Neural network 如何在原始文件中使用caffe中的空间金字塔层？

嗨，我想知道如何在proto文件中使用。 也许有人能给我解释一下如何阅读caffe文档，因为有时我很难直接理解

我的尝试基于此，但我认为它与当前版本不同

---

我这样定义层：

layers {
  name: "spatial_pyramid_pooling"
  type: "SPP"
  bottom: "conv2"
  top: "spatial_pyramid_pooling"
  spatial_pyramid_pooling_param {
    pool: MAX
    spatial_bin: 1
    spatial_bin: 2
    spatial_bin: 3
    spatial_bin: 6
    scale: 1
  }
}

layer {
  name: "spatial_pyramid_pooling"
  type: "SPP"
  bottom: "conv2"
  top: "pool2"
  spp_param {
    pyramid_height: 2
  }
} 

---

当我尝试开始学习时，会收到以下错误消息：

[libprotobuf ERROR google/protobuf/text_format.cc:287] Error parsing text-format caffe.NetParameter: 137:9: Expected integer or identifier, got: "SPP"
F0714 13:25:38.782958 2061316096 upgrade_proto.cpp:88] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file:

name: "TessDigitMean"
layer {
  name: "input"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "/Users/rvaldez/Documents/Datasets/Digits/SeperatedProviderV3_1020_batchnormalizedV2AndSPP/1/caffe/train_lmdb"
    batch_size: 64
    backend: LMDB
  }
}
layer {
  name: "input"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "/Users/rvaldez/Documents/Datasets/Digits/SeperatedProviderV3_1020_batchnormalizedV2AndSPP/1/caffe/test_lmdb"
    batch_size: 10
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "bn1"
  type: "BatchNorm"
  bottom: "pool1"
  top: "bn1"
  batch_norm_param {
    use_global_stats: false
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  include {
    phase: TRAIN
  }
}
layer {
  name: "bn1"
  type: "BatchNorm"
  bottom: "pool1"
  top: "bn1"
  batch_norm_param {
    use_global_stats: true
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  include {
    phase: TEST
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "bn1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 50
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "spatial_pyramid_pooling"
  type: "SPP"
  bottom: "conv2"
  top: "spatial_pyramid_pooling"
  spatial_pyramid_pooling_param {
    pool: MAX
    spatial_bin: 1
    spatial_bin: 2
    spatial_bin: 3
    spatial_bin: 6
    scale: 1
  }
}
layer {
  name: "bn2"
  type: "BatchNorm"
  bottom: "spatial_pyramid_pooling"
  top: "bn2"
  batch_norm_param {
    use_global_stats: false
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  include {
    phase: TRAIN
  }
}
layer {
  name: "bn2"
  type: "BatchNorm"
  bottom: "pool2"
  top: "bn2"
  batch_norm_param {
    use_global_stats: true
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  include {
    phase: TEST
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "bn2"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip2"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}

---

完整的原始文件（带有批量标准化和SPP的Lenet）：

[libprotobuf ERROR google/protobuf/text_format.cc:287] Error parsing text-format caffe.NetParameter: 137:9: Expected integer or identifier, got: "SPP"
F0714 13:25:38.782958 2061316096 upgrade_proto.cpp:88] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file:

