Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- name: "VGG_ILSVRC_16_layers"
- input: "data"
- input_shape {
- dim: 1
- dim: 3
- dim: 224
- dim: 224
- }
- input: "im_info"
- input_shape {
- dim: 1
- dim: 3
- }
- layer {
- name: "conv1_1"
- type: "Convolution"
- bottom: "data"
- top: "conv1_1"
- convolution_param {
- num_output: 64
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu1_1"
- type: "ReLU"
- bottom: "conv1_1"
- top: "conv1_1"
- }
- layer {
- name: "conv1_2"
- type: "Convolution"
- bottom: "conv1_1"
- top: "conv1_2"
- convolution_param {
- num_output: 64
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu1_2"
- type: "ReLU"
- bottom: "conv1_2"
- top: "conv1_2"
- }
- layer {
- name: "pool1"
- type: "Pooling"
- bottom: "conv1_2"
- top: "pool1"
- pooling_param {
- pool: MAX
- kernel_size: 2 stride: 2
- }
- }
- layer {
- name: "conv2_1"
- type: "Convolution"
- bottom: "pool1"
- top: "conv2_1"
- convolution_param {
- num_output: 128
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu2_1"
- type: "ReLU"
- bottom: "conv2_1"
- top: "conv2_1"
- }
- layer {
- name: "conv2_2"
- type: "Convolution"
- bottom: "conv2_1"
- top: "conv2_2"
- convolution_param {
- num_output: 128
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu2_2"
- type: "ReLU"
- bottom: "conv2_2"
- top: "conv2_2"
- }
- layer {
- name: "pool2"
- type: "Pooling"
- bottom: "conv2_2"
- top: "pool2"
- pooling_param {
- pool: MAX
- kernel_size: 2 stride: 2
- }
- }
- layer {
- name: "conv3_1"
- type: "Convolution"
- bottom: "pool2"
- top: "conv3_1"
- convolution_param {
- num_output: 256
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu3_1"
- type: "ReLU"
- bottom: "conv3_1"
- top: "conv3_1"
- }
- layer {
- name: "conv3_2"
- type: "Convolution"
- bottom: "conv3_1"
- top: "conv3_2"
- convolution_param {
- num_output: 256
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu3_2"
- type: "ReLU"
- bottom: "conv3_2"
- top: "conv3_2"
- }
- layer {
- name: "conv3_3"
- type: "Convolution"
- bottom: "conv3_2"
- top: "conv3_3"
- convolution_param {
- num_output: 256
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu3_3"
- type: "ReLU"
- bottom: "conv3_3"
- top: "conv3_3"
- }
- layer {
- name: "pool3"
- type: "Pooling"
- bottom: "conv3_3"
- top: "pool3"
- pooling_param {
- pool: MAX
- kernel_size: 2 stride: 2
- }
- }
- layer {
- name: "conv4_1"
- type: "Convolution"
- bottom: "pool3"
- top: "conv4_1"
- convolution_param {
- num_output: 512
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu4_1"
- type: "ReLU"
- bottom: "conv4_1"
- top: "conv4_1"
- }
- layer {
- name: "conv4_2"
- type: "Convolution"
- bottom: "conv4_1"
- top: "conv4_2"
- convolution_param {
- num_output: 512
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu4_2"
- type: "ReLU"
- bottom: "conv4_2"
- top: "conv4_2"
- }
- layer {
- name: "conv4_3"
- type: "Convolution"
- bottom: "conv4_2"
- top: "conv4_3"
- convolution_param {
- num_output: 512
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu4_3"
- type: "ReLU"
- bottom: "conv4_3"
- top: "conv4_3"
- }
- layer {
- name: "pool4"
- type: "Pooling"
- bottom: "conv4_3"
- top: "pool4"
- pooling_param {
- pool: MAX
- kernel_size: 2 stride: 2
- }
- }
- layer {
- name: "conv5_1"
- type: "Convolution"
- bottom: "pool4"
- top: "conv5_1"
- convolution_param {
- num_output: 512
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu5_1"
- type: "ReLU"
- bottom: "conv5_1"
- top: "conv5_1"
- }
- layer {
- name: "conv5_2"
- type: "Convolution"
- bottom: "conv5_1"
- top: "conv5_2"
- convolution_param {
- num_output: 512
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu5_2"
- type: "ReLU"
- bottom: "conv5_2"
- top: "conv5_2"
- }
- layer {
- name: "conv5_3"
- type: "Convolution"
- bottom: "conv5_2"
- top: "conv5_3"
- convolution_param {
- num_output: 512
- pad: 1 kernel_size: 3
- }
- }
- layer {
- name: "relu5_3"
- type: "ReLU"
- bottom: "conv5_3"
- top: "conv5_3"
- }
- #========= RPN ============
- layer {
- name: "rpn_conv/3x3"
- type: "Convolution"
- bottom: "conv5_3"
- top: "rpn/output"
- convolution_param {
- num_output: 512
- kernel_size: 3 pad: 1 stride: 1
- }
- }
- layer {
- name: "rpn_relu/3x3"
- type: "ReLU"
- bottom: "rpn/output"
- top: "rpn/output"
- }
- layer {
- name: "rpn_cls_score"
- type: "Convolution"
- bottom: "rpn/output"
- 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/output"
- 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 } }
- }
- #========= RoI Proposal ============
- 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"
- }
- }
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement