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Is AutoCrop layer only used in training or also in inference ? can i remove Autocrop layer in prototxt file while doing Inference? #24

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athiselvam opened this issue Feb 26, 2018 · 6 comments
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Is AutoCrop layer only used in training or also in inference ? can i remove Autocrop layer in prototxt file while doing Inference? #24

athiselvam opened this issue Feb 26, 2018 · 6 comments

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@athiselvam
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Is AutoCrop layer only used in training or also in inference ? can i remove Autocrop layer in prototxt file while doing Inference?
@yun-liu
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yun-liu commented Feb 27, 2018 •
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@athiselvam AutoCrop layer is used in both the training and the inference. You can not remove the Autocrop layer directly, because it is used to align the upsampling maps obtained by Deconvolution layer. But you can replace it with the standard Crop layer in Caffe by setting the offset to 1, 2, 4, and 0 for conv2, conv3, conv4, and conv5, respectively.

@athiselvam
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athiselvam commented Feb 27, 2018 •
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Thank You so much for your reply. Can you please send me the above change Prototxt file. Since I'm new to caffe, help me do so.

@yun-liu
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yun-liu commented Feb 28, 2018 •
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@athiselvam

name: "RCF"

input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 224
  dim: 224
}

layer { name: "conv1_1" type: "Convolution" bottom: "data" top: "conv1_1"
  convolution_param { num_output: 64 pad: 1 kernel_size: 3 } }
layer { name: "relu1_1" type: "ReLU" bottom: "conv1_1" top: "conv1_1" }
layer { name: "conv1_2" type: "Convolution" bottom: "conv1_1" top: "conv1_2"
  convolution_param { num_output: 64 pad: 1 kernel_size: 3 } }
layer { name: "relu1_2" type: "ReLU" bottom: "conv1_2" top: "conv1_2" }
layer { name: "pool1" type: "Pooling" bottom: "conv1_2" top: "pool1"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { name: "conv2_1" type: "Convolution" bottom: "pool1" top: "conv2_1"
  convolution_param { num_output: 128 pad: 1 kernel_size: 3 } }
layer { name: "relu2_1" type: "ReLU" bottom: "conv2_1" top: "conv2_1" }
layer { name: "conv2_2" type: "Convolution" bottom: "conv2_1" top: "conv2_2"
  convolution_param { num_output: 128 pad: 1 kernel_size: 3 } }
layer { name: "relu2_2" type: "ReLU" bottom: "conv2_2" top: "conv2_2" }
layer { name: "pool2" type: "Pooling" bottom: "conv2_2" top: "pool2"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { name: "conv3_1" type: "Convolution" bottom: "pool2" top: "conv3_1"
  convolution_param { num_output: 256 pad: 1 kernel_size: 3 } }
layer { name: "relu3_1" type: "ReLU" bottom: "conv3_1" top: "conv3_1" }
layer { name: "conv3_2" type: "Convolution" bottom: "conv3_1" top: "conv3_2"
  convolution_param { num_output: 256 pad: 1 kernel_size: 3 } }
layer { name: "relu3_2" type: "ReLU" bottom: "conv3_2" top: "conv3_2" }
layer { name: "conv3_3" type: "Convolution" bottom: "conv3_2" top: "conv3_3"
  convolution_param { num_output: 256 pad: 1 kernel_size: 3 } }
layer { name: "relu3_3" type: "ReLU" bottom: "conv3_3" top: "conv3_3" }
layer { name: "pool3" type: "Pooling" bottom: "conv3_3" top: "pool3"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { name: "conv4_1" type: "Convolution" bottom: "pool3" top: "conv4_1"
  convolution_param { num_output: 512 pad: 1 kernel_size: 3 } }
layer { name: "relu4_1" type: "ReLU" bottom: "conv4_1" top: "conv4_1" }
layer { name: "conv4_2" type: "Convolution" bottom: "conv4_1" top: "conv4_2"
  convolution_param { num_output: 512 pad: 1 kernel_size: 3 } }
layer { name: "relu4_2" type: "ReLU" bottom: "conv4_2" top: "conv4_2" }
layer { name: "conv4_3" type: "Convolution" bottom: "conv4_2" top: "conv4_3"
  convolution_param { num_output: 512 pad: 1 kernel_size: 3 } }
layer { name: "relu4_3" type: "ReLU" bottom: "conv4_3" top: "conv4_3" }
layer { name: "pool4" type: "Pooling" bottom: "conv4_3" top: "pool4"
  pooling_param { pool: MAX kernel_size: 2 stride: 1 } }

