def _compute_targets(rois, overlaps, labels):
"""
Compute bounding-box regression targets for an image.
for each roi find the corresponding gt_box, then compute the distance.
"""
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((rois.shape[0], 5), dtype=np.float32)
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets
def evaluate_signal_proposal(p_bbox_list, g_bbox_list, thredshold):
"""
计算单分类的网络性能
:param g_bbox_list: groud truth shape: [n,4]
:param p_bbox_list: predicted shape: [n,4] [left_top_x, left_top_y, right_bottom_x, right_bottom_y]
:return
"""
overlaps = bbox_overlaps(np.ascontiguousarray(p_bbox_list, dtype=np.float),
np.ascontiguousarray(g_bbox_list, dtype=np.float))
# precision
max_p_overlaps = np.max(overlaps, axis=1)
# print(max_p_overlaps)
filter = np.where(max_p_overlaps >= thredshold)
# print(filter[0])
precision_TP = len(filter[0])
precision = float(precision_TP) / float(len(p_bbox_list))
# print(precision, float(precision_TP), float(len(p_bbox_list)))
# recall
filted_overlaps = overlaps[filter[0]]
# print(filted_overlaps)
max_index = np.argmax(filted_overlaps, axis=1)
# print(max_index.shape)
recall = float(max_index.shape[0]) / float(len(g_bbox_list))
return precision, recall
def report(self, bbox):
"""tracker calls this function to report the result.
bbox should be in the form of (x, y, w, h)"""
gt = self._ground_truth[self._cur - 1]
# Since result bbox is reported once a time, therefore takes the first one
overlap = bbox_overlaps([gt], [bbox])[0]
self._overlaps[self._video_name].append(overlap)
def _compute_targets(rois, overlaps, labels):
"""
Compute bounding-box regression targets for an image.
for each roi find the corresponding gt_box, then compute the distance.
"""
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((rois.shape[0], 5), dtype=np.float32)
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets
def create_roidb_from_box_list(self, box_list, gt_roidb):
assert len(box_list) == self.num_images, \
'Number of boxes must match number of ground-truth images'
roidb = []
for i in range(self.num_images):
boxes = box_list[i]
num_boxes = boxes.shape[0]
overlaps = np.zeros((num_boxes, self.num_classes),
dtype=np.float32)
if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0:
gt_boxes = gt_roidb[i]['boxes']
gt_classes = gt_roidb[i]['gt_classes']
gt_overlaps = bbox_overlaps(boxes.astype(np.float),
gt_boxes.astype(np.float))
argmaxes = gt_overlaps.argmax(axis=1)
maxes = gt_overlaps.max(axis=1)
I = np.where(maxes > 0)[0]
overlaps[I, gt_classes[argmaxes[I]]] = maxes[I]
overlaps = scipy.sparse.csr_matrix(overlaps)
roidb.append({
'boxes':
boxes,
'gt_classes':
np.zeros((num_boxes, ), dtype=np.int32),
'gt_overlaps':
overlaps,
'flipped':
False,
'seg_areas':
np.zeros((num_boxes, ), dtype=np.float32),
})
return roidb
def _sample_rois(all_rois, all_scores, gt_boxes, fg_rois_per_image, rois_per_image, num_classes):
"""Generate a random sample of RoIs comprising foreground and background
examples.
"""
# overlaps: (rois x gt_boxes)
overlaps = bbox_overlaps(
np.ascontiguousarray(all_rois[:, 1:5], dtype=np.float),
np.ascontiguousarray(gt_boxes[:, :4], dtype=np.float))
gt_assignment = overlaps.argmax(axis=1)
max_overlaps = overlaps.max(axis=1)
labels = gt_boxes[gt_assignment, 4]
# Select foreground RoIs as those with >= FG_THRESH overlap
fg_inds = np.where(max_overlaps >= cfg.FLAGS.roi_fg_threshold)[0]
# 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)
bg_inds = np.where((max_overlaps < cfg.FLAGS.roi_bg_threshold_high) &
(max_overlaps >= cfg.FLAGS.roi_bg_threshold_low))[0]
print('max_overlaps', max_overlaps)
print('fg_inds', fg_inds)
print('bg_inds', bg_inds)
# Small modification to the original version where we ensure a fixed number of regions are sampled
if fg_inds.size > 0 and bg_inds.size > 0:
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:
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:
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()
# 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[int(fg_rois_per_image):] = 0
rois = all_rois[keep_inds]
roi_scores = all_scores[keep_inds]
bbox_target_data = _compute_targets(
rois[:, 1:5], gt_boxes[gt_assignment[keep_inds], :4], labels)
bbox_targets, bbox_inside_weights = \
_get_bbox_regression_labels(bbox_target_data, num_classes)
return labels, rois, roi_scores, bbox_targets, bbox_inside_weights
def _sample_rois(all_rois, all_scores, gt_boxes, fg_rois_per_image, rois_per_image, num_classes):
"""Generate a random sample of RoIs comprising foreground and background
examples.
"""
# overlaps: (rois x gt_boxes)
overlaps = bbox_overlaps(
all_rois[:, 1:5].data,
gt_boxes[:, :4].data)
max_overlaps, gt_assignment = overlaps.max(1)
labels = gt_boxes[gt_assignment, [4]]
# Select foreground RoIs as those with >= FG_THRESH overlap
fg_inds = (max_overlaps >= cfg.TRAIN.FG_THRESH).nonzero().view(-1)
# 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)
bg_inds = (((max_overlaps < cfg.TRAIN.BG_THRESH_HI) + (max_overlaps >= cfg.TRAIN.BG_THRESH_LO)) == 2).nonzero().view(-1)
# Small modification to the original version where we ensure a fixed number of regions are sampled
if fg_inds.numel() > 0 and bg_inds.numel() > 0:
fg_rois_per_image = min(fg_rois_per_image, fg_inds.numel())
fg_inds = fg_inds[torch.from_numpy(
npr.choice(np.arange(0, fg_inds.numel()), size=int(fg_rois_per_image), replace=False)).long().cuda()]
bg_rois_per_image = rois_per_image - fg_rois_per_image
to_replace = bg_inds.numel() < bg_rois_per_image
bg_inds = bg_inds[torch.from_numpy(
npr.choice(np.arange(0, bg_inds.numel()), size=int(bg_rois_per_image), replace=to_replace)).long().cuda()]
elif fg_inds.numel() > 0:
to_replace = fg_inds.numel() < rois_per_image
fg_inds = fg_inds[torch.from_numpy(
npr.choice(np.arange(0, fg_inds.numel()), size=int(rois_per_image), replace=to_replace)).long().cuda()]
fg_rois_per_image = rois_per_image
elif bg_inds.numel() > 0:
to_replace = bg_inds.numel() < rois_per_image
bg_inds = bg_inds[torch.from_numpy(
npr.choice(np.arange(0, bg_inds.numel()), size=int(rois_per_image), replace=to_replace)).long().cuda()]
fg_rois_per_image = 0
else:
import pdb
pdb.set_trace()
# The indices that we're selecting (both fg and bg)
keep_inds = torch.cat([fg_inds, bg_inds], 0)
# Select sampled values from various arrays:
labels = labels[keep_inds].contiguous()
# Clamp labels for the background RoIs to 0
labels[int(fg_rois_per_image):] = 0
rois = all_rois[keep_inds].contiguous()
roi_scores = all_scores[keep_inds].contiguous()
bbox_target_data = _compute_targets(
rois[:, 1:5].data, gt_boxes[gt_assignment[keep_inds]][:, :4].data, labels.data)
bbox_targets, bbox_inside_weights = \
_get_bbox_regression_labels(bbox_target_data, num_classes)
return labels, rois, roi_scores, bbox_targets, bbox_inside_weights
def uniform_aspect_sample(im, bbox, params, num, stype):
assert len(bbox) == 4, "Invalid ground-truth(x, y, w, h) form."
assert bbox[2] > 0 and bbox[3] > 0, "Width or height < 0."
assert len(params) == 5, "Invalid {:d}-tuple params(should be five-tuple).".format(len(params))
assert num > 0, "Number of samples should be larger than 0."
im_shape = im.shape
im_w = im_shape[1]
im_h = im_shape[0]
# Calculate average of width and height
centerx = bbox[0] + bbox[2] / 2.
centery = bbox[1] + bbox[3] / 2.
xrand = params[0] * bbox[2] * (npr.rand(num, 1) * 2 - 1)
yrand = params[1] * bbox[3] * (npr.rand(num, 1) * 2 - 1)
wrand = bbox[2] * (1.05 ** (npr.rand(num, 1) * 4 - 2))
hrand = bbox[3] * (1.05 ** (npr.rand(num, 1) * 4 - 2))
ws = wrand * (1.05 ** npr.rand(num, 1))
hs = hrand * (1.05 ** npr.rand(num, 1))
bboxes = []
for i in range(num):
cx = centerx + xrand[i, 0]
cy = centery + yrand[i, 0]
hw = ws[i, 0] / 2.
hh = hs[i, 0] / 2.
box = (
max(0, int(cx - hw)),
max(0, int(cy - hh)),
min(im_w, int(cx + hw)),
min(im_h, int(cy + hh))
)
sample = (box[0], box[1], box[2] - box[0], box[3] - box[1])
if int(sample[2]) <= 0 or int(sample[3]) <= 0:
continue
overlap = bbox_overlaps([bbox], [sample])[0]
if overlap > params[3]:
bboxes.append({
'img': im,
'box': sample,
'label': 1,
'overlap': overlap
})
elif overlap < params[4]:
bboxes.append({
'img': im,
'box': sample,
'label': 0,
'overlap': overlap
})
return bboxes
def fusion_target(rois, gt_labels, gt_boxes, gt_boxes3d):
CFG = EasyDict()
CFG.TRAIN = EasyDict()
CFG.TRAIN.RCNN_BATCH_SIZE = 128
CFG.TRAIN.RCNN_FG_FRACTION = 0.25
CFG.TRAIN.RCNN_FG_THRESH_LO = 0.5
# Include "ground-truth" in the set of candidate rois
rois = rois.reshape(-1, 5) # Proposal (i, x1, y1, x2, y2) coming from RPN
num = len(gt_boxes)
zeros = np.zeros((num, 1), dtype=np.float32)
extended_rois = np.vstack((rois, np.hstack((zeros, gt_boxes))))
assert np.all(
extended_rois[:, 0] == 0), 'Only single image batches are supported'
rois_per_image = CFG.TRAIN.RCNN_BATCH_SIZE
fg_rois_per_image = np.round(CFG.TRAIN.RCNN_FG_FRACTION * rois_per_image)
# overlaps: (rois x gt_boxes)
overlaps = bbox_overlaps(
np.ascontiguousarray(extended_rois[:, 1:5], dtype=np.float),
np.ascontiguousarray(gt_boxes, dtype=np.float))
max_overlaps = overlaps.max(axis=1)
gt_assignment = overlaps.argmax(axis=1)
labels = gt_labels[gt_assignment]
# Select foreground RoIs as those with >= FG_THRESH overlap
fg_inds = np.where(max_overlaps >= CFG.TRAIN.RCNN_FG_THRESH_LO)[0]
# Select false positive
fp_inds = np.where((max_overlaps < 0.01))[0]
# The indices that we're selecting (both fg and bg)
keep = np.append(fg_inds, fp_inds)
rois = extended_rois[keep]
labels = labels[keep]
labels[fg_inds.size:] = 0
gt_boxes3d = gt_boxes3d[gt_assignment[keep]]
et_boxes = rois[:, 1:5]
et_boxes3d = top_box_to_box3d(et_boxes)
targets = box3d_transform(et_boxes3d, gt_boxes3d)
targets[np.where(labels == 0), :, :] = 0
return rois, labels, targets
def evaluate_signal_bbox(p_bbox_list, g_bbox_list, thredshold):
"""
计算单分类的网络性能
:param p_bbox_list: predicted shape: [n,4] [left_top_x, left_top_y, right_bottom_x, right_bottom_y]
:param g_bbox_list: groud truth shape: [n,4]
:return
"""
# print(g_bbox_list)
overlaps = bbox_overlaps(np.ascontiguousarray(p_bbox_list, dtype=np.float),
np.ascontiguousarray(g_bbox_list, dtype=np.float),
1)
max_overlaps = np.max(overlaps, axis=0)
# print(max_overlaps)
filter = np.where(max_overlaps >= thredshold)[0]
recall = float(len(filter)) / float(len(g_bbox_list))
return recall
def anchor_target_layer(rpn_cls_score,
rpn_cls_prob,
im_name,
gt_boxes_large,
gt_ishard,
dontcare_areas,
im_info,
_feat_stride=[
16,
],
anchor_scales=[
16,
]):
"""
将gt_box划分为细框
实现论文中的side-refinement
arameters
----------
rpn_cls_score: (1, H, W, Ax2) bg/fg scores of previous conv layer
gt_boxes: (G, 5) vstack of [x1, y1, x2, y2, class]
gt_ishard: (G, 1), 1 or 0 indicates difficult or not
dontcare_areas: (D, 4), some areas may contains small objs but no labelling. D may be 0
im_info: a list of [image_height, image_width, scale_ratios]
_feat_stride: the downsampling ratio of feature map to the original input image
anchor_scales: the scales to the basic_anchor (basic anchor is [16, 16])
----------
:return:
"""
gt_boxes = split_frame(gt_boxes_large)
_anchors = generate_anchors(
scales=np.array(anchor_scales)) # 生成基本的anchor,一共9个
_num_anchors = _anchors.shape[0] # 9个anchor
if DEBUG:
print('anchors:')
print(_anchors)
print('anchor shapes:')
print(
np.hstack((
_anchors[:, 2::4] - _anchors[:, 0::4],
_anchors[:, 3::4] - _anchors[:, 1::4],
)))
_counts = cfg.EPS
_sums = np.zeros((1, 4))
_squared_sums = np.zeros((1, 4))
_fg_sum = 0
_bg_sum = 0
_count = 0
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
im_info = im_info[0] # 图像的高宽及通道数
assert rpn_cls_score.shape[0] == 1, \
'Only single item batches are supported'
# map of shape (..., H, W)
height, width = rpn_cls_score.shape[1:3] # feature-map的高宽
if DEBUG:
print('AnchorTargetLayer: height', height, 'width', width)
print('')
print('im_size: ({}, {})'.format(im_info[0], im_info[1]))
print('scale: {}'.format(im_info[2]))
print('height, width: ({}, {})'.format(height, width))
print('rpn: gt_boxes.shape', gt_boxes.shape)
# 1. Generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, width) * _feat_stride # (W)
shift_y = np.arange(0, height) * _feat_stride # (H)
shift_x, shift_y = np.meshgrid(
shift_x, shift_y) # in W H order # shift_x (H, W) shift_y (H, W)
# K is H x W
shifts = np.vstack(
(shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel()
)).transpose() # 生成feature-map和真实image上anchor之间的偏移量 #(H*W, 4)
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = _num_anchors # 9个anchor
K = shifts.shape[0] # 50*37，feature-map的宽乘高的大小
all_anchors = (_anchors.reshape((1, A, 4)) + shifts.reshape(
(1, K, 4)).transpose((1, 0, 2))) # 相当于复制宽高的维度，然后相加
all_anchors = all_anchors.reshape((K * A, 4))
total_anchors = int(K * A)
# only keep anchors inside the image
# 仅保留那些还在图像内部的anchor，超出图像的都删掉
inds_inside = np.where(
(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]
if DEBUG:
print('total_anchors', total_anchors)
print('inds_inside', len(inds_inside))
# keep only inside anchors
anchors = all_anchors[inds_inside, :] # 保留那些在图像内的anchor (In, 4)
