py-faster-rcnn/models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_fast_rcnn_train.pt

来源：互联网发布：html5单页面静态源码编辑：程序博客网时间：2024/05/21 10:43

py-faster-rcnn/models/pascal_voc/ZF/faster_rcnn_alt_opt/stage1_fast_rcnn_train.pt

name: "ZF"

#三个模型文件，小型网络ZF，大型网络VGG16，中型网络#VGG_CNN_M_1024

layer {

name: 'data'

type: 'Python'

top: 'data'

top: 'rois'

top: 'labels'

top: 'bbox_targets'

top: 'bbox_inside_weights'

top: 'bbox_outside_weights'

python_param {

module: 'roi_data_layer.layer'

layer: 'RoIDataLayer'

param_str:"'num_classes': 21" #训练集类别，类别数+1

}

#========= conv1-conv5 ============

layer {

name: "conv1"

type: "Convolution"

bottom: "data"

top: "conv1"

param { lr_mult: 1.0 }

param { lr_mult: 2.0 }

convolution_param {

num_output: 96

kernel_size: 7

pad: 3

stride: 2

}

layer {

name: "relu1"

type: "ReLU"

bottom: "conv1"

top: "conv1"

}

layer {

name: "norm1"

type: "LRN"

bottom: "conv1"

top: "norm1"

lrn_param {

local_size: 3

alpha: 0.00005

beta: 0.75

norm_region:WITHIN_CHANNEL

engine: CAFFE

}

layer {

name: "pool1"

type: "Pooling"

bottom: "norm1"

top: "pool1"

pooling_param {

kernel_size: 3

stride: 2

pad: 1

pool: MAX

}

layer {

name: "conv2"

type: "Convolution"

bottom: "pool1"

top: "conv2"

param { lr_mult: 1.0 }

param { lr_mult: 2.0 }

convolution_param {

num_output: 256

kernel_size: 5

pad: 2

stride: 2

}

layer {

name: "relu2"

type: "ReLU"

bottom: "conv2"

top: "conv2"

}

layer {

name: "norm2"

type: "LRN"

bottom: "conv2"

top: "norm2"

lrn_param {

local_size: 3

alpha: 0.00005

beta: 0.75

norm_region:WITHIN_CHANNEL

engine: CAFFE

}

layer {

name: "pool2"

type: "Pooling"

bottom: "norm2"

top: "pool2"

pooling_param {

kernel_size: 3

stride: 2

pad: 1

pool: MAX

}

layer {

name: "conv3"

type: "Convolution"

bottom: "pool2"

top: "conv3"

param { lr_mult: 1.0 }

param { lr_mult: 2.0 }

convolution_param {

num_output: 384

kernel_size: 3

pad: 1

stride: 1

}

layer {

name: "relu3"

type: "ReLU"

bottom: "conv3"

top: "conv3"

}

layer {

name: "conv4"

type: "Convolution"

bottom: "conv3"

top: "conv4"

param { lr_mult: 1.0 }

param { lr_mult: 2.0 }

convolution_param {

num_output: 384

kernel_size: 3

pad: 1

stride: 1

}

layer {

name: "relu4"

type: "ReLU"

bottom: "conv4"

top: "conv4"

}

layer {

name: "conv5"

type: "Convolution"

bottom: "conv4"

top: "conv5"

param { lr_mult: 1.0 }

param { lr_mult: 2.0 }

convolution_param {

num_output: 256

kernel_size: 3

pad: 1

stride: 1

}

layer {

name: "relu5"

type: "ReLU"

bottom: "conv5"

top: "conv5"

}

#========= RCNN ============

layer {

name: "roi_pool_conv5"

type: "ROIPooling"

bottom: "conv5"

bottom: "rois"

top: "roi_pool_conv5"

roi_pooling_param {

pooled_w: 6

pooled_h: 6

spatial_scale: 0.0625 # 1/16

}

layer {

name: "fc6"

type: "InnerProduct"

bottom: "roi_pool_conv5"

top: "fc6"

param { lr_mult: 1.0 }

param { lr_mult: 2.0 }

inner_product_param {

num_output: 4096

}

layer {

name: "relu6"

type: "ReLU"

bottom: "fc6"

top: "fc6"

