name: Resnets on ImageNet SINGA version: 1.1 SINGA commit: 45ec92d8ffc1fa1385a9307fdf07e21da939ee2f parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz license: Apache V2, https://github.com/facebook/fb.resnet.torch/blob/master/LICENSE --- # 用ResNet做图像分类 这个例子中，我们将Torch训练好的ResNet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/resnet/resnet-18.tar.gz $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt $ tar xvf resnet-18.tar.gz * 运行程序 $ python serve.py -h * 运行程序 # use cpu $ python serve.py --use_cpu --parameter_file resnet-18.pickle --model resnet --depth 18 & # use gpu $ python serve.py --parameter_file resnet-18.pickle --model resnet --depth 18 & 我们提供了以下模型和深度配置的参数文件: * resnet (原始 resnet), [18](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz)|[34](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-34.tar.gz)|[101](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-101.tar.gz)|[152](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-152.tar.gz) * 包括批量正则, [50](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-50.tar.gz) * wrn (宽 resnet), [50](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/wrn-50-2.tar.gz) * preact (包括 pre-activation 的 resnet) [200](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-200.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torch参数文件中提取参数值 * 运行程序 $ python convert.py -h