define up-to-date all-in-one model for pascal finetuning

diff --git a/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt b/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt
index 83f5abd..829b200 100644 (file)
--- a/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt
+++ b/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt
@@ -1,5 +1,4 @@
-train_net: "examples/finetune_pascal_detection/pascal_finetune_train.prototxt"
-test_net: "examples/finetune_pascal_detection/pascal_finetune_val.prototxt"
+net: "examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt"
 test_iter: 100
 test_interval: 1000
 base_lr: 0.001

diff --git a/examples/finetune_pascal_detection/pascal_finetune_train.prototxt b/examples/finetune_pascal_detection/pascal_finetune_train.prototxt
deleted file mode 100644 (file)
index ae7ee4c..0000000
--- a/examples/finetune_pascal_detection/pascal_finetune_train.prototxt
+++ /dev/null
@@ -1,321 +0,0 @@
-name: "CaffeNet"
-layers {
-  name: "data"
-  type: WINDOW_DATA
-  top: "data"
-  top: "label"
-  window_data_param {
-    source: "examples/finetune_pascal_detection/window_file_2007_trainval.txt"
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    batch_size: 128
-    crop_size: 227
-    mirror: true
-    fg_threshold: 0.5
-    bg_threshold: 0.5
-    fg_fraction: 0.25
-    context_pad: 16
-    crop_mode: "warp"
-  }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6"
-  type: INNER_PRODUCT
-  bottom: "pool5"
-  top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6"
-  top: "fc6"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc7"
-  type: INNER_PRODUCT
-  bottom: "fc6"
-  top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7"
-  top: "fc7"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc8_pascal"
-  type: INNER_PRODUCT
-  bottom: "fc7"
-  top: "fc8_pascal"
-  blobs_lr: 10
-  blobs_lr: 20
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 21
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "fc8_pascal"
-  bottom: "label"
-}

diff --git a/examples/finetune_pascal_detection/pascal_finetune_val.prototxt b/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
similarity index 89%
rename from examples/finetune_pascal_detection/pascal_finetune_val.prototxt
rename to examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
index 30dd164..5cd605b 100644 (file)
--- a/examples/finetune_pascal_detection/pascal_finetune_val.prototxt
+++ b/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
@@ -5,17 +5,41 @@ layers {
   top: "data"
   top: "label"
   window_data_param {
-    source: "examples/finetune_pascal_detection/window_file_2007_test.txt"
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+    source: "examples/finetune_pascal_detection/window_file_2007_trainval.txt"
     batch_size: 128
-    crop_size: 227
+    fg_threshold: 0.5
+    bg_threshold: 0.5
+    fg_fraction: 0.25
+    context_pad: 16
+    crop_mode: "warp"
+  }
+  transform_param {
     mirror: true
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "data"
+  type: WINDOW_DATA
+  top: "data"
+  top: "label"
+  window_data_param {
+    source: "examples/finetune_pascal_detection/window_file_2007_test.txt"
+    batch_size: 128
     fg_threshold: 0.5
     bg_threshold: 0.5
     fg_fraction: 0.25
     context_pad: 16
     crop_mode: "warp"
   }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
+  include: { phase: TEST }
 }
 layers {
   name: "conv1"
@@ -314,17 +338,16 @@ layers {
   }
 }
 layers {
-  name: "accuracy"
-  type: ACCURACY
+  name: "loss"
+  type: SOFTMAX_LOSS
   bottom: "fc8_pascal"
   bottom: "label"
-  top: "accuracy"
 }
 layers {
-  name: "prob"
-  type: SOFTMAX_LOSS
+  name: "accuracy"
+  type: ACCURACY
   bottom: "fc8_pascal"
   bottom: "label"
-  top: "loss"
+  top: "accuracy"
+  include { phase: TEST }
 }
-