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May 27, 2018 15:45
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| name: "VGG_ILSVRC_16_layers" | |
| layer { | |
| name: 'input-data' | |
| type: 'Python' | |
| top: 'data' | |
| top: 'im_info' | |
| top: 'gt_boxes' | |
| top: 'need_backprop' | |
| top: 'dc_label' | |
| python_param { | |
| module: 'roi_data_da_layer.layer' | |
| layer: 'RoIDataDALayer' | |
| param_str: "'num_classes': 21" | |
| } | |
| } | |
| layer { | |
| name: "conv1_1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1_1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu1_1" | |
| type: "ReLU" | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| } | |
| layer { | |
| name: "conv1_2" | |
| type: "Convolution" | |
| bottom: "conv1_1" | |
| top: "conv1_2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu1_2" | |
| type: "ReLU" | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1_2" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2_1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu2_1" | |
| type: "ReLU" | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| } | |
| layer { | |
| name: "conv2_2" | |
| type: "Convolution" | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu2_2" | |
| type: "ReLU" | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "conv2_2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3_1" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu3_1" | |
| type: "ReLU" | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| } | |
| layer { | |
| name: "conv3_2" | |
| type: "Convolution" | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu3_2" | |
| type: "ReLU" | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| } | |
| layer { | |
| name: "conv3_3" | |
| type: "Convolution" | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu3_3" | |
| type: "ReLU" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "conv3_3" | |
| top: "pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1" | |
| type: "Convolution" | |
| bottom: "pool3" | |
| top: "conv4_1" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu4_1" | |
| type: "ReLU" | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| } | |
| layer { | |
| name: "conv4_2" | |
| type: "Convolution" | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu4_2" | |
| type: "ReLU" | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| } | |
| layer { | |
| name: "conv4_3" | |
| type: "Convolution" | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu4_3" | |
| type: "ReLU" | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| } | |
| layer { | |
| name: "pool4" | |
| type: "Pooling" | |
| bottom: "conv4_3" | |
| top: "pool4" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1" | |
| type: "Convolution" | |
| bottom: "pool4" | |
| top: "conv5_1" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu5_1" | |
| type: "ReLU" | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| } | |
| layer { | |
| name: "conv5_2" | |
| type: "Convolution" | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu5_2" | |
| type: "ReLU" | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| } | |
| layer { | |
| name: "conv5_3" | |
| type: "Convolution" | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu5_3" | |
| type: "ReLU" | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| } | |
| #========= RPN ============ | |
| layer { | |
| name: "rpn_conv/3x3" | |
| type: "Convolution" | |
| bottom: "conv5_3" | |
| 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" | |
| } | |
| } | |
| #========= 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': 21" | |
| } | |
| } | |
| #========= RCNN ============ | |
| layer { | |
| name: "concat_rois" | |
| type: "Concat" | |
| bottom: "rois" | |
| bottom: "rpn_rois" | |
| top: "concat_rois" | |
| concat_param { | |
| concat_dim: 0 | |
| } | |
| } | |
| layer { | |
| name: "roi_pool5" | |
| type: "ROIPooling" | |
| bottom: "conv5_3" | |
| bottom: "concat_rois" | |
| top: "pool5" | |
| roi_pooling_param { | |
| pooled_w: 7 | |
| pooled_h: 7 | |
| spatial_scale: 0.0625 # 1/16 | |
| } | |
| } | |
| layer { | |
| name: "fc6" | |
| type: "InnerProduct" | |
| bottom: "pool5" | |
| top: "fc6" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| } | |
| } | |
| layer { | |
| name: "relu6" | |
| type: "ReLU" | |
| bottom: "fc6" | |
| top: "fc6" | |
| } | |
| layer { | |
| name: "drop6" | |
| type: "Dropout" | |
| bottom: "fc6" | |
| top: "fc6" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc7" | |
| type: "InnerProduct" | |
| bottom: "fc6" | |
| top: "fc7" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| } | |
| } | |
| layer { | |
| name: "relu7" | |
| type: "ReLU" | |
| bottom: "fc7" | |
| top: "fc7" | |
| } | |
| layer { | |
| name: "drop7" | |
| type: "Dropout" | |
| bottom: "fc7" | |
| top: "fc7" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "slice_feats" | |
| type: "Slice" | |
| bottom: "fc7" | |
| top: "feats_rois_128" | |
| top: "feats_rois_all" | |
| slice_param { | |
| axis: 0 | |
| slice_point: 128 | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| layer { | |
| name: "cls_score" | |
| type: "InnerProduct" | |
