upconv_7.prototxt 2.8 KB

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  1. name: "upconv_7"
  2. layer {
  3. name: "input"
  4. type: "Input"
  5. top: "input"
  6. input_param { shape: { dim: 1 dim: 3 dim: 142 dim: 142 } }
  7. }
  8. layer {
  9. name: "conv1_layer"
  10. type: "Convolution"
  11. bottom: "input"
  12. top: "conv1"
  13. convolution_param {
  14. num_output: 16
  15. kernel_size: 3
  16. stride: 1
  17. weight_filler {
  18. type: "gaussian"
  19. std: 0.01
  20. }
  21. }
  22. }
  23. layer {
  24. name: "conv1_relu_layer"
  25. type: "ReLU"
  26. bottom: "conv1"
  27. top: "conv1"
  28. relu_param {
  29. negative_slope: 0.1
  30. }
  31. }
  32. layer {
  33. name: "conv2_layer"
  34. type: "Convolution"
  35. bottom: "conv1"
  36. top: "conv2"
  37. convolution_param {
  38. num_output: 32
  39. kernel_size: 3
  40. stride: 1
  41. weight_filler {
  42. type: "gaussian"
  43. std: 0.01
  44. }
  45. }
  46. }
  47. layer {
  48. name: "conv2_relu_layer"
  49. type: "ReLU"
  50. bottom: "conv2"
  51. top: "conv2"
  52. relu_param {
  53. negative_slope: 0.1
  54. }
  55. }
  56. layer {
  57. name: "conv3_layer"
  58. type: "Convolution"
  59. bottom: "conv2"
  60. top: "conv3"
  61. convolution_param {
  62. num_output: 64
  63. kernel_size: 3
  64. stride: 1
  65. weight_filler {
  66. type: "gaussian"
  67. std: 0.01
  68. }
  69. }
  70. }
  71. layer {
  72. name: "conv3_relu_layer"
  73. type: "ReLU"
  74. bottom: "conv3"
  75. top: "conv3"
  76. relu_param {
  77. negative_slope: 0.1
  78. }
  79. }
  80. layer {
  81. name: "conv4_layer"
  82. type: "Convolution"
  83. bottom: "conv3"
  84. top: "conv4"
  85. convolution_param {
  86. num_output: 128
  87. kernel_size: 3
  88. stride: 1
  89. weight_filler {
  90. type: "gaussian"
  91. std: 0.01
  92. }
  93. }
  94. }
  95. layer {
  96. name: "conv4_relu_layer"
  97. type: "ReLU"
  98. bottom: "conv4"
  99. top: "conv4"
  100. relu_param {
  101. negative_slope: 0.1
  102. }
  103. }
  104. layer {
  105. name: "conv5_layer"
  106. type: "Convolution"
  107. bottom: "conv4"
  108. top: "conv5"
  109. convolution_param {
  110. num_output: 128
  111. kernel_size: 3
  112. stride: 1
  113. weight_filler {
  114. type: "gaussian"
  115. std: 0.01
  116. }
  117. }
  118. }
  119. layer {
  120. name: "conv5_relu_layer"
  121. type: "ReLU"
  122. bottom: "conv5"
  123. top: "conv5"
  124. relu_param {
  125. negative_slope: 0.1
  126. }
  127. }
  128. layer {
  129. name: "conv6_layer"
  130. type: "Convolution"
  131. bottom: "conv5"
  132. top: "conv6"
  133. convolution_param {
  134. num_output: 256
  135. kernel_size: 3
  136. stride: 1
  137. weight_filler {
  138. type: "gaussian"
  139. std: 0.01
  140. }
  141. }
  142. }
  143. layer {
  144. name: "conv6_relu_layer"
  145. type: "ReLU"
  146. bottom: "conv6"
  147. top: "conv6"
  148. relu_param {
  149. negative_slope: 0.1
  150. }
  151. }
  152. layer {
  153. name: "conv7_layer"
  154. type: "Deconvolution"
  155. bottom: "conv6"
  156. top: "conv7"
  157. convolution_param {
  158. num_output: 3
  159. kernel_size: 4
  160. stride: 2
  161. pad: 3
  162. weight_filler {
  163. type: "gaussian"
  164. std: 0.01
  165. }
  166. }
  167. }
  168. layer {
  169. name: "target"
  170. type: "MemoryData"
  171. top: "target"
  172. top: "dummy_label2"
  173. memory_data_param {
  174. batch_size: 1
  175. channels: 3
  176. height: 142
  177. width: 142
  178. }
  179. include: { phase: TRAIN }
  180. }
  181. layer {
  182. name: "loss"
  183. type: "EuclideanLoss"
  184. bottom: "conv7"
  185. bottom: "target"
  186. top: "loss"
  187. include: { phase: TRAIN }
  188. }