OHEM的论文描述在https://arxiv.org/abs/1604.03540,主要思想是首先将所有ROI区域输入fast rcnn模块求得每个ROI的loss,对所有ROI的loss从大到小排序,选择前N个最大的loss的ROI样本进行训练网络更新fast rcnn的参数.故而叫做在线困难样本挖掘,因为只有每次迭代过程中才能确定哪些ROI区域作为训练样本,而训练过程主要选择loss最大的那些ROI.算法比较简单.作者主要实现了fast rcnn的OHEM,本文主要参考fast rcnn的OHEM实现faster rcnn的OHEM.
model
RPN的model保持不变,主要修改fast rcnn部分的model,如下
stage1_fast_rcnn_ohem_train.pt
name: "VGG_ILSVRC_16_layers"
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"
}
}
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"
}
##########################
## Readonly RoI Network ##
######### Start ##########
layer {
name: "roi_pool5_readonly"
type: "ROIPooling"
bottom: "conv5_3"
bottom: "rois"
top: "pool5_readonly"
propagate_down: false
propagate_down: false
roi_pooling_param {
pooled_w: 7
pooled_h: 7
spatial_scale: 0.0625 # 1/16
}
}
layer {
name: "fc6_readonly"
type: "InnerProduct"
bottom: "pool5_readonly"
top: "fc6_readonly"
propagate_down: false
param {
name: "fc6_w"
}
param {
name: "fc6_b"
}
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu6_readonly"
type: "ReLU"
bottom: "fc6_readonly"
top: "fc6_readonly"
propagate_down: false
}
layer {
name: "drop6_readonly"
type: "Dropout"
bottom: "fc6_readonly"
top: "fc6_readonly"
propagate_down: false
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7_readonly"
type: "InnerProduct"
bottom: "fc6_readonly"
top: "fc7_readonly"
propagate_down: false
param {
name: "fc7_w"
}
param {
name: "fc7_b"
}
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu7_readonly"
type: "ReLU"
bottom: "fc7_readonly"
top: "fc7_readonly"
propagate_down: false
}
layer {
name: "drop7_readonly"
type: "Dropout"
bottom: "fc7_readonly"
top: "fc7_readonly"
propagate_down: false
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "cls_score_readonly"
type: "InnerProduct"
bottom: "fc7_readonly"
top: "cls_score_readonly"
propagate_down: false
param {
name: "cls_score_w"
}
param {
name: "cls_score_b"
}
inner_product_param {
num_output: 21
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bbox_pred_readonly"
type: "InnerProduct"
bottom: "fc7_readonly"
top: "bbox_pred_readonly"
propagate_down: false
param {
name: "bbox_pred_w"
}
param {
name: "bbox_pred_b"
}
inner_product_param {
num_output: 84
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "cls_prob_readonly"
type: "Softmax"
bottom: "cls_score_readonly"
top: "cls_prob_readonly"
propagate_down: false
}
layer {
name: "hard_roi_mining"
type: "Python"
bottom: "cls_prob_readonly"
bottom: "bbox_pred_readonly"
bottom: "rois"
bottom: "labels"
bottom: "bbox_targets"
bottom: "bbox_inside_weights"
bottom: "bbox_outside_weights"
top: "rois_hard"
top: "labels_hard"
top: "bbox_targets_hard"
top: "bbox_inside_weights_hard"
top: "bbox_outside_weights_hard"
propagate_down: false
propagate_down: false
propagate_down: false
propagate_down: false
propagate_down: false
propagate_down: false
propagate_down: false
python_param {
module: "roi_data_layer.layer"
layer: "OHEMDataLayer"
param_str: "'num_classes': 21"
}
}
########## End ###########
## Readonly RoI Network ##
##########################
layer {
name: "roi_pool5"
type: "ROIPooling"
bottom: "conv5_3"
bottom: "rois_hard"
top: "pool5"
propagate_down: true
propagate_down: false
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 {
name: "fc6_w"
lr_mult: 1
}
param {
name: "fc6_b"
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 {
name: "fc7_w"
lr_mult: 1
}
param {
name: "fc7_b"
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 {
name: "cls_score_w"
lr_mult: 1
}
param {
name: "cls_score_b"
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 {
name: "bbox_pred_w"
lr_mult: 1
}
param {
name: "bbox_pred_b"
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_hard"
top: "loss_cls"
propagate_down: true
propagate_down: false
loss_weight: 1
}
layer {
name: "loss_bbox"
type: "SmoothL1Loss"
bottom: "bbox_pred"
bottom: "bbox_targets_hard"
bottom: "bbox_inside_weights_hard"
bottom: "bbox_outside_weights_hard"
top: "loss_bbox"
propagate_down: true
propagate_down: false
propagate_down: false
propagate_down: false
loss_weight: 1
}
#========= RPN ============
# Dummy layers so that initial parameters are saved into the output net
layer {
name: "rpn_conv/3x3"
