（原）faster rcnn的tensorflow代码的理解

def create_architecture(self, mode, num_classes, tag=None, anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)):
self._image = tf.placeholder(tf.float32, shape=[1, None, None, 3]) # 由于图像宽高不定，因而第二维和第三维都是None
self._im_info = tf.placeholder(tf.float32, shape=[3]) # 图像信息，高、宽、缩放到宽为600或者高为1000的最小比例
self._gt_boxes = tf.placeholder(tf.float32, shape=[None, 5]) # ground truth框的信息。前四个为位置信息，最后一个为该框对应的类别（见roi_data_layer/minibatch.py/get_minibatch）
self._tag = tag self._num_classes = num_classes
self._mode = mode
self._anchor_scales = anchor_scales
self._num_scales = len(anchor_scales) self._anchor_ratios = anchor_ratios
self._num_ratios = len(anchor_ratios) self._num_anchors = self._num_scales * self._num_ratios # self._num_anchors=9 training = mode == 'TRAIN'
testing = mode == 'TEST' weights_regularizer = tf.contrib.layers.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY) # handle most of the regularizers here
if cfg.TRAIN.BIAS_DECAY:
biases_regularizer = weights_regularizer
else:
biases_regularizer = tf.no_regularizer # list as many types of layers as possible, even if they are not used now
with arg_scope([slim.conv2d, slim.conv2d_in_plane, slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected],
weights_regularizer=weights_regularizer, biases_regularizer=biases_regularizer, biases_initializer=tf.constant_initializer(0.0)):
# rois：256个archors的类别（训练时为每个archors的类别，测试时全0）
# cls_prob：256个archors每一类别的概率
# bbox_pred：预测位置信息的偏移
rois, cls_prob, bbox_pred = self._build_network(training) layers_to_output = {'rois': rois} for var in tf.trainable_variables():
self._train_summaries.append(var) if testing:
stds = np.tile(np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS), (self._num_classes))
means = np.tile(np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS), (self._num_classes))
self._predictions["bbox_pred"] *= stds # 训练时_region_proposal中预测的位置偏移减均值除标准差，因而测试时需要反过来。
self._predictions["bbox_pred"] += means
else:
self._add_losses()
layers_to_output.update(self._losses) val_summaries = []
with tf.device("/cpu:0"):
val_summaries.append(self._add_gt_image_summary())
for key, var in self._event_summaries.items():
val_summaries.append(tf.summary.scalar(key, var))
for key, var in self._score_summaries.items():
self._add_score_summary(key, var)
for var in self._act_summaries:
self._add_act_summary(var)
for var in self._train_summaries:
self._add_train_summary(var) self._summary_op = tf.summary.merge_all()
self._summary_op_val = tf.summary.merge(val_summaries) layers_to_output.update(self._predictions) return layers_to_output

def _build_network(self, is_training=True):
if cfg.TRAIN.TRUNCATED: # select initializers
initializer = tf.truncated_normal_initializer(mean=0.0, stddev=0.01)
initializer_bbox = tf.truncated_normal_initializer(mean=0.0, stddev=0.001)
else:
initializer = tf.random_normal_initializer(mean=0.0, stddev=0.01)
initializer_bbox = tf.random_normal_initializer(mean=0.0, stddev=0.001) net_conv = self._image_to_head(is_training) # 得到vgg16的conv5_3
with tf.variable_scope(self._scope, self._scope):
self._anchor_component() # 通过特征图及相对原始图像的缩放倍数_feat_stride得到所有archors的起点及终点坐标
rois = self._region_proposal(net_conv, is_training, initializer) # 通过rpn网络，得到256个archors的类别（训练时为每个archors的类别，测试时全0）及位置（后四维）
pool5 = self._crop_pool_layer(net_conv, rois, "pool5") # 对特征图通过rois得到候选区域，并对候选区域进行缩放，得到14*14的固定大小，进一步pooling成7*7大小 fc7 = self._head_to_tail(pool5, is_training) # 对固定大小的rois增加fc及dropout，得到4096维的特征，用于分类及回归
with tf.variable_scope(self._scope, self._scope):
cls_prob, bbox_pred = self._region_classification(fc7, is_training, initializer, initializer_bbox) # 对rois进行分类，完成目标检测；进行回归，得到预测坐标 self._score_summaries.update(self._predictions) # rois：256个archors的类别（训练时为每个archors的类别，测试时全0）
# cls_prob：256个archors每一类别的概率
# bbox_pred：预测位置信息的偏移
return rois, cls_prob, bbox_pred

def _image_to_head(self, is_training, reuse=None):
with tf.variable_scope(self._scope, self._scope, reuse=reuse):
net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3], trainable=False, scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], trainable=False, scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], trainable=is_training, scope='conv3')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, scope='conv4')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], trainable=is_training, scope='conv5') self._act_summaries.append(net)
self._layers['head'] = net return net

def _anchor_component(self):
with tf.variable_scope('ANCHOR_' + self._tag) as scope:
height = tf.to_int32(tf.ceil(self._im_info[0] / np.float32(self._feat_stride[0]))) # 图像经过vgg16得到特征图的宽高
width = tf.to_int32(tf.ceil(self._im_info[1] / np.float32(self._feat_stride[0])))
if cfg.USE_E2E_TF:
# 通过特征图宽高、_feat_stride（特征图相对原始图缩小的比例）及_anchor_scales、_anchor_ratios得到原始图像上
# 所有可能的archors（坐标可能超出原始图像边界）和archor的数量
anchors, anchor_length = generate_anchors_pre_tf(height, width, self._feat_stride, self._anchor_scales, self._anchor_ratios )
else:
anchors, anchor_length = tf.py_func(generate_anchors_pre,
[height, width, self._feat_stride, self._anchor_scales, self._anchor_ratios], [tf.float32, tf.int32], name="generate_anchors")
anchors.set_shape([None, 4]) # 起点坐标，终点坐标，共4个值
anchor_length.set_shape([])
self._anchors = anchors
self._anchor_length = anchor_length def generate_anchors_pre_tf(height, width, feat_stride=16, anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)):
shift_x = tf.range(width) * feat_stride # 得到所有archors在原始图像的起始x坐标：(0,feat_stride,2*feat_stride...)
