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December 29, 2018 01:39
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deploy_hands17_baseline.prototxt
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| name: "HANDS17-Pose-REN" | |
| input: "data" | |
| input_shape { | |
| dim: 1 | |
| dim: 1 | |
| dim: 96 | |
| dim: 96 | |
| } | |
| input: "prev_pose" | |
| input_shape { | |
| dim: 1 | |
| dim: 63 | |
| } | |
| layer { | |
| name: "conv0" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv0" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu0" | |
| type: "ReLU" | |
| bottom: "conv0" | |
| top: "conv0" | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "conv0" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "pool1" | |
| top: "pool1" | |
| } | |
| layer { | |
| name: "conv2_0" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2_0" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_0" | |
| type: "ReLU" | |
| bottom: "conv2_0" | |
| top: "conv2_0" | |
| } | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "conv2_0" | |
| top: "conv2" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "conv2" | |
| top: "conv3" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "res1" | |
| type: "Eltwise" | |
| bottom: "conv2_0" | |
| bottom: "conv3" | |
| top: "res1" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "res1" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "relu3" | |
| type: "ReLU" | |
| bottom: "pool2" | |
| top: "pool2" | |
| } | |
| layer { | |
| name: "conv3_0" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3_0" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_0" | |
| type: "ReLU" | |
| bottom: "conv3_0" | |
| top: "conv3_0" | |
| } | |
| layer { | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "conv3_0" | |
| top: "conv4" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4" | |
| type: "ReLU" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layer { | |
| name: "conv5" | |
| type: "Convolution" | |
| bottom: "conv4" | |
| top: "conv5" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "res2" | |
| type: "Eltwise" | |
| bottom: "conv3_0" | |
| bottom: "conv5" | |
| top: "res2" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "res2" | |
| top: "pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "relu5" | |
| type: "ReLU" | |
| bottom: "pool3" | |
| top: "pool3" | |
| } | |
| layer { | |
| name: "rois_0" | |
| type: "Python" | |
| bottom: "prev_pose" | |
| top: "rois_0" | |
| python_param { | |
| module: "python_layers.py_generate_roi_layer" | |
| layer: "PyGenerateROILayer" | |
| param_str: "{\'img_w\': 96, \'roi_h\': 6, \'spatial_mul\': 8, \'roi_w\': 6, \'joint_idx\': 0, \'img_h\': 96}" | |
| } | |
| } | |
| layer { | |
| name: "roi_pool_0" | |
| type: "ROIPooling" | |
| bottom: "pool3" | |
| bottom: "rois_0" | |
| top: "roi_pool_0" | |
| roi_pooling_param { | |
| pooled_h: 7 | |
| pooled_w: 7 | |
| spatial_scale: 0.125 | |
| } | |
| } | |
| layer { | |
| name: "fc1_0" | |
| type: "InnerProduct" | |
| bottom: "roi_pool_0" | |
| top: "fc1_0" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6_0" | |
| type: "ReLU" | |
| bottom: "fc1_0" | |
| top: "fc1_0" | |
