在caffe中使用SPP图层会导致“检查失败”：pad_w_< kernel_w_（1比1） [英] Using the SPP Layer in caffe results in Check failed: pad_w_ < kernel_w_ (1 vs. 1)

问题描述

好吧，我之前有一个问题，关于在caffe中使用SPP图层。
这个问题是上一个一个。

Ok, I had a previous question about using the SPP Layer in caffe. This question is a subsequent to the previous one.

使用SPP图层时，我得到以下错误输出。
似乎到达spp层时图像变得太小了？
我使用的图像很小。宽度范围在10到20像素之间，高度范围在30到35像素之间。

When using the SPP Layer I get the error output below. It seems that the images are getting too small when reaching the spp layer? The images I use are small. The width ranges between 10 and 20 px and height ranges between 30 and 35px.

I0719 12:18:22.553256 2114932736 net.cpp:406] spatial_pyramid_pooling <- conv2
I0719 12:18:22.553261 2114932736 net.cpp:380] spatial_pyramid_pooling -> pool2
F0719 12:18:22.553505 2114932736 pooling_layer.cpp:74] Check failed: pad_w_ < kernel_w_ (1 vs. 1) 
*** Check failure stack trace: ***
    @        0x106afcb6e  google::LogMessage::Fail()
    @        0x106afbfbe  google::LogMessage::SendToLog()
    @        0x106afc53a  google::LogMessage::Flush()
    @        0x106aff86b  google::LogMessageFatal::~LogMessageFatal()
    @        0x106afce55  google::LogMessageFatal::~LogMessageFatal()
    @        0x1068dc659  caffe::PoolingLayer<>::LayerSetUp()
    @        0x1068ffd98  caffe::SPPLayer<>::LayerSetUp()
    @        0x10691123f  caffe::Net<>::Init()
    @        0x10690fefe  caffe::Net<>::Net()
    @        0x106927ef8  caffe::Solver<>::InitTrainNet()
    @        0x106927325  caffe::Solver<>::Init()
    @        0x106926f95  caffe::Solver<>::Solver()
    @        0x106935b46  caffe::SGDSolver<>::SGDSolver()
    @        0x10693ae52  caffe::Creator_SGDSolver<>()
    @        0x1067e78f3  train()
    @        0x1067ea22a  main
    @     0x7fff9a3ad5ad  start
    @                0x5  (unknown)

推荐答案

我是正确的，我的图像很小。
我更换了网布，然后开始工作了。我删除了一个conv层，并用spp层替换了普通池层。我还必须将测试批次大小设置为1。准确性很高，但是我的F1得分却下降了。我不知道这是否与我不得不使用的小批量测试有关。

I was correct, my images were to small. I changed my net and it worked. I removed one conv layer and replaced the normal pool layer with the spp layer. I also had to set my test batch size to 1. Accuracy were very high, but my F1 Score went down. I dont know if this is related to the small test batch Size I had to use.

净额：

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_SPP/784/caffe/train_lmdb"
    batch_size: 1 #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_SPP/784/caffe/test_lmdb"
    batch_size: 1
    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
    pad_w: 2
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "spatial_pyramid_pooling"
  type: "SPP"
  bottom: "conv1"
  top: "pool2"
  spp_param {
    pyramid_height: 2
  }
} 
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool2"
  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"
}

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