if DEBUG:
print('anchors.shape', anchors.shape)
# 至此，anchor准备好了
# --------------------------------------------------------------
# label: 1 is positive, 0 is negative, -1 is dont care
# (A)
labels = np.empty((len(inds_inside), ), dtype=np.float32)
labels.fill(-1) # 初始化label，均为-1
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt), shape is A x G
# 计算anchor和gt-box的overlap，用来给anchor上标签
overlaps = bbox_overlaps(np.ascontiguousarray(
anchors, dtype=np.float), np.ascontiguousarray(
gt_boxes,
dtype=np.float)) # 假设anchors有x个，gt_boxes有y个，返回的是一个（x,y）的数组
# 存放每一个anchor和每一个gtbox之间的overlap
argmax_overlaps = overlaps.argmax(
axis=1) # (A)#找到和每一个anchor，overlap最大的那个gt
max_overlaps = overlaps[np.arange(
len(inds_inside)
), argmax_overlaps] # 假如在内部的anchor有900个 ，(900,), 表示的是每一个anchor最大的overlaps值
gt_argmax_overlaps = overlaps.argmax(
axis=0) # G#找到所有anchor中与gtbox，overlap最大的那个anchor # (3)
gt_max_overlaps = overlaps[
gt_argmax_overlaps, np.arange(
overlaps.shape[1]
)] # 比如有3个gt 那么就得到(3,),表示的是上一步找到的与gt的overlap最大的3个anchor的overlap值
gt_argmax_overlaps = np.where(
overlaps == gt_max_overlaps)[0] # (3, ) 表示的是哪几个与gt有最大overlap的anchor的索引
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1 # 每个位置上的9个anchor中overlap最大的认为是前景
# 是将iou小于0.5的样本标记为负样本，
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
# fg label: above threshold IOU
labels[max_overlaps >=
cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1 # overlap大于0.7的认为是前景
# 增加的修复，负样本包含了最上方最下方有字的部分，这些样本会干扰样本，因此可以去掉这些负样本中，处在最上方的和左下方的样本
bg_anchor_index = labels == 0
y_anchor = anchors[:, 3]
top_anchor_index = y_anchor < min(anchors[:, 1]) + 50
bottom_anchor_index = y_anchor > max(anchors[:, 3]) - 50
assert top_anchor_index.shape == bottom_anchor_index.shape
top_bottom_anchor_index = top_anchor_index + bottom_anchor_index
bg_topbottom_anchor_index = bg_anchor_index * top_bottom_anchor_index
labels[bg_topbottom_anchor_index] = -1
# 可视化这时候的正样本，看一下是怎样的
# vis_labels = _unmap(labels, total_anchors, inds_inside, fill=-1) # 这些anchor的label是-1，也即dontcare
# vis_training_sample(vis_labels, all_anchors, im_name, gt_boxes)
if DEBUG:
print('在过滤数量之前：')
print('正样本：' + str(len(np.where(labels == 1)[0])))
print('负样本：' + str(len(np.where(labels == 0)[0])))
print('忽略样本：' + str(len(np.where(labels == -1)[0])))
# 至此，第一次生成好了这个图片的labels，
# 生成其他部分的标签
v_target, o_target = _compute_targets(
anchors,
gt_boxes[argmax_overlaps, :]) # 根据anchor和gtbox计算得真值（anchor和gtbox之间的偏差）
# 但是计算损失函数的时候，其实是需要j索引和k索引，所以计算好这两个索引，一并返回，帮助计算损失函数
# j索引，有效索引：正锚点或者与gt的overlap大于0.5以上的锚点的索引
# 正锚点
positive_index = np.where(labels == 1)[0] # 应该是一个（p,）p应该不大于128
#
# ignore_index = np.where(labels==-1)[0] # 应该是一个（n,）n应该很大，因为忽略的anchor很多
keep_index = np.where(labels != -1)[0]
_ = np.where(max_overlaps > 0.5)[0] # 应该是一个（c,）,表示overlap大于0.5的anchor的索引
remove_ignore = list()
for i in range(_.shape[0]):
if i in keep_index:
remove_ignore.append(_[i])
remove_ignore = np.array(remove_ignore)
effect_index = np.append(positive_index, remove_ignore)
remove_repeat = np.array(list(set(list(effect_index))))
j_index = remove_repeat.astype(np.int32)
j_index1 = np.zeros((len(inds_inside)), dtype=np.int32)
j_index1[j_index] = 1
# k 索引 , 边缘索引
# 先找到所有的可以认为是边缘的gt框,这里简单的认为是边缘框和左右各自一个。
# ori_gt_box = (gt_boxes/im_info[2]).astype(np.int32, copy=False)
ori_gt_box = gt_boxes.astype(np.float32, copy=False)
# 找到左右边界框，矩阵操作实现 todo
list_left_index = list()
list_right_index = list()
for i in range(ori_gt_box.shape[0]):
if ori_gt_box[i][2] - ori_gt_box[i][0] != 15:
list_left_index.append(i)
if ori_gt_box[i][0] % 16 != 0: # 看做是左边边界框
list_left_index.append(i + 1)
if (ori_gt_box[i][2] + 1) % 16 != 0: # 看做是右边边界框
list_left_index.append(i - 1)
else:
continue
list_index1 = list_left_index + list_right_index
# 去除不属于gt中的索引和重复的索引
list_index2 = list(set(list_index1))
list_index3 = sorted(list_index2)
list_index4 = list()
for index in list_index3:
if index in range(ori_gt_box.shape[0]):
list_index4.append(index)
gt_side_index = np.array(list_index4).astype(np.int32) # 得到了边界gt框的索引
# 要得到与这些gt框有最大的overlap的anchors的索引，这些anchor是我们关心的
gt_argmax_overlaps = overlaps.argmax(axis=0)
anchor_side_index = gt_argmax_overlaps[
gt_side_index] # 得到143个与gt具有最大的overlaps的anchor的索引
# 还要去掉与边界框overlap为0的anchor，因为这些anhcor不是真的我们关心的anchor，如果不去除，还会造成o_loss异常大
# anchor_side_list = list()
anchor_fg_side_list = list()
anchor_nocare_side_list = list()
for i in range(anchor_side_index.shape[0]):
anchor_index = anchor_side_index[i]
gt_index = gt_side_index[i]
overlap = overlaps[anchor_index, gt_index]
if overlap > 0.05:
anchor_fg_side_list.append(anchor_index)
elif overlap > 0:
anchor_nocare_side_list.append(anchor_index)
else:
pass
# 找到了与所有边界框有最大交集的anchor，这些anchor中有的与gt的iou只有很小（因为gt特别窄，不够16像素），所以这些anchor我们标记为-1，意思是模型将之识别为什么我们都不关心了，但是iou大于0.4的，我们都将之标记为正样本，另模型能够正确学习正负样本
anchor_fg_side_index = np.array(anchor_fg_side_list, dtype=np.int32)
anchor_nocare_side_index = np.array(anchor_nocare_side_list,
dtype=np.int32)
anchor_fg_side_index = np.array(
sorted(list(set(list(anchor_fg_side_index))))).astype(np.int32)
anchor_nocare_side_index = np.array(
sorted(list(set(list(anchor_nocare_side_index))))).astype(np.int32)
labels[anchor_fg_side_index] = 1
labels[anchor_nocare_side_index] = -1
k_index = anchor_fg_side_index.copy()
k_index1 = np.zeros((len(inds_inside)), dtype=np.int32)
k_index1[k_index] = 1
# map up to original set of anchors
# 一开始是将超出图像范围的anchor直接丢掉的，现在在加回来
labels = _unmap(labels, total_anchors, inds_inside,
fill=-1) # 这些anchor的label是-1，也即dontcare
v_target = _unmap(v_target, total_anchors, inds_inside,
fill=0) # 这些anchor的真值是0，也即没有值
o_target = _unmap(o_target, total_anchors, inds_inside, fill=0)
j_index2 = _unmap(j_index1, total_anchors, inds_inside,
fill=0).astype(np.int32)
k_index2 = _unmap(k_index1, total_anchors, inds_inside,
fill=0).astype(np.int32)
# real_j_index = np.where(j_index2==1)[0]
# real_k_index = np.where(k_index2==1)[0]
if DEBUG:
# 可视化出我们最终选出来的正样本，确定是否合理
vis_training_sample(labels, all_anchors, im_name, gt_boxes)
if DEBUG or SHOW_SOME:
print('正样本：' + str(len(np.where(labels == 1)[0])))
print('负样本：' + str(len(np.where(labels == 0)[0])))
print('忽略样本：' + str(len(np.where(labels == -1)[0])))
# print('保存的tmp_labels')
# print('正样本：' + str(len(np.where(tmp_labels == 1)[0])))
# print('负样本：' + str(len(np.where(tmp_labels == 0)[0])))
# print('忽略样本：' + str(len(np.where(tmp_labels == -1)[0])))
return labels, v_target, o_target, j_index2, k_index2
def anchor_target_layer(rpn_cls_score, gt_boxes, im_info, _feat_stride,
all_anchors, num_anchors):
"""Same as the anchor target layer in original Fast/er RCNN """
height, width = rpn_cls_score.shape[1:3]
if DEBUG:
print('AnchorTargetLayer: height', height, 'width', width)
print('all anchors size {}'.format(all_anchors.shape))
print('im_size: ({}, {})'.format(im_info[0], im_info[1]))
print('scale: {}'.format(im_info[2]))
print('height, width: ({}, {})'.format(height, width))
print('rpn: gt_boxes.shape', gt_boxes.shape)
print('rpn: gt_boxes', gt_boxes)
A = num_anchors
total_anchors = all_anchors.shape[
0] # anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False) H*W， 4
K = total_anchors / num_anchors
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
# map of shape (..., H, W)
# only keep anchors inside the image
inds_inside = np.where(
(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]
# if DEBUG:
# print('total_anchors', total_anchors)
# print('inds_inside', len(inds_inside))
# keep only inside anchors
anchors = all_anchors[inds_inside, :]
# if DEBUG:
# print('anchors.shape', anchors.shape)
# label: 1 is positive, 0 is negative, -1 is dont care
labels = np.empty((len(inds_inside), ), dtype=np.float32)
labels.fill(-1)
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt)
overlaps = bbox.bbox_overlaps(
np.ascontiguousarray(anchors, dtype=np.float),
np.ascontiguousarray(gt_boxes, dtype=np.float))
if DEBUG:
print('anchors {} --> \n {}'.format(anchors.shape, anchors))
print('gt boxes --> \n {}'.format(gt_boxes))
print('anchors , gt boxes overlaps {} --> \n {}'.format(
overlaps.shape, overlaps))
# axis=1 按行值 axis=0 按列值. argmax(axis=1) 找到anchors每行对应重合度最大的gt_boxes
# overlaps [ N * K] N anchors length, K gt boxes length
argmax_overlaps = overlaps.argmax(
axis=1) # (A)#找到和每一个gtbox，overlap最大的那个anchor
if DEBUG:
print('arg max over laps axis = 1 --> \n {}'.format(argmax_overlaps))
max_overlaps = overlaps[np.arange(len(
inds_inside)), argmax_overlaps] # argmax_overlaps 是选择overlaps最大的一列的列位置
# argmax(axis=1) 按列扫描找到每列最大的行索引值, 找到gt_boxes 每行数据 重合度最大的anchors的行
gt_argmax_overlaps = overlaps.argmax(
axis=0) # 取得gt_boxes 对应重合度最大的 anchors的行值
if DEBUG:
print('gt arg max over laps axis = 0 --> \n {}'.format(
gt_argmax_overlaps))
gt_max_overlaps = overlaps[gt_argmax_overlaps,
np.arange(overlaps.shape[1])]
gt_argmax_overlaps = np.where(
overlaps == gt_max_overlaps)[0] # 返回overlaps=gt max overlaps
if DEBUG:
print('gt arg max over laps 222 axis = 0 --> \n {}'.format(
gt_argmax_overlaps))
# if DEBUG:
# print('arg max over laps ->', argmax_overlaps)
# print('max over laps ->', max_overlaps)
# print('gt argmax overlaps ->',len(gt_argmax_overlaps))
if not RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
# first set the negatives
labels[max_overlaps < RPN_NEGATIVE_OVERLAP] = 0
# fg label: for each gt, anchor with highest overlap
# gt_artmax_overlaps是gt_boxs的每个位置在anchros里面有最大值的位置，即每个gt_boxes在anchors里重合度最大的地方
labels[gt_argmax_overlaps] = 1
if DEBUG:
print('labels --> \n {}'.format(labels))
# fg label: above threshold IOU
labels[max_overlaps >= RPN_POSITIVE_OVERLAP] = 1
if RPN_CLOBBER_POSITIVES:
# assign bg labels last so that negative labels can clobber positives
labels[max_overlaps < RPN_NEGATIVE_OVERLAP] = 0
# subsample positive labels if we have too many
num_fg = int(RPN_FG_FRACTION * RPN_BATCHSIZE)
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
if DEBUG:
print('fg_inds --> {}'.format(fg_inds))
print('labels --> \n {}'.format(labels))
# subsample negative labels if we have too many
num_bg = RPN_BATCHSIZE - np.sum(labels == 1)
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
if DEBUG:
print('bg_inds --> {}'.format(bg_inds))
print('labels --> \n {}'.format(labels))
# if DEBUG:
# print('Number FG -> {} Number BG -> {}'.format(num_fg, num_bg))
bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
# argmax_overlaps是对应anchors每一行对应最大的列索引值，相对于gt_boxes则是其对应的行号
cgt_boxes = gt_boxes[argmax_overlaps, :]
# if DEBUG:
# print('anchors size -> {} cgt boxes size -> {}'.format(anchors.shape, cgt_boxes.shape))
# print('anchors --> \n {}'.format(anchors))
# print('cgt boxes --> \n {}'.format(cgt_boxes))
bbox_targets = _compute_targets(anchors, cgt_boxes)
# if DEBUG:
# print('bbox target {} --> \n {}'.format(bbox_targets.shape,bbox_targets))
# zz = bbox_transform_inv(torch.from_numpy(anchors[4506]).view(1,4),torch.from_numpy(bbox_targets[4506]).view(1,4))
# bbox inside 权重
bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
# only the positive ones have regression targets
bbox_inside_weights[labels == 1, :] = np.array(RPN_BBOX_INSIDE_WEIGHTS)
# bbox outside 权重
bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
if RPN_POSITIVE_WEIGHT < 0:
# uniform weighting of examples (given non-uniform sampling)
num_examples = np.sum(labels >= 0)
# positive_weights = np.ones((1, 4)) * 1.0 / num_examples
# negative_weights = np.ones((1, 4)) * 1.0 / num_examples
# 外部权重，前景是1，背景是0
positive_weights = np.ones((1, 4))
negative_weights = np.zeros((1, 4))
else:
assert ((RPN_POSITIVE_WEIGHT > 0) & (RPN_POSITIVE_WEIGHT < 1))
positive_weights = (RPN_POSITIVE_WEIGHT / np.sum(labels == 1))
negative_weights = ((1.0 - RPN_POSITIVE_WEIGHT) / np.sum(labels == 0))
# 外部权重，前景是1，背景是0
bbox_outside_weights[labels == 1, :] = positive_weights
bbox_outside_weights[labels == 0, :] = negative_weights
if DEBUG:
print('labels 2 --> \n {}'.format(labels))
# map up to original set of anchors
labels = _unmap(labels, total_anchors, inds_inside, fill=-1)
if DEBUG:
print('labels 2 --> \n {}'.format(labels))
bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)
bbox_inside_weights = _unmap(bbox_inside_weights,
total_anchors,
inds_inside,
fill=0)
bbox_outside_weights = _unmap(bbox_outside_weights,
total_anchors,
inds_inside,
fill=0)
# labels
# labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2)
# labels = labels.reshape((1, 1, A * height, width))
labels = labels.reshape((1, height, width, A))
rpn_labels = labels
# bbox_targets
bbox_targets = bbox_targets \
.reshape((1, height, width, A * 4))
rpn_bbox_targets = bbox_targets
# bbox_inside_weights
bbox_inside_weights = bbox_inside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_inside_weights = bbox_inside_weights
# bbox_outside_weights
bbox_outside_weights = bbox_outside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_outside_weights = bbox_outside_weights
return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights
def evaluate_recall(self,
candidate_boxes=None,
thresholds=None,
area='all',
limit=None):
"""Evaluate detection proposal recall metrics.
