Created
November 6, 2017 07:37
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name: "ZF" | |
input: "data" | |
input_shape { | |
dim: 1 | |
dim: 3 | |
dim: 224 | |
dim: 224 | |
} | |
input: "im_info" | |
input_shape { | |
dim: 1 | |
dim: 3 | |
} | |
# ------------------------ layer 1 ----------------------------- | |
layer { | |
name: "conv1" | |
type: "Convolution" | |
bottom: "data" | |
top: "conv1" | |
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" | |
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" | |
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" | |
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" | |
convolution_param { | |
num_output: 256 | |
kernel_size: 3 | |
pad: 1 | |
stride: 1 | |
} | |
} | |
layer { | |
name: "relu5" | |
type: "ReLU" | |
bottom: "conv5" | |
top: "conv5" | |
} | |
#-----------------------layer +------------------------- | |
layer { | |
name: "rpn_conv1" | |
type: "Convolution" | |
bottom: "conv5" | |
top: "rpn_conv1" | |
convolution_param { | |
num_output: 256 | |
kernel_size: 3 pad: 1 stride: 1 | |
} | |
} | |
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" | |
convolution_param { | |
num_output: 18 # 2(bg/fg) * 9(anchors) | |
kernel_size: 1 pad: 0 stride: 1 | |
} | |
} | |
layer { | |
name: "rpn_bbox_pred" | |
type: "Convolution" | |
bottom: "rpn_conv1" | |
top: "rpn_bbox_pred" | |
convolution_param { | |
num_output: 36 # 4 * 9(anchors) | |
kernel_size: 1 pad: 0 stride: 1 | |
} | |
} | |
layer { | |
bottom: "rpn_cls_score" | |
top: "rpn_cls_score_reshape" | |
name: "rpn_cls_score_reshape" | |
type: "Reshape" | |
reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } | |
} | |
#-----------------------output------------------------ | |
layer { | |
name: "rpn_cls_prob" | |
type: "Softmax" | |
bottom: "rpn_cls_score_reshape" | |
top: "rpn_cls_prob" | |
} | |
layer { | |
name: 'rpn_cls_prob_reshape' | |
type: 'Reshape' | |
bottom: 'rpn_cls_prob' | |
top: 'rpn_cls_prob_reshape' | |
reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } } | |
} | |
layer { | |
name: 'proposal' | |
type: 'Python' | |
bottom: 'rpn_cls_prob_reshape' | |
bottom: 'rpn_bbox_pred' | |
bottom: 'im_info' | |
top: 'rois' | |
top: 'scores' | |
python_param { | |
module: 'rpn.proposal_layer' | |
layer: 'ProposalLayer' | |
param_str: "'feat_stride': 16" | |
} | |
} |
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