shift_y = tf.range(height) * feat_stride # 得到所有archors在原始图像的起始y坐标：(0,feat_stride,2*feat_stride...)
shift_x, shift_y = tf.meshgrid(shift_x, shift_y) # shift_x：height个(0,feat_stride,2*feat_stride...);shift_y：width个(0,feat_stride,2*feat_stride...)'
sx = tf.reshape(shift_x, shape=(-1,)) # 0,feat_stride,2*feat_stride...0,feat_stride,2*feat_stride...0,feat_stride,2*feat_stride...
sy = tf.reshape(shift_y, shape=(-1,)) # 0,0,0...feat_stride,feat_stride,feat_stride...2*feat_stride,2*feat_stride,2*feat_stride..
shifts = tf.transpose(tf.stack([sx, sy, sx, sy])) # width*height个四位矩阵
K = tf.multiply(width, height) # 特征图总共像素数
shifts = tf.transpose(tf.reshape(shifts, shape=[1, K, 4]), perm=(1, 0, 2)) # 增加一维，变成1*(width*height)*4矩阵，而后变换维度为(width*height)*1*4矩阵 anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales)) #得到9个archors的在原始图像中的四个坐标（放大比例默认为16）
A = anchors.shape[0] # A=9
anchor_constant = tf.constant(anchors.reshape((1, A, 4)), dtype=tf.int32) # anchors增加维度为1*9*4 length = K * A # 总共的archors的个数（每个点对应A=9个archor，共K=height*width个点）
# 1*9*4的base archors和(width*height)*1*4的偏移矩阵进行broadcast相加，得到(width*height)*9*4，并改变形状为(width*height*9)*4，得到所有的archors的四个坐标
anchors_tf = tf.reshape(tf.add(anchor_constant, shifts), shape=(length, 4)) return tf.cast(anchors_tf, dtype=tf.float32), length def generate_anchors(base_size=16, ratios=[0.5, 1, 2], scales=2 ** np.arange(3, 6)):
"""Generate anchor (reference) windows by enumerating aspect ratios X scales wrt a reference (0, 0, 15, 15) window."""
base_anchor = np.array([1, 1, base_size, base_size]) - 1 # base archor的四个坐标
ratio_anchors = _ratio_enum(base_anchor, ratios) # 通过ratio得到3个archors的坐标（3*4矩阵）
anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales) for i in range(ratio_anchors.shape[0])]) # 3*4矩阵变成9*4矩阵，得到9个archors的坐标
return anchors def _whctrs(anchor):
""" Return width, height, x center, and y center for an anchor (window). """
w = anchor[2] - anchor[0] + 1 # 宽
h = anchor[3] - anchor[1] + 1 # 高
x_ctr = anchor[0] + 0.5 * (w - 1) # 中心x
y_ctr = anchor[1] + 0.5 * (h - 1) # 中心y
return w, h, x_ctr, y_ctr def _mkanchors(ws, hs, x_ctr, y_ctr):
""" Given a vector of widths (ws) and heights (hs) around a center (x_ctr, y_ctr), output a set of anchors (windows)."""
ws = ws[:, np.newaxis] # 3维向量变成3*1矩阵
hs = hs[:, np.newaxis] # 3维向量变成3*1矩阵
anchors = np.hstack((x_ctr - 0.5 * (ws - 1), y_ctr - 0.5 * (hs - 1), x_ctr + 0.5 * (ws - 1), y_ctr + 0.5 * (hs - 1))) # 3*4矩阵
return anchors def _ratio_enum(anchor, ratios): # 缩放比例为像素总数的比例，而非单独宽或者高的比例
""" Enumerate a set of anchors for each aspect ratio wrt an anchor. """
w, h, x_ctr, y_ctr = _whctrs(anchor) # 得到中心位置和宽高
size = w * h # 总共像素数
size_ratios = size / ratios # 缩放比例
ws = np.round(np.sqrt(size_ratios)) # 缩放后的宽,3维向量(值由大到小)
hs = np.round(ws * ratios) # 缩放后的高，两个3维向量对应元素相乘，为3维向量（值由小到大）
anchors = _mkanchors(ws, hs, x_ctr, y_ctr) # 根据中心及宽高得到3个archors的四个坐标
return anchors def _scale_enum(anchor, scales):
""" Enumerate a set of anchors for each scale wrt an anchor. """
w, h, x_ctr, y_ctr = _whctrs(anchor) # 得到中心位置和宽高
ws = w * scales # 得到宽的放大倍数
hs = h * scales # 得到宽的放大倍数
anchors = _mkanchors(ws, hs, x_ctr, y_ctr) # 根据中心及宽高得到3个archors的四个坐标
return anchors

def _region_proposal(self, net_conv, is_training, initializer): # 对输入特征图进行处理
rpn = slim.conv2d(net_conv, cfg.RPN_CHANNELS, [3, 3], trainable=is_training, weights_initializer=initializer, scope="rpn_conv/3x3") #3*3的conv，作为rpn网络
self._act_summaries.append(rpn)
rpn_cls_score = slim.conv2d(rpn, self._num_anchors * 2, [1, 1], trainable=is_training, weights_initializer=initializer, # _num_anchors为9
padding='VALID', activation_fn=None, scope='rpn_cls_score') #1*1的conv，得到每个位置的9个archors分类特征1*？*？*(9*2)（二分类），判断当前archors是正样本还是负样本
rpn_cls_score_reshape = self._reshape_layer(rpn_cls_score, 2, 'rpn_cls_score_reshape') # 1*？*？*18==>1*(?*9)*?*2
rpn_cls_prob_reshape = self._softmax_layer(rpn_cls_score_reshape, "rpn_cls_prob_reshape") # 以最后一维为特征长度，得到所有特征的概率1*(?*9)*?*2
rpn_cls_pred = tf.argmax(tf.reshape(rpn_cls_score_reshape, [-1, 2]), axis=1, name="rpn_cls_pred") # 得到每个位置的9个archors预测的类别，(1*?*9*?)的列向量
rpn_cls_prob = self._reshape_layer(rpn_cls_prob_reshape, self._num_anchors * 2, "rpn_cls_prob") # 变换会原始维度1*(?*9)*?*2==>1*?*?*(9*2)
rpn_bbox_pred = slim.conv2d(rpn, self._num_anchors * 4, [1, 1], trainable=is_training, weights_initializer=initializer,
padding='VALID', activation_fn=None, scope='rpn_bbox_pred') #1*1的conv，每个位置的9个archors回归位置偏移1*？*？*(9*4)
if is_training:
# 每个位置的9个archors的类别概率和每个位置的9个archors的回归位置偏移得到post_nms_topN=2000个archors的位置（包括全0的batch_inds）及为1的概率
rois, roi_scores = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
