<p>\u70b9\u51fb\u67d0\u4e00\u4e2a\u7b97\u5b50\u8282\u70b9\uff0c\u53ef\u4ee5\u770b\u5230\u7b97\u5b50\u7684\u5177\u4f53\u4fe1\u606f\u3002\u6bd4\u5982\u70b9\u51fb\u7b2c\u4e00\u4e2a Conv \u53ef\u4ee5\u770b\u5230\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/pic1.zhimg.com\/v2-8bc580b4d0259f197f8750a46ee1252c_r.jpg\" alt=\"\"\/><\/figure>\n\n\n\n<p>\u6bcf\u4e2a\u7b97\u5b50\u8bb0\u5f55\u4e86\u7b97\u5b50\u5c5e\u6027\u3001\u56fe\u7ed3\u6784\u3001\u6743\u91cd\u4e09\u7c7b\u4fe1\u606f\u3002<\/p>\n\n\n\n<ul><li><strong>\u7b97\u5b50\u5c5e\u6027\u4fe1\u606f<\/strong>\u5373\u56fe\u4e2d attributes \u91cc\u7684\u4fe1\u606f\uff0c\u5bf9\u4e8e\u5377\u79ef\u6765\u8bf4\uff0c\u7b97\u5b50\u5c5e\u6027\u5305\u62ec\u4e86\u5377\u79ef\u6838\u5927\u5c0f(kernel_shape)\u3001\u5377\u79ef\u6b65\u957f(strides)\u7b49\u5185\u5bb9\u3002\u8fd9\u4e9b\u7b97\u5b50\u5c5e\u6027\u6700\u7ec8\u4f1a\u7528\u6765\u751f\u6210\u4e00\u4e2a\u5177\u4f53\u7684\u7b97\u5b50\u3002<\/li><li><strong>\u56fe\u7ed3\u6784\u4fe1\u606f<\/strong>\u6307\u7b97\u5b50\u8282\u70b9\u5728\u8ba1\u7b97\u56fe\u4e2d\u7684\u540d\u79f0\u3001\u90bb\u8fb9\u7684\u4fe1\u606f\u3002\u5bf9\u4e8e\u56fe\u4e2d\u7684\u5377\u79ef\u6765\u8bf4\uff0c\u8be5\u7b97\u5b50\u8282\u70b9\u53eb\u505a Conv_2\uff0c\u8f93\u5165\u6570\u636e\u53eb\u505a 11\uff0c\u8f93\u51fa\u6570\u636e\u53eb\u505a 12\u3002\u6839\u636e\u6bcf\u4e2a\u7b97\u5b50\u8282\u70b9\u7684\u56fe\u7ed3\u6784\u4fe1\u606f\uff0c\u5c31\u80fd\u5b8c\u6574\u5730\u590d\u539f\u51fa\u7f51\u7edc\u7684\u8ba1\u7b97\u56fe\u3002<\/li><li><strong>\u6743\u91cd\u4fe1\u606f<\/strong>\u6307\u7684\u662f\u7f51\u7edc\u7ecf\u8fc7\u8bad\u7ec3\u540e\uff0c\u7b97\u5b50\u5b58\u50a8\u7684\u6743\u91cd\u4fe1\u606f\u3002\u5bf9\u4e8e\u5377\u79ef\u6765\u8bf4\uff0c\u6743\u91cd\u4fe1\u606f\u5305\u62ec\u5377\u79ef\u6838\u7684\u6743\u91cd\u503c\u548c\u5377\u79ef\u540e\u7684\u504f\u5dee\u503c\u3002\u70b9\u51fb\u56fe\u4e2d conv1.weight, conv1.bias \u540e\u9762\u7684\u52a0\u53f7\u5373\u53ef\u770b\u5230\u6743\u91cd\u4fe1\u606f\u7684\u5177\u4f53\u5185\u5bb9\u3002<\/li><\/ul>\n\n\n\n<p>\u73b0\u5728\uff0c\u6211\u4eec\u6709\u4e86 SRCNN \u7684 ONNX \u6a21\u578b\u3002\u8ba9\u6211\u4eec\u770b\u770b\u6700\u540e\u8be5\u5982\u4f55\u628a\u8fd9\u4e2a\u6a21\u578b\u8fd0\u884c\u8d77\u6765\u3002<\/p>\n\n\n\n<h3 id=\"h_477743341_5\"><strong>\u63a8\u7406\u5f15\u64ce -ONNX Runtime<\/strong><\/h3>\n\n\n\n<p class=\"has-light-pink-background-color has-background\">       <strong>ONNX