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January 5, 2019 19:46
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| name: "PVANET" | |
| layer { | |
| type: "YOU DATA LAYER" | |
| top: "mask_gt" ### 2 * 640 * 640 * 1 ### mask for text/non-text segmentation | |
| top: "mask_iou_angle" ### 1 * 640 * 640 * 5 ### iou with 4 channels and angle with one channel | |
| top: "gt_bbox" ### N * 8 ### grounding truth boxes for text (for computing loss) | |
| top: "ignore_bbox" ### N * 8 ### grounding truth boxes for text (for ignoring) | |
| } | |
| ################################################################################ | |
| ## Convolution | |
| ################################################################################ | |
| layer { | |
| name: "conv1_1/conv" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1_1/conv" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 3 | |
| pad_w: 3 | |
| kernel_h: 7 | |
| kernel_w: 7 | |
| stride_h: 2 | |
| stride_w: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv1_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv1_1/conv" | |
| top: "conv1_1/conv" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv1_1/neg" | |
| type: "Power" | |
| bottom: "conv1_1/conv" | |
| top: "conv1_1/neg" | |
| power_param { | |
| power: 1 | |
| scale: -1.0 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv1_1/concat" | |
| type: "Concat" | |
| bottom: "conv1_1/conv" | |
| bottom: "conv1_1/neg" | |
| top: "conv1_1" | |
| } | |
| layer { | |
| name: "conv1_1/scale" | |
| type: "Scale" | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv1_1/relu" | |
| type: "ReLU" | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1_1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/1/conv" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2_1/1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1/1" | |
| top: "conv2_1/2/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/2/bn_scale" | |
| type: "Scale" | |
| bottom: "conv2_1/2/pre" | |
| top: "conv2_1/2/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/2/relu" | |
| type: "ReLU" | |
| bottom: "conv2_1/2/pre" | |
| top: "conv2_1/2/pre" | |
| } | |
| layer { | |
| name: "conv2_1/2/conv" | |
| type: "Convolution" | |
| bottom: "conv2_1/2/pre" | |
| top: "conv2_1/2" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1/2" | |
| top: "conv2_1/3/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/3/neg" | |
| type: "Power" | |
| bottom: "conv2_1/3/pre" | |
| top: "conv2_1/3/neg" | |
| power_param { | |
| power: 1 | |
| scale: -1.0 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/3/concat" | |
| type: "Concat" | |
| bottom: "conv2_1/3/pre" | |
| bottom: "conv2_1/3/neg" | |
| top: "conv2_1/3/preAct" | |
| } | |
| layer { | |
| name: "conv2_1/3/scale" | |
| type: "Scale" | |
| bottom: "conv2_1/3/preAct" | |
| top: "conv2_1/3/preAct" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/3/relu" | |
| type: "ReLU" | |
| bottom: "conv2_1/3/preAct" | |
| top: "conv2_1/3/preAct" | |
| } | |
| layer { | |
| name: "conv2_1/3/conv" | |
| type: "Convolution" | |
| bottom: "conv2_1/3/preAct" | |
| top: "conv2_1/3" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/proj" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2_1/proj" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1" | |
| type: "Eltwise" | |
| bottom: "conv2_1/3" | |
| bottom: "conv2_1/proj" | |
| top: "conv2_1" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1" | |
| top: "conv2_2/1/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv2_2/1/pre" | |
| top: "conv2_2/1/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/1/relu" | |
| type: "ReLU" | |
| bottom: "conv2_2/1/pre" | |
| top: "conv2_2/1/pre" | |
| } | |
| layer { | |
| name: "conv2_2/1/conv" | |
| type: "Convolution" | |
| bottom: "conv2_2/1/pre" | |
| top: "conv2_2/1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2/1" | |
| top: "conv2_2/2/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/2/bn_scale" | |
| type: "Scale" | |
| bottom: "conv2_2/2/pre" | |
| top: "conv2_2/2/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/2/relu" | |
| type: "ReLU" | |
| bottom: "conv2_2/2/pre" | |
| top: "conv2_2/2/pre" | |
| } | |
| layer { | |
| name: "conv2_2/2/conv" | |
| type: "Convolution" | |
| bottom: "conv2_2/2/pre" | |
| top: "conv2_2/2" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2/2" | |
| top: "conv2_2/3/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/3/neg" | |
| type: "Power" | |
| bottom: "conv2_2/3/pre" | |
| top: "conv2_2/3/neg" | |
| power_param { | |
| power: 1 | |
| scale: -1.0 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/3/concat" | |
| type: "Concat" | |
| bottom: "conv2_2/3/pre" | |
| bottom: "conv2_2/3/neg" | |
| top: "conv2_2/3/preAct" | |
| } | |
| layer { | |
| name: "conv2_2/3/scale" | |
| type: "Scale" | |
| bottom: "conv2_2/3/preAct" | |
| top: "conv2_2/3/preAct" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/3/relu" | |
| type: "ReLU" | |
| bottom: "conv2_2/3/preAct" | |
| top: "conv2_2/3/preAct" | |
| } | |
| layer { | |
| name: "conv2_2/3/conv" | |
| type: "Convolution" | |
| bottom: "conv2_2/3/preAct" | |
| top: "conv2_2/3" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/input" | |
| type: "Power" | |
| bottom: "conv2_1" | |
| top: "conv2_2/input" | |
| power_param { | |
| power: 1 | |
| scale: 1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2" | |
| type: "Eltwise" | |
| bottom: "conv2_2/3" | |
| bottom: "conv2_2/input" | |
| top: "conv2_2" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2" | |
| top: "conv2_3/1/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv2_3/1/pre" | |
| top: "conv2_3/1/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/1/relu" | |
| type: "ReLU" | |
| bottom: "conv2_3/1/pre" | |
| top: "conv2_3/1/pre" | |
| } | |
| layer { | |
| name: "conv2_3/1/conv" | |
| type: "Convolution" | |
| bottom: "conv2_3/1/pre" | |
| top: "conv2_3/1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_3/1" | |
| top: "conv2_3/2/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/2/bn_scale" | |
| type: "Scale" | |
| bottom: "conv2_3/2/pre" | |
| top: "conv2_3/2/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/2/relu" | |
| type: "ReLU" | |
| bottom: "conv2_3/2/pre" | |
| top: "conv2_3/2/pre" | |
| } | |
| layer { | |
| name: "conv2_3/2/conv" | |
| type: "Convolution" | |
| bottom: "conv2_3/2/pre" | |
| top: "conv2_3/2" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_3/2" | |
| top: "conv2_3/3/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/3/neg" | |
| type: "Power" | |
| bottom: "conv2_3/3/pre" | |
| top: "conv2_3/3/neg" | |
| power_param { | |
| power: 1 | |
| scale: -1.0 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/3/concat" | |
| type: "Concat" | |
| bottom: "conv2_3/3/pre" | |
| bottom: "conv2_3/3/neg" | |
| top: "conv2_3/3/preAct" | |
| } | |
| layer { | |
| name: "conv2_3/3/scale" | |
| type: "Scale" | |
| bottom: "conv2_3/3/preAct" | |
| top: "conv2_3/3/preAct" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/3/relu" | |
| type: "ReLU" | |
| bottom: "conv2_3/3/preAct" | |
| top: "conv2_3/3/preAct" | |
| } | |
| layer { | |
| name: "conv2_3/3/conv" | |
| type: "Convolution" | |
| bottom: "conv2_3/3/preAct" | |
| top: "conv2_3/3" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/input" | |
| type: "Power" | |
| bottom: "conv2_2" | |
| top: "conv2_3/input" | |
| power_param { | |
| power: 1 | |
| scale: 1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3" | |
| type: "Eltwise" | |
| bottom: "conv2_3/3" | |
| bottom: "conv2_3/input" | |
| top: "conv2_3" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_3" | |
| top: "conv3_1/1/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv3_1/1/pre" | |
| top: "conv3_1/1/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/1/relu" | |
| type: "ReLU" | |
| bottom: "conv3_1/1/pre" | |
| top: "conv3_1/1/pre" | |
| } | |
| layer { | |
| name: "conv3_1/1/conv" | |
| type: "Convolution" | |
| bottom: "conv3_1/1/pre" | |
| top: "conv3_1/1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1/1" | |
| top: "conv3_1/2/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/2/bn_scale" | |
| type: "Scale" | |
| bottom: "conv3_1/2/pre" | |
| top: "conv3_1/2/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/2/relu" | |
| type: "ReLU" | |
| bottom: "conv3_1/2/pre" | |
| top: "conv3_1/2/pre" | |
| } | |
| layer { | |
| name: "conv3_1/2/conv" | |
| type: "Convolution" | |
| bottom: "conv3_1/2/pre" | |
| top: "conv3_1/2" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1/2" | |
| top: "conv3_1/3/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/3/neg" | |
| type: "Power" | |
| bottom: "conv3_1/3/pre" | |
| top: "conv3_1/3/neg" | |
| power_param { | |
| power: 1 | |
| scale: -1.0 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/3/concat" | |
| type: "Concat" | |
| bottom: "conv3_1/3/pre" | |
| bottom: "conv3_1/3/neg" | |
| top: "conv3_1/3/preAct" | |
| } | |
| layer { | |
| name: "conv3_1/3/scale" | |
| type: "Scale" | |
| bottom: "conv3_1/3/preAct" | |
| top: "conv3_1/3/preAct" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_1/3/preAct" | |
| top: "conv3_1/3/preAct" | |
| } | |
| layer { | |
| name: "conv3_1/3/conv" | |
| type: "Convolution" | |
| bottom: "conv3_1/3/preAct" | |
| top: "conv3_1/3" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/proj" | |
| type: "Convolution" | |
| bottom: "conv3_1/1/pre" | |
| top: "conv3_1/proj" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1" | |
| type: "Eltwise" | |
| bottom: "conv3_1/3" | |
| bottom: "conv3_1/proj" | |
| top: "conv3_1" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1" | |
| top: "conv3_2/1/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv3_2/1/pre" | |
| top: "conv3_2/1/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/1/relu" | |
| type: "ReLU" | |
| bottom: "conv3_2/1/pre" | |
| top: "conv3_2/1/pre" | |
| } | |
| layer { | |
| name: "conv3_2/1/conv" | |
| type: "Convolution" | |
| bottom: "conv3_2/1/pre" | |
| top: "conv3_2/1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2/1" | |
| top: "conv3_2/2/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/2/bn_scale" | |
| type: "Scale" | |
| bottom: "conv3_2/2/pre" | |
| top: "conv3_2/2/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/2/relu" | |
| type: "ReLU" | |
| bottom: "conv3_2/2/pre" | |
| top: "conv3_2/2/pre" | |
| } | |
| layer { | |
| name: "conv3_2/2/conv" | |
| type: "Convolution" | |
| bottom: "conv3_2/2/pre" | |
| top: "conv3_2/2" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2/2" | |
| top: "conv3_2/3/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/3/neg" | |
| type: "Power" | |
| bottom: "conv3_2/3/pre" | |
| top: "conv3_2/3/neg" | |
| power_param { | |
| power: 1 | |
| scale: -1.0 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/3/concat" | |
| type: "Concat" | |
| bottom: "conv3_2/3/pre" | |
| bottom: "conv3_2/3/neg" | |
| top: "conv3_2/3/preAct" | |
| } | |
| layer { | |
| name: "conv3_2/3/scale" | |
| type: "Scale" | |
| bottom: "conv3_2/3/preAct" | |
| top: "conv3_2/3/preAct" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_2/3/preAct" | |
| top: "conv3_2/3/preAct" | |
| } | |
| layer { | |
| name: "conv3_2/3/conv" | |
| type: "Convolution" | |
| bottom: "conv3_2/3/preAct" | |
| top: "conv3_2/3" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/input" | |
| type: "Power" | |
| bottom: "conv3_1" | |
| top: "conv3_2/input" | |
| power_param { | |
| power: 1 | |
| scale: 1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2" | |
| type: "Eltwise" | |
| bottom: "conv3_2/3" | |
| bottom: "conv3_2/input" | |
| top: "conv3_2" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2" | |
| top: "conv3_3/1/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv3_3/1/pre" | |
| top: "conv3_3/1/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/1/relu" | |
| type: "ReLU" | |
| bottom: "conv3_3/1/pre" | |
| top: "conv3_3/1/pre" | |
| } | |
| layer { | |
| name: "conv3_3/1/conv" | |
| type: "Convolution" | |
| bottom: "conv3_3/1/pre" | |
| top: "conv3_3/1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_3/1" | |
| top: "conv3_3/2/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/2/bn_scale" | |
| type: "Scale" | |
| bottom: "conv3_3/2/pre" | |
| top: "conv3_3/2/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/2/relu" | |
| type: "ReLU" | |
| bottom: "conv3_3/2/pre" | |
| top: "conv3_3/2/pre" | |
| } | |
| layer { | |
| name: "conv3_3/2/conv" | |
| type: "Convolution" | |
| bottom: "conv3_3/2/pre" | |
| top: "conv3_3/2" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_3/2" | |
| top: "conv3_3/3/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/3/neg" | |
| type: "Power" | |
| bottom: "conv3_3/3/pre" | |
| top: "conv3_3/3/neg" | |
| power_param { | |
| power: 1 | |
| scale: -1.0 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/3/concat" | |
| type: "Concat" | |
| bottom: "conv3_3/3/pre" | |
| bottom: "conv3_3/3/neg" | |
| top: "conv3_3/3/preAct" | |
| } | |
| layer { | |
| name: "conv3_3/3/scale" | |
| type: "Scale" | |
| bottom: "conv3_3/3/preAct" | |
| top: "conv3_3/3/preAct" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_3/3/preAct" | |
| top: "conv3_3/3/preAct" | |
| } | |
| layer { | |
| name: "conv3_3/3/conv" | |
| type: "Convolution" | |
| bottom: "conv3_3/3/preAct" | |
| top: "conv3_3/3" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/input" | |
| type: "Power" | |
| bottom: "conv3_2" | |
| top: "conv3_3/input" | |
| power_param { | |
| power: 1 | |
| scale: 1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3" | |
| type: "Eltwise" | |
| bottom: "conv3_3/3" | |
| bottom: "conv3_3/input" | |
| top: "conv3_3" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_3" | |
| top: "conv3_4/1/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv3_4/1/pre" | |
