本blog为github上CharlesShang/TFFRCNN版源码解析系列代码笔记
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----------------本文作者吴疆--------------
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与VGGnet_test.py相比，VGGnet_train.py需要馈入更多的变量，与train.py中train_model(...)函数定义的feed_dict相照应，此外，还增加了name为rpn-data、roi-data、drop6和drop7的网络处理层，keep_prob为dropout的比例
# train.py中train_model(...)函数定义的feed_dict
feed_dict={
self.net.data: blobs['data'],
self.net.im_info: blobs['im_info'],
self.net.keep_prob: 0.5,
self.net.gt_boxes: blobs['gt_boxes'],
self.net.gt_ishard: blobs['gt_ishard'],
self.net.dontcare_areas: blobs['dontcare_areas']
}
VGGnet_train.py代码及注释如下：
import tensorflow as tf
from network import Network
from ..fast_rcnn.config import cfg
class VGGnet_train(Network):
# 基类为Network，重构了__init__()
def __init__(self, trainable=True):
# 定义的变量比VGGnet_test.py中要多
# 下一层输入（列表）
self.inputs = []
self.data = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='data')
self.im_info = tf.placeholder(tf.float32, shape=[None, 3], name='im_info')
# 与train.py中train_model(...)函数定义的feed_dict照应
self.gt_boxes = tf.placeholder(tf.float32, shape=[None, 5], name='gt_boxes')
self.gt_ishard = tf.placeholder(tf.int32, shape=[None], name='gt_ishard')
self.dontcare_areas = tf.placeholder(tf.float32, shape=[None, 4], name='dontcare_areas')
self.keep_prob = tf.placeholder(tf.float32) # 定义dropout的比例！！！
# 各层输出（字典）
self.layers = dict({'data': self.data, 'im_info': self.im_info, 'gt_boxes': self.gt_boxes,\
'gt_ishard': self.gt_ishard, 'dontcare_areas': self.dontcare_areas})
self.trainable = trainable
self.setup()
def setup(self):
# n_classes = 2 #2018.1.30
n_classes = cfg.NCLASSES
# anchor_scales = [8, 16, 32]
anchor_scales = cfg.ANCHOR_SCALES
_feat_stride = [16, ]
(self.feed('data') #feed最后会返回self，下一层可以直接.xxx
# conv3_1后卷积核参数才被更新，之前层权值不变
.conv(3, 3, 64, 1, 1, name='conv1_1', trainable=False)
.conv(3, 3, 64, 1, 1, name='conv1_2', trainable=False)
.max_pool(2, 2, 2, 2, padding='VALID', name='pool1')
.conv(3, 3, 128, 1, 1, name='conv2_1', trainable=False)
.conv(3, 3, 128, 1, 1, name='conv2_2', trainable=False)
.max_pool(2, 2, 2, 2, padding='VALID', name='pool2')
.conv(3, 3, 256, 1, 1, name='conv3_1')
.conv(3, 3, 256, 1, 1, name='conv3_2')
.conv(3, 3, 256, 1, 1, name='conv3_3')
.max_pool(2, 2, 2, 2, padding='VALID', name='pool3')
.conv(3, 3, 512, 1, 1, name='conv4_1')
.conv(3, 3, 512, 1, 1, name='conv4_2')
.conv(3, 3, 512, 1, 1, name='conv4_3')
.max_pool(2, 2, 2, 2, padding='VALID', name='pool4')
.conv(3, 3, 512, 1, 1, name='conv5_1')
.conv(3, 3, 512, 1, 1, name='conv5_2')
.conv(3, 3, 512, 1, 1, name='conv5_3'))
#========= RPN ============
(self.feed('conv5_3')
.conv(3, 3, 512, 1, 1,name='rpn_conv/3x3'))
# (1, H, W, A x 4)
(self.feed('rpn_conv/3x3')
.conv(1, 1, len(anchor_scales) * 3 * 4, 1, 1, padding='VALID', relu=False, name='rpn_bbox_pred'))
# (1, H, W, A x 2)
(self.feed('rpn_conv/3x3')
.conv(1, 1, len(anchor_scales) * 3 * 2, 1, 1, padding='VALID', relu=False, name='rpn_cls_score'))
# generating training labels on the fly 飞速写入
# output: rpn_labels(HxWxA, 2) rpn_bbox_targets(HxWxA, 4) rpn_bbox_inside_weights rpn_bbox_outside_weights
# 相比于VGGnet_test.py多的网络层次！！！
# Produces anchor classification labels and bounding-box regression targets.
(self.feed('rpn_cls_score', 'gt_boxes', 'gt_ishard', 'dontcare_areas', 'im_info')
.anchor_target_layer(_feat_stride, anchor_scales, name='rpn-data' ))
# 先reshape后softmax再reshape回来
# shape is (1, H, W, Ax2) -> (1, H, WxA, 2)
(self.feed('rpn_cls_score')
.spatial_reshape_layer(2, name='rpn_cls_score_reshape')
.spatial_softmax(name='rpn_cls_prob'))
# shape is (1, H, WxA, 2) -> (1, H, W, Ax2)
(self.feed('rpn_cls_prob')
.spatial_reshape_layer(len(anchor_scales)*3*2, name='rpn_cls_prob_reshape'))
# ========= RoI Proposal ============
# add the delta(output) to anchors then
# choose some reasonabel boxes, considering scores, ratios, size and iou
# rpn_rois <- (1 x H x W x A, 5) e.g. [0, x1, y1, x2, y2]
# 回归后并经过一些后处理得到的proposal，见proposal_layer_tf.py
# 默认_feat_stride = [16, ]、anchor_scales = cfg.ANCHOR_SCALES = [8, 16, 32]、TEST模式
(self.feed('rpn_cls_prob_reshape', 'rpn_bbox_pred', 'im_info')
.proposal_layer(_feat_stride, anchor_scales, 'TRAIN', name='rpn_rois'))
# 相比于VGGnet_test.py多的网络层次！！！
# matching boxes and groundtruth and randomly sample some rois and labels for RCNN
(self.feed('rpn_rois', 'gt_boxes', 'gt_ishard', 'dontcare_areas')
.proposal_target_layer(n_classes, name='roi-data'))
# ========= RCNN ============
(self.feed('conv5_3', 'rois')
.roi_pool(7, 7, 1.0/16, name='pool_5')
.fc(4096, name='fc6')
.dropout(0.5, name='drop6') # 相比于VGGnet_test.py多的网络层次！！！
.fc(4096, name='fc7')
.dropout(0.5, name='drop7') # 相比于VGGnet_test.py多的网络层次！！！
.fc(n_classes, relu=False, name='cls_score')
.softmax(name='cls_prob'))
(self.feed('drop7')
.fc(n_classes*4, relu=False, name='bbox_pred'))