name: "TessDigitMean"
layer {
  name: "input"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "/Users/rvaldez/Documents/Datasets/Digits/SeperatedProviderV3_1020_batchnormalizedV2AndSPP/1/caffe/train_lmdb"
    batch_size: 64
    backend: LMDB
  }
}
layer {
  name: "input"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "/Users/rvaldez/Documents/Datasets/Digits/SeperatedProviderV3_1020_batchnormalizedV2AndSPP/1/caffe/test_lmdb"
    batch_size: 10
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "bn1"
  type: "BatchNorm"
  bottom: "pool1"
  top: "bn1"
  batch_norm_param {
    use_global_stats: false
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  include {
    phase: TRAIN
  }
}
layer {
  name: "bn1"
  type: "BatchNorm"
  bottom: "pool1"
  top: "bn1"
  batch_norm_param {
    use_global_stats: true
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  include {
    phase: TEST
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "bn1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 50
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  name: "spatial_pyramid_pooling"
  type: "SPP"
  bottom: "conv2"
  top: "spatial_pyramid_pooling"
  spatial_pyramid_pooling_param {
    pool: MAX
    spatial_bin: 1
    spatial_bin: 2
    spatial_bin: 3
    spatial_bin: 6
    scale: 1
  }
}
layer {
  name: "bn2"
  type: "BatchNorm"
  bottom: "spatial_pyramid_pooling"
  top: "bn2"
  batch_norm_param {
    use_global_stats: false
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  include {
    phase: TRAIN
  }
}
layer {
  name: "bn2"
  type: "BatchNorm"
  bottom: "pool2"
  top: "bn2"
  batch_norm_param {
    use_global_stats: true
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  include {
    phase: TEST
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "bn2"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip2"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}

好的，我找到了

---

定义SPP层的正确方法如下：

layers {
  name: "spatial_pyramid_pooling"
  type: "SPP"
  bottom: "conv2"
  top: "spatial_pyramid_pooling"
  spatial_pyramid_pooling_param {
    pool: MAX
    spatial_bin: 1
    spatial_bin: 2
    spatial_bin: 3
    spatial_bin: 6
    scale: 1
  }
}

layer {
  name: "spatial_pyramid_pooling"
  type: "SPP"
  bottom: "conv2"
  top: "pool2"
  spp_param {
    pyramid_height: 2
  }
} 

请注意，我以前编写的是

层

，而不是

层

。 此外，您可以在

spp_param{}

中为该层指定参数。 caffe的官方版本没有垃圾箱作为选项，而是金字塔高度。所以我第一次尝试所基于的版本是不正确的

---

给我自己和任何一个刚接触caffe的人一些笔记，这些人对文档的风格有点困惑

文档：

layers {
  name: "spatial_pyramid_pooling"
  type: "SPP"
  bottom: "conv2"
  top: "spatial_pyramid_pooling"
  spatial_pyramid_pooling_param {
    pool: MAX
    spatial_bin: 1
    spatial_bin: 2
    spatial_bin: 3
    spatial_bin: 6
    scale: 1
  }
}

layer {
  name: "spatial_pyramid_pooling"
  type: "SPP"
  bottom: "conv2"
  top: "pool2"
  spp_param {
    pyramid_height: 2
  }
} 

• 图层类型：SPP

注意事项：

layers {
  name: "spatial_pyramid_pooling"
  type: "SPP"
  bottom: "conv2"
  top: "spatial_pyramid_pooling"
  spatial_pyramid_pooling_param {
    pool: MAX
    spatial_bin: 1
    spatial_bin: 2
    spatial_bin: 3
    spatial_bin: 6
    scale: 1
  }
}

layer {
  name: "spatial_pyramid_pooling"
  type: "SPP"
  bottom: "conv2"
  top: "pool2"
  spp_param {
    pyramid_height: 2
  }
} 

• Layer type定义了在proto文件中声明层类型的关键字（如果您知道的话，这是一种逻辑）

• 此定义中的枚举是参数的可能值

• 参数不能与类型或名称在同一级别上定义。相反，您必须将其包装在layerspecifc参数关键字（

  spp_param

  ）中。 此关键字的构建类似于此

  \u param{}

  ，以小写字母表示

---

我将protobuf网络更改为protobuf网络，因为我非常确定它与protobuf网络无关；老实说，我怀疑这和protobuf有关，但我不太确定that@MarcGravell好的，谢谢，如果层类型包含多个单词，比如InnerProduct，那么在相应的参数中，两个单词之间会有下划线，就像在InnerProduct参数中一样