| top: "conv3_4/1/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/1/relu" | |
| type: "ReLU" | |
| bottom: "conv3_4/1/pre" | |
| top: "conv3_4/1/pre" | |
| } | |
| layer { | |
| name: "conv3_4/1/conv" | |
| type: "Convolution" | |
| bottom: "conv3_4/1/pre" | |
| top: "conv3_4/1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_4/1" | |
| top: "conv3_4/2/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/2/bn_scale" | |
| type: "Scale" | |
| bottom: "conv3_4/2/pre" | |
| top: "conv3_4/2/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/2/relu" | |
| type: "ReLU" | |
| bottom: "conv3_4/2/pre" | |
| top: "conv3_4/2/pre" | |
| } | |
| layer { | |
| name: "conv3_4/2/conv" | |
| type: "Convolution" | |
| bottom: "conv3_4/2/pre" | |
| top: "conv3_4/2" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_4/2" | |
| top: "conv3_4/3/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/3/neg" | |
| type: "Power" | |
| bottom: "conv3_4/3/pre" | |
| top: "conv3_4/3/neg" | |
| power_param { | |
| power: 1 | |
| scale: -1.0 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/3/concat" | |
| type: "Concat" | |
| bottom: "conv3_4/3/pre" | |
| bottom: "conv3_4/3/neg" | |
| top: "conv3_4/3/preAct" | |
| } | |
| layer { | |
| name: "conv3_4/3/scale" | |
| type: "Scale" | |
| bottom: "conv3_4/3/preAct" | |
| top: "conv3_4/3/preAct" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_4/3/preAct" | |
| top: "conv3_4/3/preAct" | |
| } | |
| layer { | |
| name: "conv3_4/3/conv" | |
| type: "Convolution" | |
| bottom: "conv3_4/3/preAct" | |
| top: "conv3_4/3" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/input" | |
| type: "Power" | |
| bottom: "conv3_3" | |
| top: "conv3_4/input" | |
| power_param { | |
| power: 1 | |
| scale: 1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4" | |
| type: "Eltwise" | |
| bottom: "conv3_4/3" | |
| bottom: "conv3_4/input" | |
| top: "conv3_4" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_4" | |
| top: "conv4_1/incep/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_1/incep/pre" | |
| top: "conv4_1/incep/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1/incep/pre" | |
| top: "conv4_1/incep/pre" | |
| } | |
| layer { | |
| name: "conv4_1/incep/0/conv" | |
| type: "Convolution" | |
| bottom: "conv4_1/incep/pre" | |
| top: "conv4_1/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/incep/0" | |
| top: "conv4_1/incep/0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_1/incep/0" | |
| top: "conv4_1/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/0/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1/incep/0" | |
| top: "conv4_1/incep/0" | |
| } | |
| layer { | |
| name: "conv4_1/incep/1_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv4_1/incep/pre" | |
| top: "conv4_1/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/incep/1_reduce" | |
| top: "conv4_1/incep/1_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/1_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_1/incep/1_reduce" | |
| top: "conv4_1/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1/incep/1_reduce" | |
| top: "conv4_1/incep/1_reduce" | |
| } | |
| layer { | |
| name: "conv4_1/incep/1_0/conv" | |
| type: "Convolution" | |
| bottom: "conv4_1/incep/1_reduce" | |
| top: "conv4_1/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/1_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/incep/1_0" | |
| top: "conv4_1/incep/1_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/1_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_1/incep/1_0" | |
| top: "conv4_1/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/1_0/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1/incep/1_0" | |
| top: "conv4_1/incep/1_0" | |
| } | |
| layer { | |
| name: "conv4_1/incep/2_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv4_1/incep/pre" | |
| top: "conv4_1/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/2_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/incep/2_reduce" | |
| top: "conv4_1/incep/2_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/2_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_1/incep/2_reduce" | |
| top: "conv4_1/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/2_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1/incep/2_reduce" | |
| top: "conv4_1/incep/2_reduce" | |
| } | |
| layer { | |
| name: "conv4_1/incep/2_0/conv" | |
| type: "Convolution" | |
| bottom: "conv4_1/incep/2_reduce" | |
| top: "conv4_1/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/2_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/incep/2_0" | |
| top: "conv4_1/incep/2_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/2_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_1/incep/2_0" | |
| top: "conv4_1/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/2_0/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1/incep/2_0" | |
| top: "conv4_1/incep/2_0" | |
| } | |
| layer { | |
| name: "conv4_1/incep/2_1/conv" | |
| type: "Convolution" | |
| bottom: "conv4_1/incep/2_0" | |
| top: "conv4_1/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/2_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/incep/2_1" | |
| top: "conv4_1/incep/2_1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/2_1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_1/incep/2_1" | |
| top: "conv4_1/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/2_1/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1/incep/2_1" | |
| top: "conv4_1/incep/2_1" | |
| } | |
| layer { | |
| name: "conv4_1/incep/pool" | |
| type: "Pooling" | |
| bottom: "conv4_1/incep/pre" | |
| top: "conv4_1/incep/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/poolproj/conv" | |
| type: "Convolution" | |
| bottom: "conv4_1/incep/pool" | |
| top: "conv4_1/incep/poolproj" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/poolproj/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/incep/poolproj" | |
| top: "conv4_1/incep/poolproj" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/poolproj/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_1/incep/poolproj" | |
| top: "conv4_1/incep/poolproj" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/incep/poolproj/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1/incep/poolproj" | |
| top: "conv4_1/incep/poolproj" | |
| } | |
| layer { | |
| name: "conv4_1/incep" | |
| type: "Concat" | |
| bottom: "conv4_1/incep/0" | |
| bottom: "conv4_1/incep/1_0" | |
| bottom: "conv4_1/incep/2_1" | |
| bottom: "conv4_1/incep/poolproj" | |
| top: "conv4_1/incep" | |
| } | |
| layer { | |
| name: "conv4_1/out/conv" | |
| type: "Convolution" | |
| bottom: "conv4_1/incep" | |
| top: "conv4_1/out" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/proj" | |
| type: "Convolution" | |
| bottom: "conv3_4" | |
| top: "conv4_1/proj" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1" | |
| type: "Eltwise" | |
| bottom: "conv4_1/out" | |
| bottom: "conv4_1/proj" | |
| top: "conv4_1" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1" | |
| top: "conv4_2/incep/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_2/incep/pre" | |
| top: "conv4_2/incep/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2/incep/pre" | |
| top: "conv4_2/incep/pre" | |
| } | |
| layer { | |
| name: "conv4_2/incep/0/conv" | |
| type: "Convolution" | |
| bottom: "conv4_2/incep/pre" | |
| top: "conv4_2/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/incep/0" | |
| top: "conv4_2/incep/0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_2/incep/0" | |
| top: "conv4_2/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/0/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2/incep/0" | |
| top: "conv4_2/incep/0" | |
| } | |
| layer { | |
| name: "conv4_2/incep/1_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv4_2/incep/pre" | |
| top: "conv4_2/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/incep/1_reduce" | |
| top: "conv4_2/incep/1_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/1_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_2/incep/1_reduce" | |
| top: "conv4_2/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2/incep/1_reduce" | |
| top: "conv4_2/incep/1_reduce" | |
| } | |
| layer { | |
| name: "conv4_2/incep/1_0/conv" | |
| type: "Convolution" | |
| bottom: "conv4_2/incep/1_reduce" | |
| top: "conv4_2/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/1_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/incep/1_0" | |
| top: "conv4_2/incep/1_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/1_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_2/incep/1_0" | |
| top: "conv4_2/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/1_0/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2/incep/1_0" | |
| top: "conv4_2/incep/1_0" | |
| } | |
| layer { | |
| name: "conv4_2/incep/2_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv4_2/incep/pre" | |
| top: "conv4_2/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/2_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/incep/2_reduce" | |
| top: "conv4_2/incep/2_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/2_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_2/incep/2_reduce" | |
| top: "conv4_2/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/2_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2/incep/2_reduce" | |
| top: "conv4_2/incep/2_reduce" | |
| } | |
| layer { | |
| name: "conv4_2/incep/2_0/conv" | |
| type: "Convolution" | |
| bottom: "conv4_2/incep/2_reduce" | |
| top: "conv4_2/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/2_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/incep/2_0" | |
| top: "conv4_2/incep/2_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/2_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_2/incep/2_0" | |
| top: "conv4_2/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/2_0/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2/incep/2_0" | |
| top: "conv4_2/incep/2_0" | |
| } | |
| layer { | |
| name: "conv4_2/incep/2_1/conv" | |
| type: "Convolution" | |
| bottom: "conv4_2/incep/2_0" | |
| top: "conv4_2/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/2_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/incep/2_1" | |
| top: "conv4_2/incep/2_1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/2_1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_2/incep/2_1" | |
| top: "conv4_2/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/incep/2_1/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2/incep/2_1" | |
| top: "conv4_2/incep/2_1" | |
| } | |
| layer { | |
| name: "conv4_2/incep" | |
| type: "Concat" | |
| bottom: "conv4_2/incep/0" | |
| bottom: "conv4_2/incep/1_0" | |
| bottom: "conv4_2/incep/2_1" | |
| top: "conv4_2/incep" | |
| } | |
| layer { | |
| name: "conv4_2/out/conv" | |
| type: "Convolution" | |
| bottom: "conv4_2/incep" | |
| top: "conv4_2/out" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/input" | |
| type: "Power" | |
| bottom: "conv4_1" | |
| top: "conv4_2/input" | |
| power_param { | |
| power: 1 | |
| scale: 1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2" | |
| type: "Eltwise" | |
| bottom: "conv4_2/out" | |
| bottom: "conv4_2/input" | |
| top: "conv4_2" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2" | |
| top: "conv4_3/incep/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_3/incep/pre" | |
| top: "conv4_3/incep/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3/incep/pre" | |
| top: "conv4_3/incep/pre" | |
| } | |
| layer { | |
| name: "conv4_3/incep/0/conv" | |
| type: "Convolution" | |
| bottom: "conv4_3/incep/pre" | |
| top: "conv4_3/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3/incep/0" | |
| top: "conv4_3/incep/0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_3/incep/0" | |
| top: "conv4_3/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/0/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3/incep/0" | |
| top: "conv4_3/incep/0" | |
| } | |
| layer { | |
| name: "conv4_3/incep/1_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv4_3/incep/pre" | |
| top: "conv4_3/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3/incep/1_reduce" | |
| top: "conv4_3/incep/1_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/1_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_3/incep/1_reduce" | |
| top: "conv4_3/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3/incep/1_reduce" | |
| top: "conv4_3/incep/1_reduce" | |
| } | |
| layer { | |
| name: "conv4_3/incep/1_0/conv" | |
| type: "Convolution" | |
| bottom: "conv4_3/incep/1_reduce" | |
| top: "conv4_3/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/1_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3/incep/1_0" | |
| top: "conv4_3/incep/1_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/1_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_3/incep/1_0" | |
| top: "conv4_3/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/1_0/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3/incep/1_0" | |
| top: "conv4_3/incep/1_0" | |
| } | |
| layer { | |
| name: "conv4_3/incep/2_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv4_3/incep/pre" | |
| top: "conv4_3/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/2_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3/incep/2_reduce" | |
| top: "conv4_3/incep/2_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/2_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_3/incep/2_reduce" | |
| top: "conv4_3/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/2_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3/incep/2_reduce" | |
| top: "conv4_3/incep/2_reduce" | |
| } | |
| layer { | |
| name: "conv4_3/incep/2_0/conv" | |
| type: "Convolution" | |
| bottom: "conv4_3/incep/2_reduce" | |
| top: "conv4_3/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/2_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3/incep/2_0" | |