| } | |
| layer { | |
| name: "drop1_0" | |
| type: "Dropout" | |
| bottom: "fc1_0" | |
| top: "fc1_0" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "rois_6" | |
| type: "Python" | |
| bottom: "prev_pose" | |
| top: "rois_6" | |
| python_param { | |
| module: "python_layers.py_generate_roi_layer" | |
| layer: "PyGenerateROILayer" | |
| param_str: "{\'img_w\': 96, \'roi_h\': 6, \'spatial_mul\': 8, \'roi_w\': 6, \'joint_idx\': 6, \'img_h\': 96}" | |
| } | |
| } | |
| layer { | |
| name: "roi_pool_6" | |
| type: "ROIPooling" | |
| bottom: "pool3" | |
| bottom: "rois_6" | |
| top: "roi_pool_6" | |
| roi_pooling_param { | |
| pooled_h: 7 | |
| pooled_w: 7 | |
| spatial_scale: 0.125 | |
| } | |
| } | |
| layer { | |
| name: "fc1_6" | |
| type: "InnerProduct" | |
| bottom: "roi_pool_6" | |
| top: "fc1_6" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6_6" | |
| type: "ReLU" | |
| bottom: "fc1_6" | |
| top: "fc1_6" | |
| } | |
| layer { | |
| name: "drop1_6" | |
| type: "Dropout" | |
| bottom: "fc1_6" | |
| top: "fc1_6" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "rois_8" | |
| type: "Python" | |
| bottom: "prev_pose" | |
| top: "rois_8" | |
| python_param { | |
| module: "python_layers.py_generate_roi_layer" | |
| layer: "PyGenerateROILayer" | |
| param_str: "{\'img_w\': 96, \'roi_h\': 6, \'spatial_mul\': 8, \'roi_w\': 6, \'joint_idx\': 8, \'img_h\': 96}" | |
| } | |
| } | |
| layer { | |
| name: "roi_pool_8" | |
| type: "ROIPooling" | |
| bottom: "pool3" | |
| bottom: "rois_8" | |
| top: "roi_pool_8" | |
| roi_pooling_param { | |
| pooled_h: 7 | |
| pooled_w: 7 | |
| spatial_scale: 0.125 | |
| } | |
| } | |
| layer { | |
| name: "fc1_8" | |
| type: "InnerProduct" | |
| bottom: "roi_pool_8" | |
| top: "fc1_8" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6_8" | |
| type: "ReLU" | |
| bottom: "fc1_8" | |
| top: "fc1_8" | |
| } | |
| layer { | |
| name: "drop1_8" | |
| type: "Dropout" | |
| bottom: "fc1_8" | |
| top: "fc1_8" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "rois_9" | |
| type: "Python" | |
| bottom: "prev_pose" | |
| top: "rois_9" | |
| python_param { | |
| module: "python_layers.py_generate_roi_layer" | |
| layer: "PyGenerateROILayer" | |
| param_str: "{\'img_w\': 96, \'roi_h\': 6, \'spatial_mul\': 8, \'roi_w\': 6, \'joint_idx\': 9, \'img_h\': 96}" | |
| } | |
| } | |
| layer { | |
| name: "roi_pool_9" | |
| type: "ROIPooling" | |
| bottom: "pool3" | |
| bottom: "rois_9" | |
| top: "roi_pool_9" | |
| roi_pooling_param { | |
| pooled_h: 7 | |
| pooled_w: 7 | |
| spatial_scale: 0.125 | |
| } | |
| } | |
| layer { | |
| name: "fc1_9" | |
| type: "InnerProduct" | |
| bottom: "roi_pool_9" | |
| top: "fc1_9" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6_9" | |
| type: "ReLU" | |
| bottom: "fc1_9" | |
| top: "fc1_9" | |
| } | |
| layer { | |
| name: "drop1_9" | |
| type: "Dropout" | |
| bottom: "fc1_9" | |
| top: "fc1_9" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "rois_11" | |
| type: "Python" | |
| bottom: "prev_pose" | |
| top: "rois_11" | |
| python_param { | |
| module: "python_layers.py_generate_roi_layer" | |
| layer: "PyGenerateROILayer" | |