type: "Convolution"
bottom: "conv5_3"
top: "rpn/output"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 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: 0 decay_mult: 0 }
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/output"
top: "rpn_bbox_pred"
param { lr_mult: 0 decay_mult: 0 }
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"
}stage2_fast_rcnn_ohem_train.pt
name: "VGG_ILSVRC_16_layers"
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"
}
}
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: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
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: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
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: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
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: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
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: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
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: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
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: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
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: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
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: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
name: "relu5_3"
type: "ReLU"
bottom: "conv5_3"
top: "conv5_3"
}
##########################
## Readonly RoI Network ##
######### Start ##########
layer {
name: "roi_pool5_readonly"
type: "ROIPooling"
bottom: "conv5_3"
bottom: "rois"
top: "pool5_readonly"
propagate_down: false
propagate_down: false
roi_pooling_param {
pooled_w: 7
pooled_h: 7
spatial_scale: 0.0625 # 1/16
}
}
layer {
name: "fc6_readonly"
type: "InnerProduct"
bottom: "pool5_readonly"
top: "fc6_readonly"
propagate_down: false
param {
name: "fc6_w"
}
param {
name: "fc6_b"
}
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu6_readonly"
type: "ReLU"
bottom: "fc6_readonly"
top: "fc6_readonly"
propagate_down: false
}
layer {
name: "drop6_readonly"
type: "Dropout"
bottom: "fc6_readonly"
top: "fc6_readonly"
propagate_down: false
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7_readonly"
type: "InnerProduct"
bottom: "fc6_readonly"
top: "fc7_readonly"
propagate_down: false
param {
name: "fc7_w"
}
param {
name: "fc7_b"
}
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu7_readonly"
type: "ReLU"
bottom: "fc7_readonly"
top: "fc7_readonly"
propagate_down: false
}
layer {
name: "drop7_readonly"
type: "Dropout"
bottom: "fc7_readonly"
top: "fc7_readonly"
propagate_down: false
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "cls_score_readonly"
type: "InnerProduct"
bottom: "fc7_readonly"
top: "cls_score_readonly"
propagate_down: false
param {
name: "cls_score_w"
}
param {
name: "cls_score_b"
}
inner_product_param {
num_output: 21
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "bbox_pred_readonly"
type: "InnerProduct"
bottom: "fc7_readonly"
top: "bbox_pred_readonly"
propagate_down: false
param {
name: "bbox_pred_w"
}
param {
name: "bbox_pred_b"
}
inner_product_param {
num_output: 84
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "cls_prob_readonly"
type: "Softmax"
bottom: "cls_score_readonly"
top: "cls_prob_readonly"
propagate_down: false
}
layer {
name: "hard_roi_mining"
type: "Python"
bottom: "cls_prob_readonly"
bottom: "bbox_pred_readonly"
bottom: "rois"
bottom: "labels"
bottom: "bbox_targets"
bottom: "bbox_inside_weights"
bottom: "bbox_outside_weights"
top: "rois_hard"
top: "labels_hard"
top: "bbox_targets_hard"
top: "bbox_inside_weights_hard"
top: "bbox_outside_weights_hard"
propagate_down: false
propagate_down: false
propagate_down: false
propagate_down: false
propagate_down: false
propagate_down: false
propagate_down: false
python_param {
module: "roi_data_layer.layer"
layer: "OHEMDataLayer"
param_str: "'num_classes': 21"
}
}
########## End ###########
## Readonly RoI Network ##
##########################
layer {
name: "roi_pool5"
type: "ROIPooling"
bottom: "conv5_3"
bottom: "rois_hard"
top: "pool5"
propagate_down: true