| top: "conv4_3/incep/2_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/2_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_3/incep/2_0" | |
| top: "conv4_3/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/2_0/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3/incep/2_0" | |
| top: "conv4_3/incep/2_0" | |
| } | |
| layer { | |
| name: "conv4_3/incep/2_1/conv" | |
| type: "Convolution" | |
| bottom: "conv4_3/incep/2_0" | |
| top: "conv4_3/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/2_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3/incep/2_1" | |
| top: "conv4_3/incep/2_1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/2_1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_3/incep/2_1" | |
| top: "conv4_3/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/incep/2_1/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3/incep/2_1" | |
| top: "conv4_3/incep/2_1" | |
| } | |
| layer { | |
| name: "conv4_3/incep" | |
| type: "Concat" | |
| bottom: "conv4_3/incep/0" | |
| bottom: "conv4_3/incep/1_0" | |
| bottom: "conv4_3/incep/2_1" | |
| top: "conv4_3/incep" | |
| } | |
| layer { | |
| name: "conv4_3/out/conv" | |
| type: "Convolution" | |
| bottom: "conv4_3/incep" | |
| top: "conv4_3/out" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/input" | |
| type: "Power" | |
| bottom: "conv4_2" | |
| top: "conv4_3/input" | |
| power_param { | |
| power: 1 | |
| scale: 1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3" | |
| type: "Eltwise" | |
| bottom: "conv4_3/out" | |
| bottom: "conv4_3/input" | |
| top: "conv4_3" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3" | |
| top: "conv4_4/incep/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_4/incep/pre" | |
| top: "conv4_4/incep/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4/incep/pre" | |
| top: "conv4_4/incep/pre" | |
| } | |
| layer { | |
| name: "conv4_4/incep/0/conv" | |
| type: "Convolution" | |
| bottom: "conv4_4/incep/pre" | |
| top: "conv4_4/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4/incep/0" | |
| top: "conv4_4/incep/0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_4/incep/0" | |
| top: "conv4_4/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/0/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4/incep/0" | |
| top: "conv4_4/incep/0" | |
| } | |
| layer { | |
| name: "conv4_4/incep/1_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv4_4/incep/pre" | |
| top: "conv4_4/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4/incep/1_reduce" | |
| top: "conv4_4/incep/1_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/1_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_4/incep/1_reduce" | |
| top: "conv4_4/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4/incep/1_reduce" | |
| top: "conv4_4/incep/1_reduce" | |
| } | |
| layer { | |
| name: "conv4_4/incep/1_0/conv" | |
| type: "Convolution" | |
| bottom: "conv4_4/incep/1_reduce" | |
| top: "conv4_4/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/1_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4/incep/1_0" | |
| top: "conv4_4/incep/1_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/1_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_4/incep/1_0" | |
| top: "conv4_4/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/1_0/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4/incep/1_0" | |
| top: "conv4_4/incep/1_0" | |
| } | |
| layer { | |
| name: "conv4_4/incep/2_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv4_4/incep/pre" | |
| top: "conv4_4/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/2_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4/incep/2_reduce" | |
| top: "conv4_4/incep/2_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/2_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_4/incep/2_reduce" | |
| top: "conv4_4/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/2_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4/incep/2_reduce" | |
| top: "conv4_4/incep/2_reduce" | |
| } | |
| layer { | |
| name: "conv4_4/incep/2_0/conv" | |
| type: "Convolution" | |
| bottom: "conv4_4/incep/2_reduce" | |
| top: "conv4_4/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/2_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4/incep/2_0" | |
| top: "conv4_4/incep/2_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/2_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_4/incep/2_0" | |
| top: "conv4_4/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/2_0/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4/incep/2_0" | |
| top: "conv4_4/incep/2_0" | |
| } | |
| layer { | |
| name: "conv4_4/incep/2_1/conv" | |
| type: "Convolution" | |
| bottom: "conv4_4/incep/2_0" | |
| top: "conv4_4/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/2_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4/incep/2_1" | |
| top: "conv4_4/incep/2_1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/2_1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv4_4/incep/2_1" | |
| top: "conv4_4/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/incep/2_1/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4/incep/2_1" | |
| top: "conv4_4/incep/2_1" | |
| } | |
| layer { | |
| name: "conv4_4/incep" | |
| type: "Concat" | |
| bottom: "conv4_4/incep/0" | |
| bottom: "conv4_4/incep/1_0" | |
| bottom: "conv4_4/incep/2_1" | |
| top: "conv4_4/incep" | |
| } | |
| layer { | |
| name: "conv4_4/out/conv" | |
| type: "Convolution" | |
| bottom: "conv4_4/incep" | |
| top: "conv4_4/out" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/input" | |
| type: "Power" | |
| bottom: "conv4_3" | |
| top: "conv4_4/input" | |
| power_param { | |
| power: 1 | |
| scale: 1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4" | |
| type: "Eltwise" | |
| bottom: "conv4_4/out" | |
| bottom: "conv4_4/input" | |
| top: "conv4_4" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4" | |
| top: "conv5_1/incep/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_1/incep/pre" | |
| top: "conv5_1/incep/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1/incep/pre" | |
| top: "conv5_1/incep/pre" | |
| } | |
| layer { | |
| name: "conv5_1/incep/0/conv" | |
| type: "Convolution" | |
| bottom: "conv5_1/incep/pre" | |
| top: "conv5_1/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/incep/0" | |
| top: "conv5_1/incep/0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_1/incep/0" | |
| top: "conv5_1/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/0/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1/incep/0" | |
| top: "conv5_1/incep/0" | |
| } | |
| layer { | |
| name: "conv5_1/incep/1_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv5_1/incep/pre" | |
| top: "conv5_1/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/incep/1_reduce" | |
| top: "conv5_1/incep/1_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/1_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_1/incep/1_reduce" | |
| top: "conv5_1/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1/incep/1_reduce" | |
| top: "conv5_1/incep/1_reduce" | |
| } | |
| layer { | |
| name: "conv5_1/incep/1_0/conv" | |
| type: "Convolution" | |
| bottom: "conv5_1/incep/1_reduce" | |
| top: "conv5_1/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/1_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/incep/1_0" | |
| top: "conv5_1/incep/1_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/1_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_1/incep/1_0" | |
| top: "conv5_1/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/1_0/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1/incep/1_0" | |
| top: "conv5_1/incep/1_0" | |
| } | |
| layer { | |
| name: "conv5_1/incep/2_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv5_1/incep/pre" | |
| top: "conv5_1/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/2_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/incep/2_reduce" | |
| top: "conv5_1/incep/2_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/2_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_1/incep/2_reduce" | |
| top: "conv5_1/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/2_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1/incep/2_reduce" | |
| top: "conv5_1/incep/2_reduce" | |
| } | |
| layer { | |
| name: "conv5_1/incep/2_0/conv" | |
| type: "Convolution" | |
| bottom: "conv5_1/incep/2_reduce" | |
| top: "conv5_1/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/2_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/incep/2_0" | |
| top: "conv5_1/incep/2_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/2_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_1/incep/2_0" | |
| top: "conv5_1/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/2_0/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1/incep/2_0" | |
| top: "conv5_1/incep/2_0" | |
| } | |
| layer { | |
| name: "conv5_1/incep/2_1/conv" | |
| type: "Convolution" | |
| bottom: "conv5_1/incep/2_0" | |
| top: "conv5_1/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/2_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/incep/2_1" | |
| top: "conv5_1/incep/2_1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/2_1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_1/incep/2_1" | |
| top: "conv5_1/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/2_1/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1/incep/2_1" | |
| top: "conv5_1/incep/2_1" | |
| } | |
| layer { | |
| name: "conv5_1/incep/pool" | |
| type: "Pooling" | |
| bottom: "conv5_1/incep/pre" | |
| top: "conv5_1/incep/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/poolproj/conv" | |
| type: "Convolution" | |
| bottom: "conv5_1/incep/pool" | |
| top: "conv5_1/incep/poolproj" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/poolproj/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/incep/poolproj" | |
| top: "conv5_1/incep/poolproj" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/poolproj/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_1/incep/poolproj" | |
| top: "conv5_1/incep/poolproj" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/incep/poolproj/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1/incep/poolproj" | |
| top: "conv5_1/incep/poolproj" | |
| } | |
| layer { | |
| name: "conv5_1/incep" | |
| type: "Concat" | |
| bottom: "conv5_1/incep/0" | |
| bottom: "conv5_1/incep/1_0" | |
| bottom: "conv5_1/incep/2_1" | |
| bottom: "conv5_1/incep/poolproj" | |
| top: "conv5_1/incep" | |
| } | |
| layer { | |
| name: "conv5_1/out/conv" | |
| type: "Convolution" | |
| bottom: "conv5_1/incep" | |
| top: "conv5_1/out" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/proj" | |
| type: "Convolution" | |
| bottom: "conv4_4" | |
| top: "conv5_1/proj" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1" | |
| type: "Eltwise" | |
| bottom: "conv5_1/out" | |
| bottom: "conv5_1/proj" | |
| top: "conv5_1" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1" | |
| top: "conv5_2/incep/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_2/incep/pre" | |
| top: "conv5_2/incep/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2/incep/pre" | |
| top: "conv5_2/incep/pre" | |
| } | |
| layer { | |
| name: "conv5_2/incep/0/conv" | |
| type: "Convolution" | |
| bottom: "conv5_2/incep/pre" | |
| top: "conv5_2/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2/incep/0" | |
| top: "conv5_2/incep/0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_2/incep/0" | |
| top: "conv5_2/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/0/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2/incep/0" | |
| top: "conv5_2/incep/0" | |
| } | |
| layer { | |
| name: "conv5_2/incep/1_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv5_2/incep/pre" | |
| top: "conv5_2/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2/incep/1_reduce" | |
| top: "conv5_2/incep/1_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/1_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_2/incep/1_reduce" | |
| top: "conv5_2/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2/incep/1_reduce" | |
| top: "conv5_2/incep/1_reduce" | |
| } | |
| layer { | |
| name: "conv5_2/incep/1_0/conv" | |
| type: "Convolution" | |
| bottom: "conv5_2/incep/1_reduce" | |
| top: "conv5_2/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/1_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2/incep/1_0" | |
| top: "conv5_2/incep/1_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/1_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_2/incep/1_0" | |
| top: "conv5_2/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/1_0/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2/incep/1_0" | |
| top: "conv5_2/incep/1_0" | |
| } | |
| layer { | |
| name: "conv5_2/incep/2_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv5_2/incep/pre" | |
| top: "conv5_2/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/2_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2/incep/2_reduce" | |
| top: "conv5_2/incep/2_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/2_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_2/incep/2_reduce" | |
| top: "conv5_2/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/2_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2/incep/2_reduce" | |
| top: "conv5_2/incep/2_reduce" | |
| } | |
| layer { | |
| name: "conv5_2/incep/2_0/conv" | |
| type: "Convolution" | |
| bottom: "conv5_2/incep/2_reduce" | |
| top: "conv5_2/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/2_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2/incep/2_0" | |
| top: "conv5_2/incep/2_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/2_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_2/incep/2_0" | |
| top: "conv5_2/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/2_0/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2/incep/2_0" | |
| top: "conv5_2/incep/2_0" | |
| } | |
| layer { | |
| name: "conv5_2/incep/2_1/conv" | |
