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October 3, 2016 10:51
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| name: "autocolorize" | |
| input: "data" | |
| input_dim: 1 | |
| input_dim: 1 | |
| input_dim: 514 | |
| input_dim: 514 | |
| layer { | |
| name: "data" | |
| type: "Input" | |
| top: "data" | |
| input_param { | |
| shape: { | |
| dim: 1 | |
| dim: 1 | |
| dim: 514 | |
| dim: 514 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "data" | |
| top: "conv1_1" | |
| name: "conv1_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv1_1" | |
| type: "ReLU" | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_2" | |
| name: "conv1_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv1_2" | |
| type: "ReLU" | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "pool1" | |
| name: "pool1" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "conv2_1" | |
| name: "conv2_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv2_1" | |
| type: "ReLU" | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| name: "conv2_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv2_2" | |
| type: "ReLU" | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "pool2" | |
| name: "pool2" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2" | |
| top: "conv3_1" | |
| name: "conv3_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv3_1" | |
| type: "ReLU" | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| name: "conv3_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv3_2" | |
| type: "ReLU" | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| name: "conv3_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv3_3" | |
| type: "ReLU" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "pool3" | |
| name: "pool3" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3" | |
| top: "conv4_1" | |
| name: "conv4_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv4_1" | |
| type: "ReLU" | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| name: "conv4_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv4_2" | |
| type: "ReLU" | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| name: "conv4_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv4_3" | |
| type: "ReLU" | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "pool4" | |
| name: "pool4" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4" | |
| top: "conv5_1" | |
| name: "conv5_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv5_1" | |
| type: "ReLU" | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| name: "conv5_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv5_2" | |
| type: "ReLU" | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| name: "conv5_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu_conv5_3" | |
| type: "ReLU" | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "pool5" | |
| name: "pool5" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool5" | |
| top: "fc6" | |
| name: "fc6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 4096 | |
| pad: 3 | |
| kernel_size: 7 | |
| } | |
| } | |
| layer { | |
| name: "relu_fc6" | |
| type: "ReLU" | |
| bottom: "fc6" | |
| top: "fc6" | |
| } | |
| layer { | |
| name: "dropout_fc6" | |
| type: "Dropout" | |
| bottom: "fc6" | |
| top: "fc6" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| bottom: "fc6" | |
| top: "fc7" | |
| name: "fc7" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 4096 | |
| pad: 0 | |
| kernel_size: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu_fc7" | |
| type: "ReLU" | |
| bottom: "fc7" | |
| top: "fc7" | |
| } | |
| layer { | |
| name: "dropout_fc7" | |
| type: "Dropout" | |
| bottom: "fc7" | |
| top: "fc7" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "data_full" | |
| type: "Pooling" | |
| bottom: "data" top: "data_full" | |
| pooling_param { | |
| kernel_size: 4 stride: 4 | |
| pool: AVE | |
| pad: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv1_1_full" | |
| type: "Pooling" | |
| bottom: "conv1_1" top: "conv1_1_full" | |
| pooling_param { | |
| kernel_size: 4 stride: 4 | |
| pool: AVE | |
| pad: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv1_2_full" | |
| type: "Pooling" | |
| bottom: "conv1_2" top: "conv1_2_full" | |
| pooling_param { | |
| kernel_size: 4 stride: 4 | |
| pool: AVE | |
| pad: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_full" | |
| type: "Pooling" | |
| bottom: "conv2_1" top: "conv2_1_full" | |
| pooling_param { | |
| kernel_size: 2 stride: 2 | |
| pool: AVE | |
| pad: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_full" | |
| type: "Pooling" | |
| bottom: "conv2_2" top: "conv2_2_full" | |
| pooling_param { | |
| kernel_size: 2 stride: 2 | |
| pool: AVE | |
| pad: 0 | |