Returns:
results: dictionary of results with keys
'ar': average recall
'recalls': vector recalls at each IoU overlap threshold
'thresholds': vector of IoU overlap thresholds
'gt_overlaps': vector of all ground-truth overlaps
"""
# Record max overlap value for each gt box
# Return vector of overlap values
areas = {
'all': 0,
'small': 1,
'medium': 2,
'large': 3,
'96-128': 4,
'128-256': 5,
'256-512': 6,
'512-inf': 7
}
area_ranges = [
[0**2, 1e5**2], # all
[0**2, 32**2], # small
[32**2, 96**2], # medium
[96**2, 1e5**2], # large
[96**2, 128**2], # 96-128
[128**2, 256**2], # 128-256
[256**2, 512**2], # 256-512
[512**2, 1e5**2], # 512-inf
]
assert area in areas, 'unknown area range: {}'.format(area)
area_range = area_ranges[areas[area]]
gt_overlaps = np.zeros(0)
num_pos = 0
for i in range(self.num_images):
# Checking for max_overlaps == 1 avoids including crowd annotations
# (...pretty hacking :/)
max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(
axis=1)
gt_inds = np.where((self.roidb[i]['gt_classes'] > 0)
& (max_gt_overlaps == 1))[0]
gt_boxes = self.roidb[i]['boxes'][gt_inds, :]
gt_areas = self.roidb[i]['seg_areas'][gt_inds]
valid_gt_inds = np.where((gt_areas >= area_range[0])
& (gt_areas <= area_range[1]))[0]
gt_boxes = gt_boxes[valid_gt_inds, :]
num_pos += len(valid_gt_inds)
if candidate_boxes is None:
# If candidate_boxes is not supplied, the default is to use the
# non-ground-truth boxes from this roidb
non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0]
boxes = self.roidb[i]['boxes'][non_gt_inds, :]
else:
boxes = candidate_boxes[i]
if boxes.shape[0] == 0:
continue
if limit is not None and boxes.shape[0] > limit:
boxes = boxes[:limit, :]
overlaps = bbox_overlaps(boxes.astype(np.float),
gt_boxes.astype(np.float))
_gt_overlaps = np.zeros((gt_boxes.shape[0]))
for j in range(gt_boxes.shape[0]):
# find which proposal box maximally covers each gt box
argmax_overlaps = overlaps.argmax(axis=0)
# and get the iou amount of coverage for each gt box
max_overlaps = overlaps.max(axis=0)
# find which gt box is 'best' covered (i.e. 'best' = most iou)
gt_ind = max_overlaps.argmax()
gt_ovr = max_overlaps.max()
assert (gt_ovr >= 0)
# find the proposal box that covers the best covered gt box
box_ind = argmax_overlaps[gt_ind]
# record the iou coverage of this gt box
_gt_overlaps[j] = overlaps[box_ind, gt_ind]
assert (_gt_overlaps[j] == gt_ovr)
# mark the proposal box and the gt box as used
overlaps[box_ind, :] = -1
overlaps[:, gt_ind] = -1
# append recorded iou coverage level
gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))
gt_overlaps = np.sort(gt_overlaps)
if thresholds is None:
step = 0.05
thresholds = np.arange(0.5, 0.95 + 1e-5, step)
recalls = np.zeros_like(thresholds)
# compute recall for each iou threshold
for i, t in enumerate(thresholds):
recalls[i] = (gt_overlaps >= t).sum() / float(num_pos)
# ar = 2 * np.trapz(recalls, thresholds)
ar = recalls.mean()
return {
'ar': ar,
'recalls': recalls,
'thresholds': thresholds,
'gt_overlaps': gt_overlaps
}
def anchor_target_layer(rpn_cls_score,
gt_boxes,
gt_ishard,
dontcare_areas,
im_info,
_feat_stride=[
16,
],
anchor_scales=[
16,
]):
"""
Assign anchors to ground-truth targets. Produces anchor classification
labels and bounding-box regression targets.
Parameters
----------
rpn_cls_score: (1, H, W, Ax2) bg/fg scores of previous conv layer
gt_boxes: (G, 5) vstack of [x1, y1, x2, y2, class]
gt_ishard: (G, 1), 1 or 0 indicates difficult or not
dontcare_areas: (D, 4), some areas may contains small objs but no labelling. D may be 0
im_info: a list of [image_height, image_width, scale_ratios]
_feat_stride: the downsampling ratio of feature map to the original input image
anchor_scales: the scales to the basic_anchor (basic anchor is [16, 16])
----------
Returns
----------
rpn_labels : (HxWxA, 1), for each anchor, 0 denotes bg, 1 fg, -1 dontcare
rpn_bbox_targets: (HxWxA, 4), distances of the anchors to the gt_boxes(may contains some transform)
that are the regression objectives
rpn_bbox_inside_weights: (HxWxA, 4) weights of each boxes, mainly accepts hyper param in cfg
rpn_bbox_outside_weights: (HxWxA, 4) used to balance the fg/bg,
beacuse the numbers of bgs and fgs mays significiantly different
"""
_anchors = generate_anchors(
scales=np.array(anchor_scales)) # 生成基本的anchor,一共10个
_num_anchors = _anchors.shape[0] # 10个anchor
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
# map of shape (..., H, W)
# height, width = rpn_cls_score.shape[1:3]
im_info = im_info[0] #图像的高宽及通道数
# 在feature-map上定位anchor，并加上delta，得到在实际图像中anchor的真实坐标
# Algorithm:
# for each (H, W) location i
# generate 9 anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the 9 anchors
# filter out-of-image anchors
# measure GT overlap
assert rpn_cls_score.shape[0] == 1, \
'Only single item batches are supported'
# map of shape (..., H, W)
height, width = rpn_cls_score.shape[1:3] #feature-map的高宽
# 1. Generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, width) * _feat_stride
shift_y = np.arange(0, height) * _feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y) # in W H order
# K is H x W
shifts = np.vstack(
(shift_x.ravel(), shift_y.ravel(), shift_x.ravel(),
shift_y.ravel())).transpose() # 生成feature-map和真实image上anchor之间的偏移量
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = _num_anchors # 10个anchor
K = shifts.shape[0] # 50*37，feature-map的宽乘高的大小
all_anchors = (_anchors.reshape((1, A, 4)) + shifts.reshape(
(1, K, 4)).transpose((1, 0, 2))) # 相当于复制宽高的维度，然后相加
all_anchors = all_anchors.reshape((K * A, 4))
total_anchors = int(K * A)
# only keep anchors inside the image
# 仅保留那些还在图像内部的anchor，超出图像的都删掉
inds_inside = np.where(
(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]
# if DEBUG:
# print('total_anchors', total_anchors)
# print('inds_inside', len(inds_inside))
# keep only inside anchors
anchors = all_anchors[inds_inside, :] #保留那些在图像内的anchor
# if DEBUG:
# print('anchors.shape', anchors.shape)
#至此，anchor准备好了
#--------------------------------------------------------------
# label:>=1 is prostive, 0 is negative, -1 is dont care
# (A)
labels = np.empty((len(inds_inside), ), dtype=np.float32)
labels.fill(-1) #初始化label，均为-1
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt), shape is A x G
# 计算anchor和gt-box的overlap，用来给anchor上标签
# print('anchors shape', anchors.shape) [n , 4]
# print('anchors ascontiguousarray', np.ascontiguousarray(anchors, dtype=np.float))
# print('gt_boxes shape', gt_boxes)
# print('gt_boxes ascontiguousarray', np.ascontiguousarray(gt_boxes, dtype=np.float))
# overlaps shape = [12402, 465]
# ascontiguousarray返回地址连续的数组
# print('gt_boxes',gt_boxes.shape)
overlaps = bbox_overlaps(np.ascontiguousarray(
anchors, dtype=np.float), np.ascontiguousarray(
gt_boxes,
dtype=np.float)) #假设anchors有x个，gt_boxes有y个，返回的是一个（x,y）的数组
# argmax_overlaps shape (12402,)
# 存放每一个anchor和gt最大的iou的那个gt的位置
argmax_overlaps = overlaps.argmax(
axis=1) # (A)#找到和每一个gtbox，overlap最大的那个anchor
# print('argmax_overlaps shape ', argmax_overlaps.shape, argmax_overlaps[1000:1100])
# pp_label = np.max(overlaps, axis=1)
# print('pp_label', pp_label.shape, pp_label[0:100])
# 所有anchor与groudtruth的最高得分的那个值
max_overlaps = np.max(overlaps, axis=1)
# print('max_overlaps shape', max_overlaps.shape)
# print('max_overlaps',max_overlaps[0:100])
gt_argmax_overlaps = overlaps.argmax(
axis=0) # G#找到每个位置上10个anchor中与gtbox，overlap最大的那个
# print('gt_argmax_overlaps',gt_argmax_overlaps[0:100])
gt_max_overlaps = overlaps[gt_argmax_overlaps,
np.arange(overlaps.shape[1])]
# print('gt_max_overlaps',gt_max_overlaps[0:100])
# gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
# print('gt_argmax_overlaps', gt_argmax_overlaps[0:100])
if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
labels[max_overlaps <
cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0 #先给背景上标签，小于0.3overlap的
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = gt_boxes[:,
4] # 每个位置上的10个anchor中overlap最大的认为是前景
# print('gt_boxes[gt_argmax_overlaps, 4]', gt_boxes[argmax_overlaps[gt_argmax_overlaps], 4])
# fg label: above threshold IOU
max_iou_pp = max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP # 0.7
labels[max_iou_pp] = gt_boxes[argmax_overlaps[max_iou_pp],
4] #overlap大于0.7的认为是前景
# print('labels', labels)
if cfg.TRAIN.RPN_CLOBBER_POSITIVES: # False
# assign bg labels last so that negative labels can clobber positives
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
# subsample positive labels if we have too many
# 对正样本进行采样，如果正样本的数量太多的话
# 限制正样本的数量不超过128个
# TODO 这个后期可能还需要修改，毕竟如果使用的是字符的片段，那个正样本的数量是很多的。
# cfg.TRAIN.RPN_FG_FRACTION = 0.5 ,cfg.TRAIN.RPN_BATCHSIZE = 300
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
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) # 随机去除掉一些正样本
labels[disable_inds] = -1 # 变为-1
# subsample negative labels if we have too many
# 对负样本进行采样，如果负样本的数量太多的话
# 正负样本总数是256，限制正样本数目最多128，
# 如果正样本数量小于128，差的那些就用负样本补上，凑齐256个样本
num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels >= 1)
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
# print "was %s inds, disabling %s, now %s inds" % (
# len(bg_inds), len(disable_inds), np.sum(labels == 0))
# 至此， 上好标签，开始计算rpn-box的真值
#--------------------------------------------------------------
bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_targets = _compute_targets(
anchors,
gt_boxes[argmax_overlaps, :]) #根据anchor和gtbox计算得真值（anchor和gtbox之间的偏差）
# bbox_targets.shape [ inds_inside, 4]
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) #内部权重，前景就给1，其他是0
bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0: #暂时使用uniform 权重，也就是正样本是1，负样本是0
# uniform weighting of examples (given non-uniform sampling)
num_examples = np.sum(labels >= 0) + 1
# positive_weights = np.ones((1, 4)) * 1.0 / num_examples
# negative_weights = np.ones((1, 4)) * 1.0 / num_examples
positive_weights = np.ones((1, 4))
negative_weights = np.zeros((1, 4))
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)) + 1)
negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) /
(np.sum(labels == 0)) + 1)
bbox_outside_weights[labels >= 1, :] = positive_weights #外部权重，前景是1，背景是0
bbox_outside_weights[labels == 0, :] = negative_weights
# if DEBUG:
# _sums += bbox_targets[labels == 1, :].sum(axis=0)
# _squared_sums += (bbox_targets[labels == 1, :] ** 2).sum(axis=0)
# _counts += np.sum(labels == 1)
# means = _sums / _counts
# stds = np.sqrt(_squared_sums / _counts - means ** 2)
# print('means:')
# print(means)
# print('stdevs:')
# print(stds)
# map up to original set of anchors
# 一开始是将超出图像范围的anchor直接丢掉的，现在在加回来
labels = _unmap(labels, total_anchors, inds_inside,
fill=-1) #这些anchor的label是-1，也即dontcare
bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside,
fill=0) #这些anchor的真值是0，也即没有值
bbox_inside_weights = _unmap(bbox_inside_weights,
total_anchors,
inds_inside,
fill=0) #内部权重以0填充
bbox_outside_weights = _unmap(bbox_outside_weights,
total_anchors,
inds_inside,
fill=0) #外部权重以0填充
# if DEBUG:
# print('rpn: max max_overlap', np.max(max_overlaps))
# print('rpn: num_positive', np.sum(labels == 1))
# print('rpn: num_negative', np.sum(labels == 0))
# _fg_sum += np.sum(labels == 1)
# _bg_sum += np.sum(labels == 0)
# _count += 1
# print('rpn: num_positive avg', _fg_sum / _count)
# print('rpn: num_negative avg', _bg_sum / _count)
# labels
labels = labels.reshape((1, height, width, A)) #reshap一下label
rpn_labels = labels
# bbox_targets
bbox_targets = bbox_targets \
.reshape((1, height, width, A * 4))#reshape
rpn_bbox_targets = bbox_targets
# bbox_inside_weights
bbox_inside_weights = bbox_inside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_inside_weights = bbox_inside_weights
# bbox_outside_weights
bbox_outside_weights = bbox_outside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_outside_weights = bbox_outside_weights
# print('rpn_bbox_targets', rpn_bbox_targets.shape, rpn_bbox_targets)
# print('rpn_labels shape',rpn_labels.shape)
# rpn_bbox_targets shape [1, 37, 40, 40]
# rpn_labels (1, 37, 40, 10)
# print('rpn_labels 0 num', len(np.where(rpn_labels[0]==0)[2]))
# print('rpn_labels 1 num', len(np.where(rpn_labels[0]==1)[2]))
# print('rpn_labels 2 num', len(np.where(rpn_labels[0]==2)[2]))
return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights
def mdnet_sample(im, bbox, params, num, stype):
"""Generate gaussian samples based on bbox
:arg
im: cv2's image
bbox: ground-truth box(x, y, w, h)
params: five-tuple(width, height, scale, pos_threshold, neg_threshold) of gaussian parameters
num: number of samples
:return
bboxes: list of boxes
{
'img' :img,
'box'(x, y, w, h),
'label': label,
'overlap': overlap
}
"""
assert len(bbox) == 4, "Invalid ground-truth(x, y, w, h) form."