Runtime<\/strong>&nbsp;\u662f\u7531\u5fae\u8f6f\u7ef4\u62a4\u7684\u4e00\u4e2a\u8de8\u5e73\u53f0\u673a\u5668\u5b66\u4e60\u63a8\u7406\u52a0\u901f\u5668\uff0c\u4e5f\u5c31\u662f\u6211\u4eec\u524d\u9762\u63d0\u5230\u7684\u201d\u63a8\u7406\u5f15\u64ce\u201c\u3002ONNX Runtime \u662f\u76f4\u63a5\u5bf9\u63a5 ONNX \u7684\uff0c\u5373 ONNX Runtime \u53ef\u4ee5\u76f4\u63a5\u8bfb\u53d6\u5e76\u8fd0\u884c .onnx \u6587\u4ef6, \u800c\u4e0d\u9700\u8981\u518d\u628a .onnx \u683c\u5f0f\u7684\u6587\u4ef6\u8f6c\u6362\u6210\u5176\u4ed6\u683c\u5f0f\u7684\u6587\u4ef6\u3002\u4e5f\u5c31\u662f\u8bf4\uff0c<strong><em>\u5bf9\u4e8e PyTorch &#8211; ONNX &#8211; ONNX Runtime \u8fd9\u6761\u90e8\u7f72\u6d41\u6c34\u7ebf\uff0c\u53ea\u8981\u5728\u76ee\u6807\u8bbe\u5907\u4e2d\u5f97\u5230 .onnx \u6587\u4ef6\uff0c\u5e76\u5728 ONNX Runtime \u4e0a\u8fd0\u884c\u6a21\u578b\uff0c\u6a21\u578b\u90e8\u7f72\u5c31\u7b97\u5927\u529f\u544a\u6210\u4e86\u3002<\/em><\/strong><\/p>\n\n\n\n<p>        \u901a\u8fc7\u521a\u521a\u7684\u64cd\u4f5c\uff0c\u6211\u4eec\u628a PyTorch \u7f16\u5199\u7684\u6a21\u578b\u8f6c\u6362\u6210\u4e86 ONNX \u6a21\u578b\uff0c\u5e76\u901a\u8fc7\u53ef\u89c6\u5316\u68c0\u67e5\u4e86\u6a21\u578b\u7684\u6b63\u786e\u6027\u3002\u6700\u540e\uff0c\u8ba9\u6211\u4eec\u7528 ONNX Runtime \u8fd0\u884c\u4e00\u4e0b\u6a21\u578b\uff0c\u5b8c\u6210\u6a21\u578b\u90e8\u7f72\u7684\u6700\u540e\u4e00\u6b65\u3002<\/p>\n\n\n\n<p>ONNX Runtime \u63d0\u4f9b\u4e86 Python \u63a5\u53e3\u3002\u63a5\u7740\u521a\u624d\u7684\u811a\u672c\uff0c\u6211\u4eec\u53ef\u4ee5\u6dfb\u52a0\u5982\u4e0b\u4ee3\u7801\u8fd0\u884c\u6a21\u578b\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import onnxruntime \n \nort_session = onnxruntime.InferenceSession(\"srcnn.onnx\") \nort_inputs = {'input': input_img} \nort_output = ort_session.run(&#91;'output'], ort_inputs)&#91;0] \n \nort_output = np.squeeze(ort_output, 0) \nort_output = np.clip(ort_output, 0, 255) \nort_output = np.transpose(ort_output, &#91;1, 2, 0]).astype(np.uint8) \ncv2.imwrite(\"face_ort.png\", ort_output)<\/code><\/pre>\n\n\n\n<p>\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u9664\u53bb\u540e\u5904\u7406\u64cd\u4f5c\u5916\uff0c\u548c ONNX Runtime \u76f8\u5173\u7684\u4ee3\u7801\u53ea\u6709\u4e09\u884c\u3002\u8ba9\u6211\u4eec\u7b80\u5355\u89e3\u6790\u4e00\u4e0b\u8fd9\u4e09\u884c\u4ee3\u7801\u3002<strong>onnxruntime.InferenceSession<\/strong>\u7528\u4e8e\u83b7\u53d6\u4e00\u4e2a ONNX Runtime \u63a8\u7406\u5668\uff0c\u5176\u53c2\u6570\u662f\u7528\u4e8e\u63a8\u7406\u7684 ONNX \u6a21\u578b\u6587\u4ef6\u3002\u63a8\u7406\u5668\u7684&nbsp;<strong>run<\/strong>&nbsp;\u65b9\u6cd5\u7528\u4e8e\u6a21\u578b\u63a8\u7406\uff0c\u5176\u7b2c\u4e00\u4e2a\u53c2\u6570\u4e3a\u8f93\u51fa\u5f20\u91cf\u540d\u7684\u5217\u8868\uff0c\u7b2c\u4e8c\u4e2a\u53c2\u6570\u4e3a\u8f93\u5165\u503c\u7684\u5b57\u5178\u3002\u5176\u4e2d\u8f93\u5165\u503c\u5b57\u5178\u7684 key \u4e3a\u5f20\u91cf\u540d\uff0cvalue \u4e3a numpy \u7c7b\u578b\u7684\u5f20\u91cf\u503c\u3002\u8f93\u5165\u8f93\u51fa\u5f20\u91cf\u7684\u540d\u79f0\u9700\u8981\u548c<strong>torch.onnx.export<\/strong>&nbsp;\u4e2d\u8bbe\u7f6e\u7684\u8f93\u5165\u8f93\u51fa\u540d\u5bf9\u5e94\u3002<\/p>\n\n\n\n<p>\u5982\u679c\u4ee3\u7801\u6b63\u5e38\u8fd0\u884c\u7684\u8bdd\uff0c\u53e6\u4e00\u5e45\u8d85\u5206\u8fa8\u7387\u7167\u7247\u4f1a\u4fdd\u5b58\u5728&#8221;face_ort.png&#8221;\u4e2d\u3002\u8fd9\u5e45\u56fe\u7247\u548c\u521a\u521a\u5f97\u5230\u7684&#8221;face_torch.png&#8221;\u662f\u4e00\u6a21\u4e00\u6837\u7684\u3002\u8fd9\u8bf4\u660e ONNX Runtime \u6210\u529f\u8fd0\u884c\u4e86 SRCNN \u6a21\u578b\uff0c\u6a21\u578b\u90e8\u7f72\u5b8c\u6210\u4e86\uff01\u4ee5\u540e\u6709\u7528\u6237\u60f3\u5b9e\u73b0\u8d85\u5206\u8fa8\u7387\u7684\u64cd\u4f5c\uff0c\u6211\u4eec\u53ea\u9700\u8981\u63d0\u4f9b\u4e00\u4e2a &#8220;srcnn.onnx&#8221; \u6587\u4ef6\uff0c\u5e76\u5e2e\u52a9\u7528\u6237\u914d\u7f6e\u597d ONNX Runtime \u7684 Python \u73af\u5883\uff0c\u7528\u51e0\u884c\u4ee3\u7801\u5c31\u53ef\u4ee5\u8fd0\u884c\u6a21\u578b\u4e86\u3002\u6216\u8005\u8fd8\u6709\u66f4\u7b80\u4fbf\u7684\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528 ONNX Runtime \u7f16\u8bd1\u51fa\u4e00\u4e2a\u53ef\u4ee5\u76f4\u63a5\u6267\u884c\u6a21\u578b\u7684\u5e94\u7528\u7a0b\u5e8f\u3002\u6211\u4eec\u53ea\u9700\u8981\u7ed9\u7528\u6237\u63d0\u4f9b ONNX \u6a21\u578b\u6587\u4ef6\uff0c\u5e76\u8ba9\u7528\u6237\u5728\u5e94\u7528\u7a0b\u5e8f\u9009\u62e9\u8981\u6267\u884c\u7684 ONNX \u6a21\u578b\u6587\u4ef6\u540d\u5c31\u53ef\u4ee5\u8fd0\u884c\u6a21\u578b\u4e86\u3002<\/p>\n\n\n\n<h3><strong>\u603b\u7ed3<\/strong>\uff1a<\/h3>\n\n\n\n<ul class=\"has-light-pink-background-color has-background\"><li>\u6a21\u578b\u90e8\u7f72\uff0c\u6307\u628a\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5728\u7279\u5b9a\u73af\u5883\u4e2d\u8fd0\u884c\u7684\u8fc7\u7a0b\u3002<strong>\u6a21\u578b\u90e8\u7f72\u8981\u89e3\u51b3\u6a21\u578b\u6846\u67b6\u517c\u5bb9\u6027\u5dee\u548c\u6a21\u578b\u8fd0\u884c\u901f\u5ea6\u6162\u8fd9\u4e24\u5927\u95ee\u9898\u3002<\/strong><\/li><li>\u6a21\u578b\u90e8\u7f72\u7684\u5e38\u89c1\u6d41\u6c34\u7ebf\u662f\u201c<strong>\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6-\u4e2d\u95f4\u8868\u793a-\u63a8\u7406\u5f15\u64ce<\/strong>\u201d\u3002\u5176\u4e2d\u6bd4\u8f83\u5e38\u7528\u7684\u4e00\u4e2a\u4e2d\u95f4\u8868\u793a\u662f