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October 3, 2017 18:12
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| name: "facial_point_net" | |
| input: "data" | |
| input_dim: 1 | |
| input_dim: 3 | |
| input_dim: 448 | |
| input_dim: 448 | |
| layer { | |
| name: "downsample_data" | |
| type: "SubsamplePooling" | |
| bottom: "data" | |
| top: "downsample_data" | |
| subsample_pooling_param { | |
| output_H: 60 | |
| output_W: 60 | |
| } | |
| } | |
| ########################################## | |
| layer { | |
| name: "net1_conv1" | |
| type: "Convolution" | |
| bottom: "downsample_data" | |
| top: "net1_conv1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 20 | |
| pad: 0 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "net1_PReLU1" | |
| type: "PReLU" | |
| bottom: "net1_conv1" | |
| top: "net1_conv1" | |
| } | |
| layer { | |
| name: "net1_pool1" | |
| type: "Pooling" | |
| bottom: "net1_conv1" | |
| top: "net1_pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "net1_conv2" | |
| type: "Convolution" | |
| bottom: "net1_pool1" | |
| top: "net1_conv2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| pad: 0 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "net1_PReLU2" | |
| type: "PReLU" | |
| bottom: "net1_conv2" | |
| top: "net1_conv2" | |
| } | |
| layer { | |
| name: "net1_pool2" | |
| type: "Pooling" | |
| bottom: "net1_conv2" | |
| top: "net1_pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "net1_conv3" | |
| type: "Convolution" | |
| bottom: "net1_pool2" | |
| top: "net1_conv3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 0 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "net1_PReLU3" | |
| type: "PReLU" | |
| bottom: "net1_conv3" | |
| top: "net1_conv3" | |
| } | |
| layer { | |
| name: "net1_pool3" | |
| type: "Pooling" | |
| bottom: "net1_conv3" | |
| top: "net1_pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "net1_conv4" | |
| type: "Convolution" | |
| bottom: "net1_pool3" | |
| top: "net1_conv4" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 80 | |
| pad: 0 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "net1_PReLU4" | |
| type: "PReLU" | |
| bottom: "net1_conv4" | |
| top: "net1_conv4" | |
| } | |
| layer { | |
| name: "net1_fc5_1" | |
| type: "InnerProduct" | |
| bottom: "net1_conv4" | |
| top: "net1_fc5" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 512 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "net1_PReLU5" | |
| type: "PReLU" | |
| bottom: "net1_fc5" | |
| top: "net1_fc5" | |
| } | |
| layer { | |
| name: "net1_drop6" | |
| type: "Dropout" | |
| bottom: "net1_fc5" | |
| top: "net1_fc5" | |
| dropout_param { | |
| dropout_ratio: 0.2 | |
| } | |
| } | |
| layer { | |
| name: "net1_68point" | |
| type: "InnerProduct" | |
| bottom: "net1_fc5" | |
| top: "net1_68point" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 136 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "net1_PReLU6" | |
| type: "PReLU" | |
| bottom: "net1_68point" | |
| top: "net1_68point" | |
| } | |
| ################# st ##################### | |
| layer { | |
| name: "loc_reg_" | |
| type: "InnerProduct" | |
| bottom: "net1_68point" | |
| top: "theta" | |
| param { | |
| lr_mult: 0.02 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 6 | |
| weight_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| bias_filler { | |
| type: "file" | |
| file: "affine_bias_init.txt" | |
| } | |
| } | |
| } | |
| # transform data based on theta | |
| layer { | |
| name: "st_layer" | |
| type: "SpatialTransformer" | |
| bottom: "data" | |
| bottom: "theta" | |
| top: "st_data" | |
| st_param { | |
| to_compute_dU: false | |
| output_H: 224 | |
| output_W: 224 | |
| } | |
| } | |
| ######################################### | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "st_data" | |
| top: "conv1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| # pad: 3 | |
| kernel_size: 7 | |
| stride: 2 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "norm1" | |
| type: "LRN" | |
| bottom: "conv1" | |
| top: "norm1" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0005 | |
| beta: 0.75 | |
| k: 2 | |
| } | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "norm1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 3 | |
| } | |
| } | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 5 | |
| # pad: 2 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "conv2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3" | |
| type: "ReLU" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layer { | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "conv3" | |
| top: "conv4" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4" | |
| type: "ReLU" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layer { | |
| name: "conv5" | |
| type: "Convolution" | |
| bottom: "conv4" | |
| top: "conv5" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5" | |
| type: "ReLU" | |
| bottom: "conv5" | |
| top: "conv5" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "conv5" | |
| top: "pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 3 | |
| } | |
| } | |
| layer { | |
| name: "fc6" | |
| type: "InnerProduct" | |
| bottom: "pool3" | |
| top: "fc6" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6" | |
| type: "ReLU" | |
| bottom: "fc6" | |
| top: "fc6" | |
| } | |
| layer { | |
| name: "drop6" | |
| type: "Dropout" | |
| bottom: "fc6" | |
| top: "fc6" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc7" | |
| type: "InnerProduct" | |
| bottom: "fc6" | |
| top: "fc7" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7" | |
| type: "ReLU" | |
| bottom: "fc7" | |
| top: "fc7" | |
| } | |
| layer { | |
| name: "drop7" | |
| type: "Dropout" | |
| bottom: "fc7" | |
| top: "fc7" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "68point" | |
| type: "InnerProduct" | |
| bottom: "fc7" | |
| top: "68point" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 136 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| ###################split label############################### | |
| layer { | |
| name: "seperate_label" | |
| type: "Slice" | |
| bottom: "68point" | |
| top: "sub_net1_contour_label" | |
| top: "sub_net2_Leyebrow_label" | |
| top: "sub_net3_Reyebrow_label" | |
| top: "sub_net4_Nose_label" | |
| top: "sub_net5_Leye_label" | |
| top: "sub_net6_Reye_label" | |
| top: "sub_net7_Mouth_label" | |
| slice_param { | |
| axis: 1 | |
| slice_point: 34 | |
| slice_point: 44 | |
| slice_point: 54 | |
| slice_point: 72 | |
| slice_point: 84 | |
| slice_point: 96 | |
| } | |
| } | |
| layer { | |
| name: "cat_left_label" | |
| type: "Concat" | |
| bottom: "sub_net2_Leyebrow_label" | |
| bottom: "sub_net5_Leye_label" | |
| top: "cat_left_label" | |
| } | |
| layer { | |
| name: "cat_right_label" | |
| type: "Concat" | |
| bottom: "sub_net3_Reyebrow_label" | |
| bottom: "sub_net6_Reye_label" | |
| top: "cat_right_label" | |
| } | |
| layer { | |
| name: "sub_net1_contour_label" | |
| type: "Silence" | |
| bottom: "sub_net1_contour_label" | |
| } | |
| ################# sub_net4_st ##################### | |
| layer { | |
| name: "sub_net4_theta" | |
| type: "InnerProduct" | |
| bottom: "sub_net4_Nose_label" | |
| top: "sub_net4_theta" | |
| param { | |
| lr_mult: 0.01 | |
| decay_mult: 0.01 | |
| } | |
| param { | |
| lr_mult: 0.02 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 6 | |
| weight_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| bias_filler { | |
| type: "file" | |
| file: "sub_net4_bias_init.txt" | |
| } | |
| } | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "sub_net4_label" | |
| type: "PointTransformer" | |
| bottom: "sub_net4_Nose_label" | |
| bottom: "sub_net4_theta" | |
| top: "sub_net4_label" | |
| pt_param { | |
| transform_type: AFFINE | |
| inv_trans: false | |
| } | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| # transform data based on theta | |
| layer { | |
| name: "sub_net4_st_layer" | |
| type: "SpatialTransformer" | |
| bottom: "st_data" | |
| bottom: "sub_net4_theta" | |
| top: "sub_net4_data" | |
| st_param { | |
| to_compute_dU: false | |
| output_H: 60 | |
| output_W: 60 | |
| } | |
| } | |
| ####################################################################### | |
| ####################################################################### | |
| layer { | |