propagate_down: false
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 { name: "fc6_w" lr_mult: 1 }
param { name: "fc6_b" 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 { name: "fc7_w" lr_mult: 1 }
param { name: "fc7_b" 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 { name: "cls_score_w" lr_mult: 1 }
param { name: "cls_score_b" 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 { name: "bbox_pred_w" lr_mult: 1 }
param { name: "bbox_pred_b" 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_hard"
top: "loss_cls"
propagate_down: true
propagate_down: false
loss_weight: 1
}
layer {
name: "loss_bbox"
type: "SmoothL1Loss"
bottom: "bbox_pred"
bottom: "bbox_targets_hard"
bottom: "bbox_inside_weights_hard"
bottom: "bbox_outside_weights_hard"
top: "loss_bbox"
propagate_down: true
propagate_down: false
propagate_down: false
propagate_down: false
loss_weight: 1
}
#========= RPN ============
# Dummy layers so that initial parameters are saved into the output net
layer {
name: "rpn_conv/3x3"
type: "Convolution"
bottom: "conv5_3"
top: "rpn/output"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 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: 0 decay_mult: 0 }
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/output"
top: "rpn_bbox_pred"
param { lr_mult: 0 decay_mult: 0 }
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"
}如上所示,两个过程主要增加了一个read only的模块用于计算RPN输出全部roi的loss,增加一个OHEMDataLayer层用于选择最大loss的roi参与fast rcnn的训练.注意,OHEM时所有的层不更新参数所以所有readony部分layer的bottom都需要关闭反向梯度传播”propagate_down:false”,这里顺便提一下,原版的faster rcnn不需要反传的bottom没有设置propagate_down:false也没有出错,这是因为caffe有一个智能分析哪些bottom需要反传梯度的识别机制,当这种机制失效时候就需要人工设置propagate_down:false.同时通过设置相同的类似param { name: “fc6_w” }来共享层参数.
minibatch.py中需要添加
def get_allrois_minibatch(roidb, num_classes):
"""Given a roidb, construct a minibatch sampled from it."""
num_images = len(roidb)
# Sample random scales to use for each image in this batch
random_scale_inds = npr.randint(0, high=len(cfg.TRAIN.SCALES),
size=num_images)
assert(cfg.TRAIN.BATCH_SIZE % num_images == 0), \
'num_images ({}) must divide BATCH_SIZE ({})'. \
format(num_images, cfg.TRAIN.BATCH_SIZE)
# Get the input image blob, formatted for caffe
im_blob, im_scales = _get_image_blob(roidb, random_scale_inds)
blobs = {'data': im_blob}
if cfg.TRAIN.HAS_RPN:
# Doesn't support RPN yet.
assert False
assert len(im_scales) == 1, "Single batch only"
assert len(roidb) == 1, "Single batch only"
# gt boxes: (x1, y1, x2, y2, cls)
gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
gt_boxes = np.empty((len(gt_inds), 5), dtype=np.float32)
gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :] * im_scales[0]
gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
blobs['gt_boxes'] = gt_boxes
blobs['im_info'] = np.array(
[[im_blob.shape[2], im_blob.shape[3], im_scales[0]]],
dtype=np.float32)
else: # not using RPN
# Now, build the region of interest and label blobs
rois_blob = np.zeros((0, 5), dtype=np.float32)
labels_blob = np.zeros((0), dtype=np.float32)
bbox_targets_blob = np.zeros((0, 4 * num_classes), dtype=np.float32)
bbox_inside_blob = np.zeros(bbox_targets_blob.shape, dtype=np.float32)
for im_i in xrange(num_images):
labels, overlaps, im_rois, bbox_targets, bbox_inside_weights \
= _all_rois(roidb[im_i], num_classes)
# Add to RoIs blob
rois = _project_im_rois(im_rois, im_scales[im_i])
batch_ind = im_i * np.ones((rois.shape[0], 1))
rois_blob_this_image = np.hstack((batch_ind, rois))
rois_blob = np.vstack((rois_blob, rois_blob_this_image))
# Add to labels, bbox targets, and bbox loss blobs
labels_blob = np.hstack((labels_blob, labels))
bbox_targets_blob = np.vstack((bbox_targets_blob, bbox_targets))
bbox_inside_blob = np.vstack((bbox_inside_blob, bbox_inside_weights))
blobs['rois'] = rois_blob
blobs['labels'] = labels_blob
if cfg.TRAIN.BBOX_REG:
blobs['bbox_targets'] = bbox_targets_blob
blobs['bbox_inside_weights'] = bbox_inside_blob
blobs['bbox_outside_weights'] = \
np.array(bbox_inside_blob > 0).astype(np.float32)
return blobs
def get_ohem_minibatch(loss, rois, labels, bbox_targets=None,
bbox_inside_weights=None, bbox_outside_weights=None):
"""Given rois and their loss, construct a minibatch using OHEM."""