layer { name: "conv5_1" type: "Convolution" bottom: "pool4" top: "conv5_1"
  convolution_param { num_output: 512 pad: 2 kernel_size: 3 dilation: 2 } }
layer { name: "relu5_1" type: "ReLU" bottom: "conv5_1" top: "conv5_1" }
layer { name: "conv5_2" type: "Convolution" bottom: "conv5_1" top: "conv5_2"
  convolution_param { num_output: 512 pad: 2 kernel_size: 3 dilation: 2 } }
layer { name: "relu5_2" type: "ReLU" bottom: "conv5_2" top: "conv5_2" }
layer { name: "conv5_3" type: "Convolution" bottom: "conv5_2" top: "conv5_3"
  convolution_param { num_output: 512 pad: 2 kernel_size: 3 dilation: 2 } }
layer { name: "relu5_3" type: "ReLU" bottom: "conv5_3" top: "conv5_3" }

layer { name: "conv1_1_down" type: "Convolution" bottom: "conv1_1" top: "conv1_1_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "conv1_2_down" type: "Convolution" bottom: "conv1_2" top: "conv1_2_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "score_fuse1" type: "Eltwise" bottom: "conv1_1_down" bottom: "conv1_2_down"
  top: "score_fuse1" eltwise_param { operation: SUM } }
layer { name: "score-dsn1" type: "Convolution" bottom: "score_fuse1" top: "upscore-dsn1"
  convolution_param { num_output: 1 kernel_size: 1 } }
layer { name: "sigmoid-dsn1" type: "Sigmoid" bottom: "upscore-dsn1" top: "sigmoid-dsn1"}

layer { name: "conv2_1_down" type: "Convolution" bottom: "conv2_1" top: "conv2_1_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "conv2_2_down" type: "Convolution" bottom: "conv2_2" top: "conv2_2_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "score_fuse2" type: "Eltwise" bottom: "conv2_1_down" bottom: "conv2_2_down"
  top: "score_fuse2" eltwise_param { operation: SUM } }
layer { name: "score-dsn2" type: "Convolution" bottom: "score_fuse2" top: "score-dsn2"
  convolution_param { num_output: 1 kernel_size: 1 } }
layer { name: "upsample_2" type: "Deconvolution" bottom: "score-dsn2" top: "score-dsn2-up"
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { name: "crop2" type: "Crop" bottom: "score-dsn2-up" bottom: "data" top: "upscore-dsn2"
  crop_param { axis: 2 offset: 1 } }
layer { name: "sigmoid-dsn2" type: "Sigmoid" bottom: "upscore-dsn2" top: "sigmoid-dsn2"}

layer { name: "conv3_1_down" type: "Convolution" bottom: "conv3_1" top: "conv3_1_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "conv3_2_down" type: "Convolution" bottom: "conv3_2" top: "conv3_2_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "conv3_3_down" type: "Convolution" bottom: "conv3_3" top: "conv3_3_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "score_fuse3" type: "Eltwise" bottom: "conv3_1_down" bottom: "conv3_2_down"
  bottom: "conv3_3_down" top: "score_fuse3" eltwise_param { operation: SUM } }
layer { name: "score-dsn3" type: "Convolution" bottom: "score_fuse3" top: "score-dsn3"
  convolution_param { num_output: 1 kernel_size: 1 } }
layer { name: "upsample_4" type: "Deconvolution" bottom: "score-dsn3" top: "score-dsn3-up"
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { name: "crop3" type: "Crop" bottom: "score-dsn3-up" bottom: "data" top: "upscore-dsn3"
  crop_param { axis: 2 offset: 2 } }
layer { name: "sigmoid-dsn3" type: "Sigmoid" bottom: "upscore-dsn3" top: "sigmoid-dsn3"}