| name: "sub_net4_conv1" | |
| type: "Convolution" | |
| bottom: "sub_net4_data" | |
| top: "sub_net4_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 20 | |
| pad: 0 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net4_PReLU1" | |
| type: "PReLU" | |
| bottom: "sub_net4_conv1" | |
| top: "sub_net4_conv1" | |
| } | |
| layer { | |
| name: "sub_net4_pool1" | |
| type: "Pooling" | |
| bottom: "sub_net4_conv1" | |
| top: "sub_net4_pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net4_conv2" | |
| type: "Convolution" | |
| bottom: "sub_net4_pool1" | |
| top: "sub_net4_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| pad: 0 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net4_PReLU2" | |
| type: "PReLU" | |
| bottom: "sub_net4_conv2" | |
| top: "sub_net4_conv2" | |
| } | |
| layer { | |
| name: "sub_net4_pool2" | |
| type: "Pooling" | |
| bottom: "sub_net4_conv2" | |
| top: "sub_net4_pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net4_conv3" | |
| type: "Convolution" | |
| bottom: "sub_net4_pool2" | |
| top: "sub_net4_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 0 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net4_PReLU3" | |
| type: "PReLU" | |
| bottom: "sub_net4_conv3" | |
| top: "sub_net4_conv3" | |
| } | |
| layer { | |
| name: "sub_net4_pool3" | |
| type: "Pooling" | |
| bottom: "sub_net4_conv3" | |
| top: "sub_net4_pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net4_conv4" | |
| type: "Convolution" | |
| bottom: "sub_net4_pool3" | |
| top: "sub_net4_conv4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 80 | |
| pad: 0 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net4_PReLU4" | |
| type: "PReLU" | |
| bottom: "sub_net4_conv4" | |
| top: "sub_net4_conv4" | |
| } | |
| layer { | |
| name: "sub_net4_fc5" | |
| type: "InnerProduct" | |
| bottom: "sub_net4_conv4" | |
| top: "sub_net4_fc5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 512 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net4_PReLU5" | |
| type: "PReLU" | |
| bottom: "sub_net4_fc5" | |
| top: "sub_net4_fc5" | |
| } | |
| layer { | |
| name: "sub_net4_drop6" | |
| type: "Dropout" | |
| bottom: "sub_net4_fc5" | |
| top: "sub_net4_fc5" | |
| dropout_param { | |
| dropout_ratio: 0.2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net4_Nose" | |
| type: "InnerProduct" | |
| bottom: "sub_net4_fc5" | |
| top: "sub_net4_Nose" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 18 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net4_PReLU6" | |
| type: "PReLU" | |
| bottom: "sub_net4_Nose" | |
| top: "sub_net4_Nose" | |
| } | |
| ################# sub_net5_st ##################### | |
| layer { | |
| name: "sub_net5_theta" | |
| type: "InnerProduct" | |
| bottom: "cat_left_label" | |
| top: "sub_net5_theta" | |
| param { | |
| lr_mult: 0.01 | |
| decay_mult: 0.01 | |
| } | |
| param { | |
| lr_mult: 0.02 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 6 | |
| weight_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| bias_filler { | |
| type: "file" | |
| file: "sub_net5_bias_init.txt" | |
| } | |
| } | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "sub_net5_label" | |
| type: "PointTransformer" | |
| bottom: "cat_left_label" | |
| bottom: "sub_net5_theta" | |
| top: "sub_net5_label" | |
| pt_param { | |
| transform_type: AFFINE | |
| inv_trans: false | |
| } | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| # transform data based on theta | |
| layer { | |
| name: "sub_net5_st_layer" | |
| type: "SpatialTransformer" | |
| bottom: "st_data" | |
| bottom: "sub_net5_theta" | |
| top: "sub_net5_data" | |
| st_param { | |
| to_compute_dU: false | |
| output_H: 60 | |
| output_W: 60 | |
| } | |
| } | |
| ####################################################################### | |
| ####################################################################### | |
| layer { | |
| name: "sub_net5_conv1" | |
| type: "Convolution" | |
| bottom: "sub_net5_data" | |
| top: "sub_net5_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 20 | |
| pad: 0 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net5_PReLU1" | |
| type: "PReLU" | |
| bottom: "sub_net5_conv1" | |