assert bbox[2] > 0 and bbox[3] > 0, "Width or height < 0."
assert len(
params
) == 5, "Invalid {:d}-tuple params(should be five-tuple).".format(
len(params))
assert num > 0, "Number of samples should be larger than 0."
im_shape = im.shape
im_w = im_shape[1]
im_h = im_shape[0]
# Calculate average of width and height
centerx = bbox[0] + bbox[2] / 2
centery = bbox[1] + bbox[3] / 2
bboxes = []
cur_id = 0
while cur_id < num:
# new box parameters
_mean = (bbox[2] + bbox[3]) / 2
offsetx = rd.gauss(0, params[0] * _mean)
offsety = rd.gauss(0, params[1] * _mean)
scalex = rd.gauss(1, params[2])
# scaley = rd.gauss(1, params[2])
scaley = scalex
# new box half width and half height
hw = bbox[2] * scalex / 2
hh = bbox[3] * scaley / 2
# box is in the form of (x1, y1, x2, y2)
box = (max(0, centerx + offsetx - hw), max(0, centery + offsety - hh),
min(im_w,
centerx + offsetx + hw), min(im_h, centery + offsety + hh))
# transform to (x, y, w, h)
sample = (box[0], box[1], box[2] - box[0], box[3] - box[1])
if int(sample[2]) <= 0 or int(sample[3]) <= 0:
continue
# since there is only one query box, then take the first one in the overlaps
overlap = bbox_overlaps([bbox], [sample])[0]
if overlap > params[3]:
bboxes.append({
'img': im,
'box': sample,
'label': 1,
'overlap': overlap
})
elif overlap < params[4]:
bboxes.append({
'img': im,
'box': sample,
'label': 0,
'overlap': overlap
})
else:
continue
cur_id += 1
return bboxes
def anchor_target_layer(rpn_cls_score,
gt_boxes,
gt_ishard,
dontcare_areas,
im_info,
_feat_stride=[
16,
],
anchor_scales=[
16,
]):
"""
Assign anchors to ground-truth targets. Produces anchor classification
labels and bounding-box regression targets.
Parameters
----------
rpn_cls_score: (1, H, W, Ax2) bg/fg scores of previous conv layer
gt_boxes: (G, 5) vstack of [x1, y1, x2, y2, class]
gt_ishard: (G, 1), 1 or 0 indicates difficult or not
dontcare_areas: (D, 4), some areas may contains small objs but no labelling. D may be 0
im_info: a list of [image_height, image_width, scale_ratios]
_feat_stride: the downsampling ratio of feature map to the original input image
anchor_scales: the scales to the basic_anchor (basic anchor is [16, 16])
----------
Returns
----------
rpn_labels : (HxWxA, 1), for each anchor, 0 denotes bg, 1 fg, -1 dontcare
rpn_bbox_targets: (HxWxA, 4), distances of the anchors to the gt_boxes(may contains some transform)
that are the regression objectives
rpn_bbox_inside_weights: (HxWxA, 4) weights of each boxes, mainly accepts hyper param in cfg
rpn_bbox_outside_weights: (HxWxA, 4) used to balance the fg/bg,
beacuse the numbers of bgs and fgs mays significiantly different
"""
_anchors = generate_anchors(
scales=np.array(anchor_scales)) #生成基本的anchor,一共9个
_num_anchors = _anchors.shape[0] #9个anchor
if DEBUG:
print('anchors:')
print(_anchors)
print('anchor shapes:')
print(
np.hstack((
_anchors[:, 2::4] - _anchors[:, 0::4],
_anchors[:, 3::4] - _anchors[:, 1::4],
)))
_counts = cfg.EPS
_sums = np.zeros((1, 4))
_squared_sums = np.zeros((1, 4))
_fg_sum = 0
_bg_sum = 0
_count = 0
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
# map of shape (..., H, W)
#height, width = rpn_cls_score.shape[1:3]
im_info = im_info[0] #图像的高宽及通道数
#在feature-map上定位anchor，并加上delta，得到在实际图像中anchor的真实坐标
# Algorithm:
# for each (H, W) location i
# generate 9 anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the 9 anchors
# filter out-of-image anchors
# measure GT overlap
assert rpn_cls_score.shape[0] == 1, \
'Only single item batches are supported'
# map of shape (..., H, W)
height, width = rpn_cls_score.shape[1:3] #feature-map的高宽
if DEBUG:
print('AnchorTargetLayer: height', height, 'width', width)
print('')
print('im_size: ({}, {})'.format(im_info[0], im_info[1]))
print('scale: {}'.format(im_info[2]))
print('height, width: ({}, {})'.format(height, width))
print('rpn: gt_boxes.shape', gt_boxes.shape)
print('rpn: gt_boxes', gt_boxes)
# 1. Generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, width) * _feat_stride # (W)
shift_y = np.arange(0, height) * _feat_stride #(H)
shift_x, shift_y = np.meshgrid(
shift_x, shift_y) # in W H order # shift_x (H, W) shift_y (H, W)
# K is H x W
shifts = np.vstack(
(shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel()
)).transpose() #生成feature-map和真实image上anchor之间的偏移量 #(H*W, 4)
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = _num_anchors #9个anchor
K = shifts.shape[0] #50*37，feature-map的宽乘高的大小
all_anchors = (_anchors.reshape((1, A, 4)) + shifts.reshape(
(1, K, 4)).transpose((1, 0, 2))) #相当于复制宽高的维度，然后相加
all_anchors = all_anchors.reshape((K * A, 4))
total_anchors = int(K * A)
# only keep anchors inside the image
#仅保留那些还在图像内部的anchor，超出图像的都删掉
inds_inside = np.where(
(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]
if DEBUG:
print('total_anchors', total_anchors)
print('inds_inside', len(inds_inside))
# keep only inside anchors
anchors = all_anchors[inds_inside, :] #保留那些在图像内的anchor (In, 4)
if DEBUG:
print('anchors.shape', anchors.shape)
#至此，anchor准备好了
#--------------------------------------------------------------
# label: 1 is positive, 0 is negative, -1 is dont care
# (A)
labels = np.empty((len(inds_inside), ), dtype=np.float32)
labels.fill(-1) #初始化label，均为-1
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt), shape is A x G
#计算anchor和gt-box的overlap，用来给anchor上标签
overlaps = bbox_overlaps(np.ascontiguousarray(
anchors, dtype=np.float), np.ascontiguousarray(
gt_boxes,
dtype=np.float)) #假设anchors有x个，gt_boxes有y个，返回的是一个（x,y）的数组
# 存放每一个anchor和每一个gtbox之间的overlap
argmax_overlaps = overlaps.argmax(
axis=1) # (A)#找到和每一个anchor，overlap最大的那个gt
max_overlaps = overlaps[np.arange(
len(inds_inside)
), argmax_overlaps] # 假如在内部的anchor有900个 ，(900,), 表示的是每一个anchor最大的overlaps值
gt_argmax_overlaps = overlaps.argmax(
axis=0) # G#找到所有anchor中与gtbox，overlap最大的那个anchor # (3)
if DEBUG:
print('获取所有anchor中与gt相交最大的哪几个anchor的索引')
print('gt_argmax_overlaps.shape', gt_argmax_overlaps.shape)
print('gt_argmax_overlaps', gt_argmax_overlaps)
gt_max_overlaps = overlaps[
gt_argmax_overlaps, np.arange(
overlaps.shape[1]
)] # 比如有3个gt 那么就得到(3,),表示的是上一步找到的与gt的overlap最大的3个anchor的overlap值
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[
0] # (3, ) 表示的是哪几个与gt有最大overlap的anchor的索引
if DEBUG:
print('这一步是找到那些同样与gt有最大overlap的索引，上一步找到的4个，这一步找到其他重复的')
print('gt_argmax_overlaps.shape', gt_argmax_overlaps.shape)
print('gt_argmax_overlaps', gt_argmax_overlaps)
if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
labels[max_overlaps <
cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0 #先给背景上标签，小于0.3overlap的
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1 #每个位置上的9个anchor中overlap最大的认为是前景
# fg label: above threshold IOU
labels[max_overlaps >=
cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1 #overlap大于0.7的认为是前景
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
if DEBUG:
print('在过滤数量之前：')
print('正样本：', len(np.where(labels == 1)[0]))
print('负样本：', len(np.where(labels == 0)[0]))
print('忽略样本：', len(np.where(labels == -1)[0]))
# preclude dontcare areas
if dontcare_areas is not None and dontcare_areas.shape[
0] > 0: #这里我们暂时不考虑有doncare_area的存在
# intersec shape is D x A
intersecs = bbox_intersections(
np.ascontiguousarray(dontcare_areas, dtype=np.float), # D x 4
np.ascontiguousarray(anchors, dtype=np.float) # A x 4
)
intersecs_ = intersecs.sum(axis=0) # A x 1
labels[intersecs_ > cfg.TRAIN.DONTCARE_AREA_INTERSECTION_HI] = -1
#这里我们暂时不考虑难样本的问题
# preclude hard samples that are highly occlusioned, truncated or difficult to see
if cfg.TRAIN.PRECLUDE_HARD_SAMPLES and gt_ishard is not None and gt_ishard.shape[
0] > 0:
assert gt_ishard.shape[0] == gt_boxes.shape[0]
gt_ishard = gt_ishard.astype(int)
gt_hardboxes = gt_boxes[gt_ishard == 1, :]
if gt_hardboxes.shape[0] > 0:
# H x A
hard_overlaps = bbox_overlaps(
np.ascontiguousarray(gt_hardboxes, dtype=np.float), # H x 4
np.ascontiguousarray(anchors, dtype=np.float)) # A x 4
hard_max_overlaps = hard_overlaps.max(axis=0) # (A)
labels[hard_max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = -1
max_intersec_label_inds = hard_overlaps.argmax(axis=1) # H x 1
labels[max_intersec_label_inds] = -1 #
# subsample positive labels if we have too many
#对正样本进行采样，如果正样本的数量太多的话
# 限制正样本的数量不超过128个
#TODO 这个后期可能还需要修改，毕竟如果使用的是字符的片段，那个正样本的数量是很多的。
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
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) #随机去除掉一些正样本
labels[disable_inds] = -1 #变为-1
# subsample negative labels if we have too many
#对负样本进行采样，如果负样本的数量太多的话
# 正负样本总数是256，限制正样本数目最多128，
# 如果正样本数量小于128，差的那些就用负样本补上，凑齐256个样本
num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)
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
#print "was %s inds, disabling %s, now %s inds" % (
#len(bg_inds), len(disable_inds), np.sum(labels == 0))
if DEBUG:
print("考虑均衡住正负样本以后：")
print('正样本：', len(np.where(labels == 1)[0]))
print('负样本：', len(np.where(labels == 0)[0]))
print('忽略样本：', len(np.where(labels == -1)[0]))
# 至此， 上好标签，开始计算rpn-box的真值
#--------------------------------------------------------------
bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_targets = _compute_targets(
anchors,
gt_boxes[argmax_overlaps, :]) #根据anchor和gtbox计算得真值（anchor和gtbox之间的偏差）
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) #内部权重，前景就给1，其他是0
bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0: #暂时使用uniform 权重，也就是正样本是1，负样本是0
# uniform weighting of examples (given non-uniform sampling)
num_examples = np.sum(labels >= 0) + 1
# positive_weights = np.ones((1, 4)) * 1.0 / num_examples
# negative_weights = np.ones((1, 4)) * 1.0 / num_examples
positive_weights = np.ones((1, 4))
negative_weights = np.zeros((1, 4))
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)) + 1)
negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) /
(np.sum(labels == 0)) + 1)
bbox_outside_weights[labels == 1, :] = positive_weights # 外部权重，前景是1，背景是0
bbox_outside_weights[labels == 0, :] = negative_weights
if DEBUG:
_sums += bbox_targets[labels == 1, :].sum(axis=0)
_squared_sums += (bbox_targets[labels == 1, :]**2).sum(axis=0)
_counts += np.sum(labels == 1)
means = _sums / _counts
stds = np.sqrt(_squared_sums / _counts - means**2)
print('means:')
print(means)
print('stdevs:')
print(stds)
# map up to original set of anchors
# 一开始是将超出图像范围的anchor直接丢掉的，现在在加回来
labels = _unmap(labels, total_anchors, inds_inside,
fill=-1) #这些anchor的label是-1，也即dontcare
bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside,
fill=0) #这些anchor的真值是0，也即没有值
bbox_inside_weights = _unmap(bbox_inside_weights,
total_anchors,
inds_inside,
fill=0) #内部权重以0填充
bbox_outside_weights = _unmap(bbox_outside_weights,
total_anchors,
inds_inside,
fill=0) #外部权重以0填充
if DEBUG:
print('rpn: max max_overlap', np.max(max_overlaps))
print('rpn: num_positive', np.sum(labels == 1))
print('rpn: num_negative', np.sum(labels == 0))
_fg_sum += np.sum(labels == 1)
_bg_sum += np.sum(labels == 0)
_count += 1
print('rpn: num_positive avg', _fg_sum / _count)
print('rpn: num_negative avg', _bg_sum / _count)
# labels
labels = labels.reshape((1, height, width, A)) #reshap一下label
rpn_labels = labels
# bbox_targets
bbox_targets = bbox_targets \
.reshape((1, height, width, A * 4))#reshape
rpn_bbox_targets = bbox_targets
# bbox_inside_weights
bbox_inside_weights = bbox_inside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_inside_weights = bbox_inside_weights
# bbox_outside_weights
bbox_outside_weights = bbox_outside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_outside_weights = bbox_outside_weights
return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights
def anchor_target_layer(rpn_cls_score, gt_boxes, im_info, _feat_stride,
all_anchors, num_anchors):
"""Same as the anchor target layer in original Fast/er RCNN """
A = num_anchors
total_anchors = all_anchors.size()[0]
K = total_anchors / num_anchors
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
# pytorch (bs, c, h, w)
height, width = rpn_cls_score.size()[2:4]
# only keep anchors inside the image
inds_inside = (
(all_anchors.data[:, 0] >= -_allowed_border) &
(all_anchors.data[:, 1] >= -_allowed_border) &
(all_anchors.data[:, 2] < im_info[1] + _allowed_border) & # width
(all_anchors.data[:, 3] < im_info[0] + _allowed_border) # height
).nonzero()[:, 0].long()
if DEBUG:
print('total_anchors', total_anchors)
print('inds_inside', inds_inside.size()[0])
# keep only inside anchors
anchors = all_anchors[inds_inside, :]
if DEBUG:
print('anchors.shape', anchors.size())
# label: 1 is positive, 0 is negative, -1 is dont care
labels = inds_inside.new(inds_inside.size()[0]).fill_(-1)
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt) shape is A x G
