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June 6, 2019 03:42
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| name: "darknet/16c-16x-r1/3d" | |
| layer { | |
| name: "input" | |
| type: "Input" | |
| top: "data" | |
| input_param { | |
| shape { | |
| dim: 1 | |
| dim: 800 | |
| dim: 1440 | |
| dim: 3 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "data_perm" | |
| type: "Permute" | |
| bottom: "data" | |
| top: "data_perm" | |
| permute_param { | |
| order: 0 | |
| order: 3 | |
| order: 1 | |
| order: 2 | |
| } | |
| } | |
| layer { | |
| name: "data_scale" | |
| type: "Power" | |
| bottom: "data_perm" | |
| top: "data_scale" | |
| power_param { | |
| power: 1.0 | |
| scale: 0.00392156885937 | |
| shift: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data_scale" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv1" | |
| top: "conv1" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv1_scale" | |
| type: "Scale" | |
| bottom: "conv1" | |
| top: "conv1" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| pad: 0 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv2" | |
| top: "conv2" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv2_scale" | |
| type: "Scale" | |
| bottom: "conv2" | |
| top: "conv2" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_relu" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "conv2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| pad: 0 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_scale" | |
| type: "Scale" | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_relu" | |
| type: "ReLU" | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2" | |
| type: "Convolution" | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_scale" | |
| type: "Scale" | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_relu" | |
| type: "ReLU" | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3" | |
| type: "Convolution" | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_scale" | |
| type: "Scale" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_relu" | |
| type: "ReLU" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "conv3_3" | |
| top: "pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| pad: 0 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1" | |
| type: "Convolution" | |
| bottom: "pool3" | |
| top: "conv4_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_scale" | |
| type: "Scale" | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_relu" | |
| type: "ReLU" | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2" | |
| type: "Convolution" | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_scale" | |
| type: "Scale" | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_relu" | |
| type: "ReLU" | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3" | |
| type: "Convolution" | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_scale" | |
| type: "Scale" | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_relu" | |
| type: "ReLU" | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "pool4" | |
| type: "Pooling" | |
| bottom: "conv4_3" | |
| top: "pool4" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| pad: 0 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1" | |
| type: "Convolution" | |
| bottom: "pool4" | |
| top: "conv5_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_scale" | |
| type: "Scale" | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_relu" | |
| type: "ReLU" | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2" | |
| type: "Convolution" | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_scale" | |
| type: "Scale" | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_relu" | |
| type: "ReLU" | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3" | |
| type: "Convolution" | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_scale" | |
| type: "Scale" | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_relu" | |
| type: "ReLU" | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4" | |
| type: "Convolution" | |
| bottom: "conv5_3" | |
| top: "conv5_4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_4_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_4" | |
| top: "conv5_4" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4_scale" | |
| type: "Scale" | |
| bottom: "conv5_4" | |
| top: "conv5_4" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_4_relu" | |
| type: "ReLU" | |
| bottom: "conv5_4" | |
| top: "conv5_4" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv5_5" | |
