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April 7, 2019 02:35
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| layer { | |
| name: "train-data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| mirror: true | |
| crop_size: 375 | |
| mean_file: "/home/xinge/dataset/ImageEmotion/example2/aug_emo_train_mean.binaryproto" | |
| } | |
| data_param { | |
| source: "/home/xinge/dataset/ImageEmotion/example2/aug_emo_train_lmdb/" | |
| batch_size: 64 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "val-data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| } | |
| transform_param { | |
| crop_size: 375 | |
| mean_file: "/home/xinge/dataset/ImageEmotion/example2/aug_emo_val_mean.binaryproto" | |
| } | |
| data_param { | |
| source: "/home/xinge/dataset/ImageEmotion/example2/aug_emo_val_lmdb/" | |
| batch_size: 50 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 11 | |
| stride: 4 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu0" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "cccp1" | |
| type: "Convolution" | |
| bottom: "conv1" | |
| top: "cccp1" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "cccp1" | |
| top: "cccp1" | |
| } | |
| layer { | |
| name: "cccp2" | |
| type: "Convolution" | |
| bottom: "cccp1" | |
| top: "cccp2" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2" | |
| type: "ReLU" | |
| bottom: "cccp2" | |
| top: "cccp2" | |
| } | |
| # ==== side 1 ==== | |
| # ==== side_1 ============================= | |
| layer { | |
| name: "pool0" | |
| type: "Pooling" | |
| bottom: "cccp2" | |
| top: "pool0" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| # ===== side_2 ======================= | |
| layer { | |
| name: "side_2" | |
| type: "Convolution" | |
| bottom: "pool0" | |
| top: "side_2" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "side_relu2" | |
| type: "ReLU" | |
| bottom: "side_2" | |
| top: "side_2" | |
| } | |
| layer { | |
| name: "side_pool2" | |
| type: "Pooling" | |
| bottom: "side_2" | |
| top: "side_pool2" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 46 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "side_drop2" | |
| type: "Dropout" | |
| bottom: "side_pool2" | |
| top: "side_pool2" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| #========= side_2 ========= | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "pool0" | |
| top: "conv2" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "cccp3" | |
| type: "Convolution" | |
| bottom: "conv2" | |
| top: "cccp3" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5" | |
| type: "ReLU" | |
| bottom: "cccp3" | |
| top: "cccp3" | |
| } | |
| layer { | |
| name: "cccp4" | |
| type: "Convolution" | |
| bottom: "cccp3" | |
| top: "cccp4" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6" | |
| type: "ReLU" | |
| bottom: "cccp4" | |
| top: "cccp4" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "cccp4" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| # size: 23 | |
| } | |
| } | |
| # ===== side_3 ======================= | |
| layer { | |
| name: "side_3" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "side_3" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "side_relu3" | |
| type: "ReLU" | |
| bottom: "side_3" | |
| top: "side_3" | |
| } | |
| layer { | |
| name: "side_pool3" | |
| type: "Pooling" | |
| bottom: "side_3" | |
| top: "side_pool3" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 23 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "side_drop3" | |
| type: "Dropout" | |
| bottom: "side_pool3" | |
| top: "side_pool3" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| #========= side_3 ========= | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7" | |
| type: "ReLU" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layer { | |
| name: "cccp5" | |
| type: "Convolution" | |
| bottom: "conv3" | |
| top: "cccp5" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu8" | |
| type: "ReLU" | |
| bottom: "cccp5" | |
| top: "cccp5" | |
| } | |
| layer { | |
| name: "cccp6" | |
| type: "Convolution" | |
| bottom: "cccp5" | |
| top: "cccp6" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu9" | |
