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  1. name: "FCN"
  2.  
  3. input: "data"
  4. input_dim: 1
  5. input_dim: 3
  6. input_dim: 500
  7. input_dim: 500
  8.  
  9. layer { bottom: 'data' top: 'conv1_1' name: 'conv1_1' type: "Convolution"
  10. param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  11. convolution_param { engine: CAFFE num_output: 64 pad: 35 kernel_size: 3 } }
  12. layer { bottom: 'conv1_1' top: 'conv1_1' name: 'relu1_1' type: "ReLU" }
  13. layer { bottom: 'conv1_1' top: 'conv1_2' name: 'conv1_2' type: "Convolution"
  14. param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  15. convolution_param { engine: CAFFE num_output: 64 pad: 1 kernel_size: 3 } }
  16. layer { bottom: 'conv1_2' top: 'conv1_2' name: 'relu1_2' type: "ReLU" }
  17. layer { name: 'pool1' bottom: 'conv1_2' top: 'pool1' type: "Pooling"
  18. pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
  19.  
  20. layer { name: 'conv2_1' bottom: 'pool1' top: 'conv2_1' type: "Convolution"
  21. param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  22. convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 } }
  23. layer { bottom: 'conv2_1' top: 'conv2_1' name: 'relu2_1' type: "ReLU" }
  24. layer { bottom: 'conv2_1' top: 'conv2_2' name: 'conv2_2' type: "Convolution"
  25. param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  26. convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 } }
  27. layer { bottom: 'conv2_2' top: 'conv2_2' name: 'relu2_2' type: "ReLU" }
  28. layer { bottom: 'conv2_2' top: 'pool2' name: 'pool2' type: "Pooling"
  29. pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
  30.  
  31. layer { bottom: 'pool2' top: 'conv3_1' name: 'conv3_1' type: "Convolution"
  32. param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  33. convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
  34. layer { bottom: 'conv3_1' top: 'conv3_1' name: 'relu3_1' type: "ReLU" }
  35. layer { bottom: 'conv3_1' top: 'conv3_2' name: 'conv3_2' type: "Convolution"
  36. param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  37. convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
  38. layer { bottom: 'conv3_2' top: 'conv3_2' name: 'relu3_2' type: "ReLU" }
  39. layer { bottom: 'conv3_2' top: 'conv3_3' name: 'conv3_3' type: "Convolution"
  40. param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  41. convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
  42. layer { bottom: 'conv3_3' top: 'conv3_3' name: 'relu3_3' type: "ReLU" }
  43. layer { bottom: 'conv3_3' top: 'pool3' name: 'pool3' type: "Pooling"
  44. pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
  45.  
  46. layer { bottom: 'pool3' top: 'conv4_1' name: 'conv4_1' type: "Convolution"
  47. param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  48. convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
  49. layer { bottom: 'conv4_1' top: 'conv4_1' name: 'relu4_1' type: "ReLU" }
  50. layer { bottom: 'conv4_1' top: 'conv4_2' name: 'conv4_2' type: "Convolution"
  51. param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  52. convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
  53. layer { bottom: 'conv4_2' top: 'conv4_2' name: 'relu4_2' type: "ReLU" }
  54. layer { bottom: 'conv4_2' top: 'conv4_3' name: 'conv4_3' type: "Convolution"
  55. param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  56. convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
  57. layer { bottom: 'conv4_3' top: 'conv4_3' name: 'relu4_3' type: "ReLU" }
  58. layer { bottom: 'conv4_3' top: 'pool4' name: 'pool4' type: "Pooling"
  59. pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
  60.  
  61. layer { bottom: 'pool4' top: 'conv5_1' name: 'conv5_1' type: "Convolution"
  62. param { lr_mult: 100 decay_mult: 1 } param { lr_mult: 200 decay_mult: 0}
  63. convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
  64. layer { bottom: 'conv5_1' top: 'conv5_1' name: 'relu5_1' type: "ReLU" }
  65. layer { bottom: 'conv5_1' top: 'conv5_2' name: 'conv5_2' type: "Convolution"
  66. param { lr_mult: 100 decay_mult: 1 } param { lr_mult: 200 decay_mult: 0}
  67. convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
  68. layer { bottom: 'conv5_2' top: 'conv5_2' name: 'relu5_2' type: "ReLU" }
  69. layer { bottom: 'conv5_2' top: 'conv5_3' name: 'conv5_3' type: "Convolution"
  70. param { lr_mult: 100 decay_mult: 1 } param { lr_mult: 200 decay_mult: 0}
  71. convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
  72. layer { bottom: 'conv5_3' top: 'conv5_3' name: 'relu5_3' type: "ReLU" }
  73.  