| type: "Convolution" | |
| bottom: "conv5_4" | |
| top: "conv5_5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_5_bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_5" | |
| top: "conv5_5" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv5_5_scale" | |
| type: "Scale" | |
| bottom: "conv5_5" | |
| top: "conv5_5" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_5_relu" | |
| type: "ReLU" | |
| bottom: "conv5_5" | |
| top: "conv5_5" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv6_1" | |
| type: "Convolution" | |
| bottom: "conv5_5" | |
| top: "conv6_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| engine: CAFFE | |
| group: 2 | |
| num_output: 512 | |
| bias_term: false | |
| pad: 2 | |
| kernel_size: 3 | |
| dilation: 2 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv6_1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv6_1" | |
| top: "conv6_1" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv6_1_scale" | |
| type: "Scale" | |
| bottom: "conv6_1" | |
| top: "conv6_1" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv6_1_relu" | |
| type: "ReLU" | |
| bottom: "conv6_1" | |
| top: "conv6_1" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv6_2" | |
| type: "Convolution" | |
| bottom: "conv6_1" | |
| top: "conv6_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv6_2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv6_2" | |
| top: "conv6_2" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv6_2_scale" | |
| type: "Scale" | |
| bottom: "conv6_2" | |
| top: "conv6_2" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv6_2_relu" | |
| type: "ReLU" | |
| bottom: "conv6_2" | |
| top: "conv6_2" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv6_3" | |
| type: "Convolution" | |
| bottom: "conv6_2" | |
| top: "conv6_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| engine: CAFFE | |
| group: 2 | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv6_3_bn" | |
| type: "BatchNorm" | |
| bottom: "conv6_3" | |
| top: "conv6_3" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv6_3_scale" | |
| type: "Scale" | |
| bottom: "conv6_3" | |
| top: "conv6_3" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv6_3_relu" | |
| type: "ReLU" | |
| bottom: "conv6_3" | |
| top: "conv6_3" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv6_4" | |
| type: "Convolution" | |
| bottom: "conv6_3" | |
| top: "conv6_4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv6_4_bn" | |
| type: "BatchNorm" | |
| bottom: "conv6_4" | |
| top: "conv6_4" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv6_4_scale" | |
| type: "Scale" | |
| bottom: "conv6_4" | |
| top: "conv6_4" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv6_4_relu" | |
| type: "ReLU" | |
| bottom: "conv6_4" | |
| top: "conv6_4" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv6_5" | |
| type: "Convolution" | |
| bottom: "conv6_4" | |
| top: "conv6_5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| engine: CAFFE | |
| group: 2 | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv6_5_bn" | |
| type: "BatchNorm" | |
| bottom: "conv6_5" | |
| top: "conv6_5" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv6_5_scale" | |
| type: "Scale" | |
| bottom: "conv6_5" | |
| top: "conv6_5" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv6_5_relu" | |
| type: "ReLU" | |
| bottom: "conv6_5" | |
| top: "conv6_5" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv7_1" | |
| type: "Convolution" | |
| bottom: "conv6_5" | |
| top: "conv7_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| engine: CAFFE | |
| group: 4 | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv7_1_bn" | |
| type: "BatchNorm" | |
| bottom: "conv7_1" | |
| top: "conv7_1" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv7_1_scale" | |
| type: "Scale" | |
| bottom: "conv7_1" | |
| top: "conv7_1" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv7_1_relu" | |
| type: "ReLU" | |
| bottom: "conv7_1" | |
| top: "conv7_1" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv7_2" | |
| type: "Convolution" | |
| bottom: "conv7_1" | |
| top: "conv7_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv7_2_bn" | |
| type: "BatchNorm" | |
| bottom: "conv7_2" | |
| top: "conv7_2" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv7_2_scale" | |
| type: "Scale" | |
| bottom: "conv7_2" | |
| top: "conv7_2" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv7_2_relu" | |
| type: "ReLU" | |
| bottom: "conv7_2" | |