overlaps = bbox_overlaps(anchors.data, gt_boxes[:, :4].data)
max_overlaps, argmax_overlaps = torch.max(overlaps, dim=1)
gt_max_overlaps, gt_argmax_overlaps = torch.max(overlaps, dim=0)
gt_argmax_overlaps = (overlaps == (
gt_max_overlaps.unsqueeze(0).expand_as(overlaps))).nonzero()[:, 0]
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
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1
# fg label: above threshold IOU
labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 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
# subsample positive labels if we have too many
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
fg_inds = (labels == 1).nonzero()[:, 0]
if fg_inds.numel() > num_fg:
inds = fg_inds.new(
npr.choice(np.arange(0, fg_inds.numel()),
size=int((len(fg_inds) - num_fg)),
replace=False)).long()
disable_inds = fg_inds[inds]
labels[disable_inds] = -1
# subsample negative labels if we have too many
num_bg = cfg.TRAIN.RPN_BATCHSIZE - (labels == 1).sum()
bg_inds = (labels == 0).nonzero()[:, 0]
if bg_inds.numel() > num_bg:
inds = bg_inds.new(
npr.choice(np.arange(0, bg_inds.numel()),
size=int((len(bg_inds) - num_bg)),
replace=False)).long()
disable_inds = bg_inds[inds]
labels[disable_inds] = -1
bbox_targets = _compute_targets(anchors.data,
gt_boxes[argmax_overlaps][:, :4].data)
bbox_inside_weights = bbox_targets.new(inds_inside.size()[0], 4).zero_()
# only the positive ones have regression targets
inds = (labels == 1).nonzero().view(-1)
# dim1_inds = inds.unsqueeze(1).expand(inds.size(0), 4)
# dim2_inds = inds.new((0,1,2,3)).view(-1,4).expand_as(dim1_inds)
dim_value = bbox_targets.new(cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS).view(
-1, 4).expand(inds.size(0), 4)
bbox_inside_weights[inds, :] = dim_value
bbox_outside_weights = bbox_targets.new(inds_inside.size()[0], 4).zero_()
if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0:
# uniform weighting of examples (given non-uniform sampling)
num_examples = (labels >= 0).sum()
positive_weights = np.ones((1, 4)) * 1.0 / num_examples
negative_weights = np.ones((1, 4)) * 1.0 / num_examples
else:
assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) &
(cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1))
positive_weights = (cfg.TRAIN.RPN_POSITIVE_WEIGHT /
(labels == 1).sum())
negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) /
(labels == 0).sum())
inds = (labels == 1).nonzero().view(-1)
# dim1_inds = inds.unsqueeze(1).expand(inds.size(0), 4)
# dim2_inds = inds.new((0,1,2,3)).view(-1,4).expand_as(dim1_inds)
dim_value = bbox_targets.new(positive_weights).view(-1, 4).expand(
inds.size(0), 4)
bbox_outside_weights[inds, :] = dim_value
inds = (labels == 0).nonzero().view(-1)
# dim1_inds = inds.unsqueeze(1).expand(inds.size(0), 4)
# dim2_inds = inds.new((0,1,2,3)).view(-1,4).expand_as(dim1_inds)
dim_value = bbox_targets.new(negative_weights).view(-1, 4).expand(
inds.size(0), 4)
bbox_outside_weights[inds, :] = dim_value
# map up to original set of anchors
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)
bbox_outside_weights = _unmap(bbox_outside_weights,
total_anchors,
inds_inside,
fill=0)
# labels
labels = labels.view((1, height, width, A)).permute(0, 3, 1,
2).contiguous()
labels = labels.view((1, 1, A * height, width))
rpn_labels = labels
# bbox_targets
bbox_targets = bbox_targets \
.view((1, height, width, A * 4))
rpn_bbox_targets = bbox_targets
# bbox_inside_weights
bbox_inside_weights = bbox_inside_weights \
.view((1, height, width, A * 4))
rpn_bbox_inside_weights = bbox_inside_weights
# bbox_outside_weights
bbox_outside_weights = bbox_outside_weights \
.view((1, height, width, A * 4))
rpn_bbox_outside_weights = bbox_outside_weights
return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights
def gaussian_sample(im, bbox, params, num, stype):
assert len(bbox) == 4, "Invalid ground-truth(x, y, w, h) form."
assert bbox[2] > 0 and bbox[3] > 0, "Width or height < 0."
assert len(
params
) == 5, "Invalid {:d}-tuple params(should be five-tuple).".format(
len(params))
assert num > 0, "Number of samples should be larger than 0."
im_shape = im.shape
im_w = im_shape[1]
im_h = im_shape[0]
# Calculate average of width and height
centerx = bbox[0] + bbox[2] / 2
centery = bbox[1] + bbox[3] / 2
ones = np.ones((num, 1))
neg_ones = -1 * ones
mean = round((bbox[2] + bbox[3]) / 2.)
min_ = np.min(np.hstack((ones, 0.5 * randn(num, 1))), axis=1)
min_ = min_.reshape((min_.size, 1))
max_ = np.max(np.hstack((neg_ones, min_)), axis=1)
offsetx = params[0] * mean * max_
min_ = np.min(np.hstack((ones, 0.5 * randn(num, 1))), axis=1)
min_ = min_.reshape((min_.size, 1))
max_ = np.max(np.hstack((neg_ones, min_)), axis=1)
offsety = params[1] * mean * max_
min_ = np.min(np.hstack((ones, 0.5 * randn(num, 1))), axis=1)
min_ = min_.reshape((min_.size, 1))
max_ = params[2] * np.max(np.hstack((neg_ones, min_)), axis=1)
scale = 1.05**max_
w = (bbox[2] * scale)[:, np.newaxis]
h = (bbox[3] * scale)[:, np.newaxis]
tens = np.array([10] * num)[:, np.newaxis]
w_minus_10 = np.array(w - 10)
h_minus_10 = np.array(h - 10)
if stype == 'TRAIN':
wmin_ = np.min(np.hstack((w_minus_10, w)), axis=1)[:, np.newaxis]
hmin_ = np.min(np.hstack((h_minus_10, h)), axis=1)[:, np.newaxis]
ws = np.max(np.hstack((tens, wmin_)), axis=1)
hs = np.max(np.hstack((tens, hmin_)), axis=1)
elif stype == 'TEST':
ws = np.max(np.hstack((tens, w)), axis=1)
hs = np.max(np.hstack((tens, h)), axis=1)
bboxes = []
for i in range(num):
hw = ws[i] / 2
hh = hs[i] / 2
box = (max(0, int(centerx + offsetx[i] - hw)),
max(0, int(centery + offsety[i] - hh)),
min(im_w, int(centerx + offsetx[i] + hw)),
min(im_h, int(centery + offsety[i] + hh)))
sample = (box[0], box[1], box[2] - box[0], box[3] - box[1])
if int(sample[2]) <= 0 or int(sample[3]) <= 0:
continue
overlap = bbox_overlaps([bbox], [sample])[0]
if overlap > params[3]:
bboxes.append({
'img': im,
'box': sample,
'label': 1,
'overlap': overlap
})
elif overlap < params[4]:
bboxes.append({
'img': im,
'box': sample,
'label': 0,
'overlap': overlap
})
return bboxes
def anchor_target_layer(rpn_cls_score, gt_boxes, gt_ishard, dontcare_areas, im_info, _feat_stride = [16,], anchor_scales = [16,]):
"""
Assign anchors to ground-truth targets. Produces anchor classification
labels and bounding-box regression targets.
Parameters
----------
rpn_cls_score: (1, H, W, Ax2) bg/fg scores of previous conv layer
gt_boxes: (G, 5) vstack of [x1, y1, x2, y2, class]
gt_ishard: (G, 1), 1 or 0 indicates difficult or not
dontcare_areas: (D, 4), some areas may contains small objs but no labelling. D may be 0
im_info: a list of [image_height, image_width, scale_ratios]
_feat_stride: the downsampling ratio of feature map to the original input image
anchor_scales: the scales to the basic_anchor (basic anchor is [16, 16])
----------
Returns
----------
rpn_labels : (HxWxA, 1), for each anchor, 0 denotes bg, 1 fg, -1 dontcare
rpn_bbox_targets: (HxWxA, 4), distances of the anchors to the gt_boxes(may contains some transform)
that are the regression objectives
rpn_bbox_inside_weights: (HxWxA, 4) weights of each boxes, mainly accepts hyper param in cfg
rpn_bbox_outside_weights: (HxWxA, 4) used to balance the fg/bg,
beacuse the numbers of bgs and fgs mays significiantly different
"""
_anchors = generate_anchors(scales=np.array(anchor_scales))#生成基本的anchor,一共9个
_num_anchors = _anchors.shape[0]#9个anchor
if DEBUG:
print('anchors:')
print(_anchors)
print('anchor shapes:')
print(np.hstack((
_anchors[:, 2::4] - _anchors[:, 0::4],
_anchors[:, 3::4] - _anchors[:, 1::4],
)))
_counts = cfg.EPS
_sums = np.zeros((1, 4))
_squared_sums = np.zeros((1, 4))
_fg_sum = 0
_bg_sum = 0
_count = 0
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
# map of shape (..., H, W)
#height, width = rpn_cls_score.shape[1:3]
im_info = im_info[0]#图像的高宽及通道数
#在feature-map上定位anchor，并加上delta，得到在实际图像中anchor的真实坐标
# Algorithm:
# for each (H, W) location i
# generate 9 anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the 9 anchors
# filter out-of-image anchors
# measure GT overlap
assert rpn_cls_score.shape[0] == 1, \
'Only single item batches are supported'
# map of shape (..., H, W)
height, width = rpn_cls_score.shape[1:3]#feature-map的高宽
if DEBUG:
print('AnchorTargetLayer: height', height, 'width', width)
print('')
print('im_size: ({}, {})'.format(im_info[0], im_info[1]))
print('scale: {}'.format(im_info[2]))
print('height, width: ({}, {})'.format(height, width))
print('rpn: gt_boxes.shape', gt_boxes.shape)
print('rpn: gt_boxes', gt_boxes)
# 1. Generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, width) * _feat_stride
shift_y = np.arange(0, height) * _feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y) # in W H order
# K is H x W
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
shift_x.ravel(), shift_y.ravel())).transpose()#生成feature-map和真实image上anchor之间的偏移量
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = _num_anchors#9个anchor
K = shifts.shape[0]#50*37，feature-map的宽乘高的大小
all_anchors = (_anchors.reshape((1, A, 4)) +
shifts.reshape((1, K, 4)).transpose((1, 0, 2)))#相当于复制宽高的维度，然后相加
all_anchors = all_anchors.reshape((K * A, 4))
total_anchors = int(K * A)
# only keep anchors inside the image
#仅保留那些还在图像内部的anchor，超出图像的都删掉
inds_inside = np.where(
(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]
if DEBUG:
print('total_anchors', total_anchors)
print('inds_inside', len(inds_inside))
# keep only inside anchors
anchors = all_anchors[inds_inside, :]#保留那些在图像内的anchor
if DEBUG:
print('anchors.shape', anchors.shape)
#至此，anchor准备好了
#--------------------------------------------------------------
# label: 1 is positive, 0 is negative, -1 is dont care
# (A)
labels = np.empty((len(inds_inside), ), dtype=np.float32)
labels.fill(-1)#初始化label，均为-1
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt), shape is A x G
#计算anchor和gt-box的overlap，用来给anchor上标签
overlaps = bbox_overlaps(
np.ascontiguousarray(anchors, dtype=np.float),
np.ascontiguousarray(gt_boxes, dtype=np.float))#假设anchors有x个，gt_boxes有y个，返回的是一个（x,y）的数组
# 存放每一个anchor和每一个gtbox之间的overlap
argmax_overlaps = overlaps.argmax(axis=1) # (A)#找到和每一个gtbox，overlap最大的那个anchor
max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
gt_argmax_overlaps = overlaps.argmax(axis=0) # G#找到每个位置上9个anchor中与gtbox，overlap最大的那个
gt_max_overlaps = overlaps[gt_argmax_overlaps,
np.arange(overlaps.shape[1])]
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0#先给背景上标签，小于0.3overlap的
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1#每个位置上的9个anchor中overlap最大的认为是前景
# fg label: above threshold IOU
labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1#overlap大于0.7的认为是前景
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
# preclude dontcare areas
if dontcare_areas is not None and dontcare_areas.shape[0] > 0:#这里我们暂时不考虑有doncare_area的存在
# intersec shape is D x A
intersecs = bbox_intersections(
np.ascontiguousarray(dontcare_areas, dtype=np.float), # D x 4
np.ascontiguousarray(anchors, dtype=np.float) # A x 4
)
intersecs_ = intersecs.sum(axis=0) # A x 1
labels[intersecs_ > cfg.TRAIN.DONTCARE_AREA_INTERSECTION_HI] = -1
#这里我们暂时不考虑难样本的问题
# preclude hard samples that are highly occlusioned, truncated or difficult to see
if cfg.TRAIN.PRECLUDE_HARD_SAMPLES and gt_ishard is not None and gt_ishard.shape[0] > 0:
assert gt_ishard.shape[0] == gt_boxes.shape[0]
gt_ishard = gt_ishard.astype(int)
gt_hardboxes = gt_boxes[gt_ishard == 1, :]
if gt_hardboxes.shape[0] > 0:
# H x A
hard_overlaps = bbox_overlaps(
np.ascontiguousarray(gt_hardboxes, dtype=np.float), # H x 4
np.ascontiguousarray(anchors, dtype=np.float)) # A x 4
hard_max_overlaps = hard_overlaps.max(axis=0) # (A)
labels[hard_max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = -1
max_intersec_label_inds = hard_overlaps.argmax(axis=1) # H x 1
labels[max_intersec_label_inds] = -1 #
# subsample positive labels if we have too many
#对正样本进行采样，如果正样本的数量太多的话
# 限制正样本的数量不超过128个
#TODO 这个后期可能还需要修改，毕竟如果使用的是字符的片段，那个正样本的数量是很多的。
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
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)#随机去除掉一些正样本
labels[disable_inds] = -1#变为-1
# subsample negative labels if we have too many
#对负样本进行采样，如果负样本的数量太多的话
# 正负样本总数是256，限制正样本数目最多128，
# 如果正样本数量小于128，差的那些就用负样本补上，凑齐256个样本
num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)
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
#print "was %s inds, disabling %s, now %s inds" % (
#len(bg_inds), len(disable_inds), np.sum(labels == 0))
# 至此， 上好标签，开始计算rpn-box的真值
#--------------------------------------------------------------
bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])#根据anchor和gtbox计算得真值（anchor和gtbox之间的偏差）