rpn_labels = self._anchor_target_layer(rpn_cls_score, "anchor") # rpn_labels：特征图中每个位置对应的是正样本、负样本还是不关注
with tf.control_dependencies([rpn_labels]): # Try to have a deterministic order for the computing graph, for reproducibility
rois, _ = self._proposal_target_layer(rois, roi_scores, "rpn_rois") #通过post_nms_topN个archors的位置及为1（正样本）的概率得到256个rois（第一列的全0更新为每个archors对应的类别）及对应信息
else:
if cfg.TEST.MODE == 'nms':
# 每个位置的9个archors的类别概率和每个位置的9个archors的回归位置偏移得到post_nms_topN=300个archors的位置（包括全0的batch_inds）及为1的概率
rois, _ = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
elif cfg.TEST.MODE == 'top':
rois, _ = self._proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
else:
raise NotImplementedError self._predictions["rpn_cls_score"] = rpn_cls_score # 每个位置的9个archors是正样本还是负样本
self._predictions["rpn_cls_score_reshape"] = rpn_cls_score_reshape # 每个archors是正样本还是负样本
self._predictions["rpn_cls_prob"] = rpn_cls_prob # 每个位置的9个archors是正样本和负样本的概率
self._predictions["rpn_cls_pred"] = rpn_cls_pred # 每个位置的9个archors预测的类别，(1*?*9*?)的列向量
self._predictions["rpn_bbox_pred"] = rpn_bbox_pred # 每个位置的9个archors回归位置偏移
self._predictions["rois"] = rois # 256个archors的类别（第一维）及位置（后四维） return rois # 返回256个archors的类别（第一维，训练时为每个archors的类别，测试时全0）及位置（后四维） def _reshape_layer(self, bottom, num_dim, name):
input_shape = tf.shape(bottom)
with tf.variable_scope(name) as scope:
to_caffe = tf.transpose(bottom, [0, 3, 1, 2]) # NHWC（TF数据格式）变成NCHW（caffe格式）
reshaped = tf.reshape(to_caffe, tf.concat(axis=0, values=[[1, num_dim, -1], [input_shape[2]]])) # 1*(num_dim*9)*?*?==>1*num_dim*(9*?)*? 或 1*num_dim*(9*?)*?==>1*(num_dim*9)*?*?
to_tf = tf.transpose(reshaped, [0, 2, 3, 1])
return to_tf def _softmax_layer(self, bottom, name):
if name.startswith('rpn_cls_prob_reshape'): # bottom：1*(?*9)*?*2
input_shape = tf.shape(bottom)
bottom_reshaped = tf.reshape(bottom, [-1, input_shape[-1]]) # 只保留最后一维，用于计算softmax的概率，其他的全合并：1*(?*9)*?*2==>(1*?*9*?)*2
reshaped_score = tf.nn.softmax(bottom_reshaped, name=name) # 得到所有特征的概率
return tf.reshape(reshaped_score, input_shape) # (1*?*9*?)*2==>1*(?*9)*?*2
return tf.nn.softmax(bottom, name=name)

def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name): #每个位置的9个archors的类别概率和每个位置的9个archors的回归位置偏移得到post_nms_topN个archors的位置及为1的概率
with tf.variable_scope(name) as scope:
if cfg.USE_E2E_TF: # post_nms_topN*5的rois（第一列为全0的batch_inds，后4列为坐标）；rpn_scores：post_nms_topN*1个对应的为1的概率
rois, rpn_scores = proposal_layer_tf(rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode, self._feat_stride, self._anchors, self._num_anchors)
else:
rois, rpn_scores = tf.py_func(proposal_layer, [rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode,
self._feat_stride, self._anchors, self._num_anchors], [tf.float32, tf.float32], name="proposal") rois.set_shape([None, 5])
rpn_scores.set_shape([None, 1]) return rois, rpn_scores def proposal_layer_tf(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors): #每个位置的9个archors的类别概率和每个位置的9个archors的回归位置偏移
if type(cfg_key) == bytes:
cfg_key = cfg_key.decode('utf-8')
pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N # 训练时为2000，测试时为300
nms_thresh = cfg[cfg_key].RPN_NMS_THRESH # nms的阈值，为0.7 scores = rpn_cls_prob[:, :, :, num_anchors:] # 1*?*?*(9*2)取后9个：1*?*?*9。应该是前9个代表9个archors为背景景的概率，后9个代表9个archors为前景的概率（二分类，只有背景和前景）
scores = tf.reshape(scores, shape=(-1,)) # 所有的archors为1的概率
rpn_bbox_pred = tf.reshape(rpn_bbox_pred, shape=(-1, 4)) # 所有的archors的四个坐标 proposals = bbox_transform_inv_tf(anchors, rpn_bbox_pred) # 已知archor和偏移求预测的坐标
proposals = clip_boxes_tf(proposals, im_info[:2]) # 限制预测坐标在原始图像上 indices = tf.image.non_max_suppression(proposals, scores, max_output_size=post_nms_topN, iou_threshold=nms_thresh) # 通过nms得到分值最大的post_nms_topN个坐标的索引 boxes = tf.gather(proposals, indices) # 得到post_nms_topN个对应的坐标
boxes = tf.to_float(boxes)
scores = tf.gather(scores, indices) # 得到post_nms_topN个对应的为1的概率
scores = tf.reshape(scores, shape=(-1, 1)) batch_inds = tf.zeros((tf.shape(indices)[0], 1), dtype=tf.float32) # Only support single image as input
blob = tf.concat([batch_inds, boxes], 1) # post_nms_topN*1个batch_inds和post_nms_topN*4个坐标concat，得到post_nms_topN*5的blob return blob, scores def bbox_transform_inv_tf(boxes, deltas): # 已知archor和偏移求预测的坐标
boxes = tf.cast(boxes, deltas.dtype)
widths = tf.subtract(boxes[:, 2], boxes[:, 0]) + 1.0 # 宽
heights = tf.subtract(boxes[:, 3], boxes[:, 1]) + 1.0 # 高
ctr_x = tf.add(boxes[:, 0], widths * 0.5) # 中心x
ctr_y = tf.add(boxes[:, 1], heights * 0.5) # 中心y dx = deltas[:, 0] # 预测的dx
dy = deltas[:, 1] # 预测的dy
