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name: "Faster R-CNN vgg16" | |
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" | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
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" | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
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" | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
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" | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
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" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
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" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 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" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
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" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
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" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 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" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
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" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
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" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 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" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
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" | |
param { lr_mult: 1.0 } | |
param { lr_mult: 2.0 } | |
convolution_param { | |
num_output: 512 | |
kernel_size: 3 pad: 1 stride: 1 | |
weight_filler { type: "gaussian" std: 0.01 } | |
bias_filler { type: "constant" value: 0 } | |
} | |
} | |
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" | |
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 | |
weight_filler { type: "gaussian" std: 0.01 } | |
bias_filler { type: "constant" value: 0 } | |
} | |
} | |
layer { | |
name: "rpn_bbox_pred" | |
type: "Convolution" | |
bottom: "rpn/output" | |
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 | |
weight_filler { type: "gaussian" std: 0.01 } | |
bias_filler { type: "constant" value: 0 } | |
} | |
} | |
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 } } | |
} | |
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" | |
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" | |
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 } | |
} | |
#========= 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: 'rpn_rois' | |
# top: 'rpn_scores' | |
python_param { | |
module: 'rpn.proposal_layer' | |
layer: 'ProposalLayer' | |
param_str: "'feat_stride': 16" | |
} | |
} | |
#layer { | |
# name: 'debug-data' | |
# type: 'Python' | |
# bottom: 'data' | |
# bottom: 'rpn_rois' | |
# bottom: 'rpn_scores' | |
# python_param { | |
# module: 'rpn.debug_layer' | |
# layer: 'RPNDebugLayer' | |
# } | |
#} | |
layer { | |
name: 'roi-data' | |
type: 'Python' | |
bottom: 'rpn_rois' | |
bottom: 'gt_boxes' | |
top: 'rois' | |
top: 'labels' | |
top: 'bbox_targets' | |
top: 'bbox_inside_weights' | |
top: 'bbox_outside_weights' | |
python_param { | |
module: 'rpn.proposal_target_layer' | |
layer: 'ProposalTargetLayer' | |
param_str: "'num_classes': 21" | |
} | |
} | |
#========= RCNN ============ | |
layer { | |
name: "roi_pool5" | |
type: "ROIPooling" | |
bottom: "conv5_3" | |
bottom: "rois" | |
top: "pool5" | |
roi_pooling_param { | |
pooled_w: 7 | |
pooled_h: 7 | |
spatial_scale: 0.0625 # 1/16 | |
} | |
} | |
layer { | |
name: "fc6" | |
type: "InnerProduct" | |
bottom: "pool5" | |
top: "fc6" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
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 | |
} | |
} | |
layer { | |
name: "fc7" | |
type: "InnerProduct" | |
bottom: "fc6" | |
top: "fc7" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
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 | |
} | |
} | |
layer { | |
name: "cls_score" | |
type: "InnerProduct" | |
bottom: "fc7" | |
top: "cls_score" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 21 | |
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 | |
} | |
param { | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 84 | |
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: "loss_cls" | |
loss_weight: 1 | |
} | |
layer { | |
name: "loss_bbox" | |
type: "SmoothL1Loss" | |
bottom: "bbox_pred" | |
bottom: "bbox_targets" | |
bottom: "bbox_inside_weights" | |
bottom: "bbox_outside_weights" | |
top: "loss_bbox" | |
loss_weight: 1 | |
} |
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