1 type: "Input"
1 type: "Softmax"
2 type: "Dropout"
3 type: "InnerProduct"
5 type: "Pooling"
13 type: "Convolution"
15 type: "ReLU"
1 type: "Input"
1 type: "Softmax"
2 type: "Dropout"
3 type: "InnerProduct"
5 type: "Pooling"
16 type: "Convolution"
18 type: "ReLU"
caffe2模型文件vggnet19
1 type: "Softmax"
2 type: "Dropout"
3 type: "FC"
5 type: "MaxPool"
16 type: "Conv"
18 type: "Relu"
很明显vgg19比16多了3个卷积和relu，caffe2的FC就是caffe的InnerProduct，MaxPool就是Pooling。
caffe2模型文件的resnet50
1 type: "AveragePool"
1 type: "FC"
1 type: "MaxPool"
1 type: "Softmax"
16 type: "Sum"
49 type: "Relu"
53 type: "Conv"
53 type: "SpatialBN"
比vgg多了BN层和sum算子。
caffe2的模型文件densenet121
1 type: "MaxPool"
4 type: "AveragePool"
58 type: "Concat"
121 type: "Add"
121 type: "Conv"
121 type: "Mul"
121 type: "Relu"
121 type: "SpatialBN"
caffe2的模型文件mobilenet_v2
1 type: "AveragePool"
1 type: "FC"
1 type: "Softmax"
10 type: "Sum"
36 type: "Relu"
53 type: "Conv"
1 type: "gaussian"
1 type: "Data"
1 type: "Dropout"
1 type: "MemoryData"
1 type: "Softmax"
1 type: "SoftmaxWithLoss"
4 type: "Pooling"
8 type: "Concat"
25 type: "xavier"
26 type: "Convolution"
26 type: "ReLU"
其中，gaussian和xavier都是卷积层的weight_filler。
1 type: "constant"
2 type: "Pooling"
13 type: "Slice"
16 type: "Concat"
16 type: "ShuffleChannel"
19 type: "ConvolutionDepthwise"
37 type: "ReLU"
38 type: "Convolution"
56 type: "BatchNorm"
56 type: "Scale"
57 type: "msra"
其实，看了这么多，视觉的模型主要就几个算子构成。