loss = np.array(loss)
if cfg.TRAIN.OHEM_USE_NMS:
# Do NMS using loss for de-dup and diversity
keep_inds = []
nms_thresh = cfg.TRAIN.OHEM_NMS_THRESH
source_img_ids = [roi[0] for roi in rois]
for img_id in np.unique(source_img_ids):
for label in np.unique(labels):
sel_indx = np.where(np.logical_and(labels == label, \
source_img_ids == img_id))[0]
if not len(sel_indx):
continue
boxes = np.concatenate((rois[sel_indx, 1:],
loss[sel_indx][:,np.newaxis]), axis=1).astype(np.float32)
keep_inds.extend(sel_indx[nms(boxes, nms_thresh)])
hard_keep_inds = select_hard_examples(loss[keep_inds])
hard_inds = np.array(keep_inds)[hard_keep_inds]
else:
hard_inds = select_hard_examples(loss)
blobs = {'rois_hard': rois[hard_inds, :].copy(),
'labels_hard': labels[hard_inds].copy()}
if bbox_targets is not None:
assert cfg.TRAIN.BBOX_REG
blobs['bbox_targets_hard'] = bbox_targets[hard_inds, :].copy()
blobs['bbox_inside_weights_hard'] = bbox_inside_weights[hard_inds, :].copy()
blobs['bbox_outside_weights_hard'] = bbox_outside_weights[hard_inds, :].copy()
return blobs
def select_hard_examples(loss):
"""Select hard rois."""
# Sort and select top hard examples.
sorted_indices = np.argsort(loss)[::-1]
hard_keep_inds = sorted_indices[0:np.minimum(len(loss), cfg.TRAIN.BATCH_SIZE)]
# (explore more ways of selecting examples in this function; e.g., sampling)
return hard_keep_indsminibatch.py中修改
def _sample_rois(roidb, fg_rois_per_image, rois_per_image, num_classes):
"""Generate a random sample of RoIs comprising foreground and background
examples.
"""
# label = class RoI has max overlap with
labels = roidb['max_classes']
overlaps = roidb['max_overlaps']
rois = roidb['boxes']
# Select foreground RoIs as those with >= FG_THRESH overlap
fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]
# Guard against the case when an image has fewer than fg_rois_per_image
# foreground RoIs
fg_rois_per_this_image = np.minimum(fg_rois_per_image, fg_inds.size)
# Sample foreground regions without replacement
if fg_inds.size > 0:
fg_inds = npr.choice(
fg_inds, size=fg_rois_per_this_image, replace=False)
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &
(overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
# Compute number of background RoIs to take from this image (guarding
# against there being fewer than desired)
bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image
bg_rois_per_this_image = np.minimum(bg_rois_per_this_image,
bg_inds.size)
# Sample foreground regions without replacement
if bg_inds.size > 0:
bg_inds = npr.choice(
bg_inds, size=bg_rois_per_this_image, replace=False)
# The indices that we're selecting (both fg and bg)
keep_inds = np.append(fg_inds, bg_inds)
# Select sampled values from various arrays:
labels = labels[keep_inds]
# Clamp labels for the background RoIs to 0
labels[fg_rois_per_this_image:] = 0
overlaps = overlaps[keep_inds]
rois = rois[keep_inds]
bbox_targets, bbox_inside_weights = _get_bbox_regression_labels(
roidb['bbox_targets'][keep_inds, :], num_classes)
return labels, overlaps, rois, bbox_targets, bbox_inside_weightslayer.py中添加一个OHEM层
class OHEMDataLayer(caffe.Layer):
"""Online Hard-example Mining Layer."""
def setup(self, bottom, top):
"""Setup the OHEMDataLayer."""