| } | |
| } | |
| # conv4_1 upsampling | |
| layer { | |
| name: "conv4_1_reshaped" type: "Reshape" | |
| bottom: "conv4_1" top: "conv4_1_reshaped" | |
| reshape_param { | |
| shape { dim: -1 dim: 1 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_full_reshaped" | |
| type: "Deconvolution" | |
| bottom: "conv4_1_reshaped" top: "conv4_1_full_reshaped" | |
| convolution_param { | |
| kernel_size: 4 stride: 2 | |
| num_output: 1 group: 1 | |
| pad: 1 | |
| weight_filler: { type: "bilinear" } bias_term: false | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| name: "conv4_1_full" type: "Reshape" | |
| bottom: "conv4_1_full_reshaped" top: "conv4_1_full" | |
| reshape_param { | |
| shape { dim: -1 dim: 512 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| # conv4_2 upsampling | |
| layer { | |
| name: "conv4_2_reshaped" type: "Reshape" | |
| bottom: "conv4_2" top: "conv4_2_reshaped" | |
| reshape_param { | |
| shape { dim: -1 dim: 1 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_full_reshaped" | |
| type: "Deconvolution" | |
| bottom: "conv4_2_reshaped" top: "conv4_2_full_reshaped" | |
| convolution_param { | |
| kernel_size: 4 stride: 2 | |
| num_output: 1 group: 1 | |
| pad: 1 | |
| weight_filler: { type: "bilinear" } bias_term: false | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| name: "conv4_2_full" type: "Reshape" | |
| bottom: "conv4_2_full_reshaped" top: "conv4_2_full" | |
| reshape_param { | |
| shape { dim: -1 dim: 512 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| # conv4_3 upsampling | |
| layer { | |
| name: "conv4_3_reshaped" type: "Reshape" | |
| bottom: "conv4_3" top: "conv4_3_reshaped" | |
| reshape_param { | |
| shape { dim: -1 dim: 1 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_full_reshaped" | |
| type: "Deconvolution" | |
| bottom: "conv4_3_reshaped" top: "conv4_3_full_reshaped" | |
| convolution_param { | |
| kernel_size: 4 stride: 2 | |
| num_output: 1 group: 1 | |
| pad: 1 | |
| weight_filler: { type: "bilinear" } bias_term: false | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| name: "conv4_3_full" type: "Reshape" | |
| bottom: "conv4_3_full_reshaped" top: "conv4_3_full" | |
| reshape_param { | |
| shape { dim: -1 dim: 512 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| # conv5_1 upsampling | |
| layer { | |
| name: "conv5_1_reshaped" type: "Reshape" | |
| bottom: "conv5_1" top: "conv5_1_reshaped" | |
| reshape_param { | |
| shape { dim: -1 dim: 1 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_full_reshaped" | |
| type: "Deconvolution" | |
| bottom: "conv5_1_reshaped" top: "conv5_1_full_reshaped" | |
| convolution_param { | |
| kernel_size: 8 stride: 4 | |
| num_output: 1 group: 1 | |
| pad: 2 | |
| weight_filler: { type: "bilinear" } bias_term: false | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| name: "conv5_1_full" type: "Reshape" | |
| bottom: "conv5_1_full_reshaped" top: "conv5_1_full" | |
| reshape_param { | |
| shape { dim: -1 dim: 512 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| # conv5_2 upsampling | |
| layer { | |
| name: "conv5_2_reshaped" type: "Reshape" | |
| bottom: "conv5_2" top: "conv5_2_reshaped" | |
| reshape_param { | |
| shape { dim: -1 dim: 1 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_full_reshaped" | |
| type: "Deconvolution" | |
| bottom: "conv5_2_reshaped" top: "conv5_2_full_reshaped" | |
| convolution_param { | |
| kernel_size: 8 stride: 4 | |
| num_output: 1 group: 1 | |
| pad: 2 | |
| weight_filler: { type: "bilinear" } bias_term: false | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| name: "conv5_2_full" type: "Reshape" | |
| bottom: "conv5_2_full_reshaped" top: "conv5_2_full" | |
| reshape_param { | |
| shape { dim: -1 dim: 512 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| # conv5_3 upsampling | |
| layer { | |
| name: "conv5_3_reshaped" type: "Reshape" | |
| bottom: "conv5_3" top: "conv5_3_reshaped" | |
| reshape_param { | |
| shape { dim: -1 dim: 1 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_full_reshaped" | |
| type: "Deconvolution" | |
| bottom: "conv5_3_reshaped" top: "conv5_3_full_reshaped" | |
| convolution_param { | |
| kernel_size: 8 stride: 4 | |
| num_output: 1 group: 1 | |
| pad: 2 | |
| weight_filler: { type: "bilinear" } bias_term: false | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| name: "conv5_3_full" type: "Reshape" | |
| bottom: "conv5_3_full_reshaped" top: "conv5_3_full" | |
| reshape_param { | |
| shape { dim: -1 dim: 512 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| # fc6 upsampling | |
| layer { | |
| name: "fc6_reshaped" type: "Reshape" | |
| bottom: "fc6" top: "fc6_reshaped" | |
| reshape_param { | |