dw = deltas[:, 2] # 预测的dw
dh = deltas[:, 3] # 预测的dh pred_ctr_x = tf.add(tf.multiply(dx, widths), ctr_x) # 公式2已知xa，wa，tx反过来求预测的x中心坐标
pred_ctr_y = tf.add(tf.multiply(dy, heights), ctr_y) # 公式2已知ya，ha，ty反过来求预测的y中心坐标
pred_w = tf.multiply(tf.exp(dw), widths) # 公式2已知wa，tw反过来求预测的w
pred_h = tf.multiply(tf.exp(dh), heights) # 公式2已知ha，th反过来求预测的h pred_boxes0 = tf.subtract(pred_ctr_x, pred_w * 0.5) # 预测的框的起始和终点四个坐标
pred_boxes1 = tf.subtract(pred_ctr_y, pred_h * 0.5)
pred_boxes2 = tf.add(pred_ctr_x, pred_w * 0.5)
pred_boxes3 = tf.add(pred_ctr_y, pred_h * 0.5) return tf.stack([pred_boxes0, pred_boxes1, pred_boxes2, pred_boxes3], axis=1) def clip_boxes_tf(boxes, im_info): # 限制预测坐标在原始图像上
b0 = tf.maximum(tf.minimum(boxes[:, 0], im_info[1] - 1), 0)
b1 = tf.maximum(tf.minimum(boxes[:, 1], im_info[0] - 1), 0)
b2 = tf.maximum(tf.minimum(boxes[:, 2], im_info[1] - 1), 0)
b3 = tf.maximum(tf.minimum(boxes[:, 3], im_info[0] - 1), 0)
return tf.stack([b0, b1, b2, b3], axis=1)

def _anchor_target_layer(self, rpn_cls_score, name): # rpn_cls_score:每个位置的9个archors分类特征1*？*？*(9*2)
with tf.variable_scope(name) as scope:
# rpn_labels; 特征图中每个位置对应的是正样本、负样本还是不关注（去除了边界在图像外面的archors）
# rpn_bbox_targets:# 特征图中每个位置和对应的正样本的坐标偏移（很多为0）
# rpn_bbox_inside_weights: 正样本的权重为1（去除负样本和不关注的样本，均为0）
# rpn_bbox_outside_weights: 正样本和负样本（不包括不关注的样本）归一化的权重
rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = tf.py_func(
anchor_target_layer, [rpn_cls_score, self._gt_boxes, self._im_info, self._feat_stride, self._anchors, self._num_anchors],
[tf.float32, tf.float32, tf.float32, tf.float32], name="anchor_target") rpn_labels.set_shape([1, 1, None, None])
rpn_bbox_targets.set_shape([1, None, None, self._num_anchors * 4])
rpn_bbox_inside_weights.set_shape([1, None, None, self._num_anchors * 4])
rpn_bbox_outside_weights.set_shape([1, None, None, self._num_anchors * 4]) rpn_labels = tf.to_int32(rpn_labels, name="to_int32")
self._anchor_targets['rpn_labels'] = rpn_labels # 特征图中每个位置对应的是正样本、负样本还是不关注（去除了边界在图像外面的archors）
self._anchor_targets['rpn_bbox_targets'] = rpn_bbox_targets # 特征图中每个位置和对应的正样本的坐标偏移（很多为0）
self._anchor_targets['rpn_bbox_inside_weights'] = rpn_bbox_inside_weights # 正样本的权重为1（去除负样本和不关注的样本，均为0）
self._anchor_targets['rpn_bbox_outside_weights'] = rpn_bbox_outside_weights # 正样本和负样本（不包括不关注的样本）归一化的权重 self._score_summaries.update(self._anchor_targets) return rpn_labels def anchor_target_layer(rpn_cls_score, gt_boxes, im_info, _feat_stride, all_anchors, num_anchors):# 1*？*？*(9*2); ?*5; 3; [16], ?*4; [9]
"""Same as the anchor target layer in original Fast/er RCNN """
A = num_anchors # [9]
total_anchors = all_anchors.shape[0] # 所有archors的个数，9*特征图宽*特征图高 个
K = total_anchors / num_anchors _allowed_border = 0 # allow boxes to sit over the edge by a small amount
height, width = rpn_cls_score.shape[1:3] # rpn网络得到的特征的高宽 inds_inside = np.where( # 所有archors边界可能超出图像，取在图像内部的archors的索引
(all_anchors[:, 0] >= -_allowed_border) & (all_anchors[:, 1] >= -_allowed_border) &
(all_anchors[:, 2] < im_info[1] + _allowed_border) & # width
(all_anchors[:, 3] < im_info[0] + _allowed_border) # height
)[0] anchors = all_anchors[inds_inside, :] # 得到在图像内部archors的坐标 labels = np.empty((len(inds_inside),), dtype=np.float32) # label: 1 正样本, 0 负样本, -1 不关注
labels.fill(-1) # 计算每个anchors:n*4和每个真实位置gt_boxes:m*4的重叠区域的比的矩阵:n*m
overlaps = bbox_overlaps(np.ascontiguousarray(anchors, dtype=np.float), np.ascontiguousarray(gt_boxes, dtype=np.float))
argmax_overlaps = overlaps.argmax(axis=1) # 找到每行最大值的位置，即每个archors对应的正样本的位置，得到n维的行向量
max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps] # 取出每个archors对应的正样本的重叠区域，n维向量
gt_argmax_overlaps = overlaps.argmax(axis=0) # 找到每列最大值的位置，即每个真实位置对应的archors的位置，得到m维的行向量
gt_max_overlaps = overlaps[gt_argmax_overlaps, np.arange(overlaps.shape[1])] # 取出每个真实位置对应的archors的重叠区域，m维向量
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0] # 得到从小到大顺序的位置 if not cfg.TRAIN.RPN_CLOBBER_POSITIVES: # assign bg labels first so that positive labels can clobber them first set the negatives
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0 # 将archors对应的正样本的重叠区域中小于阈值的置0 labels[gt_argmax_overlaps] = 1 # fg label: for each gt, anchor with highest overlap 每个真实位置对应的archors置1
labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1 # fg label: above threshold IOU 将archors对应的正样本的重叠区域中大于阈值的置1 if cfg.TRAIN.RPN_CLOBBER_POSITIVES: # assign bg labels last so that negative labels can clobber positives
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0 # 如果有过多的正样本，则只随机选择num_fg=0.5*256=128个正样本
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE) # subsample positive labels if we have too many