| param_str: "{\'img_w\': 96, \'roi_h\': 6, \'spatial_mul\': 8, \'roi_w\': 6, \'joint_idx\': 11, \'img_h\': 96}" | |
| } | |
| } | |
| layer { | |
| name: "roi_pool_11" | |
| type: "ROIPooling" | |
| bottom: "pool3" | |
| bottom: "rois_11" | |
| top: "roi_pool_11" | |
| roi_pooling_param { | |
| pooled_h: 7 | |
| pooled_w: 7 | |
| spatial_scale: 0.125 | |
| } | |
| } | |
| layer { | |
| name: "fc1_11" | |
| type: "InnerProduct" | |
| bottom: "roi_pool_11" | |
| top: "fc1_11" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6_11" | |
| type: "ReLU" | |
| bottom: "fc1_11" | |
| top: "fc1_11" | |
| } | |
| layer { | |
| name: "drop1_11" | |
| type: "Dropout" | |
| bottom: "fc1_11" | |
| top: "fc1_11" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "rois_12" | |
| type: "Python" | |
| bottom: "prev_pose" | |
| top: "rois_12" | |
| python_param { | |
| module: "python_layers.py_generate_roi_layer" | |
| layer: "PyGenerateROILayer" | |
| param_str: "{\'img_w\': 96, \'roi_h\': 6, \'spatial_mul\': 8, \'roi_w\': 6, \'joint_idx\': 12, \'img_h\': 96}" | |
| } | |
| } | |
| layer { | |
| name: "roi_pool_12" | |
| type: "ROIPooling" | |
| bottom: "pool3" | |
| bottom: "rois_12" | |
| top: "roi_pool_12" | |
| roi_pooling_param { | |
| pooled_h: 7 | |
| pooled_w: 7 | |
| spatial_scale: 0.125 | |
| } | |
| } | |
| layer { | |
| name: "fc1_12" | |
| type: "InnerProduct" | |
| bottom: "roi_pool_12" | |
| top: "fc1_12" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6_12" | |
| type: "ReLU" | |
| bottom: "fc1_12" | |
| top: "fc1_12" | |
| } | |
| layer { | |
| name: "drop1_12" | |
| type: "Dropout" | |
| bottom: "fc1_12" | |
| top: "fc1_12" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "rois_14" | |
| type: "Python" | |
| bottom: "prev_pose" | |
| top: "rois_14" | |
| python_param { | |
| module: "python_layers.py_generate_roi_layer" | |
| layer: "PyGenerateROILayer" | |
| param_str: "{\'img_w\': 96, \'roi_h\': 6, \'spatial_mul\': 8, \'roi_w\': 6, \'joint_idx\': 14, \'img_h\': 96}" | |
| } | |
| } | |
| layer { | |
| name: "roi_pool_14" | |
| type: "ROIPooling" | |
| bottom: "pool3" | |
| bottom: "rois_14" | |
| top: "roi_pool_14" | |
| roi_pooling_param { | |
| pooled_h: 7 | |
| pooled_w: 7 | |
| spatial_scale: 0.125 | |
| } | |
| } | |
| layer { | |
| name: "fc1_14" | |
| type: "InnerProduct" | |
| bottom: "roi_pool_14" | |
| top: "fc1_14" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6_14" | |
| type: "ReLU" | |
| bottom: "fc1_14" | |
| top: "fc1_14" | |
| } | |
| layer { | |
| name: "drop1_14" | |
| type: "Dropout" | |
| bottom: "fc1_14" | |
| top: "fc1_14" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "rois_15" | |
| type: "Python" | |
| bottom: "prev_pose" | |
| top: "rois_15" | |
| python_param { | |
| module: "python_layers.py_generate_roi_layer" | |
| layer: "PyGenerateROILayer" | |
| param_str: "{\'img_w\': 96, \'roi_h\': 6, \'spatial_mul\': 8, \'roi_w\': 6, \'joint_idx\': 15, \'img_h\': 96}" | |
| } | |
| } | |
| layer { | |
| name: "roi_pool_15" | |
| type: "ROIPooling" | |
| bottom: "pool3" | |
| bottom: "rois_15" | |
| top: "roi_pool_15" | |
| roi_pooling_param { | |
| pooled_h: 7 | |
| pooled_w: 7 | |