| top: "conv7_2" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "concat8" | |
| type: "Concat" | |
| bottom: "conv5_5" | |
| bottom: "conv7_2" | |
| top: "concat8" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv9" | |
| type: "Convolution" | |
| bottom: "concat8" | |
| top: "conv9" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| engine: CAFFE | |
| group: 4 | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv9_bn" | |
| type: "BatchNorm" | |
| bottom: "conv9" | |
| top: "conv9" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv9_scale" | |
| type: "Scale" | |
| bottom: "conv9" | |
| top: "conv9" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv9_relu" | |
| type: "ReLU" | |
| bottom: "conv9" | |
| top: "conv9" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| ################################ | |
| # add conv10 ################### | |
| ################################ | |
| layer { | |
| name: "conv10" | |
| type: "Convolution" | |
| bottom: "concat8" | |
| top: "conv10" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| engine: CAFFE | |
| group: 4 | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| dilation: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv10_bn" | |
| type: "BatchNorm" | |
| bottom: "conv10" | |
| top: "conv10" | |
| batch_norm_param { | |
| eps: 1e-06 | |
| } | |
| } | |
| layer { | |
| name: "conv10_scale" | |
| type: "Scale" | |
| bottom: "conv10" | |
| top: "conv10" | |
| scale_param { | |
| filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv10_relu" | |
| type: "ReLU" | |
| bottom: "conv10" | |
| top: "conv10" | |
| relu_param { | |
| negative_slope: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv_final_8cls" | |
| type: "Convolution" | |
| bottom: "conv9" | |
| top: "conv_final" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 208 | |
| bias_term: true | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| ################################ | |
| ###########car light############ | |
| ################################ | |
| layer { | |
| name: "brvis_ori" | |
| type: "Convolution" | |
| bottom: "conv10" | |
| top: "brvis_ori" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| bias_term: true | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "brvis_perm" | |
| type: "Permute" | |
| bottom: "brvis_ori" | |
| top: "brvis_pred" | |
| permute_param { | |
| order: 0 | |
| order: 2 | |
| order: 3 | |
| order: 1 | |
| } | |
| } | |
| layer { | |
| name: "ltvis_ori" | |
| type: "Convolution" | |
| bottom: "conv10" | |
| top: "ltvis_ori" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| bias_term: true | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "ltvis_perm" | |
| type: "Permute" | |
| bottom: "ltvis_ori" | |
| top: "ltvis_pred" | |
| permute_param { | |
| order: 0 | |
| order: 2 | |
| order: 3 | |
| order: 1 | |
| } | |
| } | |
| layer { | |
| name: "rtvis_ori" | |
| type: "Convolution" | |
| bottom: "conv10" | |
| top: "rtvis_ori" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| bias_term: true | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "rtvis_perm" | |
| type: "Permute" | |
| bottom: "rtvis_ori" | |
| top: "rtvis_pred" | |
| permute_param { | |
| order: 0 | |
| order: 2 | |
| order: 3 | |
| order: 1 | |
| } | |
| } | |
| layer { | |
| name: "brswt_ori" | |
| type: "Convolution" | |
| bottom: "conv10" | |
| top: "brswt_ori" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| bias_term: true | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "brswt_perm" | |
| type: "Permute" | |
| bottom: "brswt_ori" | |
| top: "brswt_pred" | |
| permute_param { | |
| order: 0 | |
| order: 2 | |
| order: 3 | |
| order: 1 | |
| } | |
| } | |
| layer { | |
| name: "ltswt_ori" | |
| type: "Convolution" | |
| bottom: "conv10" | |
| top: "ltswt_ori" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| bias_term: true | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "ltswt_perm" | |
| type: "Permute" | |
| bottom: "ltswt_ori" | |
| top: "ltswt_pred" | |
| permute_param { | |
| order: 0 | |
| order: 2 | |
| order: 3 | |
| order: 1 | |
| } | |
| } | |
| layer { | |
| name: "rtswt_ori" | |
| type: "Convolution" | |
| bottom: "conv10" | |
| top: "rtswt_ori" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| bias_term: true | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "rtswt_perm" | |
| type: "Permute" | |
| bottom: "rtswt_ori" | |
| top: "rtswt_pred" | |
| permute_param { | |
| order: 0 | |
| order: 2 | |