| top: "sub_net5_conv1" | |
| } | |
| layer { | |
| name: "sub_net5_pool1" | |
| type: "Pooling" | |
| bottom: "sub_net5_conv1" | |
| top: "sub_net5_pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net5_conv2" | |
| type: "Convolution" | |
| bottom: "sub_net5_pool1" | |
| top: "sub_net5_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| pad: 0 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net5_PReLU2" | |
| type: "PReLU" | |
| bottom: "sub_net5_conv2" | |
| top: "sub_net5_conv2" | |
| } | |
| layer { | |
| name: "sub_net5_pool2" | |
| type: "Pooling" | |
| bottom: "sub_net5_conv2" | |
| top: "sub_net5_pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net5_conv3" | |
| type: "Convolution" | |
| bottom: "sub_net5_pool2" | |
| top: "sub_net5_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 0 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net5_PReLU3" | |
| type: "PReLU" | |
| bottom: "sub_net5_conv3" | |
| top: "sub_net5_conv3" | |
| } | |
| layer { | |
| name: "sub_net5_pool3" | |
| type: "Pooling" | |
| bottom: "sub_net5_conv3" | |
| top: "sub_net5_pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net5_conv4" | |
| type: "Convolution" | |
| bottom: "sub_net5_pool3" | |
| top: "sub_net5_conv4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 80 | |
| pad: 0 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net5_PReLU4" | |
| type: "PReLU" | |
| bottom: "sub_net5_conv4" | |
| top: "sub_net5_conv4" | |
| } | |
| layer { | |
| name: "sub_net5_fc5" | |
| type: "InnerProduct" | |
| bottom: "sub_net5_conv4" | |
| top: "sub_net5_fc5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 512 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net5_PReLU5" | |
| type: "PReLU" | |
| bottom: "sub_net5_fc5" | |
| top: "sub_net5_fc5" | |
| } | |
| layer { | |
| name: "sub_net5_drop6" | |
| type: "Dropout" | |
| bottom: "sub_net5_fc5" | |
| top: "sub_net5_fc5" | |
| dropout_param { | |
| dropout_ratio: 0.2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net5_Leye" | |
| type: "InnerProduct" | |
| bottom: "sub_net5_fc5" | |
| top: "sub_net5_Leye" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 22 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net5_PReLU6" | |
| type: "PReLU" | |
| bottom: "sub_net5_Leye" | |
| top: "sub_net5_Leye" | |
| } | |
| ################# sub_net6_st ##################### | |
| layer { | |
| name: "sub_net6_theta" | |
| type: "InnerProduct" | |
| bottom: "cat_right_label" | |
| top: "sub_net6_theta" | |
| param { | |
| lr_mult: 0.01 | |
| decay_mult: 0.01 | |
| } | |
| param { | |
| lr_mult: 0.02 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 6 | |
| weight_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| bias_filler { | |
| type: "file" | |
| file: "sub_net6_bias_init.txt" | |
| } | |
| } | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "sub_net6_label" | |
| type: "PointTransformer" | |
| bottom: "cat_right_label" | |
| bottom: "sub_net6_theta" | |
| top: "sub_net6_label" | |
| pt_param { | |
| transform_type: AFFINE | |
| inv_trans: false | |
| } | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| # transform data based on theta | |
| layer { | |
| name: "sub_net6_st_layer" | |
| type: "SpatialTransformer" | |
| bottom: "st_data" | |
| bottom: "sub_net6_theta" | |
| top: "sub_net6_data" | |
| st_param { | |
| to_compute_dU: false | |
| output_H: 60 | |
| output_W: 60 | |
| } | |
| } | |
| ####################################################################### | |
| ####################################################################### | |
| layer { | |
| name: "sub_net6_conv1" | |
| type: "Convolution" | |
| bottom: "sub_net6_data" | |
| top: "sub_net6_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 20 | |
| pad: 0 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net6_PReLU1" | |
| type: "PReLU" | |
| bottom: "sub_net6_conv1" | |
| top: "sub_net6_conv1" | |
| } | |
| layer { | |
| name: "sub_net6_pool1" | |
| type: "Pooling" | |
| bottom: "sub_net6_conv1" | |
| top: "sub_net6_pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net6_conv2" | |
| type: "Convolution" | |
| bottom: "sub_net6_pool1" | |