| spatial_scale: 0.125 | |
| } | |
| } | |
| layer { | |
| name: "fc1_15" | |
| type: "InnerProduct" | |
| bottom: "roi_pool_15" | |
| top: "fc1_15" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6_15" | |
| type: "ReLU" | |
| bottom: "fc1_15" | |
| top: "fc1_15" | |
| } | |
| layer { | |
| name: "drop1_15" | |
| type: "Dropout" | |
| bottom: "fc1_15" | |
| top: "fc1_15" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "rois_17" | |
| type: "Python" | |
| bottom: "prev_pose" | |
| top: "rois_17" | |
| python_param { | |
| module: "python_layers.py_generate_roi_layer" | |
| layer: "PyGenerateROILayer" | |
| param_str: "{\'img_w\': 96, \'roi_h\': 6, \'spatial_mul\': 8, \'roi_w\': 6, \'joint_idx\': 17, \'img_h\': 96}" | |
| } | |
| } | |
| layer { | |
| name: "roi_pool_17" | |
| type: "ROIPooling" | |
| bottom: "pool3" | |
| bottom: "rois_17" | |
| top: "roi_pool_17" | |
| roi_pooling_param { | |
| pooled_h: 7 | |
| pooled_w: 7 | |
| spatial_scale: 0.125 | |
| } | |
| } | |
| layer { | |
| name: "fc1_17" | |
| type: "InnerProduct" | |
| bottom: "roi_pool_17" | |
| top: "fc1_17" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6_17" | |
| type: "ReLU" | |
| bottom: "fc1_17" | |
| top: "fc1_17" | |
| } | |
| layer { | |
| name: "drop1_17" | |
| type: "Dropout" | |
| bottom: "fc1_17" | |
| top: "fc1_17" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "rois_18" | |
| type: "Python" | |
| bottom: "prev_pose" | |
| top: "rois_18" | |
| python_param { | |
| module: "python_layers.py_generate_roi_layer" | |
| layer: "PyGenerateROILayer" | |
| param_str: "{\'img_w\': 96, \'roi_h\': 6, \'spatial_mul\': 8, \'roi_w\': 6, \'joint_idx\': 18, \'img_h\': 96}" | |
| } | |
| } | |
| layer { | |
| name: "roi_pool_18" | |
| type: "ROIPooling" | |
| bottom: "pool3" | |
| bottom: "rois_18" | |
| top: "roi_pool_18" | |
| roi_pooling_param { | |
| pooled_h: 7 | |
| pooled_w: 7 | |
| spatial_scale: 0.125 | |
| } | |
| } | |
| layer { | |
| name: "fc1_18" | |
| type: "InnerProduct" | |
| bottom: "roi_pool_18" | |
| top: "fc1_18" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6_18" | |
| type: "ReLU" | |
| bottom: "fc1_18" | |
| top: "fc1_18" | |
| } | |
| layer { | |
| name: "drop1_18" | |
| type: "Dropout" | |
| bottom: "fc1_18" | |
| top: "fc1_18" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "rois_20" | |
| type: "Python" | |
| bottom: "prev_pose" | |
| top: "rois_20" | |
| python_param { | |
| module: "python_layers.py_generate_roi_layer" | |
| layer: "PyGenerateROILayer" | |
| param_str: "{\'img_w\': 96, \'roi_h\': 6, \'spatial_mul\': 8, \'roi_w\': 6, \'joint_idx\': 20, \'img_h\': 96}" | |
| } | |
| } | |
| layer { | |
| name: "roi_pool_20" | |
| type: "ROIPooling" | |
| bottom: "pool3" | |
| bottom: "rois_20" | |
| top: "roi_pool_20" | |
| roi_pooling_param { | |
| pooled_h: 7 | |
| pooled_w: 7 | |
| spatial_scale: 0.125 | |
| } | |
| } | |
| layer { | |
| name: "fc1_20" | |
| type: "InnerProduct" | |
| bottom: "roi_pool_20" | |
| top: "fc1_20" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6_20" | |
| type: "ReLU" | |
| bottom: "fc1_20" | |
| top: "fc1_20" | |
| } | |
| layer { | |
| name: "drop1_20" | |
| type: "Dropout" | |