| type: "Convolution" | |
| bottom: "conv5_2/incep/2_0" | |
| top: "conv5_2/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/2_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2/incep/2_1" | |
| top: "conv5_2/incep/2_1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/2_1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_2/incep/2_1" | |
| top: "conv5_2/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/incep/2_1/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2/incep/2_1" | |
| top: "conv5_2/incep/2_1" | |
| } | |
| layer { | |
| name: "conv5_2/incep" | |
| type: "Concat" | |
| bottom: "conv5_2/incep/0" | |
| bottom: "conv5_2/incep/1_0" | |
| bottom: "conv5_2/incep/2_1" | |
| top: "conv5_2/incep" | |
| } | |
| layer { | |
| name: "conv5_2/out/conv" | |
| type: "Convolution" | |
| bottom: "conv5_2/incep" | |
| top: "conv5_2/out" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/input" | |
| type: "Power" | |
| bottom: "conv5_1" | |
| top: "conv5_2/input" | |
| power_param { | |
| power: 1 | |
| scale: 1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2" | |
| type: "Eltwise" | |
| bottom: "conv5_2/out" | |
| bottom: "conv5_2/input" | |
| top: "conv5_2" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2" | |
| top: "conv5_3/incep/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_3/incep/pre" | |
| top: "conv5_3/incep/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3/incep/pre" | |
| top: "conv5_3/incep/pre" | |
| } | |
| layer { | |
| name: "conv5_3/incep/0/conv" | |
| type: "Convolution" | |
| bottom: "conv5_3/incep/pre" | |
| top: "conv5_3/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3/incep/0" | |
| top: "conv5_3/incep/0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_3/incep/0" | |
| top: "conv5_3/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/0/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3/incep/0" | |
| top: "conv5_3/incep/0" | |
| } | |
| layer { | |
| name: "conv5_3/incep/1_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv5_3/incep/pre" | |
| top: "conv5_3/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3/incep/1_reduce" | |
| top: "conv5_3/incep/1_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/1_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_3/incep/1_reduce" | |
| top: "conv5_3/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3/incep/1_reduce" | |
| top: "conv5_3/incep/1_reduce" | |
| } | |
| layer { | |
| name: "conv5_3/incep/1_0/conv" | |
| type: "Convolution" | |
| bottom: "conv5_3/incep/1_reduce" | |
| top: "conv5_3/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/1_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3/incep/1_0" | |
| top: "conv5_3/incep/1_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/1_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_3/incep/1_0" | |
| top: "conv5_3/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/1_0/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3/incep/1_0" | |
| top: "conv5_3/incep/1_0" | |
| } | |
| layer { | |
| name: "conv5_3/incep/2_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv5_3/incep/pre" | |
| top: "conv5_3/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/2_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3/incep/2_reduce" | |
| top: "conv5_3/incep/2_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/2_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_3/incep/2_reduce" | |
| top: "conv5_3/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/2_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3/incep/2_reduce" | |
| top: "conv5_3/incep/2_reduce" | |
| } | |
| layer { | |
| name: "conv5_3/incep/2_0/conv" | |
| type: "Convolution" | |
| bottom: "conv5_3/incep/2_reduce" | |
| top: "conv5_3/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/2_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3/incep/2_0" | |
| top: "conv5_3/incep/2_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/2_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_3/incep/2_0" | |
| top: "conv5_3/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/2_0/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3/incep/2_0" | |
| top: "conv5_3/incep/2_0" | |
| } | |
| layer { | |
| name: "conv5_3/incep/2_1/conv" | |
| type: "Convolution" | |
| bottom: "conv5_3/incep/2_0" | |
| top: "conv5_3/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/2_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3/incep/2_1" | |
| top: "conv5_3/incep/2_1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/2_1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_3/incep/2_1" | |
| top: "conv5_3/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/incep/2_1/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3/incep/2_1" | |
| top: "conv5_3/incep/2_1" | |
| } | |
| layer { | |
| name: "conv5_3/incep" | |
| type: "Concat" | |
| bottom: "conv5_3/incep/0" | |
| bottom: "conv5_3/incep/1_0" | |
| bottom: "conv5_3/incep/2_1" | |
| top: "conv5_3/incep" | |
| } | |
| layer { | |
| name: "conv5_3/out/conv" | |
| type: "Convolution" | |
| bottom: "conv5_3/incep" | |
| top: "conv5_3/out" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/input" | |
| type: "Power" | |
| bottom: "conv5_2" | |
| top: "conv5_3/input" | |
| power_param { | |
| power: 1 | |
| scale: 1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3" | |
| type: "Eltwise" | |
| bottom: "conv5_3/out" | |
| bottom: "conv5_3/input" | |
| top: "conv5_3" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3" | |
| top: "conv5_4/incep/pre" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_4/incep/pre" | |
| top: "conv5_4/incep/pre" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/relu" | |
| type: "ReLU" | |
| bottom: "conv5_4/incep/pre" | |
| top: "conv5_4/incep/pre" | |
| } | |
| layer { | |
| name: "conv5_4/incep/0/conv" | |
| type: "Convolution" | |
| bottom: "conv5_4/incep/pre" | |
| top: "conv5_4/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_4/incep/0" | |
| top: "conv5_4/incep/0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_4/incep/0" | |
| top: "conv5_4/incep/0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/0/relu" | |
| type: "ReLU" | |
| bottom: "conv5_4/incep/0" | |
| top: "conv5_4/incep/0" | |
| } | |
| layer { | |
| name: "conv5_4/incep/1_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv5_4/incep/pre" | |
| top: "conv5_4/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/1_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_4/incep/1_reduce" | |
| top: "conv5_4/incep/1_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/1_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_4/incep/1_reduce" | |
| top: "conv5_4/incep/1_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_4/incep/1_reduce" | |
| top: "conv5_4/incep/1_reduce" | |
| } | |
| layer { | |
| name: "conv5_4/incep/1_0/conv" | |
| type: "Convolution" | |
| bottom: "conv5_4/incep/1_reduce" | |
| top: "conv5_4/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/1_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_4/incep/1_0" | |
| top: "conv5_4/incep/1_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/1_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_4/incep/1_0" | |
| top: "conv5_4/incep/1_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/1_0/relu" | |
| type: "ReLU" | |
| bottom: "conv5_4/incep/1_0" | |
| top: "conv5_4/incep/1_0" | |
| } | |
| layer { | |
| name: "conv5_4/incep/2_reduce/conv" | |
| type: "Convolution" | |
| bottom: "conv5_4/incep/pre" | |
| top: "conv5_4/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/2_reduce/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_4/incep/2_reduce" | |
| top: "conv5_4/incep/2_reduce" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/2_reduce/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_4/incep/2_reduce" | |
| top: "conv5_4/incep/2_reduce" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/2_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_4/incep/2_reduce" | |
| top: "conv5_4/incep/2_reduce" | |
| } | |
| layer { | |
| name: "conv5_4/incep/2_0/conv" | |
| type: "Convolution" | |
| bottom: "conv5_4/incep/2_reduce" | |
| top: "conv5_4/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/2_0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_4/incep/2_0" | |
| top: "conv5_4/incep/2_0" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/2_0/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_4/incep/2_0" | |
| top: "conv5_4/incep/2_0" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/2_0/relu" | |
| type: "ReLU" | |
| bottom: "conv5_4/incep/2_0" | |
| top: "conv5_4/incep/2_0" | |
| } | |
| layer { | |
| name: "conv5_4/incep/2_1/conv" | |
| type: "Convolution" | |
| bottom: "conv5_4/incep/2_0" | |
| top: "conv5_4/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 1 | |
| pad_w: 1 | |
| kernel_h: 3 | |
| kernel_w: 3 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/2_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_4/incep/2_1" | |
| top: "conv5_4/incep/2_1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/2_1/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_4/incep/2_1" | |
| top: "conv5_4/incep/2_1" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/incep/2_1/relu" | |
| type: "ReLU" | |
| bottom: "conv5_4/incep/2_1" | |
| top: "conv5_4/incep/2_1" | |
| } | |
| layer { | |
| name: "conv5_4/incep" | |
| type: "Concat" | |
| bottom: "conv5_4/incep/0" | |
| bottom: "conv5_4/incep/1_0" | |
| bottom: "conv5_4/incep/2_1" | |
| top: "conv5_4/incep" | |
| } | |
| layer { | |
| name: "conv5_4/out/conv" | |
| type: "Convolution" | |
| bottom: "conv5_4/incep" | |
| top: "conv5_4/out" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0.1 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| bias_term: false | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| pad_h: 0 | |
| pad_w: 0 | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/out/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_4/out" | |
| top: "conv5_4/out" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/out/bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_4/out" | |
| top: "conv5_4/out" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/input" | |
| type: "Power" | |
| bottom: "conv5_3" | |
| top: "conv5_4/input" | |
| power_param { | |
| power: 1 | |
| scale: 1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4" | |
| type: "Eltwise" | |
| bottom: "conv5_4/out" | |
| bottom: "conv5_4/input" | |
| top: "conv5_4" | |
| eltwise_param { | |
| operation: SUM | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/last_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_4" | |
| top: "conv5_4" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/last_bn_scale" | |
| type: "Scale" | |
| bottom: "conv5_4" | |
| top: "conv5_4" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/last_relu" | |
| type: "ReLU" | |
| bottom: "conv5_4" | |
| top: "conv5_4" | |
| } | |
| ################################################## | |
| layer { | |
| name: "upsample_tmp" | |
| type: "Deconvolution" | |
| bottom: "conv5_4" | |
| top: "upsample" | |
| param { lr_mult: 0 decay_mult: 0} | |
| convolution_param { | |
| num_output: 384 kernel_size: 4 pad: 1 stride: 2 group: 384 | |
| weight_filler: {type: "bilinear" } | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "concat_tmp" | |
| bottom: "conv4_4" | |
| bottom: "upsample" | |
| top: "concat" | |
| type: "Concat" | |
| concat_param { axis: 1 } | |
| } | |
| layer { | |
| name: "convfrpn_tmp" | |
| type: "Convolution" | |
| bottom: "concat" | |
| top: "convf_rpn" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0 } | |
| convolution_param { | |
| num_output: 128 kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "xavier" std: 0.1 } | |
| bias_filler { type: "constant" value: 0.1 } | |
| } | |
| } | |
| layer { | |
| name: "reluf_rpn_tmp" | |
| type: "ReLU" | |
| bottom: "convf_rpn" | |
| top: "convf_rpn" | |
| } | |
| layer { | |
| name: "convf2_tmp" | |
| type: "Convolution" | |
| bottom: "concat" | |
| top: "convf_2" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0 } | |
| convolution_param { | |
| num_output: 384 kernel_size: 1 pad: 0 stride: 1 | |
| weight_filler { type: "xavier" std: 0.1 } | |
| bias_filler { type: "constant" value: 0.1 } | |
| } | |
| } | |
| layer { | |
| name: "reluf_2_tmp" | |
| type: "ReLU" | |
| bottom: "convf_2" | |
| top: "convf_2" | |
| } | |
| layer { | |
| name: "concat_convf_tmp" | |
| bottom: "convf_rpn" | |
| bottom: "convf_2" | |
| top: "convf" | |
| type: "Concat" | |
| concat_param { axis: 1 } | |
| } | |
| ########################################################################### | |
| layer { | |
| name: "upsample_conv4_tmp" | |
| type: "Deconvolution" | |
| bottom: "convf" | |
| top: "upsample_conv4" | |
| param { lr_mult: 0 decay_mult: 0} | |
| convolution_param { | |
| num_output: 512 kernel_size: 4 pad: 1 stride: 2 group: 512 | |
| weight_filler: {type: "bilinear" } | |
| bias_term: false | |
| } | |
| } | |
| ##### | |
| layer { | |
| name: "conv3_4_bn_tmp" | |
| type: "BatchNorm" | |
| bottom: "conv3_4" | |
| top: "conv3_4_2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 0.001 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_2_scale_tmp" | |
| type: "Scale" | |
| bottom: "conv3_4_2" | |
| top: "conv3_4_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_2_relu_tmp" | |
| type: "ReLU" | |
| bottom: "conv3_4_2" | |
| top: "conv3_4_2" | |
| } | |
| ###### | |
| ###### | |
| layer { | |
| name: "concat_8x_tmp" | |
| bottom: "upsample_conv4" | |
| bottom: "conv3_4_2" | |
| top: "concat_8x" | |
| type: "Concat" | |
| concat_param { axis: 1 } | |
| } | |
| layer { | |
| name: "conv_8s_1x1_tmp" | |
| type: "Convolution" | |
| bottom: "concat_8x" | |
| top: "conv_8s_1x1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv_8s_3x3_tmp" | |
| type: "Convolution" | |