ONNX\u3002<\/li><li><strong>\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u5b9e\u9645\u4e0a\u5c31\u662f\u4e00\u4e2a\u8ba1\u7b97\u56fe\u3002\u6a21\u578b\u90e8\u7f72\u65f6\u901a\u5e38\u628a\u6a21\u578b\u8f6c\u6362\u6210\u9759\u6001\u7684\u8ba1\u7b97\u56fe\uff0c\u5373\u6ca1\u6709\u63a7\u5236\u6d41\uff08\u5206\u652f\u8bed\u53e5\u3001\u5faa\u73af\u8bed\u53e5\uff09\u7684\u8ba1\u7b97\u56fe\u3002<\/strong><\/li><li>PyTorch \u6846\u67b6\u81ea\u5e26\u5bf9 ONNX \u7684\u652f\u6301\uff0c\u53ea\u9700\u8981\u6784\u9020\u4e00\u7ec4\u968f\u673a\u7684\u8f93\u5165\uff0c\u5e76\u5bf9\u6a21\u578b\u8c03\u7528&nbsp;<strong>torch.onnx.export<\/strong>&nbsp;\u5373\u53ef\u5b8c\u6210 PyTorch \u5230 ONNX \u7684\u8f6c\u6362\u3002<\/li><li><strong>\u63a8\u7406\u5f15\u64ce ONNX Runtime \u5bf9 ONNX \u6a21\u578b\u6709\u539f\u751f\u7684\u652f\u6301\u3002\u7ed9\u5b9a\u4e00\u4e2a .onnx \u6587\u4ef6\uff0c\u53ea\u9700\u8981\u7b80\u5355\u4f7f\u7528 ONNX Runtime \u7684 Python API \u5c31\u53ef\u4ee5\u5b8c\u6210\u6a21\u578b\u63a8\u7406\u3002<\/strong><\/li><\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u8f6c\u81ea\uff1ammdeploy \u6a21\u578b\u8f6c\u6362\u5de5\u5177\uff1a https:\/\/convertmodel.com\/ \u5b98\u7f511\uff1ahttps &hellip; <a href=\"http:\/\/139.9.1.231\/index.php\/2023\/02\/01\/onnx-pytorch\/\" class=\"more-link\">\u7ee7\u7eed\u9605\u8bfb<span class=\"screen-reader-text\">ONNX&#8212;-\u6a21\u578b\u90e8\u7f72\u6559\u7a0b\uff081\uff09<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[11,26],"tags":[],"_links":{"self":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts\/12421"}],"collection":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/comments?post=12421"}],"version-history":[{"count":46,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts\/12421\/revisions"}],"predecessor-version":[{"id":14511,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts\/12421\/revisions\/14511"}],"wp:attachment":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/media?parent=12421"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/categories?post=12421"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/tags?post=12421"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}