Created
December 13, 2017 10:18
-
-
Save sriharsha0806/551f6b72e308c68e72517031eb5c9069 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| name: "VGG_ILSVRC_16_layer" | |
| layer { | |
| name: "data" | |
| type: "DenseImageData" | |
| top: "data" | |
| top: "label" | |
| dense_image_data_param { | |
| source: "/SegNet/CamVid/test.txt" # Change this to the absolute path to your data file | |
| batch_size: 4 # Change this to be the number of Monte Carlo Dropout samples you wish to make | |
| } | |
| } | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| name: "conv1_1_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| name: "conv1_2_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| name: "conv2_1_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| name: "conv2_2_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| name: "conv3_1_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| name: "conv3_2_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| name: "conv3_3_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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 { | |
| name: "encdrop3" | |
| type: "Dropout" | |
| bottom: "pool3" | |
| top: "pool3" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| sample_weights_test: true | |
| } | |
| } | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| name: "conv4_1_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| name: "conv4_2_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| name: "conv4_3_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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 { | |
| name: "encdrop4" | |
| type: "Dropout" | |
| bottom: "pool4" | |
| top: "pool4" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| sample_weights_test: true | |
| } | |
| } | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| name: "conv5_1_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| name: "conv5_2_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| name: "conv5_3_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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: "encdrop5" | |
| type: "Dropout" | |
| bottom: "pool5" | |
| top: "pool5" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| sample_weights_test: true | |
| } | |
| } | |
| layer { | |
| name: "upsample5" | |
| type: "Upsample" | |
| bottom: "pool5" | |
| top: "pool5_D" | |
| bottom: "pool5_mask" | |
| upsample_param { | |
| scale: 2 | |
| upsample_w: 30 | |
| upsample_h: 23 | |
| } | |
| } | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3_D" | |
| top: "conv5_3_D" | |
| name: "conv5_3_D_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2_D" | |
| top: "conv5_2_D" | |
| name: "conv5_2_D_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1_D" | |
| top: "conv5_1_D" | |
| name: "conv5_1_D_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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: "decdrop5" | |
| type: "Dropout" | |
| bottom: "conv5_1_D" | |
| top: "conv5_1_D" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| sample_weights_test: true | |
| } | |
| } | |
| layer { | |
| name: "upsample4" | |
| type: "Upsample" | |
| bottom: "conv5_1_D" | |
| top: "pool4_D" | |
| bottom: "pool4_mask" | |
| upsample_param { | |
| scale: 2 | |
| upsample_w: 60 | |
| upsample_h: 45 | |
| } | |
| } | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3_D" | |
| top: "conv4_3_D" | |
| name: "conv4_3_D_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2_D" | |
| top: "conv4_2_D" | |
| name: "conv4_2_D_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1_D" | |
| top: "conv4_1_D" | |
| name: "conv4_1_D_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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: "decdrop4" | |
| type: "Dropout" | |
| bottom: "conv4_1_D" | |
| top: "conv4_1_D" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| sample_weights_test: true | |
| } | |
| } | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3_D" | |
| top: "conv3_3_D" | |
| name: "conv3_3_D_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2_D" | |
| top: "conv3_2_D" | |
| name: "conv3_2_D_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1_D" | |
| top: "conv3_1_D" | |
| name: "conv3_1_D_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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: "decdrop3" | |
| type: "Dropout" | |
| bottom: "conv3_1_D" | |
| top: "conv3_1_D" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| sample_weights_test: true | |
| } | |
| } | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2_D" | |
| top: "conv2_2_D" | |
| name: "conv2_2_D_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1_D" | |
| top: "conv2_1_D" | |
| name: "conv2_1_D_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2_D" | |
| top: "conv1_2_D" | |
| name: "conv1_2_D_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| 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" | |
| value: 0 | |
| } | |
| num_output: 11 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "prob" | |
| type: "Softmax" | |
| bottom: "conv1_1_D" | |
| top: "prob" | |
| softmax_param {engine: CAFFE} | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment