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cascade rpn det
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| name: "ResNet-50" | |
| layer { | |
| name: 'input-data' | |
| type: 'Python' | |
| top: 'data' | |
| top: 'im_info' | |
| top: 'gt_boxes' | |
| python_param { | |
| module: 'roi_data_layer.layer' | |
| layer: 'RoIDataLayer' | |
| param_str: "'num_classes': 81" | |
| } | |
| } | |
| #===============CONV1=========== | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "conv1_scale" | |
| type: "Scale" | |
| bottom: "conv1" | |
| top: "conv1" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_relu" | |
| bottom: "conv1" | |
| top: "conv1" | |
| type: "ReLU" | |
| } | |
| #===rpn1=== | |
| layer { | |
| name: "rpn_cls_score1" | |
| type: "Convolution" | |
| bottom: "conv1" | |
| top: "rpn_cls_score1" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0 } | |
| convolution_param { | |
| num_output: 18 # 2(bg/fg) * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_bbox_pred1" | |
| type: "Convolution" | |
| bottom: "conv1" | |
| top: "rpn_bbox_pred1" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0 } | |
| convolution_param { | |
| num_output: 36 # 4 * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| bottom: "rpn_cls_score1" | |
| top: "rpn_cls_score_reshape1" | |
| name: "rpn_cls_score_reshape1" | |
| type: "Reshape" | |
| reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } | |
| } | |
| #===============CONV2=========== | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "conv1" | |
| top: "conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "conv2_scale" | |
| type: "Scale" | |
| bottom: "conv2" | |
| top: "conv2" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_relu" | |
| bottom: "conv2" | |
| top: "conv2" | |
| type: "ReLU" | |
| } | |
| #===rpn2=== | |
| layer { | |
| name: "rpn_cls_score2" | |
| type: "Convolution" | |
| bottom: "conv2" | |
| top: "rpn_cls_score2" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0 } | |
| convolution_param { | |
| num_output: 18 # 2(bg/fg) * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_bbox_pred2" | |
| type: "Convolution" | |
| bottom: "conv2" | |
| top: "rpn_bbox_pred2" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0 } | |
| convolution_param { | |
| num_output: 36 # 4 * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| bottom: "rpn_cls_score2" | |
| top: "rpn_cls_score_reshape2" | |
| name: "rpn_cls_score_reshape2" | |
| type: "Reshape" | |
| reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } | |
| } | |
| #===============CONV3=========== | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "conv2" | |
| top: "conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layer { | |
| name: "conv3_scale" | |
| type: "Scale" | |
| bottom: "conv3" | |
| top: "conv3" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_relu" | |
| bottom: "conv3" | |
| top: "conv3" | |
| type: "ReLU" | |
| } | |
| #===rpn3=== | |
| layer { | |
| name: "rpn_cls_score3" | |
| type: "Convolution" | |
| bottom: "conv3" | |
| top: "rpn_cls_score3" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0 } | |
| convolution_param { | |
| num_output: 18 # 2(bg/fg) * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_bbox_pred3" | |
| type: "Convolution" | |
| bottom: "conv3" | |
| top: "rpn_bbox_pred3" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0 } | |
| convolution_param { | |
| num_output: 36 # 4 * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| bottom: "rpn_cls_score3" | |
| top: "rpn_cls_score_reshape3" | |
| name: "rpn_cls_score_reshape3" | |
| type: "Reshape" | |
| reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } | |
| } | |
| #===============CONV4=========== | |
| layer { | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "conv3" | |
| top: "conv4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layer { | |
| name: "conv4_scale" | |
| type: "Scale" | |
| bottom: "conv4" | |
| top: "conv4" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_relu" | |
| bottom: "conv4" | |
| top: "conv4" | |
| type: "ReLU" | |
| } | |
| #===rpn4=== | |
| layer { | |
| name: "rpn_cls_score4" | |
| type: "Convolution" | |
| bottom: "conv4" | |
| top: "rpn_cls_score4" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0 } | |
| convolution_param { | |
| num_output: 18 # 2(bg/fg) * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_bbox_pred4" | |
| type: "Convolution" | |
| bottom: "conv4" | |
| top: "rpn_bbox_pred4" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0 } | |
| convolution_param { | |
| num_output: 36 # 4 * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| bottom: "rpn_cls_score4" | |
| top: "rpn_cls_score_reshape4" | |
| name: "rpn_cls_score_reshape4" | |
| type: "Reshape" | |
| reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } | |
| } | |
| #===============CONV5=========== | |
| layer { | |
| name: "conv5" | |
| type: "Convolution" | |
| bottom: "conv4" | |
| top: "conv5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5" | |
| top: "conv5" | |
| } | |
| layer { | |
| name: "conv5_scale" | |
| type: "Scale" | |
| bottom: "conv5" | |
| top: "conv5" | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_relu" | |
| bottom: "conv5" | |
| top: "conv5" | |
| type: "ReLU" | |
| } | |
| #===rpn5=== | |
| layer { | |
| name: "rpn_cls_score5" | |
| type: "Convolution" | |
| bottom: "conv5" | |
| top: "rpn_cls_score5" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0 } | |
| convolution_param { | |
| num_output: 18 # 2(bg/fg) * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_bbox_pred5" | |
| type: "Convolution" | |
| bottom: "conv5" | |
| top: "rpn_bbox_pred5" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0 } | |
| convolution_param { | |
| num_output: 36 # 4 * 9(anchors) | |
| kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| bottom: "rpn_cls_score5" | |
| top: "rpn_cls_score_reshape5" | |
| name: "rpn_cls_score_reshape5" | |
| type: "Reshape" | |
| reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } | |
| } | |
| #===loss=== | |
| layer { | |
| name: "rpn_loss_bbox" | |
| type: "SmoothL1Loss" | |
| bottom: "rpn_bbox_pred1" | |
| bottom: "rpn_bbox_pred2" | |
| bottom: "rpn_bbox_pred3" | |
| bottom: "rpn_bbox_pred4" | |
| bottom: "rpn_bbox_pred5" | |
| bottom: "gt_boxes" | |
| top: "rpn_loss_bbox" | |
| loss_weight: 1 | |
| smooth_l1_loss_param { sigma: 3.0 } | |
| } | |
| layer { | |
| name: "rpn_loss_cls" | |
| type: "SoftmaxWithLoss" | |
| bottom: "rpn_cls_score_reshape1" | |
| bottom: "rpn_cls_score_reshape2" | |
| bottom: "rpn_cls_score_reshape3" | |
| bottom: "rpn_cls_score_reshape4" | |
| bottom: "rpn_cls_score_reshape5" | |
| bottom: "gt_boxes" | |
| propagate_down: 1 | |
| propagate_down: 0 | |
| top: "rpn_cls_loss" | |
| loss_weight: 1 | |
| loss_param { | |
| ignore_label: -1 | |
| normalize: true | |
| } | |
| } |
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