}

layer {

name: "drop6"

type: "Dropout"

bottom: "fc6"

top: "fc6"

dropout_param {

dropout_ratio: 0.5

scale_train: false

}

layer {

name: "fc7"

type: "InnerProduct"

bottom: "fc6"

top: "fc7"

param { lr_mult: 1.0 }

param { lr_mult: 2.0 }

inner_product_param {

num_output: 4096

}

layer {

name: "relu7"

type: "ReLU"

bottom: "fc7"

top: "fc7"

}

layer {

name: "drop7"

type: "Dropout"

bottom: "fc7"

top: "fc7"

dropout_param {

dropout_ratio: 0.5

scale_train: false

}

layer {

name: "cls_score"

type: "InnerProduct"

bottom: "fc7"

top: "cls_score"

param { lr_mult: 1.0 }

param { lr_mult: 2.0 }

inner_product_param {

num_output: 21#训练集类别，类别数+1

weight_filler {

type: "gaussian"

std: 0.01

}

bias_filler {

type: "constant"

value: 0

}

layer {

name: "bbox_pred"

type: "InnerProduct"

bottom: "fc7"

top: "bbox_pred"

param { lr_mult: 1.0 }

param { lr_mult: 2.0 }

inner_product_param {

num_output: 84#训练集类别，（类别数+1）*4

weight_filler {

type: "gaussian"

std: 0.001

}

bias_filler {

type: "constant"

value: 0

}

layer {

name: "loss_cls"

type: "SoftmaxWithLoss"

bottom: "cls_score"

bottom: "labels"

propagate_down: 1

propagate_down: 0

top: "cls_loss"

loss_weight: 1

loss_param {

ignore_label: -1

normalize: true

}

layer {

name: "loss_bbox"

type: "SmoothL1Loss"

bottom: "bbox_pred"

bottom: "bbox_targets"

bottom:"bbox_inside_weights"

bottom:"bbox_outside_weights"

top: "bbox_loss"

loss_weight: 1

}

#========= RPN ============

# Dummy layers so that initial parameters are saved into the output net

layer {

name: "rpn_conv1"

type: "Convolution"

bottom: "conv5"

top: "rpn_conv1"

param { lr_mult: 0 decay_mult: 0 }

convolution_param {

num_output: 256

kernel_size: 3 pad: 1 stride: 1

weight_filler { type:"gaussian" std: 0.01 }

bias_filler { type:"constant" value: 0 }

}

layer {

name: "rpn_relu1"

type: "ReLU"

bottom: "rpn_conv1"

top: "rpn_conv1"

}

layer {

name: "rpn_cls_score"

type: "Convolution"

bottom: "rpn_conv1"

top: "rpn_cls_score"

param { lr_mult: 0 decay_mult: 0 }

convolution_param {

num_output: 18 # 2(bg/fg) * 9(anchors)

kernel_size: 1 pad: 0 stride: 1

weight_filler { type:"gaussian" std: 0.01 }

bias_filler { type:"constant" value: 0 }

}

layer {

name: "rpn_bbox_pred"

type: "Convolution"

bottom: "rpn_conv1"

top: "rpn_bbox_pred"

param { lr_mult: 0 decay_mult: 0 }

convolution_param {

num_output: 36 # 4 * 9(anchors)

kernel_size: 1 pad: 0 stride: 1

weight_filler { type:"gaussian" std: 0.01 }

bias_filler { type:"constant" value: 0 }

}

layer {

name:"silence_rpn_cls_score"

type: "Silence"

bottom: "rpn_cls_score"

}

layer {

name:"silence_rpn_bbox_pred"

type: "Silence"

bottom: "rpn_bbox_pred"

}

阅读全文

0 0