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deeplab_largeFOV_train
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| # VGG 16-layer network convolutional finetuning | |
| # Network modified to have smaller receptive field (128 pixels) | |
| # and smaller stride (8 pixels) when run in convolutional mode. | |
| # | |
| # In this model we also change max pooling size in the first 4 layers | |
| # from 2 to 3 while retaining stride = 2 | |
| # which makes it easier to exactly align responses at different layers. | |
| # | |
| # For alignment to work, we set (we choose 32x so as to be able to evaluate | |
| # the model for all different subsampling sizes): | |
| # (1) input dimension equal to | |
| # $n = 32 * k - 31$, e.g., 321 (for k = 11) | |
| # Dimension after pooling w. subsampling: | |
| # (16 * k - 15); (8 * k - 7); (4 * k - 3); (2 * k - 1); (k). | |
| # For k = 11, these translate to | |
| # 161; 81; 41; 21; 11 | |
| # | |
| name: "DeepLab-LargeFOV" | |
| layers { | |
| name: "data" | |
| type: IMAGE_SEG_DATA | |
| top: "data" | |
| top: "label" | |
| image_data_param { | |
| root_folder: "exper/voc12/data" | |
| source: "exper/voc12/list/trainval.txt" | |
| label_type: PIXEL | |
| batch_size: 30 | |
| shuffle: true | |
| } | |
| transform_param { | |
| mean_value: 104.008 | |
| mean_value: 116.669 | |
| mean_value: 122.675 | |
| crop_size: 321 | |
| mirror: true | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| ### NETWORK ### | |
| layers { | |
| bottom: "data" | |
| top: "conv1_1" | |
| name: "conv1_1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| name: "relu1_1" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv1_1" | |
| top: "conv1_2" | |
| name: "conv1_2" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| name: "relu1_2" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv1_2" | |
| top: "pool1" | |
| name: "pool1" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 1 | |
| } | |
| } | |
| layers { | |
| bottom: "pool1" | |
| top: "conv2_1" | |
| name: "conv2_1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| name: "relu2_1" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| name: "conv2_2" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| name: "relu2_2" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv2_2" | |
| top: "pool2" | |
| name: "pool2" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 1 | |
| } | |
| } | |
| layers { | |
| bottom: "pool2" | |
| top: "conv3_1" | |
| name: "conv3_1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| name: "relu3_1" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| name: "conv3_2" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| name: "relu3_2" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| name: "conv3_3" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| name: "relu3_3" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv3_3" | |
| top: "pool3" | |
| name: "pool3" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 1 | |
| } | |
| } | |
| layers { | |
| bottom: "pool3" | |
| top: "conv4_1" | |
| name: "conv4_1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| name: "relu4_1" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| name: "conv4_2" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| name: "relu4_2" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| name: "conv4_3" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| name: "relu4_3" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv4_3" | |
| top: "pool4" | |
| name: "pool4" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| } | |
| } | |
| layers { | |
| bottom: "pool4" | |
| top: "conv5_1" | |
| name: "conv5_1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 512 | |
| #pad: 1 | |
| pad: 2 | |
| hole: 2 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| name: "relu5_1" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| name: "conv5_2" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 512 | |
| #pad: 1 | |
| pad: 2 | |
| hole: 2 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| name: "relu5_2" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| name: "conv5_3" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 512 | |
| #pad: 1 | |
| pad: 2 | |
| hole: 2 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| name: "relu5_3" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv5_3" | |
| top: "pool5" | |
| name: "pool5" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| #kernel_size: 2 | |
| #stride: 2 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layers { | |
| bottom: "pool5" | |
| top: "pool5a" | |
| name: "pool5a" | |
| type: POOLING | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layers { | |
| bottom: "pool5a" | |
| top: "fc6" | |
| name: "fc6" | |
| type: CONVOLUTION | |
| # strict_dim: false | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 1024 | |
| pad: 12 | |
| hole: 12 | |
| kernel_size: 3 | |
| } | |
| } | |
| layers { | |
| bottom: "fc6" | |
| top: "fc6" | |
| name: "relu6" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "fc6" | |
| top: "fc6" | |
| name: "drop6" | |
| type: DROPOUT | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layers { | |
| bottom: "fc6" | |
| top: "fc7" | |
| name: "fc7" | |
| type: CONVOLUTION | |
| # strict_dim: false | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 1024 | |
| kernel_size: 1 | |
| } | |
| } | |
| layers { | |
| bottom: "fc7" | |
| top: "fc7" | |
| name: "relu7" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "fc7" | |
| top: "fc7" | |
| name: "drop7" | |
| type: DROPOUT | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layers { | |
| bottom: "fc7" | |
| top: "fc8_exper/voc12" | |
| name: "fc8_exper/voc12" | |
| type: CONVOLUTION | |
| # strict_dim: false | |
| blobs_lr: 10 | |
| blobs_lr: 20 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 21 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "label" | |
| top: "label_shrink" | |
| name: "label_shrink" | |
| type: INTERP | |
| interp_param { | |
| shrink_factor: 8 | |
| pad_beg: 0 | |
| pad_end: 0 | |
| } | |
| } | |
| layers { | |
| name: "loss" | |
| type: SOFTMAX_LOSS | |
| bottom: "fc8_exper/voc12" | |
| bottom: "label_shrink" | |
| softmaxloss_param { | |
| #weight_source: "exper/voc12/loss_weight/loss_weight_train.txt" | |
| ignore_label: 255 | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| layers { | |
| name: "accuracy" | |
| type: SEG_ACCURACY | |
| bottom: "fc8_exper/voc12" | |
| bottom: "label_shrink" | |
| top: "accuracy" | |
| seg_accuracy_param { | |
| ignore_label: 255 | |
| } | |
| } |
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