fg_inds = np.where(labels == 1)[0]
if len(fg_inds) > num_fg:
disable_inds = npr.choice(fg_inds, size=(len(fg_inds) - num_fg), replace=False)
labels[disable_inds] = -1 # 将多于的正样本设置为不关注 # 如果有过多的负样本，则只随机选择 num_bg=256-正样本个数 个负样本
num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1) # subsample negative labels if we have too many
bg_inds = np.where(labels == 0)[0]
if len(bg_inds) > num_bg:
disable_inds = npr.choice(bg_inds, size=(len(bg_inds) - num_bg), replace=False)
labels[disable_inds] = -1 # 将多于的负样本设置为不关注 bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :]) # 通过archors和archors对应的正样本计算坐标的偏移 bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_inside_weights[labels == 1, :] = np.array(cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS) # 正样本的四个坐标的权重均设置为1 bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0: # uniform weighting of examples (given non-uniform sampling)
num_examples = np.sum(labels >= 0) # 正样本和负样本的总数（去除不关注的样本）
positive_weights = np.ones((1, 4)) * 1.0 / num_examples # 归一化的权重
negative_weights = np.ones((1, 4)) * 1.0 / num_examples # 归一化的权重
else:
assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) & (cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1))
positive_weights = (cfg.TRAIN.RPN_POSITIVE_WEIGHT / np.sum(labels == 1))
negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) / np.sum(labels == 0))
bbox_outside_weights[labels == 1, :] = positive_weights # 归一化的权重
bbox_outside_weights[labels == 0, :] = negative_weights # 归一化的权重 # 由于上面使用了inds_inside，此处将labels，bbox_targets，bbox_inside_weights，bbox_outside_weights映射到原始的archors（包含未知
# 参数超出图像边界的archors）对应的labels，bbox_targets，bbox_inside_weights，bbox_outside_weights，同时将不需要的填充fill的值
labels = _unmap(labels, total_anchors, inds_inside, fill=-1)
bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)
bbox_inside_weights = _unmap(bbox_inside_weights, total_anchors, inds_inside, fill=0) # 所有archors中正样本的四个坐标的权重均设置为1，其他为0
bbox_outside_weights = _unmap(bbox_outside_weights, total_anchors, inds_inside, fill=0) labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2) # (1*？*？)*9==>1*？*？*9==>1*9*？*？
labels = labels.reshape((1, 1, A * height, width)) # 1*9*？*？==>1*1*(9*？)*？
rpn_labels = labels # 特征图中每个位置对应的是正样本、负样本还是不关注（去除了边界在图像外面的archors） bbox_targets = bbox_targets.reshape((1, height, width, A * 4)) # 1*(9*？)*？*4==>1*？*？*(9*4) rpn_bbox_targets = bbox_targets # 特征图中每个位置和对应的正样本的坐标偏移（很多为0）
bbox_inside_weights = bbox_inside_weights.reshape((1, height, width, A * 4)) # 1*(9*？)*？*4==>1*？*？*(9*4)
rpn_bbox_inside_weights = bbox_inside_weights
bbox_outside_weights = bbox_outside_weights.reshape((1, height, width, A * 4)) # 1*(9*？)*？*4==>1*？*？*(9*4)
rpn_bbox_outside_weights = bbox_outside_weights # 归一化的权重
return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights def _unmap(data, count, inds, fill=0):
""" Unmap a subset of item (data) back to the original set of items (of size count) """
if len(data.shape) == 1:
ret = np.empty((count,), dtype=np.float32) # 得到1维矩阵
ret.fill(fill) # 默认填充fill的值
ret[inds] = data # 有效位置填充具体数据
else:
ret = np.empty((count,) + data.shape[1:], dtype=np.float32) # 得到对应维数的矩阵
ret.fill(fill) # 默认填充fill的值
ret[inds, :] = data # 有效位置填充具体数据
return ret def _compute_targets(ex_rois, gt_rois):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 5 # 通过公式2后四个，结合archor和对应的正样本的坐标计算坐标的偏移
return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False) # 由于gt_rois是5列，去掉最后一列的batch_inds def bbox_transform(ex_rois, gt_rois):
ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0 # archor的宽
ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0 # archor的高
ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths #archor的中心x
ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights #archor的中心y gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0 # 真实正样本w
gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0 # 真实正样本h
gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths # 真实正样本中心x
gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights # 真实正样本中心y targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths # 通过公式2后四个的x*，xa，wa得到dx
targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights # 通过公式2后四个的y*，ya，ha得到dy
targets_dw = np.log(gt_widths / ex_widths) # 通过公式2后四个的w*，wa得到dw
targets_dh = np.log(gt_heights / ex_heights) # 通过公式2后四个的h*，ha得到dh targets = np.vstack((targets_dx, targets_dy, targets_dw, targets_dh)).transpose()
return targets

def bbox_overlaps(
np.ndarray[DTYPE_t, ndim=2] boxes,
np.ndarray[DTYPE_t, ndim=2] query_boxes):
"""
Parameters
----------