| order: 3 | |
| order: 1 | |
| } | |
| } | |
| ######################### | |
| layer { | |
| name: "conv_final_permute" | |
| type: "Permute" | |
| bottom: "conv_final" | |
| top: "conv_final_permute" | |
| permute_param { | |
| order: 0 | |
| order: 2 | |
| order: 3 | |
| order: 1 | |
| } | |
| } | |
| layer { | |
| name: "slice" | |
| type: "Slice" | |
| bottom: "conv_final_permute" | |
| top: "detect1_loc_pred" | |
| top: "obj_perm" | |
| top: "cls_perm" | |
| slice_param { | |
| slice_point: 64 | |
| slice_point: 80 | |
| axis: 3 | |
| } | |
| } | |
| layer { | |
| name: "cls_reshape" | |
| type: "Reshape" | |
| bottom: "cls_perm" | |
| top: "cls_reshape" | |
| reshape_param { | |
| shape { | |
| dim: 0 | |
| dim: 0 | |
| dim: -1 | |
| dim: 8 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "cls_pred_prob" | |
| type: "Softmax" | |
| bottom: "cls_reshape" | |
| top: "cls_pred_prob" | |
| softmax_param { | |
| axis: 3 | |
| } | |
| } | |
| layer { | |
| name: "cls_pred" | |
| type: "Reshape" | |
| bottom: "cls_pred_prob" | |
| top: "detect1_cls_pred" | |
| reshape_param { | |
| shape { | |
| dim: 0 | |
| dim: 0 | |
| dim: -1 | |
| dim: 128 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "obj_pred" | |
| type: "Sigmoid" | |
| bottom: "obj_perm" | |
| top: "detect1_obj_pred" | |
| } | |
| ########################### | |
| ######## BBOX3D ########### | |
| ########################### | |
| layer { | |
| name: "ori_origin" | |
| type: "Convolution" | |
| bottom: "conv9" | |
| top: "ori_origin" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: true | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "ori_pred" | |
| type: "Permute" | |
| bottom: "ori_origin" | |
| top: "detect1_ori_pred" | |
| permute_param { | |
| order: 0 | |
| order: 2 | |
| order: 3 | |
| order: 1 | |
| } | |
| } | |
| layer { | |
| name: "dim_origin" | |
| type: "Convolution" | |
| bottom: "conv9" | |
| top: "dim_origin" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| bias_term: true | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "dim_pred" | |
| type: "Permute" | |
| bottom: "dim_origin" | |
| top: "detect1_dim_pred" | |
| permute_param { | |
| order: 0 | |
| order: 2 | |
| order: 3 | |
| order: 1 | |
| } | |
| } | |
| ################################ | |
| # visible prediction ########### | |
| ################################ | |
| layer { | |
| name: "vis_pack" | |
| type: "Convolution" | |
| bottom: "conv10" | |
| top: "vis_pack" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: true | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "vis_perm" | |
| type: "Permute" | |
| bottom: "vis_pack" | |
| top: "vis_perm" | |
| permute_param { | |
| order: 0 | |
| order: 2 | |
| order: 3 | |
| order: 1 | |
| } | |
| } | |
| layer { | |
| name: "vis_perm_reshape" | |
| type: "Reshape" | |
| bottom: "vis_perm" | |
| top: "vis_perm_reshape" | |
| reshape_param { | |
| shape { | |
| dim: 0 | |
| dim: 0 | |
| dim: -1 | |
| dim: 4 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "vis_pred" | |
| type: "Sigmoid" | |
| bottom: "vis_perm_reshape" | |
| top: "vis_pred" | |
| } | |
| layer { | |
| name: "conv_area_id_half" | |
| type: "Convolution" | |
| bottom: "conv10" | |
| top: "conv_area_id" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: true | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "area_id_perm" | |
| type: "Permute" | |
| bottom: "conv_area_id" | |
| top: "area_id_perm" | |
| permute_param { | |
| order: 0 | |
| order: 2 | |
| order: 3 | |
| order: 1 | |
| } | |
| } | |
| layer { | |
| name: "area_id_perm_reshape" | |
| type: "Reshape" | |
| bottom: "area_id_perm" | |
| top: "area_id_pred" | |
| reshape_param { | |
| shape { | |
| dim: 0 | |
| dim: 0 | |
| dim: 0 | |
| dim: -1 | |
| dim: 4 | |
| } | |
| } | |
| } | |
| ################################ | |
| # cut off prediction ########### | |
| ################################ | |
| layer { | |
| name: "cut_4d_origin" | |
| type: "Convolution" | |
| bottom: "conv10" | |
| top: "cut_4d_origin" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: true | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "cut_sig" | |
| type: "Sigmoid" | |
| bottom: "cut_4d_origin" | |
| top: "cut_sig" | |
| } | |
| layer { | |
| name: "cut_pred" | |
| type: "Permute" | |
| bottom: "cut_sig" | |
| top: "cut_pred" | |
| permute_param { | |
| order: 0 | |
| order: 2 | |
| order: 3 | |
| order: 1 | |
| } | |
| } |
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