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)#内部权重，前景就给1，其他是0
bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0:#暂时使用uniform 权重，也就是正样本是1，负样本是0
# uniform weighting of examples (given non-uniform sampling)
num_examples = np.sum(labels >= 0) + 1
# positive_weights = np.ones((1, 4)) * 1.0 / num_examples
# negative_weights = np.ones((1, 4)) * 1.0 / num_examples
positive_weights = np.ones((1, 4))
negative_weights = np.zeros((1, 4))
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)) + 1)
negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) /
(np.sum(labels == 0)) + 1)
bbox_outside_weights[labels == 1, :] = positive_weights#外部权重，前景是1，背景是0
bbox_outside_weights[labels == 0, :] = negative_weights
if DEBUG:
_sums += bbox_targets[labels == 1, :].sum(axis=0)
_squared_sums += (bbox_targets[labels == 1, :] ** 2).sum(axis=0)
_counts += np.sum(labels == 1)
means = _sums / _counts
stds = np.sqrt(_squared_sums / _counts - means ** 2)
print('means:')
print(means)
print('stdevs:')
print(stds)
# map up to original set of anchors
# 一开始是将超出图像范围的anchor直接丢掉的，现在在加回来
labels = _unmap(labels, total_anchors, inds_inside, fill=-1)#这些anchor的label是-1，也即dontcare
bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)#这些anchor的真值是0，也即没有值
bbox_inside_weights = _unmap(bbox_inside_weights, total_anchors, inds_inside, fill=0)#内部权重以0填充
bbox_outside_weights = _unmap(bbox_outside_weights, total_anchors, inds_inside, fill=0)#外部权重以0填充
if DEBUG:
print('rpn: max max_overlap', np.max(max_overlaps))
print('rpn: num_positive', np.sum(labels == 1))
print('rpn: num_negative', np.sum(labels == 0))
_fg_sum += np.sum(labels == 1)
_bg_sum += np.sum(labels == 0)
_count += 1
print('rpn: num_positive avg', _fg_sum / _count)
print('rpn: num_negative avg', _bg_sum / _count)
# labels
labels = labels.reshape((1, height, width, A))#reshap一下label
rpn_labels = labels
# bbox_targets
bbox_targets = bbox_targets \
.reshape((1, height, width, A * 4))#reshape
rpn_bbox_targets = bbox_targets
# bbox_inside_weights
bbox_inside_weights = bbox_inside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_inside_weights = bbox_inside_weights
# bbox_outside_weights
bbox_outside_weights = bbox_outside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_outside_weights = bbox_outside_weights
return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights
def anchor_target_layer(rpn_cls_score,
gt_boxes_large,
gt_ishard,
dontcare_areas,
im_info,
_feat_stride=[
16,
],
anchor_scales=[
16,
]):
"""
将gt_box划分为细框
实现论文中的side-refinement
arameters
----------
rpn_cls_score: (1, H, W, Ax2) bg/fg scores of previous conv layer
gt_boxes: (G, 5) vstack of [x1, y1, x2, y2, class]
gt_ishard: (G, 1), 1 or 0 indicates difficult or not
dontcare_areas: (D, 4), some areas may contains small objs but no labelling. D may be 0
im_info: a list of [image_height, image_width, scale_ratios]
_feat_stride: the downsampling ratio of feature map to the original input image
anchor_scales: the scales to the basic_anchor (basic anchor is [16, 16])
----------
:return:
"""
gt_boxes = split_frame(gt_boxes_large)
# gt_width = gt_boxes[:,2]-gt_boxes[:,0]
_anchors = generate_anchors(
scales=np.array(anchor_scales)) # 生成基本的anchor,一共9个
_num_anchors = _anchors.shape[0] # 9个anchor
if DEBUG:
print('anchors:')
print(_anchors)
print('anchor shapes:')
print(
np.hstack((
_anchors[:, 2::4] - _anchors[:, 0::4],
_anchors[:, 3::4] - _anchors[:, 1::4],
)))
_counts = cfg.EPS
_sums = np.zeros((1, 4))
_squared_sums = np.zeros((1, 4))
_fg_sum = 0
_bg_sum = 0
_count = 0
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
im_info = im_info[0] # 图像的高宽及通道数
assert rpn_cls_score.shape[0] == 1, \
'Only single item batches are supported'
# map of shape (..., H, W)
height, width = rpn_cls_score.shape[1:3] # feature-map的高宽
if DEBUG:
print('AnchorTargetLayer: height', height, 'width', width)
print('')
print('im_size: ({}, {})'.format(im_info[0], im_info[1]))
print('scale: {}'.format(im_info[2]))
print('height, width: ({}, {})'.format(height, width))
print('rpn: gt_boxes.shape', gt_boxes.shape)
# 1. Generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, width) * _feat_stride # (W)
shift_y = np.arange(0, height) * _feat_stride # (H)
shift_x, shift_y = np.meshgrid(
shift_x, shift_y) # in W H order # shift_x (H, W) shift_y (H, W)
# K is H x W
shifts = np.vstack(
(shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel()
)).transpose() # 生成feature-map和真实image上anchor之间的偏移量 #(H*W, 4)
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = _num_anchors # 9个anchor
K = shifts.shape[0] # 50*37，feature-map的宽乘高的大小
all_anchors = (_anchors.reshape((1, A, 4)) + shifts.reshape(
(1, K, 4)).transpose((1, 0, 2))) # 相当于复制宽高的维度，然后相加
all_anchors = all_anchors.reshape((K * A, 4))
total_anchors = int(K * A)
# only keep anchors inside the image
# 仅保留那些还在图像内部的anchor，超出图像的都删掉
inds_inside = np.where(
(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]
if DEBUG:
print('total_anchors', total_anchors)
print('inds_inside', len(inds_inside))
# keep only inside anchors
anchors = all_anchors[inds_inside, :] #保留那些在图像内的anchor (In, 4)
if DEBUG:
print('anchors.shape', anchors.shape)
#至此，anchor准备好了
#--------------------------------------------------------------
# label: 1 is positive, 0 is negative, -1 is dont care
# (A)
labels = np.empty((len(inds_inside), ), dtype=np.float32)
labels.fill(0) #初始化label，均为-1
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt), shape is A x G
# 计算anchor和gt-box的overlap，用来给anchor上标签
overlaps = bbox_overlaps(np.ascontiguousarray(
anchors, dtype=np.float), np.ascontiguousarray(
gt_boxes,
dtype=np.float)) # 假设anchors有x个，gt_boxes有y个，返回的是一个（x,y）的数组
# 存放每一个anchor和每一个gtbox之间的overlap
argmax_overlaps = overlaps.argmax(
axis=1) # (A)#找到和每一个anchor，overlap最大的那个gt
max_overlaps = overlaps[np.arange(
len(inds_inside)
), argmax_overlaps] # 假如在内部的anchor有900个 ，(900,), 表示的是每一个anchor最大的overlaps值
gt_argmax_overlaps = overlaps.argmax(
axis=0) # G#找到所有anchor中与gtbox，overlap最大的那个anchor # (3)
if DEBUG:
print('获取所有anchor中与gt相交最大的哪几个anchor的索引')
print('gt_argmax_overlaps.shape', gt_argmax_overlaps.shape)
gt_max_overlaps = overlaps[
gt_argmax_overlaps, np.arange(
overlaps.shape[1]
)] # 比如有3个gt 那么就得到(3,),表示的是上一步找到的与gt的overlap最大的3个anchor的overlap值
gt_argmax_overlaps = np.where(
overlaps == gt_max_overlaps)[0] # (3, ) 表示的是哪几个与gt有最大overlap的anchor的索引
if DEBUG:
print('这一步是找到那些同样与gt有最大overlap的索引，上一步找到的4个，这一步找到其他重复的')
print('gt_argmax_overlaps.shape', gt_argmax_overlaps.shape)
if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
labels[max_overlaps <
cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0 #先给背景上标签，小于0.3overlap的
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1 # 每个位置上的9个anchor中overlap最大的认为是前景
# fg label: above threshold IOU
labels[max_overlaps >=
cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1 #overlap大于0.7的认为是前景
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
if DEBUG:
print('在过滤数量之前：')
print('正样本：', len(np.where(labels == 1)[0]))
print('负样本：', len(np.where(labels == 0)[0]))
print('忽略样本：', len(np.where(labels == -1)[0]))
# 不再限制正负样本的数量
if DEBUG:
print("考虑均衡住正负样本以后：")
print('正样本：', len(np.where(labels == 1)[0]))
print('负样本：', len(np.where(labels == 0)[0]))
print('忽略样本：', len(np.where(labels == -1)[0]))
# 至此， 上好标签，开始计算rpn-box的真值
#
v_target, o_target = _compute_targets(anchors, gt_boxes[
argmax_overlaps, :]) # 根据anchor和gtbox计算得真值（anchor和gtbox之间的偏差）
# 但是计算损失函数的时候，其实是需要j索引和k索引，所以计算好这两个索引，一并返回，帮助计算损失函数
# j索引，有效索引：正锚点或者与gt的overlap大于0.5以上的锚点的索引
# 正锚点
positive_index = np.where(labels == 1)[0] # 应该是一个（p,）p应该不大于128
#
# ignore_index = np.where(labels==-1)[0] # 应该是一个（n,）n应该很大，因为忽略的anchor很多
keep_index = np.where(labels != -1)[0]
_ = np.where(max_overlaps > 0.5)[0] # 应该是一个（c,）,表示overlap大于0.5的anchor的索引
remove_ignore = list()
for i in range(_.shape[0]):
if i in keep_index:
remove_ignore.append(_[i])
remove_ignore = np.array(remove_ignore)
effect_index = np.append(positive_index, remove_ignore)
remove_repeat = np.array(list(set(list(effect_index))))
j_index = remove_repeat.astype(np.int32)
j_index1 = np.zeros((len(inds_inside)), dtype=np.int32)
j_index1[j_index] = 1
# k 索引 , 边缘索引
# 先找到所有的可以认为是边缘的gt框,这里简单的认为是边缘框和左右各自一个。
#ori_gt_box = (gt_boxes/im_info[2]).astype(np.int32, copy=False)
ori_gt_box = gt_boxes.astype(np.float32, copy=False)
# 找到左右边界框，矩阵操作实现 todo
list_left_index = list()
list_right_index = list()
for i in range(ori_gt_box.shape[0]):
if ori_gt_box[i][2] - ori_gt_box[i][0] != 15:
list_left_index.append(i)
else:
continue
list_index1 = list_left_index + list_right_index
# 去除不属于gt中的索引和重复的索引
list_index2 = list(set(list_index1))
list_index3 = sorted(list_index2)
list_index4 = list()
for index in list_index3:
if index in range(ori_gt_box.shape[0]):
list_index4.append(index)
# if DEBUG:
# print("list_left_index", list_left_index)
# print("list_right_index", list_right_index)
# print("list_index1", list_index1)
# print("list_index2", list_index2)
# print("list_index3", list_index3)
# print("list_index4", list_index4)
gt_side_index = np.array(list_index4).astype(np.int32) # 得到了边界gt框的索引
# 要得到与这些gt框有最大的overlap的anchors的索引，这些anchor是我们关心的
gt_argmax_overlaps = overlaps.argmax(axis=0)
anchor_side_index = gt_argmax_overlaps[
gt_side_index] # 得到143个与gt具有最大的overlaps的anchor的索引
# 还要去掉与边界框overlap为0的anchor，因为这些anhcor不是真的我们关心的anchor，如果不去除，还会造成o_loss异常大
anchor_side_list = list()
for i in range(anchor_side_index.shape[0]):
anchor_index = anchor_side_index[i]
gt_index = gt_side_index[i]
overlap = overlaps[anchor_index, gt_index]
if overlap > 0:
anchor_side_list.append(anchor_index)
anchor_side_index = np.array(anchor_side_list, dtype=np.int32)
anchor_side_index1 = np.array(sorted(list(set(
list(anchor_side_index))))).astype(np.int32)
k_index = anchor_side_index1 # (s,) s个边界索引，但是并不是包括之前去除的超过边界框的索引值，所以需要之后的操作
k_index1 = np.zeros((len(inds_inside)), dtype=np.int32)
k_index1[k_index] = 1
if DEBUG:
print('jIndex1:', j_index1.shape)
print('k_index1:', k_index1.shape)
# map up to original set of anchors
# 一开始是将超出图像范围的anchor直接丢掉的，现在在加回来
labels = _unmap(labels, total_anchors, inds_inside,
fill=-1) # 这些anchor的label是-1，也即dontcare
v_target = _unmap(v_target, total_anchors, inds_inside,
fill=0) # 这些anchor的真值是0，也即没有值
o_target = _unmap(o_target, total_anchors, inds_inside, fill=0)
j_index2 = _unmap(j_index1, total_anchors, inds_inside,
fill=0).astype(np.int32)
k_index2 = _unmap(k_index1, total_anchors, inds_inside,
fill=0).astype(np.int32)
# real_j_index = np.where(j_index2==1)[0]
# real_k_index = np.where(k_index2==1)[0]
if DEBUG:
print('loss_1 index:', np.where(labels != -1)[0].shape[0])
print('j_index:', j_index.shape)
print('k_index:', k_index.shape)
print('j_index2:', j_index2.shape)
print('k_index2:', k_index2.shape)
print('label shape', labels.shape)
print('v_target shape', v_target.shape)
print('o_target shape', o_target.shape)
return labels, v_target, o_target, j_index2, k_index2
def anchor_target_layer(rpn_cls_score,
rpn_cls_prob,
im_name,
gt_boxes_large,
gt_ishard,
dontcare_areas,
im_info,
_feat_stride=[
16,
],
anchor_scales=[
16,
]):
"""
将gt_box划分为细框
实现论文中的side-refinement
arameters
----------
rpn_cls_score: (1, H, W, Ax2) bg/fg scores of previous conv layer
gt_boxes: (G, 5) vstack of [x1, y1, x2, y2, class]
gt_ishard: (G, 1), 1 or 0 indicates difficult or not
dontcare_areas: (D, 4), some areas may contains small objs but no labelling. D may be 0
im_info: a list of [image_height, image_width, scale_ratios]
_feat_stride: the downsampling ratio of feature map to the original input image
anchor_scales: the scales to the basic_anchor (basic anchor is [16, 16])
----------
:return:
"""
global img_name
if img_name != im_name: # 第一次训练这个图片
flag_first = True
else:
flag_first = False
img_name = im_name
if flag_first: # 如果是第一次见到这个图片，就要重新生成所有tmp对象
gt_boxes = split_frame(gt_boxes_large)
# gt_width = gt_boxes[:,2]-gt_boxes[:,0]
_anchors = generate_anchors(
scales=np.array(anchor_scales)) # 生成基本的anchor,一共9个
_num_anchors = _anchors.shape[0] # 9个anchor
if DEBUG:
print('anchors:')
print(_anchors)
print('anchor shapes:')
print(
np.hstack((
_anchors[:, 2::4] - _anchors[:, 0::4],
_anchors[:, 3::4] - _anchors[:, 1::4],
)))
_counts = cfg.EPS
_sums = np.zeros((1, 4))
_squared_sums = np.zeros((1, 4))
_fg_sum = 0
_bg_sum = 0
_count = 0
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
im_info = im_info[0] # 图像的高宽及通道数
assert rpn_cls_score.shape[0] == 1, \
'Only single item batches are supported'
# map of shape (..., H, W)
height, width = rpn_cls_score.shape[1:3] # feature-map的高宽
if DEBUG:
print('AnchorTargetLayer: height', height, 'width', width)
print('')
print('im_size: ({}, {})'.format(im_info[0], im_info[1]))
print('scale: {}'.format(im_info[2]))
print('height, width: ({}, {})'.format(height, width))
print('rpn: gt_boxes.shape', gt_boxes.shape)
# 1. Generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, width) * _feat_stride # (W)
shift_y = np.arange(0, height) * _feat_stride # (H)
shift_x, shift_y = np.meshgrid(
shift_x,
shift_y) # in W H order # shift_x (H, W) shift_y (H, W)
# K is H x W
shifts = np.vstack(
(shift_x.ravel(), shift_y.ravel(), shift_x.ravel(),
shift_y.ravel()
)).transpose() # 生成feature-map和真实image上anchor之间的偏移量 #(H*W, 4)
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = _num_anchors # 9个anchor
K = shifts.shape[0] # 50*37，feature-map的宽乘高的大小
all_anchors = (_anchors.reshape((1, A, 4)) + shifts.reshape(
(1, K, 4)).transpose((1, 0, 2))) # 相当于复制宽高的维度，然后相加
all_anchors = all_anchors.reshape((K * A, 4))
total_anchors = int(K * A)
# only keep anchors inside the image
# 仅保留那些还在图像内部的anchor，超出图像的都删掉
inds_inside = np.where(
(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]
if DEBUG:
print('total_anchors', total_anchors)
print('inds_inside', len(inds_inside))
# keep only inside anchors
anchors = all_anchors[inds_inside, :] # 保留那些在图像内的anchor (In, 4)
if DEBUG:
print('anchors.shape', anchors.shape)
# 至此，anchor准备好了
# --------------------------------------------------------------
# label: 1 is positive, 0 is negative, -1 is dont care
# (A)
labels = np.empty((len(inds_inside), ), dtype=np.float32)
labels.fill(-1) # 初始化label，均为-1
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt), shape is A x G
# 计算anchor和gt-box的overlap，用来给anchor上标签
overlaps = bbox_overlaps(
np.ascontiguousarray(anchors, dtype=np.float),
np.ascontiguousarray(
gt_boxes,
dtype=np.float)) # 假设anchors有x个，gt_boxes有y个，返回的是一个（x,y）的数组
# 存放每一个anchor和每一个gtbox之间的overlap
argmax_overlaps = overlaps.argmax(
axis=1) # (A)#找到和每一个anchor，overlap最大的那个gt
max_overlaps = overlaps[np.arange(
len(inds_inside)
), argmax_overlaps] # 假如在内部的anchor有900个 ，(900,), 表示的是每一个anchor最大的overlaps值
gt_argmax_overlaps = overlaps.argmax(
axis=0) # G#找到所有anchor中与gtbox，overlap最大的那个anchor # (3)
gt_max_overlaps = overlaps[
gt_argmax_overlaps,
np.arange(
overlaps.shape[1]
)] # 比如有3个gt 那么就得到(3,),表示的是上一步找到的与gt的overlap最大的3个anchor的overlap值
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[
0] # (3, ) 表示的是哪几个与gt有最大overlap的anchor的索引
if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
labels[max_overlaps <
cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0 # 先给背景上标签，小于0.3overlap的
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1 # 每个位置上的9个anchor中overlap最大的认为是前景
# fg label: above threshold IOU
labels[max_overlaps >=
cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1 # overlap大于0.7的认为是前景
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
before_filter_labels = labels.copy() # 过滤之前的标签，方便用来计算hard negtive
all_bg_index = before_filter_labels == 0
if DEBUG:
print('在过滤数量之前：')
print('正样本：' + str(len(np.where(labels == 1)[0])))
print('负样本：' + str(len(np.where(labels == 0)[0])))
print('忽略样本：' + str(len(np.where(labels == -1)[0])))
# preclude dontcare areas
if dontcare_areas is not None and dontcare_areas.shape[
0] > 0: # 这里我们暂时不考虑有doncare_area的存在
# intersec shape is D x A
intersecs = bbox_intersections(
np.ascontiguousarray(dontcare_areas, dtype=np.float), # D x 4
np.ascontiguousarray(anchors, dtype=np.float) # A x 4
)
intersecs_ = intersecs.sum(axis=0) # A x 1
labels[intersecs_ > cfg.TRAIN.DONTCARE_AREA_INTERSECTION_HI] = -1
# 这里我们暂时不考虑难样本的问题
# preclude hard samples that are highly occlusioned, truncated or difficult to see
if cfg.TRAIN.PRECLUDE_HARD_SAMPLES and gt_ishard is not None and gt_ishard.shape[
0] > 0 and 0:
assert gt_ishard.shape[0] == gt_boxes.shape[0]
gt_ishard = gt_ishard.astype(int)
gt_hardboxes = gt_boxes[gt_ishard == 1, :]
if gt_hardboxes.shape[0] > 0:
# H x A
hard_overlaps = bbox_overlaps(
np.ascontiguousarray(gt_hardboxes,
dtype=np.float), # H x 4
np.ascontiguousarray(anchors, dtype=np.float)) # A x 4
hard_max_overlaps = hard_overlaps.max(axis=0) # (A)
labels[
hard_max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = -1
max_intersec_label_inds = hard_overlaps.argmax(axis=1) # H x 1
labels[max_intersec_label_inds] = -1 #
# subsample positive labels if we have too many
# 对正样本进行采样，如果正样本的数量太多的话
# 限制正样本的数量不超过128个
# TODO 这个后期可能还需要修改，毕竟如果使用的是字符的片段，那个正样本的数量是很多的。
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
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) # 随机去除掉一些正样本
labels[disable_inds] = -1 # 变为-1
# subsample negative labels if we have too many
# 对负样本进行采样，如果负样本的数量太多的话
# 正负样本总数是512，限制正样本数目最多128，
# 如果正样本数量小于128，差的那些就用负样本补上，凑齐256个样本
num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)
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
# print "was %s inds, disabling %s, now %s inds" % (
# len(bg_inds), len(disable_inds), np.sum(labels == 0))
if DEBUG:
print("考虑均衡住正负样本以后：")
print('正样本：' + str(len(np.where(labels == 1)[0])))
print('负样本：' + str(len(np.where(labels == 0)[0])))
print('忽略样本：' + str(len(np.where(labels == -1)[0])))
# 至此，第一次生成好了这个图片的labels，随机抽了512个
# 生成其他部分的标签
v_target, o_target = _compute_targets(anchors, gt_boxes[
argmax_overlaps, :]) # 根据anchor和gtbox计算得真值（anchor和gtbox之间的偏差）
# 但是计算损失函数的时候，其实是需要j索引和k索引，所以计算好这两个索引，一并返回，帮助计算损失函数
# j索引，有效索引：正锚点或者与gt的overlap大于0.5以上的锚点的索引
# 正锚点
positive_index = np.where(labels == 1)[0] # 应该是一个（p,）p应该不大于128
#
# ignore_index = np.where(labels==-1)[0] # 应该是一个（n,）n应该很大，因为忽略的anchor很多
keep_index = np.where(labels != -1)[0]
_ = np.where(
max_overlaps > 0.5)[0] # 应该是一个（c,）,表示overlap大于0.5的anchor的索引
remove_ignore = list()
for i in range(_.shape[0]):
if i in keep_index:
remove_ignore.append(_[i])
remove_ignore = np.array(remove_ignore)
effect_index = np.append(positive_index, remove_ignore)
remove_repeat = np.array(list(set(list(effect_index))))
j_index = remove_repeat.astype(np.int32)
j_index1 = np.zeros((len(inds_inside)), dtype=np.int32)
j_index1[j_index] = 1
# k 索引 , 边缘索引
# 先找到所有的可以认为是边缘的gt框,这里简单的认为是边缘框和左右各自一个。
# ori_gt_box = (gt_boxes/im_info[2]).astype(np.int32, copy=False)
ori_gt_box = gt_boxes.astype(np.float32, copy=False)
# 找到左右边界框，矩阵操作实现 todo
list_left_index = list()
list_right_index = list()
for i in range(ori_gt_box.shape[0]):
if ori_gt_box[i][2] - ori_gt_box[i][0] != 15:
list_left_index.append(i)
else:
continue
list_index1 = list_left_index + list_right_index
# 去除不属于gt中的索引和重复的索引
list_index2 = list(set(list_index1))
list_index3 = sorted(list_index2)
list_index4 = list()
for index in list_index3:
if index in range(ori_gt_box.shape[0]):
list_index4.append(index)
gt_side_index = np.array(list_index4).astype(np.int32) # 得到了边界gt框的索引
# 要得到与这些gt框有最大的overlap的anchors的索引，这些anchor是我们关心的
gt_argmax_overlaps = overlaps.argmax(axis=0)
anchor_side_index = gt_argmax_overlaps[
gt_side_index] # 得到143个与gt具有最大的overlaps的anchor的索引
# 还要去掉与边界框overlap为0的anchor，因为这些anhcor不是真的我们关心的anchor，如果不去除，还会造成o_loss异常大
anchor_side_list = list()
for i in range(anchor_side_index.shape[0]):
anchor_index = anchor_side_index[i]
gt_index = gt_side_index[i]
overlap = overlaps[anchor_index, gt_index]
if overlap > 0:
anchor_side_list.append(anchor_index)
anchor_side_index = np.array(anchor_side_list, dtype=np.int32)
anchor_side_index1 = np.array(
sorted(list(set(list(anchor_side_index))))).astype(np.int32)
k_index = anchor_side_index1 # (s,) s个边界索引，但是并不是包括之前去除的超过边界框的索引值，所以需要之后的操作
k_index1 = np.zeros((len(inds_inside)), dtype=np.int32)
k_index1[k_index] = 1
in_labels = labels.copy()
# map up to original set of anchors
# 一开始是将超出图像范围的anchor直接丢掉的，现在在加回来
labels = _unmap(labels, total_anchors, inds_inside,
fill=-1) # 这些anchor的label是-1，也即dontcare
v_target = _unmap(v_target, total_anchors, inds_inside,
fill=0) # 这些anchor的真值是0，也即没有值
o_target = _unmap(o_target, total_anchors, inds_inside, fill=0)
j_index2 = _unmap(j_index1, total_anchors, inds_inside,
fill=0).astype(np.int32)
k_index2 = _unmap(k_index1, total_anchors, inds_inside,
fill=0).astype(np.int32)
# real_j_index = np.where(j_index2==1)[0]
# real_k_index = np.where(k_index2==1)[0]
global tmp_labels, tmp_all_bg_index, tmp_v_target, tmp_o_target, tmp_j_index2, tmp_k_index2, tmp_inds_inside
tmp_labels = in_labels
tmp_all_bg_index = all_bg_index
tmp_v_target = v_target
tmp_o_target = o_target
tmp_j_index2 = j_index2
tmp_k_index2 = k_index2
tmp_inds_inside = inds_inside
if DEBUG or SHOW_SOME:
print('第一次这张图')
print('正样本：' + str(len(np.where(labels == 1)[0])))
print('负样本：' + str(len(np.where(labels == 0)[0])))
print('忽略样本：' + str(len(np.where(labels == -1)[0])))
# print('保存的tmp_labels')
# print('正样本：' + str(len(np.where(tmp_labels == 1)[0])))
# print('负样本：' + str(len(np.where(tmp_labels == 0)[0])))
# print('忽略样本：' + str(len(np.where(tmp_labels == -1)[0])))
return labels, v_target, o_target, j_index2, k_index2
else: # 第二次见过这个图，只用生成hard neg添加进去
if DEBUG and SHOW_SOME:
print('不是第一次')
# 先找出负样本
bg_index = tmp_all_bg_index
inds_inside = tmp_inds_inside
# 找出得分高于某个阈值的
rpn_cls_prob = np.reshape(rpn_cls_prob, [-1, 2])
rpn_cls_prob = rpn_cls_prob[inds_inside, :]
fg_score = rpn_cls_prob[:, 1]
high_score = fg_score > 0.5
# 找出即是负样本，又是分数很高的样本
assert bg_index.shape == high_score.shape
# 得到了hard negtive的索引,这个是
hard_neg = bg_index * high_score
if DEBUG:
print('负样本的数量：' + str(len(np.where(bg_index == True)[0])))
print('得分高于0.5的数量：' + str(len(np.where(high_score == True)[0])))
print('hard negtive 数量' + str(len(np.where(hard_neg == True)[0])))
# 如果是第二次训练这张图片，训练样本是第一次随机生成的正负样本加这一次的hard negtive
labels = tmp_labels.copy()
first_gen_index = labels != -1
hard_neg_index = hard_neg
assert first_gen_index.shape == hard_neg_index.shape, 'line 282'
diff = hard_neg_index * 1 - first_gen_index * 1
new_hard_index = diff == 1
assert labels.shape == new_hard_index.shape
if DEBUG:
print('加载进来的第一次的labels的负样本的数量：' +
str(len(np.where(labels == 0)[0])))
print('属于难负样本不属于第一次样本的数量：' +
str(len(np.where(new_hard_index == True)[0])))
print('加难样本之前的负样本数量：' + str(len(np.where(labels == 0)[0])))
labels[new_hard_index] = 0
if DEBUG or SHOW_SOME:
print('加难样本之后的负样本数量：' + str(len(np.where(labels == 0)[0])))
print('正样本：' + str(len(np.where(labels == 1)[0])))
print('负样本：' + str(len(np.where(labels == 0)[0])))
print('忽略样本：' + str(len(np.where(labels == -1)[0])))
# print('第一次保存的tmp_labels的负样本数量应该不变的'+ str(len(np.where(tmp_labels == 0)[0])))
# 至此，准备好了labels
# 其他标签不变，加载上次保存的就好
v_target = tmp_v_target
o_target = tmp_o_target
j_index2 = tmp_j_index2
k_index2 = tmp_k_index2
return labels, v_target, o_target, j_index2, k_index2
def anchor_target_layer(rpn_cls_score, gt_boxes, im_info, _feat_stride,
all_anchors, num_anchors):
"""Same as the anchor target layer in original Fast/er RCNN """
A = num_anchors
total_anchors = all_anchors.shape[0]
K = total_anchors / num_anchors
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
# pytorch (bs, c, h, w)
height, width = rpn_cls_score.shape[2:4]
# only keep anchors inside the image
inds_inside = np.where(
(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]
# keep only inside anchors
anchors = all_anchors[inds_inside, :]
# label: 1 is positive, 0 is negative, -1 is dont care
labels = np.empty((len(inds_inside), ), dtype=np.float32)
labels.fill(-1)
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt)
overlaps = bbox_overlaps(np.ascontiguousarray(anchors, dtype=np.float),
np.ascontiguousarray(gt_boxes, dtype=np.float))
argmax_overlaps = overlaps.argmax(axis=1)
max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
gt_argmax_overlaps = overlaps.argmax(axis=0)
gt_max_overlaps = overlaps[gt_argmax_overlaps,
np.arange(overlaps.shape[1])]
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
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
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1
# fg label: above threshold IOU
labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 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
# subsample positive labels if we have too many
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
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
# subsample negative labels if we have too many
num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)
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
bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])
bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
# only the positive ones have regression targets
bbox_inside_weights[labels == 1, :] = np.array(
cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS)
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)
positive_weights = np.ones((1, 4)) * 1.0 / num_examples
negative_weights = np.ones((1, 4)) * 1.0 / num_examples
# positive_weights = np.ones((1, 4))
# negative_weights = np.zeros((1, 4))
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