| bottom: "fc1_20" | |
| top: "fc1_20" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "concate_1_0" | |
| type: "Concat" | |
| bottom: "fc1_0" | |
| bottom: "fc1_6" | |
| bottom: "fc1_8" | |
| top: "concate_1_0" | |
| } | |
| layer { | |
| name: "fc2_0" | |
| type: "InnerProduct" | |
| bottom: "concate_1_0" | |
| top: "fc2_0" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7_0" | |
| type: "ReLU" | |
| bottom: "fc2_0" | |
| top: "fc2_0" | |
| } | |
| layer { | |
| name: "drop2_0" | |
| type: "Dropout" | |
| bottom: "fc2_0" | |
| top: "fc2_0" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "concate_1_1" | |
| type: "Concat" | |
| bottom: "fc1_0" | |
| bottom: "fc1_9" | |
| bottom: "fc1_11" | |
| top: "concate_1_1" | |
| } | |
| layer { | |
| name: "fc2_1" | |
| type: "InnerProduct" | |
| bottom: "concate_1_1" | |
| top: "fc2_1" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7_1" | |
| type: "ReLU" | |
| bottom: "fc2_1" | |
| top: "fc2_1" | |
| } | |
| layer { | |
| name: "drop2_1" | |
| type: "Dropout" | |
| bottom: "fc2_1" | |
| top: "fc2_1" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "concate_1_2" | |
| type: "Concat" | |
| bottom: "fc1_0" | |
| bottom: "fc1_12" | |
| bottom: "fc1_14" | |
| top: "concate_1_2" | |
| } | |
| layer { | |
| name: "fc2_2" | |
| type: "InnerProduct" | |
| bottom: "concate_1_2" | |
| top: "fc2_2" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7_2" | |
| type: "ReLU" | |
| bottom: "fc2_2" | |
| top: "fc2_2" | |
| } | |
| layer { | |
| name: "drop2_2" | |
| type: "Dropout" | |
| bottom: "fc2_2" | |
| top: "fc2_2" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "concate_1_3" | |
| type: "Concat" | |
| bottom: "fc1_0" | |
| bottom: "fc1_15" | |
| bottom: "fc1_17" | |
| top: "concate_1_3" | |
| } | |
| layer { | |
| name: "fc2_3" | |
| type: "InnerProduct" | |
| bottom: "concate_1_3" | |
| top: "fc2_3" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7_3" | |
| type: "ReLU" | |
| bottom: "fc2_3" | |
| top: "fc2_3" | |
| } | |
| layer { | |
| name: "drop2_3" | |
| type: "Dropout" | |
| bottom: "fc2_3" | |
| top: "fc2_3" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "concate_1_4" | |
| type: "Concat" | |
| bottom: "fc1_0" | |
| bottom: "fc1_18" | |
| bottom: "fc1_20" | |
| top: "concate_1_4" | |
| } | |
| layer { | |
| name: "fc2_4" | |
| type: "InnerProduct" | |
| bottom: "concate_1_4" | |
| top: "fc2_4" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7_4" | |
| type: "ReLU" | |
| bottom: "fc2_4" | |
| top: "fc2_4" | |
| } | |
| layer { | |
| name: "drop2_4" | |
| type: "Dropout" | |
| bottom: "fc2_4" | |
| top: "fc2_4" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc_concat" | |
| type: "Concat" | |
| bottom: "fc2_0" | |
| bottom: "fc2_1" | |
| bottom: "fc2_2" | |
| bottom: "fc2_3" | |
| bottom: "fc2_4" | |
| top: "fc_concat" | |
| } | |
| layer { | |
| name: "fc3_0" | |
| type: "InnerProduct" | |
| bottom: "fc_concat" | |
| top: "predict" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| inner_product_param { | |
| num_output: 63 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } |
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