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faster_rcnn_caffe_net.txt
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faster_rcnn_caffe_net.txt
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name: "ZF"
layer {
name: 'input-data' #这一层就是最开始数据输入
type: 'Python'
top: 'data' # top表示该层的输出，所以可以看到这一层输出三组数据，data，真值框gt_boxes，和相关信息im_info
top: 'im_info' # 这些都是存储在矩阵中的
top: 'gt_boxes'
python_param {
module: 'roi_data_layer.layer'
layer: 'RoIDataLayer'
param_str: "'num_classes': 21"
}
}
#========= conv1-conv5 ============
layer {
name: "conv1"
type: "Convolution"
bottom: "data" # 输入data
top: "conv1" # 输出conv1，这里conv1就代表了这一层输出数据的名称，存储在对应的矩阵中
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 96
kernel_size: 7
pad: 3 # 这里可以看到卷积1层 填充了3个像素
stride: 2
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1" # 做归一化操作，通俗点说就是做个除法
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 # 池化的时候，又做了填充
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"
}
#========= RPN ============
# 到我们的RPN网络部分了，前面的都是共享的5层卷积层的部分
layer {
name: "rpn_conv1"
type: "Convolution"
bottom: "conv5"
top: "rpn_conv1"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 256
kernel_size: 3 pad: 1 stride: 1 #这里作者把每个滑窗3*3，通过3*3*256*256的卷积核输出256维，完整的输出其实是12*12*256,
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: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 18 # 2(bg/fg) * 9(anchors)
kernel_size: 1 pad: 0 stride: 1 #这里看的很清楚，作者通过1*1*256*18的卷积核，将前面的256维数据转换成了18个输出
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: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 36 # 4 * 9(anchors)
kernel_size: 1 pad: 0 stride: 1 <span style="font-family: Arial, Helvetica, sans-serif;">#这里看的很清楚，作者通过1*1*256*36的卷积核，将前面的256维数据转换成了36个输出</span>
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}
layer {
bottom: "rpn_cls_score"
top: "rpn_cls_score_reshape" # 我们之前说过，其实这一层是12*12*256的，所以后面我们要送给损失函数，需要将这个矩阵reshape一下，我们需要的是144个滑窗，每个对应的256的向量
name: "rpn_cls_score_reshape"
type: "Reshape"
reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
}
layer {
name: 'rpn-data'
type: 'Python'
bottom: 'rpn_cls_score'
bottom: 'gt_boxes'
bottom: 'im_info'
bottom: 'data'
top: 'rpn_labels'
top: 'rpn_bbox_targets'
top: 'rpn_bbox_inside_weights'
top: 'rpn_bbox_outside_weights'
python_param {
module: 'rpn.anchor_target_layer'
layer: 'AnchorTargetLayer'
param_str: "'feat_stride': 16"
}
}
layer {
name: "rpn_loss_cls"
type: "SoftmaxWithLoss" # 很明显这里是计算softmax的损失，输入labels和cls layer的18个输出（中间reshape了一下），输出损失函数的具体值
bottom: "rpn_cls_score_reshape"
bottom: "rpn_labels"
propagate_down: 1
propagate_down: 0
top: "rpn_cls_loss"
loss_weight: 1
loss_param {
ignore_label: -1
normalize: true
}
}
layer {
name: "rpn_loss_bbox"
type: "SmoothL1Loss" # 这里计算的框回归损失函数具体的值
bottom: "rpn_bbox_pred"
bottom: "rpn_bbox_targets"
bottom: "rpn_bbox_inside_weights"
bottom: "rpn_bbox_outside_weights"
top: "rpn_loss_bbox"
loss_weight: 1
smooth_l1_loss_param { sigma: 3.0 }
}
#========= RCNN ============
# Dummy layers so that initial parameters are saved into the output net
layer {
name: "dummy_roi_pool_conv5"
type: "DummyData"
top: "dummy_roi_pool_conv5"
dummy_data_param {
shape { dim: 1 dim: 9216 }
data_filler { type: "gaussian" std: 0.01 }
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "dummy_roi_pool_conv5"
top: "fc6"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
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
}
}
layer {
name: "silence_fc7"
type: "Silence"
bottom: "fc7"
}