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May 21, 2016 12:38
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| name: "ResNet-18" | |
| 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': 40" | |
| } | |
| } | |
| layer { | |
| bottom: "data" | |
| top: "conv1" | |
| name: "conv1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv1" | |
| name: "bn_conv1" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv1" | |
| name: "scale_conv1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv1" | |
| name: "conv1_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "pool1" | |
| name: "pool1" | |
| type: "Pooling" | |
| pooling_param { | |
| kernel_size: 3 | |
| stride: 2 | |
| pool: MAX | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "res2a_branch1" | |
| name: "res2a_branch1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch1" | |
| top: "res2a_branch1" | |
| name: "bn2a_branch1" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch1" | |
| top: "res2a_branch1" | |
| name: "scale2a_branch1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "res2a_branch2a" | |
| name: "res2a_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2a" | |
| top: "res2a_branch2a" | |
| name: "bn2a_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2a" | |
| top: "res2a_branch2a" | |
| name: "scale2a_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2a" | |
| top: "res2a_branch2a" | |
| name: "res2a_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2a_branch2a" | |
| top: "res2a_branch2b" | |
| name: "res2a_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2b" | |
| top: "res2a_branch2b" | |
| name: "bn2a_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch2b" | |
| top: "res2a_branch2b" | |
| name: "scale2a_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch1" | |
| bottom: "res2a_branch2b" | |
| top: "res2a" | |
| name: "res2a" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res2a" | |
| top: "res2a" | |
| name: "res2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2a" | |
| top: "res2b_branch2a" | |
| name: "res2b_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2a" | |
| top: "res2b_branch2a" | |
| name: "bn2b_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2a" | |
| top: "res2b_branch2a" | |
| name: "scale2b_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2a" | |
| top: "res2b_branch2a" | |
| name: "res2b_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2b_branch2a" | |
| top: "res2b_branch2b" | |
| name: "res2b_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2b" | |
| top: "res2b_branch2b" | |
| name: "bn2b_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2b_branch2b" | |
| top: "res2b_branch2b" | |
| name: "scale2b_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2a" | |
| bottom: "res2b_branch2b" | |
| top: "res2b" | |
| name: "res2b" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res2b" | |
| top: "res2b" | |
| name: "res2b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2b" | |
| top: "res3a_branch1" | |
| name: "res3a_branch1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch1" | |
| top: "res3a_branch1" | |
| name: "bn3a_branch1" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch1" | |
| top: "res3a_branch1" | |
| name: "scale3a_branch1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res2b" | |
| top: "res3a_branch2a" | |
| name: "res3a_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2a" | |
| top: "res3a_branch2a" | |
| name: "bn3a_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2a" | |
| top: "res3a_branch2a" | |
| name: "scale3a_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2a" | |
| top: "res3a_branch2a" | |
| name: "res3a_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3a_branch2a" | |
| top: "res3a_branch2b" | |
| name: "res3a_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2b" | |
| top: "res3a_branch2b" | |
| name: "bn3a_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch2b" | |
| top: "res3a_branch2b" | |
| name: "scale3a_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch1" | |
| bottom: "res3a_branch2b" | |
| top: "res3a" | |
| name: "res3a" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res3a" | |
| top: "res3a" | |
| name: "res3a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3a" | |
| top: "res3b_branch2a" | |
| name: "res3b_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2a" | |
| top: "res3b_branch2a" | |
| name: "bn3b_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2a" | |
| top: "res3b_branch2a" | |
| name: "scale3b_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2a" | |
| top: "res3b_branch2a" | |
| name: "res3b_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3b_branch2a" | |
| top: "res3b_branch2b" | |
| name: "res3b_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2b" | |
| top: "res3b_branch2b" | |
| name: "bn3b_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3b_branch2b" | |
| top: "res3b_branch2b" | |