| type: "ReLU" | |
| bottom: "cccp6" | |
| top: "cccp6" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "cccp6" | |
| top: "pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "drop" | |
| type: "Dropout" | |
| bottom: "pool3" | |
| top: "pool3" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| # ===== side_4 ======================= | |
| layer { | |
| name: "side_4" | |
| type: "Convolution" | |
| bottom: "pool3" | |
| top: "side_4" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "side_relu4" | |
| type: "ReLU" | |
| bottom: "side_4" | |
| top: "side_4" | |
| } | |
| layer { | |
| name: "side_pool4" | |
| type: "Pooling" | |
| bottom: "side_4" | |
| top: "side_pool4" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 11 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "side_drop4" | |
| type: "Dropout" | |
| bottom: "side_pool4" | |
| top: "side_pool4" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| #========= side_4 ========= | |
| layer { | |
| name: "conv4-1024" | |
| type: "Convolution" | |
| bottom: "pool3" | |
| top: "conv4" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| pad: 1 | |
| kernel_size: 4 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu10" | |
| type: "ReLU" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layer { | |
| name: "cccp7-1024" | |
| type: "Convolution" | |
| bottom: "conv4" | |
| top: "cccp7" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu11" | |
| type: "ReLU" | |
| bottom: "cccp7" | |
| top: "cccp7" | |
| } | |
| layer { | |
| name: "cccp8-1024" | |
| type: "Convolution" | |
| bottom: "cccp7" | |
| top: "cccp8" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu12" | |
| type: "ReLU" | |
| bottom: "cccp8" | |
| top: "cccp8" | |
| } | |
| layer { | |
| name: "pool4" | |
| type: "Pooling" | |
| bottom: "cccp8" | |
| top: "pool4" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| #=======side branch_5 ====== | |
| layer { | |
| name: "side_5" | |
| type: "Convolution" | |
| bottom: "pool4" | |
| top: "side_5" | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0.0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "side_relu5" | |
| type: "ReLU" | |
| bottom: "side_5" | |
| top: "side_5" | |
| } | |
| layer { | |
| name: "side_pool5" | |
| type: "Pooling" | |
| bottom: "side_5" | |
| top: "side_pool5" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 5 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "side_drop5" | |
| type: "Dropout" | |
| bottom: "side_pool5" | |
| top: "side_pool5" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| #=========================== | |
| layer { | |
| name: "concat" | |
| bottom: "side_pool5" | |
| bottom: "side_pool2" | |
| bottom: "side_pool3" | |
| bottom: "side_pool4" | |
| top: "out1" | |
| type: "Concat" | |
| concat_param { | |
| axis: 2 | |
| } | |
| } | |
| layer { | |
| name: "relu_con_1" | |
| type: "ReLU" | |
| bottom: "out1" | |
| top: "out1" | |
| } | |
| layer { | |
| name: "fc1" | |
| type: "InnerProduct" | |
| # learning rate and decay multipliers for the weights | |
| param { lr_mult: 2 decay_mult: 1 } | |
| # learning rate and decay multipliers for the biases | |
| param { lr_mult: 2 decay_mult: 0 } | |
| inner_product_param { | |
| num_output: 512 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| bottom: "out1" | |
| top: "fc1" | |
| } | |
| layer { | |
| name: "relu_con_2" | |
| type: "ReLU" | |
| bottom: "fc1" | |
| top: "fc1" | |
| } | |
| layer { | |
| name: "fc2" | |
| type: "InnerProduct" | |
| # learning rate and decay multipliers for the weights | |
| param { lr_mult: 2 decay_mult: 1 } | |
| # learning rate and decay multipliers for the biases | |
| param { lr_mult: 2 decay_mult: 0 } | |
| inner_product_param { | |
| num_output: 8 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| bottom: "fc1" | |
| top: "fc2" | |
| } | |
| layer { | |
| name: "accuracy1" | |
| type: "Accuracy" | |
| bottom: "fc2" | |
| bottom: "label" | |
| top: "accuracy1" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc2" | |
| bottom: "label" | |
| top: "loss" | |
| loss_weight: 1 | |
| } |
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