boxes: (N, 4) ndarray of float
query_boxes: (K, 4) ndarray of float
Returns
-------
overlaps: (N, K) ndarray of overlap between boxes and query_boxes
"""
cdef unsigned int N = boxes.shape[0]
cdef unsigned int K = query_boxes.shape[0]
cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE)
cdef DTYPE_t iw, ih, box_area
cdef DTYPE_t ua
cdef unsigned int k, n
for k in range(K):
box_area = (
(query_boxes[k, 2] - query_boxes[k, 0] + 1) *
(query_boxes[k, 3] - query_boxes[k, 1] + 1)
)
for n in range(N):
iw = (
min(boxes[n, 2], query_boxes[k, 2]) -
max(boxes[n, 0], query_boxes[k, 0]) + 1
)
if iw > 0:
ih = (
min(boxes[n, 3], query_boxes[k, 3]) -
max(boxes[n, 1], query_boxes[k, 1]) + 1
)
if ih > 0:
ua = float(
(boxes[n, 2] - boxes[n, 0] + 1) *
(boxes[n, 3] - boxes[n, 1] + 1) +
box_area - iw * ih
)
overlaps[n, k] = iw * ih / ua
return overlaps

def _proposal_target_layer(self, rois, roi_scores, name): # post_nms_topN个archors的位置及为1（正样本）的概率
# 只在训练时使用该层，从post_nms_topN个archors中选择256个archors
with tf.variable_scope(name) as scope:
# labels：正样本和负样本对应的真实的类别
# rois：从post_nms_topN个archors中选择256个archors（第一列的全0更新为每个archors对应的类别）
# roi_scores：256个archors对应的为正样本的概率
# bbox_targets：256*(4*21)的矩阵，只有为正样本时，对应类别的坐标才不为0，其他类别的坐标全为0
# bbox_inside_weights：256*(4*21)的矩阵，正样本时，对应类别四个坐标的权重为1，其他全为0
# bbox_outside_weights：256*(4*21)的矩阵，正样本时，对应类别四个坐标的权重为1，其他全为0
rois, roi_scores, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights = tf.py_func(
proposal_target_layer, [rois, roi_scores, self._gt_boxes, self._num_classes],
[tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32], name="proposal_target") rois.set_shape([cfg.TRAIN.BATCH_SIZE, 5])
roi_scores.set_shape([cfg.TRAIN.BATCH_SIZE])
labels.set_shape([cfg.TRAIN.BATCH_SIZE, 1])
bbox_targets.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4])
bbox_inside_weights.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4])
bbox_outside_weights.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4]) self._proposal_targets['rois'] = rois
self._proposal_targets['labels'] = tf.to_int32(labels, name="to_int32")
self._proposal_targets['bbox_targets'] = bbox_targets
self._proposal_targets['bbox_inside_weights'] = bbox_inside_weights
self._proposal_targets['bbox_outside_weights'] = bbox_outside_weights self._score_summaries.update(self._proposal_targets) return rois, roi_scores def proposal_target_layer(rpn_rois, rpn_scores, gt_boxes, _num_classes):
"""Assign object detection proposals to ground-truth targets. Produces proposal classification labels and bounding-box regression targets."""
# Proposal ROIs (0, x1, y1, x2, y2) coming from RPN (i.e., rpn.proposal_layer.ProposalLayer), or any other source
all_rois = rpn_rois # rpn_rois为post_nms_topN*5的矩阵
all_scores = rpn_scores # rpn_scores为post_nms_topN的矩阵，代表对应的archors为正样本的概率 if cfg.TRAIN.USE_GT: # Include ground-truth boxes in the set of candidate rois; USE_GT=False，未使用这段代码
zeros = np.zeros((gt_boxes.shape[0], 1), dtype=gt_boxes.dtype)
all_rois = np.vstack((all_rois, np.hstack((zeros, gt_boxes[:, :-1]))))
all_scores = np.vstack((all_scores, zeros)) # not sure if it a wise appending, but anyway i am not using it num_images = 1 # 该程序只能一次处理一张图片
rois_per_image = cfg.TRAIN.BATCH_SIZE / num_images # 每张图片中最终选择的rois
fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image) # 正样本的个数：0.25*rois_per_image # Sample rois with classification labels and bounding box regression targets
# labels：正样本和负样本对应的真实的类别
# rois：从post_nms_topN个archors中选择256个archors（第一列的全0更新为每个archors对应的类别）
# roi_scores：256个archors对应的为正样本的概率
# bbox_targets：256*(4*21)的矩阵，只有为正样本时，对应类别的坐标才不为0，其他类别的坐标全为0
# bbox_inside_weights：256*(4*21)的矩阵，正样本时，对应类别四个坐标的权重为1，其他全为0
labels, rois, roi_scores, bbox_targets, bbox_inside_weights = _sample_rois(all_rois, all_scores, gt_boxes, fg_rois_per_image, rois_per_image, _num_classes) # 选择256个archors rois = rois.reshape(-1, 5)
roi_scores = roi_scores.reshape(-1)
labels = labels.reshape(-1, 1)
bbox_targets = bbox_targets.reshape(-1, _num_classes * 4)
bbox_inside_weights = bbox_inside_weights.reshape(-1, _num_classes * 4)
bbox_outside_weights = np.array(bbox_inside_weights > 0).astype(np.float32) # 256*(4*21)的矩阵，正样本时，对应类别四个坐标的权重为1，其他全为0 return rois, roi_scores, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights def _get_bbox_regression_labels(bbox_target_data, num_classes):
"""Bounding-box regression targets (bbox_target_data) are stored in a compact form N x (class, tx, ty, tw, th)
This function expands those targets into the 4-of-4*K representation used by the network (i.e. only one class has non-zero targets).