bbox_outside_weights[labels == 0, :] = negative_weights
# map up to original set of anchors
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)
bbox_outside_weights = _unmap(bbox_outside_weights,
total_anchors,
inds_inside,
fill=0)
# labels
labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2)
labels = labels.reshape((1, 1, A * height, width))
rpn_labels = labels
# bbox_targets
bbox_targets = bbox_targets \
.reshape((1, height, width, A * 4))
rpn_bbox_targets = bbox_targets
# bbox_inside_weights
bbox_inside_weights = bbox_inside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_inside_weights = bbox_inside_weights
# bbox_outside_weights
bbox_outside_weights = bbox_outside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_outside_weights = bbox_outside_weights
return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights
def anchor_target_layer(rpn_cls_score_list, gt_boxes, im_info, _feat_stride,
all_anchors, num_anchors):
"""Same as the anchor target layer in original Fast/er RCNN """
A_s = num_anchors
total_anchors = all_anchors.shape[0]
# K = total_anchors / num_anchors
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
# pytorch (bs, c, h, w)
heights = [rpn_cls_score.shape[2] for rpn_cls_score in rpn_cls_score_list]
widths = [rpn_cls_score.shape[3] for rpn_cls_score in rpn_cls_score_list]
# only keep anchors inside the image
inds_inside = np.where(
(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]
if DEBUG:
print('inds_inside', len(inds_inside))
print('total anchors', total_anchors)
# keep only inside anchors
anchors = all_anchors[inds_inside, :]
# label: 1 is positive, 0 is negative, -1 is dont care
labels = np.empty((len(inds_inside), ), dtype=np.float32)
labels.fill(-1)
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt)
overlaps = bbox_overlaps(np.ascontiguousarray(anchors, dtype=np.float),
np.ascontiguousarray(gt_boxes, dtype=np.float))
argmax_overlaps = overlaps.argmax(axis=1)
max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
gt_argmax_overlaps = overlaps.argmax(axis=0)
gt_max_overlaps = overlaps[gt_argmax_overlaps,
np.arange(overlaps.shape[1])]
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0] #laji
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
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1
# fg label: above threshold IOU
labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 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
# --------------------ignore handling----------------------------
#tmp = [gt for gt in gt_boxes if gt[4] == 1]
tttinds = np.where(gt_boxes[:, 4] == 1)[0]
if (len(tttinds > 0)):
tmp = gt_boxes[tttinds, :]
# calculate overlaps between anchors and ignore regions
overlaps2 = bbox_overlaps(
np.ascontiguousarray(anchors, dtype=np.float),
np.ascontiguousarray(tmp, dtype=np.float))
# find max value
argmax_overlaps2 = overlaps2.argmax(axis=1)
max_overlaps2 = overlaps2[np.arange(len(inds_inside)),
argmax_overlaps2]
# ignore high overlaps by setting them to -1 (ignore)
labels[max_overlaps2 >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = -1
# --------------------ignore handling----------------------------
# import pdb; pdb.set_trace()
# subsample positive labels if we have too many
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
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
# subsample negative labels if we have too many
num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)
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
bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])
bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
# only the positive ones have regression targets
bbox_inside_weights[labels == 1, :] = np.array(
cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS)
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)
positive_weights = np.ones((1, 4)) * 1.0 / num_examples
negative_weights = np.ones((1, 4)) * 1.0 / num_examples
# positive_weights = np.ones((1, 4))
# negative_weights = np.zeros((1, 4))
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
bbox_outside_weights[labels == 0, :] = negative_weights
# map up to original set of anchors
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)
bbox_outside_weights = _unmap(bbox_outside_weights,
total_anchors,
inds_inside,
fill=0)
begin_cnt = 0
end_cnt = 0
begin_cnt_bbox = 0
end_cnt_bbox = 0
labels_list = list()
bbox_targets_list = list()
bbox_inside_weights_list = list()
bbox_outside_weights_list = list()
for height, width, A in zip(heights, widths, A_s):
begin_cnt = end_cnt
end_cnt += 1 * height * width * A
labels_part = labels[begin_cnt:end_cnt]
# labels
labels_part = labels_part.reshape(
(1, height, width, A)).transpose(0, 3, 1, 2)
labels_part = labels_part.reshape((1, 1, A * height, width)).reshape(
(-1, ))
labels_list.append(labels_part)
# begin_cnt_bbox = end_cnt_bbox
# end_cnt_bbox += 1*height*width*A*4
# bbox_targets_part = bbox_targets[begin_cnt_bbox:end_cnt_bbox]
# bbox_inside_weights_part = bbox_inside_weights[begin_cnt_bbox:end_cnt_bbox]
# bbox_outside_weights_part = bbox_outside_weights[begin_cnt_bbox:end_cnt_bbox]
#
# # bbox_targets
# bbox_targets_part = bbox_targets_part.reshape((1, height, width, A * 4))
# bbox_targets_list.append(bbox_targets_part)
#
# # bbox_inside_weights
# bbox_inside_weights_part = bbox_inside_weights_part.reshape((1, height, width, A * 4))
# bbox_inside_weights_list.append(bbox_inside_weights_part)
#
# # bbox_outside_weights
# bbox_outside_weights_part = bbox_outside_weights_part.reshape((1, height, width, A * 4))
# bbox_outside_weights_list.append(bbox_outside_weights_part)
assert total_anchors == end_cnt
labels = np.concatenate(labels_list, axis=0)
# labels
# labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2)
# labels = labels.reshape((1, 1, A * height, width))
rpn_labels = labels
# bbox_targets
# bbox_targets = bbox_targets \
# .reshape((1, height, width, A * 4))
rpn_bbox_targets = bbox_targets
# bbox_inside_weights
# bbox_inside_weights = bbox_inside_weights \
# .reshape((1, height, width, A * 4))
rpn_bbox_inside_weights = bbox_inside_weights
# bbox_outside_weights
# bbox_outside_weights = bbox_outside_weights \
# .reshape((1, height, width, A * 4))
rpn_bbox_outside_weights = bbox_outside_weights
return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights
def _sample_rois(all_rois, all_scores, gt_boxes, fg_rois_per_image,
rois_per_image, num_classes):
"""Generate a random sample of RoIs comprising foreground and background
examples.
"""
# overlaps: (rois x gt_boxes)
overlaps = bbox_overlaps(all_rois[:, 1:5].data, gt_boxes[:, :4].data)
max_overlaps, gt_assignment = overlaps.max(1)
labels = gt_boxes[gt_assignment, [4]]
# 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)
bg_inds = (
(max_overlaps < cfg.TRAIN.BG_THRESH_HI) +
(max_overlaps >= cfg.TRAIN.BG_THRESH_LO) == 2).nonzero().view(-1)
#-----------------------ignore handling--------------------
# Select foreground RoIs as those with >= FG_THRESH overlap
# import pdb; pdb.set_trace()
# gt_boxes2 = [gt for gt in gt_boxes if gt[4].data[0]==15]
# if len(gt_boxes2) == 0:
# import pdb; pdb.set_trace()
# then we choose positive regions
# we only keep pedestrain regions
# import pdb;pdb.set_trace()
#
# gt_boxes = [gt for gt in gt_boxes if gt[4].data[0]==15]
#
# gt_boxes = torch.stack(gt_boxes)
tttinds = (gt_boxes[:, 4] == 15).nonzero().view(-1)
gt_boxes = gt_boxes[tttinds]
# import pdb; pdb.set_trace()
overlaps = bbox_overlaps(all_rois[:, 1:5].data, gt_boxes[:, :4].data)
max_overlaps, gt_assignment = overlaps.max(1)
labels = gt_boxes[gt_assignment, [4]]
fg_inds = (max_overlaps >= cfg.TRAIN.FG_THRESH).nonzero().view(-1)
#-----------------------ignore handling--------------------
# print('fg_rois_per_image:',fg_rois_per_image,'fg_inds.numel():',fg_inds.numel())
# Small modification to the original version where we ensure a fixed number of regions are sampled
if fg_inds.numel() > 0 and bg_inds.numel() > 0:
'''
to_replace = fg_inds.numel() < fg_rois_per_image
fg_inds = fg_inds[torch.from_numpy(
npr.choice(np.arange(0, fg_inds.numel()), size=int(fg_rois_per_image), replace=to_replace)).long().cuda()]
'''
fg_rois_per_image = min(fg_rois_per_image, fg_inds.numel())
fg_inds = fg_inds[torch.from_numpy(
npr.choice(np.arange(0, fg_inds.numel()),
size=int(fg_rois_per_image),
replace=False)).long().cuda()]
bg_rois_per_image = rois_per_image - fg_rois_per_image
#print(bg_rois_per_image,fg_rois_per_image,'bg_rois_per_image,fg_rois_per_image proposal_target_layer.py(167)')
to_replace = bg_inds.numel() < bg_rois_per_image
if to_replace:
bg_inds = bg_inds[torch.from_numpy(
npr.choice(np.arange(0, bg_inds.numel()),
size=int(bg_rois_per_image),
replace=to_replace)).long().cuda()]
else:
bg_inds = bg_inds[:int(bg_rois_per_image)]
elif fg_inds.numel() > 0:
to_replace = fg_inds.numel() < rois_per_image
fg_inds = fg_inds[torch.from_numpy(
npr.choice(np.arange(0, fg_inds.numel()),
size=int(rois_per_image),
replace=to_replace)).long().cuda()]
fg_rois_per_image = rois_per_image
elif bg_inds.numel() > 0:
to_replace = bg_inds.numel() < rois_per_image
bg_inds = bg_inds[torch.from_numpy(
npr.choice(np.arange(0, bg_inds.numel()),
size=int(rois_per_image),
replace=to_replace)).long().cuda()]
fg_rois_per_image = 0
else:
import pdb
pdb.set_trace()
# The indices that we're selecting (both fg and bg)
keep_inds = torch.cat([fg_inds, bg_inds], 0)
# Select sampled values from various arrays:
labels = labels[keep_inds].contiguous()
# Clamp labels for the background RoIs to 0
# import pdb;pdb.set_trace()
# print(fg_rois_per_image,len(labels))
labels[int(fg_rois_per_image):] = 0
rois = all_rois[keep_inds].contiguous()
roi_scores = all_scores[keep_inds].contiguous()
bbox_target_data = _compute_targets(
rois[:, 1:5].data, gt_boxes[gt_assignment[keep_inds]][:, :4].data,
labels.data)
bbox_targets, bbox_inside_weights = \
_get_bbox_regression_labels(bbox_target_data, num_classes)
return labels, rois, roi_scores, bbox_targets, bbox_inside_weights
def rpn_target(anchors, inside_inds, gt_labels, gt_boxes):
def box_transform(et_boxes, gt_boxes):
et_ws = et_boxes[:, 2] - et_boxes[:, 0] + 1.0
et_hs = et_boxes[:, 3] - et_boxes[:, 1] + 1.0
et_cxs = et_boxes[:, 0] + 0.5 * et_ws
et_cys = et_boxes[:, 1] + 0.5 * et_hs
gt_ws = gt_boxes[:, 2] - gt_boxes[:, 0] + 1.0
gt_hs = gt_boxes[:, 3] - gt_boxes[:, 1] + 1.0
gt_cxs = gt_boxes[:, 0] + 0.5 * gt_ws
gt_cys = gt_boxes[:, 1] + 0.5 * gt_hs
dxs = (gt_cxs - et_cxs) / et_ws
dys = (gt_cys - et_cys) / et_hs
dws = np.log(gt_ws / et_ws)
dhs = np.log(gt_hs / et_hs)
deltas = np.vstack((dxs, dys, dws, dhs)).transpose()
return deltas
CFG = EasyDict()
CFG.TRAIN = EasyDict()
CFG.TRAIN.RPN_BATCHSIZE = 100
CFG.TRAIN.RPN_FG_FRACTION = 0.5
CFG.TRAIN.RPN_FG_THRESH_LO = 0.7
CFG.TRAIN.RPN_BG_THRESH_HI = 0.3
inside_anchors = anchors[inside_inds, :]
# label: 1 is positive, 0 is negative, -1 is dont care
labels = np.empty((len(inside_inds), ), dtype=np.int32)
labels.fill(-1)
# overlaps between the anchors and the gt process
overlaps = bbox_overlaps(
np.ascontiguousarray(inside_anchors, dtype=np.float),
np.ascontiguousarray(gt_boxes, dtype=np.float))
argmax_overlaps = overlaps.argmax(axis=1)
max_overlaps = overlaps[np.arange(len(inside_inds)), argmax_overlaps]
gt_argmax_overlaps = overlaps.argmax(axis=0)
gt_max_overlaps = overlaps[gt_argmax_overlaps,
np.arange(overlaps.shape[1])]
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
labels[max_overlaps < CFG.TRAIN.RPN_BG_THRESH_HI] = 0 # bg label
labels[
gt_argmax_overlaps] = 1 # fg label: for each gt, anchor with highest overlap
labels[max_overlaps >=
CFG.TRAIN.RPN_FG_THRESH_LO] = 1 # fg label: above threshold IOU
# subsample positive labels
num_fg = int(CFG.TRAIN.RPN_FG_FRACTION * CFG.TRAIN.RPN_BATCHSIZE)
fg_inds = np.where(labels == 1)[0]
if len(fg_inds) > num_fg:
disable_inds = np.random.choice(fg_inds,
size=(len(fg_inds) - num_fg),
replace=False)
labels[disable_inds] = -1
# subsample negative labels
num_bg = CFG.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)
bg_inds = np.where(labels == 0)[0]
if len(bg_inds) > num_bg:
disable_inds = np.random.choice(bg_inds,
size=(len(bg_inds) - num_bg),
replace=False)
labels[disable_inds] = -1
idx_label = np.where(labels != -1)[0]
idx_target = np.where(labels == 1)[0]
pos_neg_inds = inside_inds[idx_label]
labels = labels[idx_label]
pos_inds = inside_inds[idx_target]
pos_anchors = inside_anchors[idx_target]
pos_gt_boxes = gt_boxes[argmax_overlaps][idx_target]
targets = box_transform(pos_anchors, pos_gt_boxes)
return pos_neg_inds, pos_inds, labels, targets