| bottom: "conv_8s_1x1" | |
| top: "conv_8s_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| ##### | |
| layer { | |
| name: "conv_8s_3x3_bn_tmp" | |
| type: "BatchNorm" | |
| bottom: "conv_8s_3x3" | |
| top: "conv_8s_3x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 0.001 | |
| } | |
| } | |
| layer { | |
| name: "conv_8s_3x3_scale_tmp" | |
| type: "Scale" | |
| bottom: "conv_8s_3x3" | |
| top: "conv_8s_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv_8s_3x3_relu_tmp" | |
| type: "ReLU" | |
| bottom: "conv_8s_3x3" | |
| top: "conv_8s_3x3" | |
| } | |
| ##### | |
| ##### | |
| ########################################################################### | |
| layer { | |
| name: "upsample_conv3_tmp" | |
| type: "Deconvolution" | |
| bottom: "conv_8s_3x3" | |
| top: "upsample_conv3" | |
| param { lr_mult: 0 decay_mult: 0} | |
| convolution_param { | |
| num_output: 128 kernel_size: 4 pad: 1 stride: 2 group: 128 | |
| weight_filler: {type: "bilinear" } | |
| bias_term: false | |
| } | |
| } | |
| ##### | |
| layer { | |
| name: "conv2_3_bn_tmp" | |
| type: "BatchNorm" | |
| bottom: "conv2_3" | |
| top: "conv2_3_2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 0.001 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_2_scale_tmp" | |
| type: "Scale" | |
| bottom: "conv2_3_2" | |
| top: "conv2_3_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_2_relu_tmp" | |
| type: "ReLU" | |
| bottom: "conv2_3_2" | |
| top: "conv2_3_2" | |
| } | |
| ##### | |
| ##### | |
| layer { | |
| name: "concat_4x_tmp" | |
| bottom: "upsample_conv3" | |
| bottom: "conv2_3_2" | |
| top: "concat_4x" | |
| type: "Concat" | |
| concat_param { axis: 1 } | |
| } | |
| layer { | |
| name: "conv_4s_1x1" | |
| type: "Convolution" | |
| bottom: "concat_4x" | |
| top: "conv_4s_1x1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv_4s_3x3" | |
| type: "Convolution" | |
| bottom: "conv_4s_1x1" | |
| top: "conv_4s_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| ##### | |
| layer { | |
| name: "conv_4s_3x3_bn" | |
| type: "BatchNorm" | |
| bottom: "conv_4s_3x3" | |
| top: "conv_4s_3x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 0.001 | |
| } | |
| } | |
| layer { | |
| name: "conv_4s_3x3_scale" | |
| type: "Scale" | |
| bottom: "conv_4s_3x3" | |
| top: "conv_4s_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv_4s_3x3_relu" | |
| type: "ReLU" | |
| bottom: "conv_4s_3x3" | |
| top: "conv_4s_3x3" | |
| } | |
| ##### | |
| ##### | |
| ######################################## | |
| layer { | |
| name: "conv_final" | |
| type: "Convolution" | |
| bottom: "conv_4s_3x3" | |
| top: "conv_final" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv_final_bn" | |
| type: "BatchNorm" | |
| bottom: "conv_final" | |
| top: "conv_final" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 0.001 | |
| } | |
| } | |
| layer { | |
| name: "conv_final_scale" | |
| type: "Scale" | |
| bottom: "conv_final" | |
| top: "conv_final" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv_final_relu" | |
| type: "ReLU" | |
| bottom: "conv_final" | |
| top: "conv_final" | |
| } | |
| ############################################################################## | |
| layer { | |
| name: "concat_final_conv" | |
| bottom: "conv_final" | |
| bottom: "conv2_3" | |
| top: "concat_final_conv" | |
| type: "Concat" | |
| concat_param { axis: 1 } | |
| } | |
| layer { | |
| name: "conv_finalrecognition" | |
| type: "Convolution" | |
| bottom: "concat_final_conv" | |
| top: "conv_final_recognition" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: 'iou_maps_angles' | |
| type: 'Python' | |
| bottom: 'gt_bbox' | |
| top: 'rois' | |
| top: 'sample_gt_cont' | |
| top: 'sample_gt_label_input' | |
| top: "sample_gt_label_output" | |
| python_param { | |
| module: 'tool_layers' | |
| layer: 'gen_gts_layer' | |
| param_str: "'sel_num': 20" | |
| } | |
| } | |
| layer { | |
| name: 'iou_maps_angles' | |
| type: 'Python' | |
| bottom: 'rois' ### you can change this to ground truth boxes for sampling points with gt | |
| top: 'sample_points' | |
| top: 'sample_points_id' | |
| python_param { | |
| module: 'tool_layers' | |
| layer: 'sample_points_layer' | |
| param_str: "'hor_num': 64 \n'ver_num': 8" | |
| } | |
| } | |
| layer { | |
| name: "reshape_pts" | |
| type: "Reshape" | |
| bottom: "sample_points" | |
| top: "reshape_pts" | |
| reshape_param { | |
| shape { dim: -1 dim: 2 } | |
| } | |
| } | |
| layer { | |
| name: "reshape_pts_id" | |
| type: "Reshape" | |
| bottom: "sample_points_id" | |
| top: "reshape_pts_id" | |
| reshape_param { | |
| shape { dim: -1 dim: 2 } | |
| } | |
| } | |
| layer { | |
| name: "bilinear_fea1" | |
| type: "PointBilinear" | |
| bottom: "conv_final_recognition" | |
| bottom: "reshape_pts" | |
| bottom: "reshape_pts_id" | |
| top: "bilinear_fea1" | |
| point_bilinear_param { | |
| ratio: 0.25 | |
| } | |
| propagate_down: true ### you can control this to decide whether to back propagate loss to detection | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "reshape_fea" | |
| type: "Reshape" | |
| bottom: "bilinear_fea1" | |
| top: "reshape_fea" | |
| reshape_param { | |
| shape { dim: -1 dim: 8 dim: 64 dim: 128 } | |
| } | |
| } | |
| layer { | |
| name: "reshape_fea_transposet" | |
| bottom: "reshape_fea" | |
| top: "reshape_fea_transposet" | |
| type: "Transpose" | |
| transpose_param { dim: 0 dim: 3 dim: 1 dim: 2} | |
| } | |
| ############################################## | |
| layer { | |
| name: "pool2_conv/15" | |
| type: "Convolution" | |
| bottom: "reshape_fea_transposet" | |
| top: "pool2_conv/15" | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/16_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/15" | |
| top: "pool2_conv/15_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/16_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/15_batchnorm" | |
| top: "pool2_bn/16" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/17" | |
| type: "ReLU" | |
| bottom: "pool2_bn/16" | |
| top: "pool2_bn/16" | |
| } | |
| layer { | |
| name: "pool2_conv/18" | |
| type: "Convolution" | |
| bottom: "reshape_fea_transposet" | |
| top: "pool2_conv/18" | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/19_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/18" | |
| top: "pool2_conv/18_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/19_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/18_batchnorm" | |
| top: "pool2_bn/19" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/20" | |
| type: "ReLU" | |
| bottom: "pool2_bn/19" | |
| top: "pool2_bn/19" | |
| } | |
| layer { | |
| name: "pool2_conv/21" | |
| type: "Convolution" | |
| bottom: "pool2_bn/19" | |
| top: "pool2_conv/21" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/22_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/21" | |
| top: "pool2_conv/21_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/22_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/21_batchnorm" | |
| top: "pool2_bn/22" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/23" | |
| type: "ReLU" | |
| bottom: "pool2_bn/22" | |
| top: "pool2_bn/22" | |
| } | |
| layer { | |
| name: "pool2_conv/24" | |
| type: "Convolution" | |
| bottom: "reshape_fea_transposet" | |
| top: "pool2_conv/24" | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/25_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/24" | |
| top: "pool2_conv/24_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/25_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/24_batchnorm" | |
| top: "pool2_bn/25" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/26" | |
| type: "ReLU" | |
| bottom: "pool2_bn/25" | |
| top: "pool2_bn/25" | |
| } | |
| layer { | |
| name: "pool2_conv/27" | |
| type: "Convolution" | |
| bottom: "pool2_bn/25" | |
| top: "pool2_conv/27" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/28_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/27" | |
| top: "pool2_conv/27_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/28_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/27_batchnorm" | |
| top: "pool2_bn/28" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/29" | |
| type: "ReLU" | |
| bottom: "pool2_bn/28" | |
| top: "pool2_bn/28" | |
| } | |
| layer { | |
| name: "pool2_conv/30" | |
| type: "Convolution" | |
| bottom: "pool2_bn/28" | |
| top: "pool2_conv/30" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/31_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/30" | |
| top: "pool2_conv/30_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/31_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/30_batchnorm" | |
| top: "pool2_bn/31" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/32" | |
| type: "ReLU" | |
| bottom: "pool2_bn/31" | |
| top: "pool2_bn/31" | |
| } | |
| layer { | |
| name: "pool2_pool/33" | |
| type: "Pooling" | |
| bottom: "reshape_fea_transposet" | |
| top: "pool2_pool/33" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/34" | |
| type: "Convolution" | |
| bottom: "pool2_pool/33" | |
| top: "pool2_conv/34" | |
| convolution_param { | |
| num_output: 16 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/35_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/34" | |
| top: "pool2_conv/34_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/35_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/34_batchnorm" | |
| top: "pool2_bn/35" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/36" | |
| type: "ReLU" | |
| bottom: "pool2_bn/35" | |
| top: "pool2_bn/35" | |
| } | |
| layer { | |
| name: "pool2_Concat/37" | |
| type: "Concat" | |
| bottom: "pool2_bn/16" | |
| bottom: "pool2_bn/22" | |
| bottom: "pool2_bn/31" | |
| bottom: "pool2_bn/35" | |
| top: "pool2_Concat/37" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/38" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/37" | |
| top: "pool2_conv/38" | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/39_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/38" | |
| top: "pool2_conv/38_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/39_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/38_batchnorm" | |
| top: "pool2_bn/39" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/40" | |
| type: "ReLU" | |
| bottom: "pool2_bn/39" | |
| top: "pool2_bn/39" | |
| } | |
| layer { | |
| name: "pool2_conv/41" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/37" | |
| top: "pool2_conv/41" | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/42_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/41" | |
| top: "pool2_conv/41_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/42_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/41_batchnorm" | |
| top: "pool2_bn/42" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/43" | |
| type: "ReLU" | |
| bottom: "pool2_bn/42" | |
| top: "pool2_bn/42" | |
| } | |
| layer { | |
| name: "pool2_conv/44" | |
| type: "Convolution" | |
| bottom: "pool2_bn/42" | |
| top: "pool2_conv/44" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/45_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/44" | |
| top: "pool2_conv/44_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/45_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/44_batchnorm" | |
| top: "pool2_bn/45" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/46" | |
| type: "ReLU" | |
| bottom: "pool2_bn/45" | |
| top: "pool2_bn/45" | |
| } | |
| layer { | |
| name: "pool2_conv/47" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/37" | |
| top: "pool2_conv/47" | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/48_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/47" | |
| top: "pool2_conv/47_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/48_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/47_batchnorm" | |
| top: "pool2_bn/48" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/49" | |
| type: "ReLU" | |
| bottom: "pool2_bn/48" | |
| top: "pool2_bn/48" | |
| } | |
| layer { | |
| name: "pool2_conv/50" | |
| type: "Convolution" | |
| bottom: "pool2_bn/48" | |
| top: "pool2_conv/50" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/51_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/50" | |
| top: "pool2_conv/50_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/51_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/50_batchnorm" | |
| top: "pool2_bn/51" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/52" | |
| type: "ReLU" | |
| bottom: "pool2_bn/51" | |
| top: "pool2_bn/51" | |
| } | |
| layer { | |
| name: "pool2_conv/53" | |
| type: "Convolution" | |
| bottom: "pool2_bn/51" | |
| top: "pool2_conv/53" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/54_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/53" | |
| top: "pool2_conv/53_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/54_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/53_batchnorm" | |
| top: "pool2_bn/54" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/55" | |
| type: "ReLU" | |
| bottom: "pool2_bn/54" | |
| top: "pool2_bn/54" | |
| } | |
| layer { | |
| name: "pool2_pool/56" | |
| type: "Pooling" | |
| bottom: "pool2_Concat/37" | |
| top: "pool2_pool/56" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/57" | |
| type: "Convolution" | |
| bottom: "pool2_pool/56" | |
| top: "pool2_conv/57" | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/58_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/57" | |
| top: "pool2_conv/57_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/58_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/57_batchnorm" | |
| top: "pool2_bn/58" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/59" | |
| type: "ReLU" | |
| bottom: "pool2_bn/58" | |
| top: "pool2_bn/58" | |
| } | |
| layer { | |
| name: "pool2_Concat/60" | |
| type: "Concat" | |
| bottom: "pool2_bn/39" | |
| bottom: "pool2_bn/45" | |
| bottom: "pool2_bn/54" | |
| bottom: "pool2_bn/58" | |