Returns:
bbox_target (ndarray): N x 4K blob of regression targets
bbox_inside_weights (ndarray): N x 4K blob of loss weights
"""
clss = bbox_target_data[:, 0] # 第1列，为类别
bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32) # 256*(4*21)的矩阵
bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
inds = np.where(clss > 0)[0] # 正样本的索引
for ind in inds:
cls = clss[ind] # 正样本的类别
start = int(4 * cls) # 每个正样本的起始坐标
end = start + 4 # 每个正样本的终止坐标（由于坐标为4）
bbox_targets[ind, start:end] = bbox_target_data[ind, 1:] # 对应的坐标偏移赋值给对应的类别
bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS # 对应的权重(1.0, 1.0, 1.0, 1.0)赋值给对应的类别 # bbox_targets：256*(4*21)的矩阵，只有为正样本时，对应类别的坐标才不为0，其他类别的坐标全为0
# bbox_inside_weights：256*(4*21)的矩阵，正样本时，对应类别四个坐标的权重为1，其他全为0
return bbox_targets, bbox_inside_weights def _compute_targets(ex_rois, gt_rois, labels):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 4 targets = bbox_transform(ex_rois, gt_rois) # 通过公式2后四个，结合256个archor和对应的正样本的坐标计算坐标的偏移
if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED: # Optionally normalize targets by a precomputed mean and stdev
targets = ((targets - np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS)) / np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS)) # 坐标减去均值除以标准差，进行归一化
return np.hstack((labels[:, np.newaxis], targets)).astype(np.float32, copy=False) # 之前的bbox第一列为全0，此处第一列为对应的类别 def _sample_rois(all_rois, all_scores, gt_boxes, fg_rois_per_image, rois_per_image, num_classes): # all_rois第一列全0，后4列为坐标；gt_boxes前4列为坐标，最后一列为类别
"""Generate a random sample of RoIs comprising foreground and background examples."""
# 计算archors和gt_boxes重叠区域面积的比值
overlaps = bbox_overlaps(np.ascontiguousarray(all_rois[:, 1:5], dtype=np.float), np.ascontiguousarray(gt_boxes[:, :4], dtype=np.float)) # overlaps: (rois x gt_boxes)
gt_assignment = overlaps.argmax(axis=1) # 得到每个archors对应的gt_boxes的索引
max_overlaps = overlaps.max(axis=1) # 得到每个archors对应的gt_boxes的重叠区域的值
labels = gt_boxes[gt_assignment, 4] # 得到每个archors对应的gt_boxes的类别 # 每个archors对应的gt_boxes的重叠区域的值大于阈值的作为正样本，得到正样本的索引
fg_inds = np.where(max_overlaps >= cfg.TRAIN.FG_THRESH)[0] # Select foreground RoIs as those with >= FG_THRESH overlap
# Guard against the case when an image has fewer than fg_rois_per_image. Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
# 每个archors对应的gt_boxes的重叠区域的值在给定阈值内的作为负样本，得到负样本的索引
bg_inds = np.where((max_overlaps < cfg.TRAIN.BG_THRESH_HI) & (max_overlaps >= cfg.TRAIN.BG_THRESH_LO))[0] # Small modification to the original version where we ensure a fixed number of regions are sampled
# 最终选择256个archors
if fg_inds.size > 0 and bg_inds.size > 0: # 正负样本均存在，则选择最多fg_rois_per_image个正样本，不够的话，补充负样本
fg_rois_per_image = min(fg_rois_per_image, fg_inds.size)
fg_inds = npr.choice(fg_inds, size=int(fg_rois_per_image), replace=False)
bg_rois_per_image = rois_per_image - fg_rois_per_image
to_replace = bg_inds.size < bg_rois_per_image
bg_inds = npr.choice(bg_inds, size=int(bg_rois_per_image), replace=to_replace)
elif fg_inds.size > 0: # 只有正样本，选择rois_per_image个正样本
to_replace = fg_inds.size < rois_per_image
fg_inds = npr.choice(fg_inds, size=int(rois_per_image), replace=to_replace)
fg_rois_per_image = rois_per_image
elif bg_inds.size > 0: # 只有负样本，选择rois_per_image个负样本
to_replace = bg_inds.size < rois_per_image
bg_inds = npr.choice(bg_inds, size=int(rois_per_image), replace=to_replace)
fg_rois_per_image = 0
else:
import pdb
pdb.set_trace() keep_inds = np.append(fg_inds, bg_inds) # 正样本和负样本的索引
labels = labels[keep_inds] # 正样本和负样本对应的真实的类别
labels[int(fg_rois_per_image):] = 0 # 负样本对应的类别设置为0
rois = all_rois[keep_inds] # 从post_nms_topN个archors中选择256个archors
roi_scores = all_scores[keep_inds] # 256个archors对应的为正样本的概率 # 通过256个archors的坐标和每个archors对应的gt_boxes的坐标及这些archors的真实类别得到坐标偏移（将rois第一列的全0更新为每个archors对应的类别）
bbox_target_data = _compute_targets(rois[:, 1:5], gt_boxes[gt_assignment[keep_inds], :4], labels)
# bbox_targets：256*(4*21)的矩阵，只有为正样本时，对应类别的坐标才不为0，其他类别的坐标全为0
# bbox_inside_weights：256*(4*21)的矩阵，正样本时，对应类别四个坐标的权重为1，其他全为0
bbox_targets, bbox_inside_weights = _get_bbox_regression_labels(bbox_target_data, num_classes) # labels：正样本和负样本对应的真实的类别
# rois：从post_nms_topN个archors中选择256个archors（第一列的全0更新为每个archors对应的类别）
# roi_scores：256个archors对应的为正样本的概率
# bbox_targets：256*(4*21)的矩阵，只有为正样本时，对应类别的坐标才不为0，其他类别的坐标全为0
# bbox_inside_weights：256*(4*21)的矩阵，正样本时，对应类别四个坐标的权重为1，其他全为0
return labels, rois, roi_scores, bbox_targets, bbox_inside_weights

def _crop_pool_layer(self, bottom, rois, name):
with tf.variable_scope(name) as scope:
batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name="batch_id"), [1]) # 得到第一列，为类别
bottom_shape = tf.shape(bottom) # Get the normalized coordinates of bounding boxes
height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(self._feat_stride[0])
width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(self._feat_stride[0])
x1 = tf.slice(rois, [0, 1], [-1, 1], name="x1") / width # 由于crop_and_resize的bboxes范围为0-1，得到归一化的坐标
