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shufflenetSimple2
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| name: "shufflenet" | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| data_param { | |
| source: "/u02/xchen/char/ImageData/char/gray/lmdb128train/" | |
| batch_size: 256 | |
| backend: LMDB | |
| } | |
| transform_param { | |
| scale: 0.00390625 | |
| mean_file: "/u02/xchen/char/ImageData/char/gray/gray128mean.binaryproto" | |
| crop_size: 128 | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| } | |
| data_param { | |
| source: "/u02/xchen/char/ImageData/char/gray/lmdb128val/" | |
| batch_size: 1 | |
| backend: LMDB | |
| } | |
| transform_param { | |
| scale: 0.00390625 | |
| mean_file: "/u02/xchen/char/ImageData/char/gray/gray128mean.binaryproto" | |
| crop_size: 128 | |
| } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1" | |
| convolution_param { | |
| num_output: 48 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| bias_term: false | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_relu" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "resx1_match_conv" | |
| type: "Pooling" | |
| bottom: "conv1" | |
| top: "resx1_match_conv" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 0 | |
| } | |
| } | |
| layer { | |
| name: "resx1_conv1" | |
| type: "Convolution" | |
| bottom: "conv1" | |
| top: "resx1_conv1" | |
| convolution_param { | |
| num_output: 12 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| bias_term: false | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "resx1_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx1_conv1" | |
| top: "resx1_conv1" | |
| } | |
| layer { | |
| name: "resx1_conv2" | |
| type: "ConvolutionDepthwise" | |
| bottom: "resx1_conv1" | |
| top: "resx1_conv2" | |
| convolution_param { | |
| num_output: 12 | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 1 | |
| bias_term: false | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "resx1_conv3" | |
| type: "Convolution" | |
| bottom: "resx1_conv2" | |
| top: "resx1_conv3" | |
| convolution_param { | |
| num_output: 81 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| group: 3 | |
| bias_term: false | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "resx1_concat" | |
| type: "Concat" | |
| bottom: "resx1_match_conv" | |
| bottom: "resx1_conv3" | |
| top: "resx1_concat" | |
| } | |
| layer { | |
| name: "resx1_concat_relu" | |
| type: "ReLU" | |
| bottom: "resx1_concat" | |
| top: "resx1_concat" | |
| } | |
| layer { | |
| name: "resx5_match_conv" | |
| type: "Pooling" | |
| bottom: "resx1_concat" | |
| top: "resx5_match_conv" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "resx5_conv1" | |
| type: "Convolution" | |
| bottom: "resx1_concat" | |
| top: "resx5_conv1" | |
| convolution_param { | |
| num_output: 33 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| group: 3 | |
| bias_term: false | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "resx5_conv1_relu" | |
| type: "ReLU" | |
| bottom: "resx5_conv1" | |
| top: "resx5_conv1" | |
| } | |
| layer { | |
| name: "shuffle5" | |
| type: "ShuffleChannel" | |
| bottom: "resx5_conv1" | |
| top: "shuffle5" | |
| shuffle_channel_param { | |
| group: 3 | |
| } | |
| } | |
| layer { | |
| name: "resx5_conv2" | |
| type: "ConvolutionDepthwise" | |
| bottom: "shuffle5" | |
| top: "resx5_conv2" | |
| convolution_param { | |
| num_output: 33 | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 1 | |
| bias_term: false | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "resx5_conv3" | |
| type: "Convolution" | |
| bottom: "resx5_conv2" | |
| top: "resx5_conv3" | |
| convolution_param { | |
| num_output: 384 | |
| kernel_size: 1 | |
| stride: 1 | |
| pad: 0 | |
| group: 3 | |
| bias_term: false | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "resx5_concat" | |
| type: "Concat" | |
| bottom: "resx5_match_conv" | |
| bottom: "resx5_conv3" | |
| top: "resx5_concat" | |
| } | |
| layer { | |
| name: "resx5_concat_relu" | |
| type: "ReLU" | |
| bottom: "resx5_concat" | |
| top: "resx5_concat" | |
| } | |
| layer { | |
| name: "pool_ave" | |
| type: "Pooling" | |
| bottom: "resx5_concat" | |
| top: "pool_ave" | |
| pooling_param { | |
| global_pooling : true | |
| pool: AVE | |
| } | |
| } | |
| layer { | |
| name: "fc7" | |
| type: "Convolution" | |
| bottom: "pool_ave" | |
| top: "fc7" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 7906 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc7" | |
| bottom: "label" | |
| top: "loss" | |
| } | |
| layer { | |
| name: "accuracy" | |
| type: "Accuracy" | |
| bottom: "fc7" | |
| bottom: "label" | |
| top: "accuracy" | |
| include { | |
| phase: TEST | |
| } | |
| } |
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