| top: "pool2_Concat/60" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/61" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/60" | |
| top: "pool2_conv/61" | |
| convolution_param { | |
| num_output: 63 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/62_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/61" | |
| top: "pool2_conv/61_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/62_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/61_batchnorm" | |
| top: "pool2_bn/62" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/63" | |
| type: "ReLU" | |
| bottom: "pool2_bn/62" | |
| top: "pool2_bn/62" | |
| } | |
| layer { | |
| name: "pool2_conv/64" | |
| type: "Convolution" | |
| bottom: "pool2_bn/62" | |
| top: "pool2_conv/64" | |
| convolution_param { | |
| num_output: 80 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 2 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/65_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/64" | |
| top: "pool2_conv/64_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/65_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/64_batchnorm" | |
| top: "pool2_bn/65" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/66" | |
| type: "ReLU" | |
| bottom: "pool2_bn/65" | |
| top: "pool2_bn/65" | |
| } | |
| layer { | |
| name: "pool2_conv/67" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/60" | |
| top: "pool2_conv/67" | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/68_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/67" | |
| top: "pool2_conv/67_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/68_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/67_batchnorm" | |
| top: "pool2_bn/68" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/69" | |
| type: "ReLU" | |
| bottom: "pool2_bn/68" | |
| top: "pool2_bn/68" | |
| } | |
| layer { | |
| name: "pool2_conv/70" | |
| type: "Convolution" | |
| bottom: "pool2_bn/68" | |
| top: "pool2_conv/70" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/71_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/70" | |
| top: "pool2_conv/70_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/71_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/70_batchnorm" | |
| top: "pool2_bn/71" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/72" | |
| type: "ReLU" | |
| bottom: "pool2_bn/71" | |
| top: "pool2_bn/71" | |
| } | |
| layer { | |
| name: "pool2_conv/73" | |
| type: "Convolution" | |
| bottom: "pool2_bn/71" | |
| top: "pool2_conv/73" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 2 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/74_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/73" | |
| top: "pool2_conv/73_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/74_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/73_batchnorm" | |
| top: "pool2_bn/74" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/75" | |
| type: "ReLU" | |
| bottom: "pool2_bn/74" | |
| top: "pool2_bn/74" | |
| } | |
| layer { | |
| name: "pool2_pool/76" | |
| type: "Pooling" | |
| bottom: "pool2_Concat/60" | |
| top: "pool2_pool/76" | |
| pooling_param { | |
| pool: MAX | |
| pad: 0 | |
| kernel_h: 2 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_Concat/77" | |
| type: "Concat" | |
| bottom: "pool2_bn/65" | |
| bottom: "pool2_bn/74" | |
| bottom: "pool2_pool/76" | |
| top: "pool2_Concat/77" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/78" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/77" | |
| top: "pool2_conv/78" | |
| convolution_param { | |
| num_output: 112 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/79_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/78" | |
| top: "pool2_conv/78_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/79_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/78_batchnorm" | |
| top: "pool2_bn/79" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/80" | |
| type: "ReLU" | |
| bottom: "pool2_bn/79" | |
| top: "pool2_bn/79" | |
| } | |
| layer { | |
| name: "pool2_conv/81" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/77" | |
| top: "pool2_conv/81" | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/82_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/81" | |
| top: "pool2_conv/81_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/82_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/81_batchnorm" | |
| top: "pool2_bn/82" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/83" | |
| type: "ReLU" | |
| bottom: "pool2_bn/82" | |
| top: "pool2_bn/82" | |
| } | |
| layer { | |
| name: "pool2_conv/84" | |
| type: "Convolution" | |
| bottom: "pool2_bn/82" | |
| top: "pool2_conv/84" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/85_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/84" | |
| top: "pool2_conv/84_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/85_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/84_batchnorm" | |
| top: "pool2_bn/85" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/86" | |
| type: "ReLU" | |
| bottom: "pool2_bn/85" | |
| top: "pool2_bn/85" | |
| } | |
| layer { | |
| name: "pool2_conv/87" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/77" | |
| top: "pool2_conv/87" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/88_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/87" | |
| top: "pool2_conv/87_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/88_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/87_batchnorm" | |
| top: "pool2_bn/88" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/89" | |
| type: "ReLU" | |
| bottom: "pool2_bn/88" | |
| top: "pool2_bn/88" | |
| } | |
| layer { | |
| name: "pool2_conv/90" | |
| type: "Convolution" | |
| bottom: "pool2_bn/88" | |
| top: "pool2_conv/90" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/91_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/90" | |
| top: "pool2_conv/90_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/91_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/90_batchnorm" | |
| top: "pool2_bn/91" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/92" | |
| type: "ReLU" | |
| bottom: "pool2_bn/91" | |
| top: "pool2_bn/91" | |
| } | |
| layer { | |
| name: "pool2_conv/93" | |
| type: "Convolution" | |
| bottom: "pool2_bn/91" | |
| top: "pool2_conv/93" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/94_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/93" | |
| top: "pool2_conv/93_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/94_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/93_batchnorm" | |
| top: "pool2_bn/94" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/95" | |
| type: "ReLU" | |
| bottom: "pool2_bn/94" | |
| top: "pool2_bn/94" | |
| } | |
| layer { | |
| name: "pool2_pool/96" | |
| type: "Pooling" | |
| bottom: "pool2_Concat/77" | |
| top: "pool2_pool/96" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/97" | |
| type: "Convolution" | |
| bottom: "pool2_pool/96" | |
| top: "pool2_conv/97" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/98_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/97" | |
| top: "pool2_conv/97_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/98_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/97_batchnorm" | |
| top: "pool2_bn/98" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/99" | |
| type: "ReLU" | |
| bottom: "pool2_bn/98" | |
| top: "pool2_bn/98" | |
| } | |
| layer { | |
| name: "pool2_Concat/100" | |
| type: "Concat" | |
| bottom: "pool2_bn/79" | |
| bottom: "pool2_bn/85" | |
| bottom: "pool2_bn/94" | |
| bottom: "pool2_bn/98" | |
| top: "pool2_Concat/100" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/109" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/100" | |
| top: "pool2_conv/109" | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/110_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/109" | |
| top: "pool2_conv/109_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/110_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/109_batchnorm" | |
| top: "pool2_bn/110" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/111" | |
| type: "ReLU" | |
| bottom: "pool2_bn/110" | |
| top: "pool2_bn/110" | |
| } | |
| layer { | |
| name: "pool2_conv/112" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/100" | |
| top: "pool2_conv/112" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/113_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/112" | |
| top: "pool2_conv/112_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/113_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/112_batchnorm" | |
| top: "pool2_bn/113" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/114" | |
| type: "ReLU" | |
| bottom: "pool2_bn/113" | |
| top: "pool2_bn/113" | |
| } | |
| layer { | |
| name: "pool2_conv/115" | |
| type: "Convolution" | |
| bottom: "pool2_bn/113" | |
| top: "pool2_conv/115" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/116_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/115" | |
| top: "pool2_conv/115_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/116_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/115_batchnorm" | |
| top: "pool2_bn/116" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/117" | |
| type: "ReLU" | |
| bottom: "pool2_bn/116" | |
| top: "pool2_bn/116" | |
| } | |
| layer { | |
| name: "pool2_conv/118" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/100" | |
| top: "pool2_conv/118" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/119_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/118" | |
| top: "pool2_conv/118_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/119_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/118_batchnorm" | |
| top: "pool2_bn/119" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/120" | |
| type: "ReLU" | |
| bottom: "pool2_bn/119" | |
| top: "pool2_bn/119" | |
| } | |
| layer { | |
| name: "pool2_conv/121" | |
| type: "Convolution" | |
| bottom: "pool2_bn/119" | |
| top: "pool2_conv/121" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/122_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/121" | |
| top: "pool2_conv/121_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/122_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/121_batchnorm" | |
| top: "pool2_bn/122" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/123" | |
| type: "ReLU" | |
| bottom: "pool2_bn/122" | |
| top: "pool2_bn/122" | |
| } | |
| layer { | |
| name: "pool2_conv/124" | |
| type: "Convolution" | |
| bottom: "pool2_bn/122" | |
| top: "pool2_conv/124" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/125_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/124" | |
| top: "pool2_conv/124_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/125_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/124_batchnorm" | |
| top: "pool2_bn/125" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/126" | |
| type: "ReLU" | |
| bottom: "pool2_bn/125" | |
| top: "pool2_bn/125" | |
| } | |
| layer { | |
| name: "pool2_pool/127" | |
| type: "Pooling" | |
| bottom: "pool2_Concat/100" | |
| top: "pool2_pool/127" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/128" | |
| type: "Convolution" | |
| bottom: "pool2_pool/127" | |
| top: "pool2_conv/128" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/129_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/128" | |
| top: "pool2_conv/128_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/129_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/128_batchnorm" | |
| top: "pool2_bn/129" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/130" | |
| type: "ReLU" | |
| bottom: "pool2_bn/129" | |
| top: "pool2_bn/129" | |
| } | |
| layer { | |
| name: "pool2_Concat/131" | |
| type: "Concat" | |
| bottom: "pool2_bn/110" | |
| bottom: "pool2_bn/116" | |
| bottom: "pool2_bn/125" | |
| bottom: "pool2_bn/129" | |
| top: "pool2_Concat/131" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/132" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/131" | |
| top: "pool2_conv/132" | |
| convolution_param { | |
| num_output: 80 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/133_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/132" | |
| top: "pool2_conv/132_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/133_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/132_batchnorm" | |
| top: "pool2_bn/133" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/134" | |
| type: "ReLU" | |
| bottom: "pool2_bn/133" | |
| top: "pool2_bn/133" | |
| } | |
| layer { | |
| name: "pool2_conv/135" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/131" | |
| top: "pool2_conv/135" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/136_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/135" | |
| top: "pool2_conv/135_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/136_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/135_batchnorm" | |
| top: "pool2_bn/136" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/137" | |
| type: "ReLU" | |
| bottom: "pool2_bn/136" | |
| top: "pool2_bn/136" | |
| } | |
| layer { | |
| name: "pool2_conv/138" | |
| type: "Convolution" | |
| bottom: "pool2_bn/136" | |
| top: "pool2_conv/138" | |
| convolution_param { | |
| num_output: 80 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/139_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/138" | |
| top: "pool2_conv/138_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/139_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/138_batchnorm" | |
| top: "pool2_bn/139" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/140" | |
| type: "ReLU" | |
| bottom: "pool2_bn/139" | |
| top: "pool2_bn/139" | |
| } | |
| layer { | |
| name: "pool2_conv/141" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/131" | |
| top: "pool2_conv/141" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/142_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/141" | |
