caffe deploy

6eanut · Jul 11, 2024 · 8ddd49e · 8ddd49e
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diff --git a/24-07/caffe-aarch64.md b/24-07/caffe-aarch64.md
@@ -786,7 +786,7 @@ caffe time -model my_models/3DMM_CNN/deploy_network.prototxt
 
 ![1720064717585](image/caffe-aarch64/1720064717585.png)
 
-#### cascadedfcn/step1
+#### cascadedfcn_step1
 
 自带[deploy.prototxt](https://github.com/IBBM/Cascaded-FCN/tree/master/models/cascadedfcn/step1)文件。
 
@@ -796,7 +796,7 @@ caffe time -model my_models/Cascaded-FCN/models/cascadedfcn/step1/step1_deploy.p
 
 ![1720064596125](image/caffe-aarch64/1720064596125.png)
 
-#### cascadedfcn/step2
+#### cascadedfcn_step2
 
 自带[deploy.prototxt](https://github.com/IBBM/Cascaded-FCN/tree/master/models/cascadedfcn/step2)文件。
 
@@ -851,3 +851,9 @@ caffe time -model my_models/Using-Ranking-CNN-for-Age-Estimation/basic_16to66/de
 ```
 
 ![1720065166608](image/caffe-aarch64/1720065166608.png)
+
+## 4-附件
+
+50个models的deploy文件
+
+caffe在aarch64（鲲鹏920）上的自动化构建脚本
diff --git a/24-07/caffe/deploy/04-VGG_CNN_S.prototxt b/24-07/caffe/deploy/04-VGG_CNN_S.prototxt
@@ -0,0 +1,198 @@
+name: "VGG_CNN_S"
+input: "data"
+input_dim: 10
+input_dim: 3
+input_dim: 224
+input_dim: 224
+layers {
+  bottom: "data"
+  top: "conv1"
+  name: "conv1"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 96
+    kernel_size: 7
+    stride: 2
+  }
+}
+layers {
+  bottom: "conv1"
+  top: "conv1"
+  name: "relu1"
+  type: RELU
+}
+layers {
+  bottom: "conv1"
+  top: "norm1"
+  name: "norm1"
+  type: LRN
+  lrn_param {
+    local_size: 5
+    alpha: 0.0005
+    beta: 0.75
+    k: 2
+  }
+}
+layers {
+  bottom: "norm1"
+  top: "pool1"
+  name: "pool1"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 3
+  }
+}
+layers {
+  bottom: "pool1"
+  top: "conv2"
+  name: "conv2"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 256
+    kernel_size: 5
+  }
+}
+layers {
+  bottom: "conv2"
+  top: "conv2"
+  name: "relu2"
+  type: RELU
+}
+layers {
+  bottom: "conv2"
+  top: "pool2"
+  name: "pool2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool2"
+  top: "conv3"
+  name: "conv3"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  bottom: "conv3"
+  top: "conv3"
+  name: "relu3"
+  type: RELU
+}
+layers {
+  bottom: "conv3"
+  top: "conv4"
+  name: "conv4"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  bottom: "conv4"
+  top: "conv4"
+  name: "relu4"
+  type: RELU
+}
+layers {
+  bottom: "conv4"
+  top: "conv5"
+  name: "conv5"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  bottom: "conv5"
+  top: "conv5"
+  name: "relu5"
+  type: RELU
+}
+layers {
+  bottom: "conv5"
+  top: "pool5"
+  name: "pool5"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 3
+  }
+}
+layers {
+  bottom: "pool5"
+  top: "fc6"
+  name: "fc6"
+  type: INNER_PRODUCT
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layers {
+  bottom: "fc6"
+  top: "fc6"
+  name: "relu6"
+  type: RELU
+}
+layers {
+  bottom: "fc6"
+  top: "fc6"
+  name: "drop6"
+  type: DROPOUT
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  bottom: "fc6"
+  top: "fc7"
+  name: "fc7"
+  type: INNER_PRODUCT
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layers {
+  bottom: "fc7"
+  top: "fc7"
+  name: "relu7"
+  type: RELU
+}
+layers {
+  bottom: "fc7"
+  top: "fc7"
+  name: "drop7"
+  type: DROPOUT
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  bottom: "fc7"
+  top: "fc8"
+  name: "fc8"
+  type: INNER_PRODUCT
+  inner_product_param {
+    num_output: 1000
+  }
+}
+layers {
+  bottom: "fc8"
+  top: "prob"
+  name: "prob"
+  type: SOFTMAX
+}