| name: "scale3b_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3a" | |
| bottom: "res3b_branch2b" | |
| top: "res3b" | |
| name: "res3b" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res3b" | |
| top: "res3b" | |
| name: "res3b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3b" | |
| top: "res4a_branch1" | |
| name: "res4a_branch1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch1" | |
| top: "res4a_branch1" | |
| name: "bn4a_branch1" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch1" | |
| top: "res4a_branch1" | |
| name: "scale4a_branch1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res3b" | |
| top: "res4a_branch2a" | |
| name: "res4a_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2a" | |
| top: "res4a_branch2a" | |
| name: "bn4a_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2a" | |
| top: "res4a_branch2a" | |
| name: "scale4a_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2a" | |
| top: "res4a_branch2a" | |
| name: "res4a_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4a_branch2a" | |
| top: "res4a_branch2b" | |
| name: "res4a_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2b" | |
| top: "res4a_branch2b" | |
| name: "bn4a_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch2b" | |
| top: "res4a_branch2b" | |
| name: "scale4a_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a_branch1" | |
| bottom: "res4a_branch2b" | |
| top: "res4a" | |
| name: "res4a" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res4a" | |
| top: "res4a" | |
| name: "res4a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4a" | |
| top: "res4b_branch2a" | |
| name: "res4b_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2a" | |
| top: "res4b_branch2a" | |
| name: "bn4b_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2a" | |
| top: "res4b_branch2a" | |
| name: "scale4b_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2a" | |
| top: "res4b_branch2a" | |
| name: "res4b_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res4b_branch2a" | |
| top: "res4b_branch2b" | |
| name: "res4b_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2b" | |
| top: "res4b_branch2b" | |
| name: "bn4b_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4b_branch2b" | |
| top: "res4b_branch2b" | |
| name: "scale4b_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res4a" | |
| bottom: "res4b_branch2b" | |
| top: "res4b" | |
| name: "res4b" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res4b" | |
| top: "res4b" | |
| name: "res4b_relu" | |
| type: "ReLU" | |
| } | |
| #### Add RPN network #### | |
| layer { | |
| name: "rpn_conv/3x3" | |
| type: "Convolution" | |
| bottom: "res4b" | |
| top: "rpn/output" | |
| param { lr_mult: 1.0 } | |
| param { lr_mult: 2.0 } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 pad: 1 stride: 1 | |
| weight_filler { type: "gaussian" std: 0.01 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "rpn_relu/3x3" | |
| type: "ReLU" | |
| bottom: "rpn/output" | |
| top: "rpn/output" | |
| } | |
| layer { | |
| name: "rpn_cls_score" | |
| type: "Convolution" | |
| bottom: "rpn/output" | |
| top: "rpn_cls_score" | |
| param { lr_mult: 1.0 } | |
| param { lr_mult: 2.0 } | |
| 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_pred" | |
| type: "Convolution" | |
| bottom: "rpn/output" | |
| top: "rpn_bbox_pred" | |
| param { lr_mult: 1.0 } | |
| param { lr_mult: 2.0 } | |
| 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_score" | |
| top: "rpn_cls_score_reshape" | |
| name: "rpn_cls_score_reshape" | |
| type: "Reshape" | |
| reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } | |
| } | |
| layer { | |
| name: 'rpn-data' | |
| type: 'Python' | |
| bottom: 'rpn_cls_score' | |
| bottom: 'gt_boxes' | |
| bottom: 'im_info' | |
| bottom: 'data' | |
| top: 'rpn_labels' | |
| top: 'rpn_bbox_targets' | |
| top: 'rpn_bbox_inside_weights' | |
| top: 'rpn_bbox_outside_weights' | |
| python_param { | |
| module: 'rpn.anchor_target_layer' | |
| layer: 'AnchorTargetLayer' | |
| param_str: "'feat_stride': 16" | |
| } | |
| } | |
| layer { | |
| name: "rpn_loss_cls" | |
| type: "SoftmaxWithLoss" | |
| bottom: "rpn_cls_score_reshape" | |
| bottom: "rpn_labels" | |
| propagate_down: 1 | |
| propagate_down: 0 | |
| top: "rpn_cls_loss" | |
| loss_weight: 1 | |
| loss_param { | |
| ignore_label: -1 | |
| normalize: true | |
| } | |
| } | |
| layer { | |
| name: "rpn_loss_bbox" | |
| type: "SmoothL1Loss" | |
| bottom: "rpn_bbox_pred" | |
| bottom: "rpn_bbox_targets" | |
| bottom: 'rpn_bbox_inside_weights' | |
| bottom: 'rpn_bbox_outside_weights' | |
| top: "rpn_loss_bbox" | |
| loss_weight: 1 | |
| smooth_l1_loss_param { sigma: 3.0 } | |
| } | |
| #========= RoI Proposal ============ | |
| layer { | |
| name: "rpn_cls_prob" | |
| type: "Softmax" | |
| bottom: "rpn_cls_score_reshape" | |
| top: "rpn_cls_prob" | |
| } | |
| layer { | |
| name: 'rpn_cls_prob_reshape' | |
| type: 'Reshape' | |
| bottom: 'rpn_cls_prob' | |
| top: 'rpn_cls_prob_reshape' | |
| reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } } | |
| } | |
| layer { | |
| name: 'proposal' | |
| type: 'Python' | |
| bottom: 'rpn_cls_prob_reshape' | |
| bottom: 'rpn_bbox_pred' | |
| bottom: 'im_info' | |
| top: 'rpn_rois' | |
| # top: 'rpn_scores' | |
| python_param { | |
| module: 'rpn.proposal_layer' | |
| layer: 'ProposalLayer' | |
| param_str: "'feat_stride': 16" | |
| } | |
| } | |
| #layer { | |
| # name: 'debug-data' | |
| # type: 'Python' | |
| # bottom: 'data' | |
| # bottom: 'rpn_rois' | |
| # bottom: 'rpn_scores' | |
| # python_param { | |
| # module: 'rpn.debug_layer' | |
| # layer: 'RPNDebugLayer' | |
| # } | |
| #} | |
| layer { | |
| name: 'roi-data' | |
| type: 'Python' | |
| bottom: 'rpn_rois' | |
| bottom: 'gt_boxes' | |
| top: 'rois' | |
| top: 'labels' | |
| top: 'bbox_targets' | |
| top: 'bbox_inside_weights' | |
| top: 'bbox_outside_weights' | |
| python_param { | |
| module: 'rpn.proposal_target_layer' | |
| layer: 'ProposalTargetLayer' | |
| param_str: "'num_classes': 40" | |
| } | |
| } | |
| ##### Stop Resnet till conv4x and add ROIpooling | |
| layer { | |
| name: "roi_pool5" | |
| type: "ROIPooling" | |
| bottom: "res4b" | |
| bottom: "rois" | |
| top: "roipool5" | |
| roi_pooling_param { | |
| pooled_w: 14 | |
| pooled_h: 14 | |
| spatial_scale: 0.0625 # 1/16 | |
| } | |
| } | |
| ##### Resume Resnet conv5x layers after ROIpooling layers | |
| layer { | |
| bottom: "roipool5" | |
| top: "res5a_branch1" | |
| name: "res5a_branch1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch1" | |
| top: "res5a_branch1" | |
| name: "bn5a_branch1" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch1" | |
| top: "res5a_branch1" | |
| name: "scale5a_branch1" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "roipool5" | |
| top: "res5a_branch2a" | |
| name: "res5a_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 2 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2a" | |
| top: "res5a_branch2a" | |
| name: "bn5a_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2a" | |
| top: "res5a_branch2a" | |
| name: "scale5a_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2a" | |
| top: "res5a_branch2a" | |
| name: "res5a_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5a_branch2a" | |
| top: "res5a_branch2b" | |
| name: "res5a_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2b" | |
| top: "res5a_branch2b" | |
| name: "bn5a_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch2b" | |
| top: "res5a_branch2b" | |
| name: "scale5a_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a_branch1" | |
| bottom: "res5a_branch2b" | |
| top: "res5a" | |
| name: "res5a" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res5a" | |
| top: "res5a" | |
| name: "res5a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5a" | |
| top: "res5b_branch2a" | |
| name: "res5b_branch2a" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2a" | |
| top: "res5b_branch2a" | |
| name: "bn5b_branch2a" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2a" | |
| top: "res5b_branch2a" | |
| name: "scale5b_branch2a" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2a" | |
| top: "res5b_branch2a" | |
| name: "res5b_branch2a_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5b_branch2a" | |
| top: "res5b_branch2b" | |
| name: "res5b_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2b" | |
| top: "res5b_branch2b" | |
| name: "bn5b_branch2b" | |
| type: "BatchNorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5b_branch2b" | |
| top: "res5b_branch2b" | |
| name: "scale5b_branch2b" | |
| type: "Scale" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| bottom: "res5a" | |
| bottom: "res5b_branch2b" | |
| top: "res5b" | |
| name: "res5b" | |
| type: "Eltwise" | |
| } | |
| layer { | |
| bottom: "res5b" | |
| top: "res5b" | |
| name: "res5b_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res5b" | |
| top: "pool5" | |
| name: "pool5" | |
| type: "Pooling" | |
| pooling_param { | |
| kernel_size: 7 | |
| stride: 1 | |
| pool: AVE | |
| } | |
| } | |
| ######### Add faster RCNN cls and bbox layer | |
| layer { | |
| name: "cls_score" | |
| type: "InnerProduct" | |
| bottom: "pool5" | |
| top: "cls_score" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 40 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bbox_pred" | |
| type: "InnerProduct" | |
| bottom: "pool5" | |
| top: "bbox_pred" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 160 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "loss_cls" | |
| type: "SoftmaxWithLoss" | |
| bottom: "cls_score" | |
| bottom: "labels" | |
| propagate_down: 1 | |
| propagate_down: 0 | |
| top: "loss_cls" | |
| loss_weight: 1 | |
| } | |
| layer { | |
| name: "loss_bbox" | |
| type: "SmoothL1Loss" | |
| bottom: "bbox_pred" | |
| bottom: "bbox_targets" | |
| bottom: "bbox_inside_weights" | |
| bottom: "bbox_outside_weights" | |
| top: "loss_bbox" | |
| loss_weight: 1 | |
| } |
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