Miscalculation of output from max pooling

[Jarvis Du]

Hi,

Theoretically, a max pooling layer with input 64(channel)*27(height)*27(width), pad of 0, kernel_size of 2 and stride of 2 should give an output of 64*12*12.

When I tried to create such a layer in caffe, it gives out 64*13*13

Here is the prototxt file.

name: "net"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 231
input_dim: 231
state {
  phase: TEST
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 48
    pad: 4
    kernel_h: 9
    kernel_w: 9
    stride_h: 4
    stride_w: 4
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2"
  convolution_param {
    num_output: 64
    pad: 0
    kernel_h: 5
    kernel_w: 5
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    pad: 0
    kernel_h: 2
    kernel_w: 2
    stride_h: 2
    stride_w: 2
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "pool2"
  top: "pool2"
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  convolution_param {
    num_output: 64
    pad: 0
    kernel_h: 3
    kernel_w: 3
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: MAX
    pad: 0
    kernel_h: 2
    kernel_w: 2
    stride_h: 2
    stride_w: 2
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "pool3"
  top: "pool3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4"
  convolution_param {
    num_output: 64
    pad: 0
    kernel_h: 3
    kernel_w: 3
    stride_h: 1
    stride_w: 1
  }
}
layer {                                                                      
  name: "pool4"
  type: "Pooling"
  bottom: "conv4"
  top: "pool4"
  pooling_param {
    pool: MAX
    pad: 0
    kernel_h: 2
    kernel_w: 2
    stride_h: 2
    stride_w: 2
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "pool4"
  top: "pool4"
}layer {
  name: "conv5"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5"
  convolution_param {
    num_output: 32
    pad: 0
    kernel_h: 3
    kernel_w: 3
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}

I guess the calculation of output size for pooling is to take ceiling instead of floor for dividing in

h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1. (See here).

Though for convolutional layer, it is to take floor here.

#1318 mentioned this problem, but no solution is given on this.

[Jarvis Du]

Typos here, the input size is 64*25*25.

[Evan Shelhamer]

See https://github.com/BVLC/caffe/issues/1318#issuecomment-133150235 for explanation. Currently pooling "covers" the input in that pooling regions that extend past the input + padding are computed.

Evan Shelhamer

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