| bottom: "feats_rois_128" | |
| top: "cls_score" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 21 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bbox_pred" | |
| type: "InnerProduct" | |
| bottom: "feats_rois_128" | |
| top: "bbox_pred" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 84 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| # ===== all losses ===== | |
| layer { | |
| name: "rpn_loss_cls_filter" | |
| type: "GradientSilent" | |
| bottom: "rpn_cls_score_reshape" | |
| bottom: "need_backprop" | |
| top: "rpn_cls_score_reshape_filter" | |
| } | |
| layer { | |
| name: "rpn_loss_cls" | |
| type: "SoftmaxWithLoss" | |
| bottom: "rpn_cls_score_reshape_filter" | |
| 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_filter" | |
| type: "GradientSilent" | |
| bottom: "rpn_bbox_pred" | |
| bottom: "need_backprop" | |
| top: "rpn_bbox_pred_filter" | |
| } | |
| layer { | |
| name: "rpn_loss_bbox" | |
| type: "SmoothL1Loss" | |
| bottom: "rpn_bbox_pred_filter" | |
| 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 } | |
| } | |
| layer { | |
| name: "loss_cls_filter" | |
| type: "GradientSilent" | |
| bottom: "cls_score" | |
| bottom: "need_backprop" | |
| top: "cls_score_filter" | |
| } | |
| layer { | |
| name: "loss_cls" | |
| type: "SoftmaxWithLoss" | |
| bottom: "cls_score_filter" | |
| bottom: "labels" | |
| propagate_down: 1 | |
| propagate_down: 0 | |
| top: "loss_cls" | |
| loss_weight: 1 | |
| } | |
| layer { | |
| name: "loss_bbox_filter" | |
| type: "GradientSilent" | |
| bottom: "bbox_pred" | |
| bottom: "need_backprop" | |
| top: "bbox_pred_filter" | |
| } | |
| layer { | |
| name: "loss_bbox" | |
| type: "SmoothL1Loss" | |
| bottom: "bbox_pred_filter" | |
| bottom: "bbox_targets" | |
| bottom: "bbox_inside_weights" | |
| bottom: "bbox_outside_weights" | |
| top: "loss_bbox" | |
| loss_weight: 1 | |
| } | |
| #========= DA loss ============ | |
| layer { | |
| name: "grl" | |
| type: "GradientScaler" | |
| bottom: "feats_rois_all" | |
| top: "grl" | |
| gradient_scaler_param { | |
| scale_factor: -0.1 | |
| } | |
| } | |
| layer { | |
| name: "dc_ip1" | |
| type: "InnerProduct" | |
| bottom: "grl" | |
| top: "dc_ip1" | |
| param { | |
| lr_mult: 10 | |
| } | |
| param { | |
| lr_mult: 20 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "dc_relu1" | |
| type: "ReLU" | |
| bottom: "dc_ip1" | |
| top: "dc_ip1" | |
| } | |
| layer { | |
| name: "dc_drop1" | |
| type: "Dropout" | |
| bottom: "dc_ip1" | |
| top: "dc_ip1" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "dc_ip2" | |
| type: "InnerProduct" | |
| bottom: "dc_ip1" | |
| top: "dc_ip2" | |
| param { | |
| lr_mult: 10 | |
| } | |
| param { | |
| lr_mult: 20 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "dc_relu2" | |
| type: "ReLU" | |
| bottom: "dc_ip2" | |
| top: "dc_ip2" | |
| } | |
| layer { | |
| name: "dc_drop2" | |
| type: "Dropout" | |
| bottom: "dc_ip2" | |
| top: "dc_ip2" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "dc_ip3" | |
| type: "InnerProduct" | |
| bottom: "dc_ip2" | |
| top: "dc_ip3" | |
| param { | |
| lr_mult: 10 | |
| } | |
| param { | |
| lr_mult: 20 | |
| } | |
| inner_product_param { | |
| num_output: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| # std: 0.3 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: 'dc_label_resize' | |
| type: 'Python' | |
| bottom: 'dc_ip3' | |
| bottom: 'dc_label' | |
| top: 'dc_label_resize' | |
| python_param { | |
| module: 'utils.label_resize_layer' | |
| layer: 'LabelResizeLayer' | |
| } | |
| } | |
| layer { | |
| name: "dc_loss" | |
| type: "SigmoidCrossEntropyLoss" | |
| bottom: "dc_ip3" | |
| bottom: "dc_label_resize" | |
| propagate_down: 1 | |
| propagate_down: 0 | |
| top: "dc_loss" | |
| loss_weight: 0.1 | |
| } | |
| # semantic segmentation section | |
| layer { | |
| name: "da_conv_grl" | |
| type: "GradientScaler" | |
| bottom: "conv5_3" | |
| top: "da_conv_grl" | |
| gradient_scaler_param { | |
| scale_factor: -0.1 | |
| } | |
| } | |
| layer { | |
| name: "da_conv_ss_6" | |
| type: "Convolution" | |
| bottom: "da_conv_grl" | |
| top: "da_conv_ss_6" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: "da_relu_SS_6" | |
| type: "ReLU" | |
| bottom: "da_conv_ss_6" | |
| top: "da_conv_ss_6" | |
| } | |
| layer { | |
| name: "da_score_ss" | |
| type: "Convolution" | |
| bottom: "da_conv_ss_6" | |
| top: "da_score_ss" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { type: "gaussian" std: 0.001 } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| } | |
| layer { | |
| name: 'da_label_ss_resize' | |
| type: 'Python' | |
| bottom:'da_score_ss' | |
| bottom:'need_backprop' | |
| top: 'da_label_ss_resize' | |
| python_param { | |
| module: 'utils.resize_sslb' | |
| layer: 'LabelResizeLayer' | |
| } | |
| } | |
| layer { | |
| name: "da_conv_loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "da_score_ss" | |
| bottom: "da_label_ss_resize" | |
| top: "da_conv_loss" | |
| loss_param { | |
| ignore_label: 255 | |
| normalize: 1 | |
| } | |
| propagate_down: 1 | |
| propagate_down: 0 | |
| loss_weight: 1 | |
| } |
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