| top: "pool2_conv/141_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/142_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/141_batchnorm" | |
| top: "pool2_bn/142" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/143" | |
| type: "ReLU" | |
| bottom: "pool2_bn/142" | |
| top: "pool2_bn/142" | |
| } | |
| layer { | |
| name: "pool2_conv/144" | |
| type: "Convolution" | |
| bottom: "pool2_bn/142" | |
| top: "pool2_conv/144" | |
| convolution_param { | |
| num_output: 80 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/145_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/144" | |
| top: "pool2_conv/144_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/145_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/144_batchnorm" | |
| top: "pool2_bn/145" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/146" | |
| type: "ReLU" | |
| bottom: "pool2_bn/145" | |
| top: "pool2_bn/145" | |
| } | |
| layer { | |
| name: "pool2_conv/147" | |
| type: "Convolution" | |
| bottom: "pool2_bn/145" | |
| top: "pool2_conv/147" | |
| convolution_param { | |
| num_output: 80 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/148_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/147" | |
| top: "pool2_conv/147_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/148_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/147_batchnorm" | |
| top: "pool2_bn/148" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/149" | |
| type: "ReLU" | |
| bottom: "pool2_bn/148" | |
| top: "pool2_bn/148" | |
| } | |
| layer { | |
| name: "pool2_pool/150" | |
| type: "Pooling" | |
| bottom: "pool2_Concat/131" | |
| top: "pool2_pool/150" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/151" | |
| type: "Convolution" | |
| bottom: "pool2_pool/150" | |
| top: "pool2_conv/151" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/152_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/151" | |
| top: "pool2_conv/151_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/152_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/151_batchnorm" | |
| top: "pool2_bn/152" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/153" | |
| type: "ReLU" | |
| bottom: "pool2_bn/152" | |
| top: "pool2_bn/152" | |
| } | |
| layer { | |
| name: "pool2_Concat/154" | |
| type: "Concat" | |
| bottom: "pool2_bn/133" | |
| bottom: "pool2_bn/139" | |
| bottom: "pool2_bn/148" | |
| bottom: "pool2_bn/152" | |
| top: "pool2_Concat/154" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/155" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/154" | |
| top: "pool2_conv/155" | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/156_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/155" | |
| top: "pool2_conv/155_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/156_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/155_batchnorm" | |
| top: "pool2_bn/156" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/157" | |
| type: "ReLU" | |
| bottom: "pool2_bn/156" | |
| top: "pool2_bn/156" | |
| } | |
| layer { | |
| name: "pool2_conv/158" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/154" | |
| top: "pool2_conv/158" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/159_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/158" | |
| top: "pool2_conv/158_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/159_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/158_batchnorm" | |
| top: "pool2_bn/159" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/160" | |
| type: "ReLU" | |
| bottom: "pool2_bn/159" | |
| top: "pool2_bn/159" | |
| } | |
| layer { | |
| name: "pool2_conv/161" | |
| type: "Convolution" | |
| bottom: "pool2_bn/159" | |
| top: "pool2_conv/161" | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/162_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/161" | |
| top: "pool2_conv/161_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/162_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/161_batchnorm" | |
| top: "pool2_bn/162" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/163" | |
| type: "ReLU" | |
| bottom: "pool2_bn/162" | |
| top: "pool2_bn/162" | |
| } | |
| layer { | |
| name: "pool2_conv/164" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/154" | |
| top: "pool2_conv/164" | |
| convolution_param { | |
| num_output: 80 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/165_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/164" | |
| top: "pool2_conv/164_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/165_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/164_batchnorm" | |
| top: "pool2_bn/165" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/166" | |
| type: "ReLU" | |
| bottom: "pool2_bn/165" | |
| top: "pool2_bn/165" | |
| } | |
| layer { | |
| name: "pool2_conv/167" | |
| type: "Convolution" | |
| bottom: "pool2_bn/165" | |
| top: "pool2_conv/167" | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/168_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/167" | |
| top: "pool2_conv/167_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/168_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/167_batchnorm" | |
| top: "pool2_bn/168" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/169" | |
| type: "ReLU" | |
| bottom: "pool2_bn/168" | |
| top: "pool2_bn/168" | |
| } | |
| layer { | |
| name: "pool2_conv/170" | |
| type: "Convolution" | |
| bottom: "pool2_bn/168" | |
| top: "pool2_conv/170" | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/171_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/170" | |
| top: "pool2_conv/170_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/171_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/170_batchnorm" | |
| top: "pool2_bn/171" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/172" | |
| type: "ReLU" | |
| bottom: "pool2_bn/171" | |
| top: "pool2_bn/171" | |
| } | |
| layer { | |
| name: "pool2_pool/173" | |
| type: "Pooling" | |
| bottom: "pool2_Concat/154" | |
| top: "pool2_pool/173" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/174" | |
| type: "Convolution" | |
| bottom: "pool2_pool/173" | |
| top: "pool2_conv/174" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/175_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/174" | |
| top: "pool2_conv/174_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/175_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/174_batchnorm" | |
| top: "pool2_bn/175" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/176" | |
| type: "ReLU" | |
| bottom: "pool2_bn/175" | |
| top: "pool2_bn/175" | |
| } | |
| layer { | |
| name: "pool2_Concat/177" | |
| type: "Concat" | |
| bottom: "pool2_bn/156" | |
| bottom: "pool2_bn/162" | |
| bottom: "pool2_bn/171" | |
| bottom: "pool2_bn/175" | |
| top: "pool2_Concat/177" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/186" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/177" | |
| top: "pool2_conv/186" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/187_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/186" | |
| top: "pool2_conv/186_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/187_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/186_batchnorm" | |
| top: "pool2_bn/187" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/188" | |
| type: "ReLU" | |
| bottom: "pool2_bn/187" | |
| top: "pool2_bn/187" | |
| } | |
| layer { | |
| name: "pool2_conv/189" | |
| type: "Convolution" | |
| bottom: "pool2_bn/187" | |
| top: "pool2_conv/189" | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 2 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/190_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/189" | |
| top: "pool2_conv/189_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/190_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/189_batchnorm" | |
| top: "pool2_bn/190" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/191" | |
| type: "ReLU" | |
| bottom: "pool2_bn/190" | |
| top: "pool2_bn/190" | |
| } | |
| layer { | |
| name: "pool2_conv/192" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/177" | |
| top: "pool2_conv/192" | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/193_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/192" | |
| top: "pool2_conv/192_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/193_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/192_batchnorm" | |
| top: "pool2_bn/193" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/194" | |
| type: "ReLU" | |
| bottom: "pool2_bn/193" | |
| top: "pool2_bn/193" | |
| } | |
| layer { | |
| name: "pool2_conv/195" | |
| type: "Convolution" | |
| bottom: "pool2_bn/193" | |
| top: "pool2_conv/195" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/196_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/195" | |
| top: "pool2_conv/195_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/196_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/195_batchnorm" | |
| top: "pool2_bn/196" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/197" | |
| type: "ReLU" | |
| bottom: "pool2_bn/196" | |
| top: "pool2_bn/196" | |
| } | |
| layer { | |
| name: "pool2_conv/198" | |
| type: "Convolution" | |
| bottom: "pool2_bn/196" | |
| top: "pool2_conv/198" | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 2 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/199_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/198" | |
| top: "pool2_conv/198_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/199_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/198_batchnorm" | |
| top: "pool2_bn/199" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/200" | |
| type: "ReLU" | |
| bottom: "pool2_bn/199" | |
| top: "pool2_bn/199" | |
| } | |
| layer { | |
| name: "pool2_pool/201" | |
| type: "Pooling" | |
| bottom: "pool2_Concat/177" | |
| top: "pool2_pool/201" | |
| pooling_param { | |
| pool: MAX | |
| pad: 0 | |
| kernel_h: 2 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_Concat/202" | |
| type: "Concat" | |
| bottom: "pool2_bn/190" | |
| bottom: "pool2_bn/199" | |
| bottom: "pool2_pool/201" | |
| top: "pool2_Concat/202" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/203" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/202" | |
| top: "pool2_conv/203" | |
| convolution_param { | |
| num_output: 176 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/204_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/203" | |
| top: "pool2_conv/203_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/204_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/203_batchnorm" | |
| top: "pool2_bn/204" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/205" | |
| type: "ReLU" | |
| bottom: "pool2_bn/204" | |
| top: "pool2_bn/204" | |
| } | |
| layer { | |
| name: "pool2_conv/206" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/202" | |
| top: "pool2_conv/206" | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/207_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/206" | |
| top: "pool2_conv/206_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/207_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/206_batchnorm" | |
| top: "pool2_bn/207" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/208" | |
| type: "ReLU" | |
| bottom: "pool2_bn/207" | |
| top: "pool2_bn/207" | |
| } | |
| layer { | |
| name: "pool2_conv/209" | |
| type: "Convolution" | |
| bottom: "pool2_bn/207" | |
| top: "pool2_conv/209" | |
| convolution_param { | |
| num_output: 160 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/210_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/209" | |
| top: "pool2_conv/209_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/210_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/209_batchnorm" | |
| top: "pool2_bn/210" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/211" | |
| type: "ReLU" | |
| bottom: "pool2_bn/210" | |
| top: "pool2_bn/210" | |
| } | |
| layer { | |
| name: "pool2_conv/212" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/202" | |
| top: "pool2_conv/212" | |
| convolution_param { | |
| num_output: 80 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/213_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/212" | |
| top: "pool2_conv/212_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/213_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/212_batchnorm" | |
| top: "pool2_bn/213" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/214" | |
| type: "ReLU" | |
| bottom: "pool2_bn/213" | |
| top: "pool2_bn/213" | |
| } | |
| layer { | |
| name: "pool2_conv/215" | |
| type: "Convolution" | |
| bottom: "pool2_bn/213" | |
| top: "pool2_conv/215" | |
| convolution_param { | |
| num_output: 112 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/216_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/215" | |
| top: "pool2_conv/215_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/216_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/215_batchnorm" | |
| top: "pool2_bn/216" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/217" | |
| type: "ReLU" | |
| bottom: "pool2_bn/216" | |
| top: "pool2_bn/216" | |
| } | |
| layer { | |
| name: "pool2_conv/218" | |
| type: "Convolution" | |
| bottom: "pool2_bn/216" | |
| top: "pool2_conv/218" | |
| convolution_param { | |
| num_output: 112 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/219_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/218" | |
| top: "pool2_conv/218_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/219_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/218_batchnorm" | |
| top: "pool2_bn/219" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/220" | |
| type: "ReLU" | |
| bottom: "pool2_bn/219" | |
| top: "pool2_bn/219" | |
| } | |
| layer { | |
| name: "pool2_pool/221" | |
| type: "Pooling" | |