layer { name: "conv4_1_down" type: "Convolution" bottom: "conv4_1" top: "conv4_1_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "conv4_2_down" type: "Convolution" bottom: "conv4_2" top: "conv4_2_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "conv4_3_down" type: "Convolution" bottom: "conv4_3" top: "conv4_3_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "score_fuse4" type: "Eltwise" bottom: "conv4_1_down" bottom: "conv4_2_down"
  bottom: "conv4_3_down" top: "score_fuse4" eltwise_param { operation: SUM } }
layer { name: "score-dsn4" type: "Convolution" bottom: "score_fuse4" top: "score-dsn4"
  convolution_param { num_output: 1 kernel_size: 1 } }
layer { name: "upsample_8" type: "Deconvolution" bottom: "score-dsn4" top: "score-dsn4-up"
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { name: "crop4" type: "Crop" bottom: "score-dsn4-up" bottom: "data" top: "upscore-dsn4"
  crop_param { axis: 2 offset: 4 } }
layer { name: "sigmoid-dsn4" type: "Sigmoid" bottom: "upscore-dsn4" top: "sigmoid-dsn4"}

layer { name: "conv5_1_down" type: "Convolution" bottom: "conv5_1" top: "conv5_1_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "conv5_2_down" type: "Convolution" bottom: "conv5_2" top: "conv5_2_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "conv5_3_down" type: "Convolution" bottom: "conv5_3" top: "conv5_3_down"
  convolution_param { num_output: 21 kernel_size: 1 } }
layer { name: "score_fuse5" type: "Eltwise" bottom: "conv5_1_down" bottom: "conv5_2_down"
  bottom: "conv5_3_down" top: "score_fuse5" eltwise_param { operation: SUM } }
layer { name: "score-dsn5" type: "Convolution" bottom: "score_fuse5" top: "score-dsn5"
  convolution_param { num_output: 1 kernel_size: 1 } }
layer { name: "upsample_8_5" type: "Deconvolution" bottom: "score-dsn5" top: "score-dsn5-up"
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { name: "crop5" type: "Crop" bottom: "score-dsn5-up" bottom: "data" top: "upscore-dsn5"
  crop_param { axis: 2 offset: 0 } }
layer { name: "sigmoid-dsn5" type: "Sigmoid" bottom: "upscore-dsn5" top: "sigmoid-dsn5"}

layer { name: "concat" bottom: "upscore-dsn1"  bottom: "upscore-dsn2" bottom: "upscore-dsn3"
  bottom: "upscore-dsn4" bottom: "upscore-dsn5" top: "concat-upscore" type: "Concat"
  concat_param { concat_dim: 1 } }
layer { name: "new-score-weighting" type: "Convolution" bottom: "concat-upscore" top: "upscore-fuse"
  convolution_param { num_output: 1 kernel_size: 1 } }
layer { name: "sigmoid-fuse" type: "Sigmoid" bottom: "upscore-fuse" top: "sigmoid-fuse"}

@athiselvam
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@yun-liu Thanks a lot, man. It's working perfectly.

@yun-liu yun-liu mentioned this issue Jul 29, 2018
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@piaobuliao
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@yun-liu 224*224的图像的crop这样配置 还是任意图像的都这样配置？

@yun-liu
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yun-liu commented Aug 5, 2018

@piaobuliao 任意的

@piaobuliao piaobuliao mentioned this issue Aug 6, 2018
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Repository owner deleted a comment from zchrissirhcz Jul 15, 2019
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@piaobuliao @athiselvam @yun-liu