name: VGG on ImageNet SINGA version: 1.1.1 SINGA commit: license: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py --- # 用VGG做图像分类 这个例子中，我们将PyTorch训练好的VGG转换为SINGA模型以用作图像分类。 ## 操作说明 * 下载参数的checkpoint文件到如下目录 $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11.tar.gz $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt $ tar xvf vgg11.tar.gz * 运行程序 $ python serve.py -h * 例子 # use cpu $ python serve.py --use_cpu --parameter_file vgg11.pickle --depth 11 & # use gpu $ python serve.py --parameter_file vgg11.pickle --depth 11 & 我们提供了以下模型和深度配置的参数文件: * 不使用批量正则, [11](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11.tar.gz), [13](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg13.tar.gz), [16](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg16.tar.gz), [19](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg19.tar.gz) * 使用批量正则, [11](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11_bn.tar.gz), [13](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg13_bn.tar.gz), [16](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg16_bn.tar.gz), [19](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg19_bn.tar.gz) * 提交图片进行分类 $ curl -i -F image=@image1.jpg http://localhost:9999/api $ curl -i -F image=@image2.jpg http://localhost:9999/api $ curl -i -F image=@image3.jpg http://localhost:9999/api image1.jpg, image2.jpg和image3.jpg应该在执行指令前就已被下载。 ## 详细信息 用`convert.py`从Pytorch参数文件中提取参数值 * 运行程序 $ python convert.py -h