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| name: "VGG_ILSVRC_16_layer" | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| data_param { | |
| source: "data_lmdb" # Change this to the absolute path to your | |
| batch_size: 1 # Change this number to a batch size that will fit on your GPU | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "label" | |
| data_param { | |
| source: "label" # Change this to the absolute path to | |
| batch_size: 1 # Change this number to a batch size that will fit on your GPU | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| bottom: "data" | |
| top: "conv1_1" | |
| name: "conv1_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| name: "conv1_1_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| name: "relu1_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_2" | |
| name: "conv1_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| name: "conv1_2_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| name: "relu1_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "pool1" | |
| top: "pool1_mask" | |
| name: "pool1" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "conv2_1" | |
| name: "conv2_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| name: "conv2_1_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| name: "relu2_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| name: "conv2_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| name: "conv2_2_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| name: "relu2_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "pool2" | |
| top: "pool2_mask" | |
| name: "pool2" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2" | |
| top: "conv3_1" | |
| name: "conv3_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| name: "conv3_1_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| name: "relu3_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| name: "conv3_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| name: "conv3_2_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| name: "relu3_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| name: "conv3_3" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| name: "conv3_3_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| name: "relu3_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "pool3" | |
| top: "pool3_mask" | |
| name: "pool3" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3" | |
| top: "conv4_1" | |
| name: "conv4_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| name: "conv4_1_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| name: "relu4_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| name: "conv4_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| name: "conv4_2_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| name: "relu4_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| name: "conv4_3" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| name: "conv4_3_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| name: "relu4_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "pool4" | |
| top: "pool4_mask" | |
| name: "pool4" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4" | |
| top: "conv5_1" | |
| name: "conv5_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| name: "conv5_1_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| name: "relu5_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| name: "conv5_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| name: "conv5_2_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| name: "relu5_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| name: "conv5_3" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| name: "conv5_3_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| name: "relu5_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "pool5" | |
| top: "pool5_mask" | |
| name: "pool5" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "upsample5" | |
| type: "Upsample" | |
| bottom: "pool5" | |
| top: "pool5_D" | |
| bottom: "pool5_mask" | |
| upsample_param { | |
| scale: 2 | |
| upsample_w: 16 | |
| upsample_h: 16 | |
| } | |
| } | |
| layer { | |
| bottom: "pool5_D" | |
| top: "conv5_3_D" | |
| name: "conv5_3_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3_D" | |
| top: "conv5_3_D" | |
| name: "conv5_3_D_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3_D" | |
| top: "conv5_3_D" | |
| name: "relu5_3_D" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_3_D" | |
| top: "conv5_2_D" | |
| name: "conv5_2_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2_D" | |
| top: "conv5_2_D" | |
| name: "conv5_2_D_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2_D" | |
| top: "conv5_2_D" | |
| name: "relu5_2_D" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_2_D" | |
| top: "conv5_1_D" | |
| name: "conv5_1_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1_D" | |
| top: "conv5_1_D" | |
| name: "conv5_1_D_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1_D" | |
| top: "conv5_1_D" | |
| name: "relu5_1_D" | |
| type: "ReLU" | |
| } | |
| layer { | |
| name: "upsample4" | |
| type: "Upsample" | |
| bottom: "conv5_1_D" | |
| top: "pool4_D" | |
| bottom: "pool4_mask" | |
| upsample_param { | |
| scale: 2 | |
| upsample_w: 32 | |
| upsample_h: 32 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4_D" | |
| top: "conv4_3_D" | |
| name: "conv4_3_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3_D" | |
| top: "conv4_3_D" | |
| name: "conv4_3_D_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3_D" | |
| top: "conv4_3_D" | |
| name: "relu4_3_D" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_3_D" | |
| top: "conv4_2_D" | |
| name: "conv4_2_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2_D" | |
| top: "conv4_2_D" | |
| name: "conv4_2_D_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2_D" | |
| top: "conv4_2_D" | |
| name: "relu4_2_D" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_2_D" | |
| top: "conv4_1_D" | |
| name: "conv4_1_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1_D" | |
| top: "conv4_1_D" | |
| name: "conv4_1_D_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1_D" | |
| top: "conv4_1_D" | |
| name: "relu4_1_D" | |
| type: "ReLU" | |
| } | |
| layer { | |
| name: "upsample3" | |
| type: "Upsample" | |
| bottom: "conv4_1_D" | |
| top: "pool3_D" | |
| bottom: "pool3_mask" | |
| upsample_param { | |
| scale: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3_D" | |
| top: "conv3_3_D" | |
| name: "conv3_3_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3_D" | |
| top: "conv3_3_D" | |
| name: "conv3_3_D_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3_D" | |
| top: "conv3_3_D" | |
| name: "relu3_3_D" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_3_D" | |
| top: "conv3_2_D" | |
| name: "conv3_2_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2_D" | |
| top: "conv3_2_D" | |
| name: "conv3_2_D_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2_D" | |
| top: "conv3_2_D" | |
| name: "relu3_2_D" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_2_D" | |
| top: "conv3_1_D" | |
| name: "conv3_1_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1_D" | |
| top: "conv3_1_D" | |
| name: "conv3_1_D_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1_D" | |
| top: "conv3_1_D" | |
| name: "relu3_1_D" | |
| type: "ReLU" | |
| } | |
| layer { | |
| name: "upsample2" | |
| type: "Upsample" | |
| bottom: "conv3_1_D" | |
| top: "pool2_D" | |
| bottom: "pool2_mask" | |
| upsample_param { | |
| scale: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2_D" | |
| top: "conv2_2_D" | |
| name: "conv2_2_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2_D" | |
| top: "conv2_2_D" | |
| name: "conv2_2_D_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2_D" | |
| top: "conv2_2_D" | |
| name: "relu2_2_D" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_2_D" | |
| top: "conv2_1_D" | |
| name: "conv2_1_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1_D" | |
| top: "conv2_1_D" | |
| name: "conv2_1_D_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1_D" | |
| top: "conv2_1_D" | |
| name: "relu2_1_D" | |
| type: "ReLU" | |
| } | |
| layer { | |
| name: "upsample1" | |
| type: "Upsample" | |
| bottom: "conv2_1_D" | |
| top: "pool1_D" | |
| bottom: "pool1_mask" | |
| upsample_param { | |
| scale: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1_D" | |
| top: "conv1_2_D" | |
| name: "conv1_2_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2_D" | |
| top: "conv1_2_D" | |
| name: "conv1_2_D_bn" | |
| type: "BN" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2_D" | |
| top: "conv1_2_D" | |
| name: "relu1_2_D" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_2_D" | |
| top: "conv1_1_D" | |
| name: "conv1_1_D" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| num_output: 2 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "conv1_1_D" | |
| bottom: "label" | |
| top: "loss" | |
| loss_param: { | |
| weight_by_label_freqs: true | |
| class_weighting: 0.5 | |
| class_weighting: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "accuracy" | |
| type: "Accuracy" | |
| bottom: "conv1_1_D" | |
| bottom: "label" | |
| top: "accuracy" | |
| top: "per_class_accuracy" | |
| } |
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