y1 = tf.slice(rois, [0, 2], [-1, 1], name="y1") / height
x2 = tf.slice(rois, [0, 3], [-1, 1], name="x2") / width
y2 = tf.slice(rois, [0, 4], [-1, 1], name="y2") / height
bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], axis=1)) # Won't be back-propagated to rois anyway, but to save time
pre_pool_size = cfg.POOLING_SIZE * 2 # 根据bboxes裁剪出256个特征，并缩放到14*14（channels和bottom的channels一样），batchsize为256
crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [pre_pool_size, pre_pool_size], name="crops") return slim.max_pool2d(crops, [2, 2], padding='SAME') # amx pool后得到7*7的特征

def _head_to_tail(self, pool5, is_training, reuse=None):
with tf.variable_scope(self._scope, self._scope, reuse=reuse):
pool5_flat = slim.flatten(pool5, scope='flatten')
fc6 = slim.fully_connected(pool5_flat, 4096, scope='fc6')
if is_training:
fc6 = slim.dropout(fc6, keep_prob=0.5, is_training=True, scope='dropout6')
fc7 = slim.fully_connected(fc6, 4096, scope='fc7')
if is_training:
fc7 = slim.dropout(fc7, keep_prob=0.5, is_training=True, scope='dropout7') return fc7

def _region_classification(self, fc7, is_training, initializer, initializer_bbox):
# 增加fc层，输出为总共类别的个数，进行分类
cls_score = slim.fully_connected(fc7, self._num_classes, weights_initializer=initializer, trainable=is_training, activation_fn=None, scope='cls_score')
cls_prob = self._softmax_layer(cls_score, "cls_prob") # 得到每一类别的概率
cls_pred = tf.argmax(cls_score, axis=1, name="cls_pred") # 得到预测的类别
# 增加fc层，预测位置信息的偏移
bbox_pred = slim.fully_connected(fc7, self._num_classes * 4, weights_initializer=initializer_bbox, trainable=is_training, activation_fn=None, scope='bbox_pred') self._predictions["cls_score"] = cls_score # 用于rcnn分类的256个archors的特征
self._predictions["cls_pred"] = cls_pred
self._predictions["cls_prob"] = cls_prob
self._predictions["bbox_pred"] = bbox_pred return cls_prob, bbox_pred

def _add_losses(self, sigma_rpn=3.0):
with tf.variable_scope('LOSS_' + self._tag) as scope:
rpn_cls_score = tf.reshape(self._predictions['rpn_cls_score_reshape'], [-1, 2]) # 每个archors是正样本还是负样本
rpn_label = tf.reshape(self._anchor_targets['rpn_labels'], [-1]) # 特征图中每个位置对应的是正样本、负样本还是不关注（去除了边界在图像外面的archors）
rpn_select = tf.where(tf.not_equal(rpn_label, -1)) # 不关注的archor到的索引
rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2]) # 去除不关注的archor
rpn_label = tf.reshape(tf.gather(rpn_label, rpn_select), [-1]) # 去除不关注的label
rpn_cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, labels=rpn_label)) # rpn二分类的损失 rpn_bbox_pred = self._predictions['rpn_bbox_pred'] # 每个位置的9个archors回归位置偏移
rpn_bbox_targets = self._anchor_targets['rpn_bbox_targets'] # 特征图中每个位置和对应的正样本的坐标偏移（很多为0）
rpn_bbox_inside_weights = self._anchor_targets['rpn_bbox_inside_weights'] # 正样本的权重为1（去除负样本和不关注的样本，均为0）
rpn_bbox_outside_weights = self._anchor_targets['rpn_bbox_outside_weights'] # 正样本和负样本（不包括不关注的样本）归一化的权重
rpn_loss_box = self._smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights, sigma=sigma_rpn, dim=[1, 2, 3]) cls_score = self._predictions["cls_score"] # 用于rcnn分类的256个archors的特征
label = tf.reshape(self._proposal_targets["labels"], [-1]) # 正样本和负样本对应的真实的类别
cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=cls_score, labels=label)) # rcnn分类的损失 bbox_pred = self._predictions['bbox_pred'] # RCNN, bbox loss
bbox_targets = self._proposal_targets['bbox_targets'] # 256*(4*21)的矩阵，只有为正样本时，对应类别的坐标才不为0，其他类别的坐标全为0
bbox_inside_weights = self._proposal_targets['bbox_inside_weights'] # 256*(4*21)的矩阵，正样本时，对应类别四个坐标的权重为1，其他全为0
bbox_outside_weights = self._proposal_targets['bbox_outside_weights'] # 256*(4*21)的矩阵，正样本时，对应类别四个坐标的权重为1，其他全为0
loss_box = self._smooth_l1_loss(bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights) self._losses['cross_entropy'] = cross_entropy
self._losses['loss_box'] = loss_box
self._losses['rpn_cross_entropy'] = rpn_cross_entropy
self._losses['rpn_loss_box'] = rpn_loss_box loss = cross_entropy + loss_box + rpn_cross_entropy + rpn_loss_box # 总共的损失
regularization_loss = tf.add_n(tf.losses.get_regularization_losses(), 'regu')
self._losses['total_loss'] = loss + regularization_loss self._event_summaries.update(self._losses) return loss

def _smooth_l1_loss(self, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights, sigma=1.0, dim=[1]):
sigma_2 = sigma ** 2
box_diff = bbox_pred - bbox_targets # 预测的和真实的相减
in_box_diff = bbox_inside_weights * box_diff # 乘以正样本的权重1（rpn：去除负样本和不关注的样本，rcnn：去除负样本）
abs_in_box_diff = tf.abs(in_box_diff) # 绝对值
smoothL1_sign = tf.stop_gradient(tf.to_float(tf.less(abs_in_box_diff, 1. / sigma_2))) # 小于阈值的截断的标志位
in_loss_box = tf.pow(in_box_diff, 2) * (sigma_2 / 2.) * smoothL1_sign + (abs_in_box_diff - (0.5 / sigma_2)) * (1. - smoothL1_sign) # smooth l1 loss
out_loss_box = bbox_outside_weights * in_loss_box # rpn：除以有效样本总数（不考虑不关注的样本），进行归一化；rcnn：正样本四个坐标权重为1，负样本为0
loss_box = tf.reduce_mean(tf.reduce_sum(out_loss_box, axis=dim))
return loss_box