| bottom: "pool2_Concat/202" | |
| top: "pool2_pool/221" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/222" | |
| type: "Convolution" | |
| bottom: "pool2_pool/221" | |
| top: "pool2_conv/222" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/223_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/222" | |
| top: "pool2_conv/222_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/223_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/222_batchnorm" | |
| top: "pool2_bn/223" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/224" | |
| type: "ReLU" | |
| bottom: "pool2_bn/223" | |
| top: "pool2_bn/223" | |
| } | |
| layer { | |
| name: "pool2_Concat/225" | |
| type: "Concat" | |
| bottom: "pool2_bn/204" | |
| bottom: "pool2_bn/210" | |
| bottom: "pool2_bn/219" | |
| bottom: "pool2_bn/223" | |
| top: "pool2_Concat/225" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/226" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/225" | |
| top: "pool2_conv/226" | |
| convolution_param { | |
| num_output: 176 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/227_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/226" | |
| top: "pool2_conv/226_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/227_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/226_batchnorm" | |
| top: "pool2_bn/227" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/228" | |
| type: "ReLU" | |
| bottom: "pool2_bn/227" | |
| top: "pool2_bn/227" | |
| } | |
| layer { | |
| name: "pool2_conv/229" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/225" | |
| top: "pool2_conv/229" | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/230_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/229" | |
| top: "pool2_conv/229_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/230_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/229_batchnorm" | |
| top: "pool2_bn/230" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/231" | |
| type: "ReLU" | |
| bottom: "pool2_bn/230" | |
| top: "pool2_bn/230" | |
| } | |
| layer { | |
| name: "pool2_conv/232" | |
| type: "Convolution" | |
| bottom: "pool2_bn/230" | |
| top: "pool2_conv/232" | |
| convolution_param { | |
| num_output: 160 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/233_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/232" | |
| top: "pool2_conv/232_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/233_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/232_batchnorm" | |
| top: "pool2_bn/233" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/234" | |
| type: "ReLU" | |
| bottom: "pool2_bn/233" | |
| top: "pool2_bn/233" | |
| } | |
| layer { | |
| name: "pool2_conv/235" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/225" | |
| top: "pool2_conv/235" | |
| convolution_param { | |
| num_output: 96 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/236_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/235" | |
| top: "pool2_conv/235_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/236_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/235_batchnorm" | |
| top: "pool2_bn/236" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/237" | |
| type: "ReLU" | |
| bottom: "pool2_bn/236" | |
| top: "pool2_bn/236" | |
| } | |
| layer { | |
| name: "pool2_conv/238" | |
| type: "Convolution" | |
| bottom: "pool2_bn/236" | |
| top: "pool2_conv/238" | |
| convolution_param { | |
| num_output: 112 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/239_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/238" | |
| top: "pool2_conv/238_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/239_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/238_batchnorm" | |
| top: "pool2_bn/239" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/240" | |
| type: "ReLU" | |
| bottom: "pool2_bn/239" | |
| top: "pool2_bn/239" | |
| } | |
| layer { | |
| name: "pool2_conv/241" | |
| type: "Convolution" | |
| bottom: "pool2_bn/239" | |
| top: "pool2_conv/241" | |
| convolution_param { | |
| num_output: 112 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| stride_h: 1 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/242_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/241" | |
| top: "pool2_conv/241_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/242_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/241_batchnorm" | |
| top: "pool2_bn/242" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/243" | |
| type: "ReLU" | |
| bottom: "pool2_bn/242" | |
| top: "pool2_bn/242" | |
| } | |
| layer { | |
| name: "pool2_pool/244" | |
| type: "Pooling" | |
| bottom: "pool2_Concat/225" | |
| top: "pool2_pool/244" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/245" | |
| type: "Convolution" | |
| bottom: "pool2_pool/244" | |
| top: "pool2_conv/245" | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/246_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/245" | |
| top: "pool2_conv/245_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/246_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/245_batchnorm" | |
| top: "pool2_bn/246" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/247" | |
| type: "ReLU" | |
| bottom: "pool2_bn/246" | |
| top: "pool2_bn/246" | |
| } | |
| layer { | |
| name: "pool2_Concat/248" | |
| type: "Concat" | |
| bottom: "pool2_bn/227" | |
| bottom: "pool2_bn/233" | |
| bottom: "pool2_bn/242" | |
| bottom: "pool2_bn/246" | |
| top: "pool2_Concat/248" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_conv/249" | |
| type: "Convolution" | |
| bottom: "pool2_Concat/248" | |
| top: "pool2_conv/249" | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| kernel_h: 2 | |
| kernel_w: 1 | |
| stride_h: 2 | |
| stride_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/250_batchnorm_batchnorm" | |
| type: "BatchNorm" | |
| param { lr_mult: 0 decay_mult: 0 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| bottom: "pool2_conv/249" | |
| top: "pool2_conv/249_batchnorm" | |
| batch_norm_param { | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_bn/250_batchnorm_scale" | |
| type: "Scale" | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| param { lr_mult: 0.1 decay_mult: 0 } | |
| bottom: "pool2_conv/249_batchnorm" | |
| top: "pool2_bn/250" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_ReLU/251" | |
| type: "ReLU" | |
| bottom: "pool2_bn/250" | |
| top: "pool2_bn/250" | |
| } | |
| ########################################################## | |
| layer { | |
| name: "feature_transpose" | |
| type: "Transpose" | |
| bottom: "pool2_bn/250" | |
| top: "feature_transpose" | |
| transpose_param { | |
| dim: 3 | |
| dim: 0 | |
| dim: 1 | |
| dim: 2 | |
| } | |
| } | |
| layer { | |
| name: "reshape_feature_transpose" | |
| type: "Reshape" | |
| bottom: "feature_transpose" | |
| top: "reshape_feature_transpose" | |
| reshape_param { | |
| shape { dim: 0 dim: 0 dim: -1 } | |
| } | |
| } | |
| ########################################################### | |
| ########################################################### | |
| ########################################################### | |
| ########################################################### | |
| ########################################################### | |
| ########################################################### | |
| layer { | |
| name: "embedding_letter" | |
| type: "Embed" | |
| bottom: "sample_gt_label_input" | |
| top: "embedding_letter" | |
| param { | |
| lr_mult: 1 | |
| } | |
| embed_param { | |
| bias_term: false | |
| input_dim: 69 | |
| num_output: 128 | |
| weight_filler { type: "uniform" min: -0.08 max: 0.08 } | |
| } | |
| propagate_down: false | |
| } | |
| ########################################## | |
| layer { | |
| name: "llstmoutput" | |
| type: "Lstm" | |
| bottom: "reshape_feature_transpose" | |
| top: "llstm" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0.0 } | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| lstm_param { | |
| num_output: 256 | |
| weight_filler { type: "gaussian" std: 0.01} | |
| bias_filler { type: "constant" } | |
| clipping_threshold: 1 | |
| } | |
| } | |
| # ===================== rlstm =================== | |
| layer { | |
| name: "rlstm_input" | |
| type: "ReverseAxis" | |
| bottom: "reshape_feature_transpose" | |
| top: "rlstm_input" | |
| reverse_axis_param { | |
| axis: 0 | |
| } | |
| } | |
| layer { | |
| name: "rlstm_output" | |
| type: "Lstm" | |
| bottom: "rlstm_input" | |
| top: "rlstm_output" | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| param { lr_mult: 2.0 decay_mult: 0.0 } | |
| param { lr_mult: 1.0 decay_mult: 1.0 } | |
| lstm_param { | |
| num_output: 256 | |
| weight_filler { type: "gaussian" std: 0.01} | |
| bias_filler { type: "constant" } | |
| clipping_threshold: 1 | |
| } | |
| } | |
| layer { | |
| name: "lstm_reverse2" | |
| type: "ReverseAxis" | |
| bottom: "rlstm_output" | |
| top: "rlstm" | |
| reverse_axis_param { | |
| axis: 0 | |
| } | |
| } | |
| # merge lstm and rlstm 64 * N * 128 | |
| layer { | |
| name: "lstm_encoders" | |
| type: "Eltwise" | |
| bottom: "llstm" | |
| bottom: "rlstm" | |
| top: "lstm_encoders" | |
| eltwise_param { operation: SUM coeff: 1 coeff: 1 } | |
| } | |
| ############################################################# | |
| ### 64*N*128*1 | |
| layer { | |
| name: "reshape_lstm_encoders" | |
| type: "Reshape" | |
| bottom: "lstm_encoders" | |
| top: "reshape_lstm_encoders" | |
| reshape_param { | |
| shape { dim: 0 dim: 0 dim: 0 dim: 1} | |
| } | |
| } | |
| ### 64*N*128*1 ==> 1*N*128*64 | |
| layer { | |
| name: "reshape_lstm_encoders_transpose" | |
| type: "Transpose" | |
| bottom: "reshape_lstm_encoders" | |
| top: "reshape_lstm_encoders_transpose" | |
| transpose_param { dim: 3 dim: 1 dim: 2 dim: 0 } | |
| } | |
| ### 1*N*128*64 ==> 25*N*128*64 | |
| layer { | |
| name: "tile_encoder" | |
| type: "Tile" | |
| bottom: "reshape_lstm_encoders_transpose" | |
| top: "tile_encoder" | |
| tile_param { | |
| axis: 0 | |
| tiles: 25 | |
| } | |
| } | |
| layer { | |
| name: "decoder" | |
| type: "AttLstm" | |
| bottom: "tile_encoder" | |
| bottom: "sample_gt_cont" | |
| top: "decoder" | |
| top: "att_weights" | |
| param { lr_mult: 10 decay_mult: 1 } | |
| param { lr_mult: 10 decay_mult: 1 } | |
| param { lr_mult: 10 decay_mult: 1 } | |
| recurrent_param { | |
| num_output: 256 | |
| weight_filler { type: "xavier" } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| propagate_down: true | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "concat_embedding" | |
| bottom: "decoder" | |
| bottom: "embedding_letter" | |
| top: "concat_embedding" | |
| type: "Concat" | |
| concat_param { axis: 2 } | |
| } | |
| layer { | |
| name: "pred_lstm" | |
| type: "LSTMNew" | |
| bottom: "concat_embedding" | |
| bottom: "sample_gt_cont" | |
| top: "pred_lstm" | |
| param { lr_mult: 1 decay_mult: 1 } | |
| param { lr_mult: 1 decay_mult: 1 } | |
| param { lr_mult: 1 decay_mult: 1 } | |
| recurrent_param { | |
| num_output: 256 | |
| weight_filler { type: "xavier" } | |
| bias_filler { type: "constant" value: 0 } | |
| } | |
| propagate_down: true | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "predictip" | |
| type: "InnerProduct" | |
| bottom: "pred_lstm" | |
| top: "predict_ip" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 256 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| axis: 2 | |
| } | |
| } | |
| layer { | |
| name: "predictforward" | |
| type: "InnerProduct" | |
| bottom: "predict_ip" | |
| top: "predict_forward" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 69 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| axis: 2 | |
| } | |
| } | |
| layer { | |
| name: "pred" | |
| type: "SoftmaxWithLoss" | |
| bottom: "predict_forward" | |
| bottom: "sample_gt_label_output" | |
| top: "pred" | |
| loss_param { | |
| ignore_label: -1 | |
| } | |
| softmax_param { | |
| axis: 2 | |
| } | |
| loss_weight: 1 | |
| propagate_down: true | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "pred_soft" | |
| type: "Softmax" | |
| bottom: "predict_forward" | |
| top: "pred_soft" | |
| softmax_param { | |
| axis: 2 | |
| } | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "accuracy" | |
| type: "Accuracy" | |
| bottom: "pred_soft" | |
| bottom: "sample_gt_label_output" | |
| top: "accuracy" | |
| accuracy_param { ignore_label: -1 axis: 2} | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "score_4s" | |
| type: "Convolution" | |
| bottom: "conv_final" | |
| top: "score_4s" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "loss_4s" | |
| type: "SoftmaxWithLoss" | |
| bottom: "score_4s" | |
| bottom: "mask_gt" | |
| top: "loss_4s" | |
| loss_weight:1 | |
| propagate_down: true | |
| propagate_down: false | |
| loss_param { | |
| ignore_label: 255 | |
| normalize: true | |
| } | |
| } | |
| layer { | |
| name: "conv_feature_prior" | |
| type: "Convolution" | |
| bottom: "conv_final" | |
| top: "conv_feature_prior" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv_feature_prior_relu" | |
| type: "ReLU" | |
| bottom: "conv_feature_prior" | |
| top: "conv_feature_prior" | |
| } | |
| layer { | |
| name: "conv_maps" | |
| type: "Convolution" | |
| bottom: "conv_feature_prior" | |
| top: "conv_maps" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 4 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv_maps/relu" | |
| type: "ReLU" | |
| bottom: "conv_maps" | |
| top: "conv_maps" | |
| } | |
| layer { | |
| name: "conv_orient" | |
| type: "Convolution" | |
| bottom: "conv_feature_prior" | |
| top: "conv_orient" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "concat_bbox_orient" | |
| type: "Concat" | |
| bottom: "conv_maps" | |
| bottom: "conv_orient" | |
| top: "pre_bbox_orient" | |
| } | |
| layer { | |
| name: "YOUR IOULOSS LAYER" | |
| type: "YOUR IOULOSS LAYER" | |
| bottom: "pre_bbox_orient" | |
| top: iouloss | |
| } | |
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