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
self._num_classes = layer_params['num_classes']
self._name_to_bottom_map = {
'cls_prob_readonly': 0,
'bbox_pred_readonly': 1,
'rois': 2,
'labels': 3}
if cfg.TRAIN.BBOX_REG:
self._name_to_bottom_map['bbox_targets'] = 4
self._name_to_bottom_map['bbox_loss_weights'] = 5
self._name_to_top_map = {}
assert cfg.TRAIN.HAS_RPN == False
# data blob: holds a batch of N images, each with 3 channels
idx = 0
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[idx].reshape(1, 5)
self._name_to_top_map['rois_hard'] = idx
idx += 1
# labels blob: R categorical labels in [0, ..., K] for K foreground
# classes plus background
top[idx].reshape(1)
self._name_to_top_map['labels_hard'] = idx
idx += 1
if cfg.TRAIN.BBOX_REG:
# bbox_targets blob: R bounding-box regression targets with 4
# targets per class
top[idx].reshape(1, self._num_classes * 4)
self._name_to_top_map['bbox_targets_hard'] = idx
idx += 1
# bbox_inside_weights blob: At most 4 targets per roi are active;
# thisbinary vector sepcifies the subset of active targets
top[idx].reshape(1, self._num_classes * 4)
self._name_to_top_map['bbox_inside_weights_hard'] = idx
idx += 1
top[idx].reshape(1, self._num_classes * 4)
self._name_to_top_map['bbox_outside_weights_hard'] = idx
idx += 1
print 'OHEMDataLayer: name_to_top:', self._name_to_top_map
assert len(top) == len(self._name_to_top_map)
def forward(self, bottom, top):
"""Compute loss, select RoIs using OHEM. Use RoIs to get blobs and copy them into this layer's top blob vector."""
cls_prob = bottom[0].data
bbox_pred = bottom[1].data
rois = bottom[2].data
labels = bottom[3].data
if cfg.TRAIN.BBOX_REG:
bbox_target = bottom[4].data
bbox_inside_weights = bottom[5].data
bbox_outside_weights = bottom[6].data
else:
bbox_target = None
bbox_inside_weights = None
bbox_outside_weights = None
flt_min = np.finfo(float).eps
# classification loss
loss = [ -1 * np.log(max(x, flt_min)) \
for x in [cls_prob[i,label] for i, label in enumerate(labels)]]
if cfg.TRAIN.BBOX_REG:
# bounding-box regression loss
# d := w * (b0 - b1)
# smoothL1(x) = 0.5 * x^2 if |x| < 1
# |x| - 0.5 otherwise
def smoothL1(x):
if abs(x) < 1:
return 0.5 * x * x
else:
return abs(x) - 0.5
bbox_loss = np.zeros(labels.shape[0])
for i in np.where(labels > 0 )[0]:
indices = np.where(bbox_inside_weights[i,:] != 0)[0]
bbox_loss[i] = sum(bbox_outside_weights[i,indices] * [smoothL1(x) \
for x in bbox_inside_weights[i,indices] * (bbox_pred[i,indices] - bbox_target[i,indices])])
loss += bbox_loss
blobs = get_ohem_minibatch(loss, rois, labels, bbox_target, \
bbox_inside_weights, bbox_outside_weights)
for blob_name, blob in blobs.iteritems():
top_ind = self._name_to_top_map[blob_name]
# Reshape net's input blobs
top[top_ind].reshape(*(blob.shape))
# Copy data into net's input blobs
top[top_ind].data[...] = blob.astype(np.float32, copy=False)
def backward(self, top, propagate_down, bottom):
"""This layer does not propagate gradients."""
pass
def reshape(self, bottom, top):
"""Reshaping happens during the call to forward."""
pass修改layer.py中的_get_next_minibatch,run函数
def _get_next_minibatch(self):
"""Return the blobs to be used for the next minibatch.
If cfg.TRAIN.USE_PREFETCH is True, then blobs will be computed in a
separate process and made available through self._blob_queue.
