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multivariate regression example in Caffe(Deeplearning.ir)

Coderx
Dec 16th, 2016
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  1. #Multivariate Regression in Caffe
  2. #DeepLearning.ir
  3. #https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/gaze-based-human-computer-#interaction/appearance-based-gaze-estimation-in-the-wild-mpiigaze/
  4. #https://www.mpi-inf.mpg.de/fileadmin/inf/d2/xucong/MPIIGaze/train_test.prototxt
  5.  
  6. name: "MPIIGaze"
  7. layers {
  8. name: "MPII_train"
  9. type: HDF5_DATA
  10. top: "data"
  11. top: "label"
  12. hdf5_data_param {
  13. source: "../train_list.txt"
  14. batch_size: 1000
  15. }
  16. include: { phase: TRAIN }
  17. }
  18.  
  19.  
  20. layers {
  21. name: "MPII_test"
  22. type: HDF5_DATA
  23. top: "data"
  24. top: "label"
  25. hdf5_data_param {
  26. source: "../test_list.txt"
  27. batch_size: 1000
  28. }
  29. include: { phase: TEST }
  30. }
  31.  
  32. layers {
  33. name: "cutLabel"
  34. type: SLICE
  35. bottom: "label"
  36. top: "gaze"
  37. top: "headpose"
  38. slice_param {
  39. slice_dim: 1
  40. slice_point: 2
  41. }
  42. }
  43.  
  44. layers {
  45. name: "conv1"
  46. type: CONVOLUTION
  47. bottom: "data"
  48. top: "conv1"
  49. blobs_lr: 1
  50. blobs_lr: 2
  51. convolution_param {
  52. num_output: 20
  53. kernel_size: 5
  54. stride: 1
  55. weight_filler {
  56. type: "gaussian"
  57. std: 0.1
  58. }
  59. bias_filler {
  60. type: "constant"
  61. }
  62. }
  63. }
  64. layers {
  65. name: "pool1"
  66. type: POOLING
  67. bottom: "conv1"
  68. top: "pool1"
  69. pooling_param {
  70. pool: MAX
  71. kernel_size: 2
  72. stride: 2
  73. }
  74. }
  75.  
  76. layers {
  77. name: "conv2"
  78. type: CONVOLUTION
  79. bottom: "pool1"
  80. top: "conv2"
  81. blobs_lr: 1
  82. blobs_lr: 2
  83. convolution_param {
  84. num_output: 50
  85. kernel_size: 5
  86. stride: 1
  87. weight_filler {
  88. type: "gaussian"
  89. std: 0.01
  90. }
  91. bias_filler {
  92. type: "constant"
  93. }
  94. }
  95. }
  96.  
  97. layers {
  98. name: "pool2"
  99. type: POOLING
  100. bottom: "conv2"
  101. top: "pool2"
  102. pooling_param {
  103. pool: MAX
  104. kernel_size: 2
  105. stride: 2
  106. }
  107. }
  108. layers {
  109. name: "ip1"
  110. type: INNER_PRODUCT
  111. bottom: "pool2"
  112. top: "ip1"
  113. blobs_lr: 1
  114. blobs_lr: 2
  115. inner_product_param {
  116. num_output: 500
  117. weight_filler {
  118. type: "xavier"
  119. }
  120. bias_filler {
  121. type: "constant"
  122. }
  123. }
  124. }
  125.  
  126. layers {
  127. name: "relu1"
  128. type: RELU
  129. bottom: "ip1"
  130. top: "ip1"
  131. }
  132.  
  133. layers {
  134. name: "concat_headpose_eyeappearance"
  135. type: CONCAT
  136. bottom: "ip1"
  137. bottom: "headpose"
  138. top: "cat"
  139. }
  140.  
  141.  
  142. layers {
  143. name: "ip2"
  144. type: INNER_PRODUCT
  145. bottom: "cat"
  146. top: "ip2"
  147. blobs_lr: 1
  148. blobs_lr: 2
  149. inner_product_param {
  150. num_output: 2
  151. weight_filler {
  152. type: "xavier"
  153. }
  154. bias_filler {
  155. type: "constant"
  156. }
  157. }
  158. }
  159. layers {
  160. name: "accuracy"
  161. type: ACCURACY
  162. bottom: "ip2"
  163. bottom: "gaze"
  164. top: "accuracy"
  165. include: { phase: TEST }
  166. }
  167. layers {
  168. name: "loss"
  169. type: EUCLIDEAN_LOSS
  170. bottom: "ip2"
  171. bottom: "gaze"
  172. top: "loss"
  173. }
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