layer { name: "input" type: "Input" top: "data" input_param { # These dimensions are purely for sake of example; # see infer.py for how to reshape the net to the given input size. shape { dim: 1 dim: 3 dim: 500 dim: 500 } } } layer { name: "conv1_1" type: "Convolution" bottom: "data" top: "conv1_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 pad: 100 kernel_size: 3 stride: 1 } } layer { name: "relu1_1" type: "ReLU" bottom: "conv1_1" top: "conv1_1" } layer { name: "conv1_2" type: "Convolution" bottom: "conv1_1" top: "conv1_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu1_2" type: "ReLU" bottom: "conv1_2" top: "conv1_2" } layer { name: "pool1" type: "Pooling" bottom: "conv1_2" top: "pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv2_1" type: "Convolution" bottom: "pool1" top: "conv2_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu2_1" type: "ReLU" bottom: "conv2_1" top: "conv2_1" } layer { name: "conv2_2" type: "Convolution" bottom: "conv2_1" top: "conv2_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu2_2" type: "ReLU" bottom: "conv2_2" top: "conv2_2" } layer { name: "pool2" type: "Pooling" bottom: "conv2_2" top: "pool2" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv3_1" type: "Convolution" bottom: "pool2" top: "conv3_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu3_1" type: "ReLU" bottom: "conv3_1" top: "conv3_1" } layer { name: "conv3_2" type: "Convolution" bottom: "conv3_1" top: "conv3_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu3_2" type: "ReLU" bottom: "conv3_2" top: "conv3_2" } layer { name: "conv3_3" type: "Convolution" bottom: "conv3_2" top: "conv3_3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu3_3" type: "ReLU" bottom: "conv3_3" top: "conv3_3" } layer { name: "pool3" type: "Pooling" bottom: "conv3_3" top: "pool3" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv4_1" type: "Convolution" bottom: "pool3" top: "conv4_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu4_1" type: "ReLU" bottom: "conv4_1" top: "conv4_1" } layer { name: "conv4_2" type: "Convolution" bottom: "conv4_1" top: "conv4_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu4_2" type: "ReLU" bottom: "conv4_2" top: "conv4_2" } layer { name: "conv4_3" type: "Convolution" bottom: "conv4_2" top: "conv4_3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu4_3" type: "ReLU" bottom: "conv4_3" top: "conv4_3" } layer { name: "pool4" type: "Pooling" bottom: "conv4_3" top: "pool4" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv5_1" type: "Convolution" bottom: "pool4" top: "conv5_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu5_1" type: "ReLU" bottom: "conv5_1" top: "conv5_1" } layer { name: "conv5_2" type: "Convolution" bottom: "conv5_1" top: "conv5_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu5_2" type: "ReLU" bottom: "conv5_2" top: "conv5_2" } layer { name: "conv5_3" type: "Convolution" bottom: "conv5_2" top: "conv5_3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 stride: 1 } } layer { name: "relu5_3" type: "ReLU" bottom: "conv5_3" top: "conv5_3" } layer { name: "pool5" type: "Pooling" bottom: "conv5_3" top: "pool5" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "fc6" type: "Convolution" bottom: "pool5" top: "fc6" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 4096 pad: 0 kernel_size: 7 stride: 1 } } layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6" } layer { name: "fc7" type: "Convolution" bottom: "fc6" top: "fc7" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 4096 pad: 0 kernel_size: 1 stride: 1 } } layer { name: "relu7" type: "ReLU" bottom: "fc7" top: "fc7" } layer { name: "score_fr" type: "Convolution" bottom: "fc7" top: "score_fr" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 21 pad: 0 kernel_size: 1 } } layer { name: "upscore2" type: "Deconvolution" bottom: "score_fr" top: "upscore2" param { lr_mult: 0 } convolution_param { num_output: 21 bias_term: false kernel_size: 4 stride: 2 } } layer { name: "score_pool4" type: "Convolution" bottom: "pool4" top: "score_pool4" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 21 pad: 0 kernel_size: 1 } } layer { name: "score_pool4c" type: "Crop" bottom: "score_pool4" bottom: "upscore2" top: "score_pool4c" crop_param { axis: 2 offset: 5 } } layer { name: "fuse_pool4" type: "Eltwise" bottom: "upscore2" bottom: "score_pool4c" top: "fuse_pool4" eltwise_param { operation: SUM } } layer { name: "upscore_pool4" type: "Deconvolution" bottom: "fuse_pool4" top: "upscore_pool4" param { lr_mult: 0 } convolution_param { num_output: 21 bias_term: false kernel_size: 4 stride: 2 } } layer { name: "score_pool3" type: "Convolution" bottom: "pool3" top: "score_pool3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 21 pad: 0 kernel_size: 1 } } layer { name: "score_pool3c" type: "Crop" bottom: "score_pool3" bottom: "upscore_pool4" top: "score_pool3c" crop_param { axis: 2 offset: 9 } } layer { name: "fuse_pool3" type: "Eltwise" bottom: "upscore_pool4" bottom: "score_pool3c" top: "fuse_pool3" eltwise_param { operation: SUM } } layer { name: "upscore8" type: "Deconvolution" bottom: "fuse_pool3" top: "upscore8" param { lr_mult: 0 } convolution_param { num_output: 21 bias_term: false kernel_size: 16 stride: 8 } } layer { name: "score" type: "Crop" bottom: "upscore8" bottom: "data" top: "score" crop_param { axis: 2 offset: 31 } }