"""
if cfg.TRAIN.USE_PREFETCH:
return self._blob_queue.get()
else:
db_inds = self._get_next_minibatch_inds()
minibatch_db = [self._roidb[i] for i in db_inds]
if cfg.TRAIN.USE_OHEM:
blobs = get_allrois_minibatch(minibatch_db, self._num_classes)
else:
blobs = get_minibatch(minibatch_db, self._num_classes)
return blobs
def run(self):
print 'BlobFetcher started'
while True:
db_inds = self._get_next_minibatch_inds()
minibatch_db = [self._roidb[i] for i in db_inds]
if cfg.TRAIN.USE_OHEM:
blobs = get_allrois_minibatch(minibatch_db, self._num_classes)
else:
blobs = get_minibatch(minibatch_db, self._num_classes)
self._queue.put(blobs)在train_faster_rcnn_alt_opt.py中修改
def get_solvers(net_name):
# Faster R-CNN Alternating Optimization
n = 'faster_rcnn_alt_opt'
# Solver for each training stage
solvers = [[net_name, n, 'stage1_rpn_solver60k80k.pt'],
[net_name, n, 'stage1_fast_rcnn_ohem_solver30k40k.pt'],
[net_name, n, 'stage2_rpn_solver60k80k.pt'],
[net_name, n, 'stage2_fast_rcnn_ohem_solver30k40k.pt']]
solvers = [os.path.join(cfg.ROOT_DIR, 'models', *s) for s in solvers]
# Iterations for each training stage
max_iters = [80000, 40000, 80000, 40000]
#max_iters = [50, 50, 50, 50]
# Test prototxt for the RPN
rpn_test_prototxt = os.path.join(
cfg.ROOT_DIR, 'models', net_name, n, 'rpn_test.pt')
return solvers, max_iters, rpn_test_prototxt
def train_rpn(queue=None, imdb_name=None, init_model=None, solver=None,
max_iters=None, cfg=None):
"""Train a Region Proposal Network in a separate training process.
"""
# Not using any proposals, just ground-truth boxes
cfg.TRAIN.USE_OHEM = False
cfg.TRAIN.HAS_RPN = True
cfg.TRAIN.BBOX_REG = False # applies only to Fast R-CNN bbox regression
cfg.TRAIN.PROPOSAL_METHOD = 'gt'
cfg.TRAIN.IMS_PER_BATCH = 1
print 'Init model: {}'.format(init_model)
print('Using config:')
pprint.pprint(cfg)
import caffe
_init_caffe(cfg)
roidb, imdb = get_roidb(imdb_name)
print 'roidb len: {}'.format(len(roidb))
output_dir = get_output_dir(imdb, None)
print 'Output will be saved to `{:s}`'.format(output_dir)
model_paths = train_net(solver, roidb, output_dir,
pretrained_model=init_model,
max_iters=max_iters)
# Cleanup all but the final model
for i in model_paths[:-1]:
os.remove(i)
rpn_model_path = model_paths[-1]
# Send final model path through the multiprocessing queue
queue.put({'model_path': rpn_model_path})
def train_fast_rcnn(queue=None, imdb_name=None, init_model=None, solver=None,
max_iters=None, cfg=None, rpn_file=None):
"""Train a Fast R-CNN using proposals generated by an RPN.
"""
cfg.TRAIN.USE_OHEM = True
cfg.TRAIN.BG_THRESH_LO = 0.0
cfg.ASPECT_GROUPING = False
cfg.TRAIN.OHEM_USE_NMS = False
cfg.TRAIN.HAS_RPN = False # not generating prosals on-the-fly
cfg.TRAIN.PROPOSAL_METHOD = 'rpn' # use pre-computed RPN proposals instead
cfg.TRAIN.IMS_PER_BATCH = 1
print 'Init model: {}'.format(init_model)
print 'RPN proposals: {}'.format(rpn_file)
print('Using config:')
pprint.pprint(cfg)
import caffe
_init_caffe(cfg)
roidb, imdb = get_roidb(imdb_name, rpn_file=rpn_file)
output_dir = get_output_dir(imdb, None)
print 'Output will be saved to `{:s}`'.format(output_dir)
# Train Fast R-CNN
model_paths = train_net(solver, roidb, output_dir,
pretrained_model=init_model,
max_iters=max_iters)
# Cleanup all but the final model
for i in model_paths[:-1]:
os.remove(i)
fast_rcnn_model_path = model_paths[-1]
# Send Fast R-CNN model path over the multiprocessing queue
queue.put({'model_path': fast_rcnn_model_path})项目地址:https://github.com/manutdzou/py-faster-rcnn-OHEM
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