| top: "sub_net6_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| pad: 0 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net6_PReLU2" | |
| type: "PReLU" | |
| bottom: "sub_net6_conv2" | |
| top: "sub_net6_conv2" | |
| } | |
| layer { | |
| name: "sub_net6_pool2" | |
| type: "Pooling" | |
| bottom: "sub_net6_conv2" | |
| top: "sub_net6_pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net6_conv3" | |
| type: "Convolution" | |
| bottom: "sub_net6_pool2" | |
| top: "sub_net6_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 0 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net6_PReLU3" | |
| type: "PReLU" | |
| bottom: "sub_net6_conv3" | |
| top: "sub_net6_conv3" | |
| } | |
| layer { | |
| name: "sub_net6_pool3" | |
| type: "Pooling" | |
| bottom: "sub_net6_conv3" | |
| top: "sub_net6_pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net6_conv4" | |
| type: "Convolution" | |
| bottom: "sub_net6_pool3" | |
| top: "sub_net6_conv4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 80 | |
| pad: 0 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net6_PReLU4" | |
| type: "PReLU" | |
| bottom: "sub_net6_conv4" | |
| top: "sub_net6_conv4" | |
| } | |
| layer { | |
| name: "sub_net6_fc5" | |
| type: "InnerProduct" | |
| bottom: "sub_net6_conv4" | |
| top: "sub_net6_fc5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 512 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net6_PReLU5" | |
| type: "PReLU" | |
| bottom: "sub_net6_fc5" | |
| top: "sub_net6_fc5" | |
| } | |
| layer { | |
| name: "sub_net6_drop6" | |
| type: "Dropout" | |
| bottom: "sub_net6_fc5" | |
| top: "sub_net6_fc5" | |
| dropout_param { | |
| dropout_ratio: 0.2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net6_Reye" | |
| type: "InnerProduct" | |
| bottom: "sub_net6_fc5" | |
| top: "sub_net6_Reye" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 22 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net6_PReLU6" | |
| type: "PReLU" | |
| bottom: "sub_net6_Reye" | |
| top: "sub_net6_Reye" | |
| } | |
| ################# sub_net7_st ##################### | |
| layer { | |
| name: "sub_net7_theta" | |
| type: "InnerProduct" | |
| bottom: "sub_net7_Mouth_label" | |
| top: "sub_net7_theta" | |
| param { | |
| lr_mult: 0.01 | |
| decay_mult: 0.01 | |
| } | |
| param { | |
| lr_mult: 0.02 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 6 | |
| weight_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| bias_filler { | |
| type: "file" | |
| file: "sub_net7_bias_init.txt" | |
| } | |
| } | |
| propagate_down: false | |
| } | |
| layer { | |
| name: "sub_net7_label" | |
| type: "PointTransformer" | |
| bottom: "sub_net7_Mouth_label" | |
| bottom: "sub_net7_theta" | |
| top: "sub_net7_label" | |
| pt_param { | |
| transform_type: AFFINE | |
| inv_trans: false | |
| } | |
| propagate_down: false | |
| propagate_down: false | |
| } | |
| # transform data based on theta | |
| layer { | |
| name: "sub_net7_st_layer" | |
| type: "SpatialTransformer" | |
| bottom: "st_data" | |
| bottom: "sub_net7_theta" | |
| top: "sub_net7_data" | |
| st_param { | |
| to_compute_dU: false | |
| output_H: 60 | |
| output_W: 60 | |
| } | |
| } | |
| ####################################################################### | |
| ####################################################################### | |
| layer { | |
| name: "sub_net7_conv1" | |
| type: "Convolution" | |
| bottom: "sub_net7_data" | |
| top: "sub_net7_conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 20 | |
| pad: 0 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net7_PReLU1" | |
| type: "PReLU" | |
| bottom: "sub_net7_conv1" | |
| top: "sub_net7_conv1" | |
| } | |
| layer { | |
| name: "sub_net7_pool1" | |
| type: "Pooling" | |
| bottom: "sub_net7_conv1" | |
| top: "sub_net7_pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net7_conv2" | |
| type: "Convolution" | |
| bottom: "sub_net7_pool1" | |
| top: "sub_net7_conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| pad: 0 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net7_PReLU2" | |
| type: "PReLU" | |
| bottom: "sub_net7_conv2" | |
| top: "sub_net7_conv2" | |
| } | |