  74. ## DSN conv 1 ###
  75. layer { name: 'score-dsn1' type: "Convolution" bottom: 'conv1_2' top: 'score-dsn1-up'
  76. param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0}
  77. convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 } }
  78. layer { type: "Crop" name: 'crop' bottom: 'score-dsn1-up' bottom: 'data' top: 'upscore-dsn1' }
  79. layer { type: "Sigmoid" name: "sigmoid-dsn1" bottom: "upscore-dsn1" top:"sigmoid-dsn1"}
  80.  
  81. ### DSN conv 2 ###
  82. layer { name: 'score-dsn2' type: "Convolution" bottom: 'conv2_2' top: 'score-dsn2'
  83. param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0}
  84. convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 } }
  85. layer { type: "Deconvolution" name: 'upsample_2' bottom: 'score-dsn2' top: 'score-dsn2-up'
  86. param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  87. convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
  88. layer { type: "Crop" name: 'crop' bottom: 'score-dsn2-up' bottom: 'data' top: 'upscore-dsn2' }
  89. layer { type: "Sigmoid" name: "sigmoid-dsn2" bottom: "upscore-dsn2" top:"sigmoid-dsn2"}
  90.  
  91. ### DSN conv 3 ###
  92. layer { name: 'score-dsn3' type: "Convolution" bottom: 'conv3_3' top: 'score-dsn3'
  93. param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0}
  94. convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 } }
  95. layer { type: "Deconvolution" name: 'upsample_4' bottom: 'score-dsn3' top: 'score-dsn3-up'
  96. param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  97. convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
  98. layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up' bottom: 'data' top: 'upscore-dsn3' }
  99. layer { type: "Sigmoid" name: "sigmoid-dsn3" bottom: "upscore-dsn3" top:"sigmoid-dsn3"}
  100.  
  101. ###DSN conv 4###
  102. layer { name: 'score-dsn4' type: "Convolution" bottom: 'conv4_3' top: 'score-dsn4'
  103. param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0}
  104. convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 } }
  105. layer { type: "Deconvolution" name: 'upsample_8' bottom: 'score-dsn4' top: 'score-dsn4-up'
  106. param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  107. convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
  108. layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up' bottom: 'data' top: 'upscore-dsn4' }
  109. layer { type: "Sigmoid" name: "sigmoid-dsn4" bottom: "upscore-dsn4" top:"sigmoid-dsn4"}
  110.  
  111. ###DSN conv 5###
  112. layer { name: 'score-dsn5' type: "Convolution" bottom: 'conv5_3' top: 'score-dsn5'
  113. param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0}
  114. convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 } }
  115. layer { type: "Deconvolution" name: 'upsample_16' bottom: 'score-dsn5' top: 'score-dsn5-up'
  116. param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  117. convolution_param { kernel_size: 32 stride: 16 num_output: 1 } }
  118. layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up' bottom: 'data' top: 'upscore-dsn5' }
  119. layer { type: "Sigmoid" name: "sigmoid-dsn5" bottom: "upscore-dsn5" top:"sigmoid-dsn5"}
  120.  
  121. ### Concat and multiscale weight layer ###
  122. layer { name: "concat" bottom: "upscore-dsn1" bottom: "upscore-dsn2" bottom: "upscore-dsn3"
  123. bottom: "upscore-dsn4" bottom: "upscore-dsn5" top: "concat-upscore" type: "Concat"
  124. concat_param { concat_dim: 1} }
  125. layer { name: 'new-score-weighting' type: "Convolution" bottom: 'concat-upscore' top: 'upscore-fuse'
  126. param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0}
  127. convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 weight_filler {type: "constant" value: 0.2} } }
  128. layer { type: "Sigmoid" name: "sigmoid-fuse" bottom: "upscore-fuse" top:"sigmoid-fuse"}
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