| shape { dim: -1 dim: 1 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| layer { | |
| name: "fc6_full_reshaped" | |
| type: "Deconvolution" | |
| bottom: "fc6_reshaped" top: "fc6_full_reshaped" | |
| convolution_param { | |
| kernel_size: 16 stride: 8 | |
| num_output: 1 group: 1 | |
| pad: 4 | |
| weight_filler: { type: "bilinear" } bias_term: false | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| name: "fc6_full" type: "Reshape" | |
| bottom: "fc6_full_reshaped" top: "fc6_full" | |
| reshape_param { | |
| shape { dim: -1 dim: 4096 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| # fc7 upsampling | |
| layer { | |
| name: "fc7_reshaped" type: "Reshape" | |
| bottom: "fc7" top: "fc7_reshaped" | |
| reshape_param { | |
| shape { dim: -1 dim: 1 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| layer { | |
| name: "fc7_full_reshaped" | |
| type: "Deconvolution" | |
| bottom: "fc7_reshaped" top: "fc7_full_reshaped" | |
| convolution_param { | |
| kernel_size: 16 stride: 8 | |
| num_output: 1 group: 1 | |
| pad: 4 | |
| weight_filler: { type: "bilinear" } bias_term: false | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| name: "fc7_full" type: "Reshape" | |
| bottom: "fc7_full_reshaped" top: "fc7_full" | |
| reshape_param { | |
| shape { dim: -1 dim: 4096 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| layer { | |
| name: "dense_hypercolumn" | |
| type: "Concat" | |
| bottom: "data_full" | |
| bottom: "conv1_1_full" | |
| bottom: "conv1_2_full" | |
| bottom: "conv2_1_full" | |
| bottom: "conv2_2_full" | |
| bottom: "conv3_1" | |
| bottom: "conv3_2" | |
| bottom: "conv3_3" | |
| bottom: "conv4_1_full" | |
| bottom: "conv4_2_full" | |
| bottom: "conv4_3_full" | |
| bottom: "conv5_1_full" | |
| bottom: "conv5_2_full" | |
| bottom: "conv5_3_full" | |
| bottom: "fc6_full" | |
| bottom: "fc7_full" | |
| top: "dense_hypercolumn" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| bottom: "dense_hypercolumn" | |
| top: "h_fc1" | |
| name: "h_fc1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 1024 | |
| pad: 0 | |
| kernel_size: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu_h_fc1" | |
| type: "ReLU" | |
| bottom: "h_fc1" | |
| top: "h_fc1" | |
| } | |
| layer { | |
| bottom: "h_fc1" | |
| top: "prediction_h" | |
| name: "prediction_h" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 32 | |
| pad: 0 | |
| kernel_size: 1 | |
| } | |
| } | |
| layer { | |
| bottom: "h_fc1" | |
| top: "prediction_c" | |
| name: "prediction_c" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 32 | |
| pad: 0 | |
| kernel_size: 1 | |
| } | |
| } | |
| layer { | |
| name: "prediction_h_softmax" | |
| type: "Softmax" | |
| bottom: "prediction_h" | |
| top: "prediction_h_softmax" | |
| } | |
| layer { | |
| name: "prediction_c_softmax" | |
| type: "Softmax" | |
| bottom: "prediction_c" | |
| top: "prediction_c_softmax" | |
| } | |
| # prediction_h upsample | |
| layer { | |
| name: "prediction_h_softmax_reshaped" type: "Reshape" | |
| bottom: "prediction_h_softmax" top: "prediction_h_softmax_reshaped" | |
| reshape_param { | |
| shape { dim: -1 dim: 1 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| layer { | |
| name: "prediction_h_full_reshaped" | |
| type: "Deconvolution" | |
| bottom: "prediction_h_softmax_reshaped" | |
| top: "prediction_h_full_reshaped" | |
| convolution_param { | |
| kernel_size: 8 stride: 4 | |
| num_output: 1 group: 1 | |
| pad: 2 | |
| weight_filler: { type: "bilinear" } | |
| bias_term: false | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| name: "prediction_h_full" type: "Reshape" | |
| bottom: "prediction_h_full_reshaped" top: "prediction_h_full" | |
| reshape_param { | |
| shape { dim: -1 dim: 32 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| # prediction_c upsample | |
| layer { | |
| name: "prediction_c_softmax_reshaped" type: "Reshape" | |
| bottom: "prediction_c_softmax" top: "prediction_c_softmax_reshaped" | |
| reshape_param { | |
| shape { dim: -1 dim: 1 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } | |
| layer { | |
| name: "prediction_c_full_reshaped" | |
| type: "Deconvolution" | |
| bottom: "prediction_c_softmax_reshaped" | |
| top: "prediction_c_full_reshaped" | |
| convolution_param { | |
| kernel_size: 8 stride: 4 | |
| num_output: 1 group: 1 | |
| pad: 2 | |
| weight_filler: { type: "bilinear" } | |
| bias_term: false | |
| } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| } | |
| layer { | |
| name: "prediction_c_full" type: "Reshape" | |
| bottom: "prediction_c_full_reshaped" top: "prediction_c_full" | |
| reshape_param { | |
| shape { dim: -1 dim: 32 } | |
| axis: 0 | |
| num_axes: 2 | |
| } | |
| } |
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