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July 20, 2017 13:50
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mvcnn.prototxt
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| name: "siameseMVCNN" | |
| layer { | |
| name: "inputdata" | |
| type: "Python" | |
| top: "data" | |
| top: "label" | |
| python_param { | |
| module: 'MVCNNDataLayer' | |
| layer: 'MVCNNDataLayer' | |
| param_str: "{'phase': 'train', 'batch_size': 1, 'view_size': 12,'channel_size': 3, 'data_path': './modelnet40v1'}" | |
| } | |
| include { | |
| phase: TRAIN | |
| } | |
| } | |
| layer { | |
| name: "inputdata" | |
| type: "Python" | |
| top: "data" | |
| top: "label" | |
| python_param { | |
| module: 'MVCNNDataLayer' | |
| layer: 'MVCNNDataLayer' | |
| param_str: "{'phase': 'test', 'batch_size': 1, 'view_size': 12,'channel_size': 3, 'data_path': './modelnet40v1'}" | |
| } | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 11 | |
| stride: 4 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "norm1" | |
| type: "LRN" | |
| bottom: "conv1" | |
| top: "norm1" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "norm1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 2 | |
| kernel_size: 5 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "norm2" | |
| type: "LRN" | |
| bottom: "conv2" | |
| top: "norm2" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "norm2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3" | |
| type: "ReLU" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layer { | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "conv3" | |
| top: "conv4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4" | |
| type: "ReLU" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layer { | |
| name: "conv5" | |
| type: "Convolution" | |
| bottom: "conv4" | |
| top: "conv5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5" | |
| type: "ReLU" | |
| bottom: "conv5" | |
| top: "conv5" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "conv5" | |
| top: "pool5" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "splitView" | |
| type : "Slice" | |
| bottom: "pool5" | |
| top : "model_view0" | |
| top : "model_view1" | |
| top : "model_view2" | |
| top : "model_view3" | |
| top : "model_view4" | |
| top : "model_view5" | |
| top : "model_view6" | |
| top : "model_view7" | |
| top : "model_view8" | |
| top : "model_view9" | |
| top : "model_view10" | |
| top : "model_view11" | |
| slice_param{ | |
| axis : 0 | |
| slice_point: 1 | |
| slice_point: 2 | |
| slice_point: 3 | |
| slice_point: 4 | |
| slice_point: 5 | |
| slice_point: 6 | |
| slice_point: 7 | |
| slice_point: 8 | |
| slice_point: 9 | |
| slice_point: 10 | |
| slice_point: 11 | |
| } | |
| } | |
| layer { | |
| name: "view_pooling_max" | |
| type: "Eltwise" | |
| bottom: "model_view0" | |
| bottom: "model_view1" | |
| bottom: "model_view2" | |
| bottom: "model_view3" | |
| bottom: "model_view4" | |
| bottom: "model_view5" | |
| bottom: "model_view6" | |
| bottom: "model_view7" | |
| bottom: "model_view8" | |
| bottom: "model_view9" | |
| bottom: "model_view10" | |
| bottom: "model_view11" | |
| top: "view_pooling_max" | |
| eltwise_param { | |
| operation: MAX | |
| } | |
| } | |
| layer { | |
| bottom: "view_pooling_max" | |
| top: "fc6" | |
| name: "fc6" | |
| type: "InnerProduct" | |
| inner_product_param { | |
| num_output: 4096 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "fc6" | |
| top: "fc6" | |
| name: "relu6" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "fc6" | |
| top: "fc6" | |
| name: "drop6" | |
| type: "Dropout" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc7" | |
| type: "InnerProduct" | |
| bottom: "fc6" | |
| top: "fc7" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| 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: "modelNet40" | |
| type: "InnerProduct" | |
| bottom: "fc7" | |
| top: "modelNet40" | |
| 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: "accuracy" | |
| type: "Accuracy" | |
| bottom: "modelNet40" | |
| bottom: "label" | |
| top: "accuracy" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "modelNet40" | |
| bottom: "label" | |
| top: "loss" | |
| } | |
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