| layer { | |
| name: "sub_net7_pool2" | |
| type: "Pooling" | |
| bottom: "sub_net7_conv2" | |
| top: "sub_net7_pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net7_conv3" | |
| type: "Convolution" | |
| bottom: "sub_net7_pool2" | |
| top: "sub_net7_conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 0 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net7_PReLU3" | |
| type: "PReLU" | |
| bottom: "sub_net7_conv3" | |
| top: "sub_net7_conv3" | |
| } | |
| layer { | |
| name: "sub_net7_pool3" | |
| type: "Pooling" | |
| bottom: "sub_net7_conv3" | |
| top: "sub_net7_pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net7_conv4" | |
| type: "Convolution" | |
| bottom: "sub_net7_pool3" | |
| top: "sub_net7_conv4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 80 | |
| pad: 0 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net7_PReLU4" | |
| type: "PReLU" | |
| bottom: "sub_net7_conv4" | |
| top: "sub_net7_conv4" | |
| } | |
| layer { | |
| name: "sub_net7_fc5" | |
| type: "InnerProduct" | |
| bottom: "sub_net7_conv4" | |
| top: "sub_net7_fc5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 512 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net7_PReLU5" | |
| type: "PReLU" | |
| bottom: "sub_net7_fc5" | |
| top: "sub_net7_fc5" | |
| } | |
| layer { | |
| name: "sub_net7_drop6" | |
| type: "Dropout" | |
| bottom: "sub_net7_fc5" | |
| top: "sub_net7_fc5" | |
| dropout_param { | |
| dropout_ratio: 0.2 | |
| } | |
| } | |
| layer { | |
| name: "sub_net7_Mouth" | |
| type: "InnerProduct" | |
| bottom: "sub_net7_fc5" | |
| top: "sub_net7_Mouth" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 40 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub_net7_PReLU6" | |
| type: "PReLU" | |
| bottom: "sub_net7_Mouth" | |
| top: "sub_net7_Mouth" | |
| } | |
| ########################################################### | |
| layer { | |
| name: "net4_Nose" | |
| type: "Eltwise" | |
| bottom: "sub_net4_label" | |
| bottom: "sub_net4_Nose" | |
| top: "net4_Nose" | |
| eltwise_param { | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "pre_Nose_label" | |
| type: "PointTransformer" | |
| bottom: "net4_Nose" | |
| bottom: "sub_net4_theta" | |
| top: "pre_Nose_label" | |
| pt_param { | |
| transform_type: AFFINE | |
| inv_trans: true | |
| } | |
| } | |
| layer { | |
| name: "net5_Leye" | |
| type: "Eltwise" | |
| bottom: "sub_net5_label" | |
| bottom: "sub_net5_Leye" | |
| top: "net5_Leye" | |
| eltwise_param { | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "pre_Leye_label" | |
| type: "PointTransformer" | |
| bottom: "net5_Leye" | |
| bottom: "sub_net5_theta" | |
| top: "pre_Leye_label" | |
| pt_param { | |
| transform_type: AFFINE | |
| inv_trans: true | |
| } | |
| } | |
| layer { | |
| name: "net6_Reye" | |
| type: "Eltwise" | |
| bottom: "sub_net6_label" | |
| bottom: "sub_net6_Reye" | |
| top: "net6_Reye" | |
| eltwise_param { | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "pre_Reye_label" | |
| type: "PointTransformer" | |
| bottom: "net6_Reye" | |
| bottom: "sub_net6_theta" | |
| top: "pre_Reye_label" | |
| pt_param { | |
| transform_type: AFFINE | |
| inv_trans: true | |
| } | |
| } | |
| layer { | |
| name: "net7_Mouth" | |
| type: "Eltwise" | |
| bottom: "sub_net7_label" | |
| bottom: "sub_net7_Mouth" | |
| top: "net7_Mouth" | |
| eltwise_param { | |
| coeff: 1 | |
| coeff: 1 | |
| } | |
| } | |
| layer { | |
| name: "pre_Mouth_label" | |
| type: "PointTransformer" | |
| bottom: "net7_Mouth" | |
| bottom: "sub_net7_theta" | |
| top: "pre_Mouth_label" | |
| pt_param { | |
| transform_type: AFFINE | |
| inv_trans: true | |
| } | |
| } | |
| layer { | |
| name: "cat_label" | |
| type: "Concat" | |
| bottom: "sub_net1_contour_label" | |
| bottom: "pre_Nose_label" | |
| bottom: "pre_Leye_label" | |
| bottom: "pre_Reye_label" | |
| bottom: "pre_Mouth_label" | |
| top: "cat_label" | |
| } | |
| layer { | |
| name: "pre_label" | |
| type: "PointTransformer" | |
| bottom: "cat_label" | |
| bottom: "theta" | |
| top: "pre_label" | |
| pt_param { | |
| transform_type: AFFINE | |
| inv_trans: true | |
| } | |
| } |
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