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雷鋒網(wǎng) AI科技評論按：本文首發(fā)于知乎專欄Learning Machine，作者張瀟捷，雷鋒網(wǎng) AI科技評論獲其授權(quán)轉(zhuǎn)載。
前段時間學完Udacity的機器學習和深度學習的課程，感覺只能算剛剛摸到深度學習的門檻，于是開始看斯坦福的cs231n（http://cs231n.stanford.edu/syllabus.html），一不小心便入了計算機視覺的坑。原來除了識別物體，還可以進行定位(localization)，檢測(object detection)，語義分割(semantic segmentation)，實例分割(instance segmentation)，左右手互搏(GAN)，風格學習(transfer learning)等等。。。真是一下開了眼。從detection學起，開干！
detection的話，自然是rgb大神的一系列工作，從rcnn一路到Y(jié)OLO。這里貼一個YOLO的視頻，給各位看官鑒賞一下:YOLO: Real-Time Object Detection（https://www.youtube.com/watch?v=VOC3huqHrss&feature=youtu.be） 。也可以直接看這個地址，有更詳細的內(nèi)容：YOLO: Real-Time Object Detection（https://pjreddie.com/darknet/yolo/）。Faster-rcnn的原文在這里：Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks（https://arxiv.org/abs/1506.01497）。
由于tensorflow使用的不是很熟練，大部分項目都是用keras做的 ，因此在github上找到了一個keras版的faster-rcnn（https://github.com/yhenon/keras-frcnn），學習一下。基本上clone下來以后稍微調(diào)整幾處代碼就能成功跑起來了。我用Oxford的pet數(shù)據(jù)集進行了訓練，在我的老爺卡gtx970上訓練了差不多1個多小時，就能夠比較有效的實現(xiàn)detection了。下面是效果圖。
接下來就是理解代碼了，faster-rcnn的核心思想就是通過RPN替代過往的獨立的步驟進行region proposal，實現(xiàn)完全的end-to-end學習，從而對算法進行了提速。所以讀懂RPN是理解faster-rcnn的第一步。下面的代碼是如何得到用于訓練RPN的ground truth的，完全理解之后也就理解RPN的原理了。
計算過程比較長，但沒有復(fù)雜的數(shù)學知識，我畫了一個大概的流程圖，在此基礎(chǔ)上理解應(yīng)該就容易多了。
下面來看代碼：
def calc_rpn(C, img_data, width, height, resized_width, resized_height, img_length_calc_function):
downscale = float(C.rpn_stride)
anchor_sizes = C.anchor_box_scales
anchor_ratios = C.anchor_box_ratios
num_anchors = len(anchor_sizes) * len(anchor_ratios)
# calculate the output map size based on the network architecture
(output_width, output_height) = img_length_calc_function(resized_width, resized_height)
n_anchratios = len(anchor_ratios)
# initialise empty output objectives
y_rpn_overlap = np.zeros((output_height, output_width, num_anchors))
y_is_box_valid = np.zeros((output_height, output_width, num_anchors))
y_rpn_regr = np.zeros((output_height, output_width, num_anchors * 4))
num_bboxes = len(img_data['bboxes'])
num_anchors_for_bbox = np.zeros(num_bboxes).astype(int)
best_anchor_for_bbox = -1*np.ones((num_bboxes, 4)).astype(int)
best_iou_for_bbox = np.zeros(num_bboxes).astype(np.float32)
best_x_for_bbox = np.zeros((num_bboxes, 4)).astype(int)
best_dx_for_bbox = np.zeros((num_bboxes, 4)).astype(np.float32)
# get the GT box coordinates, and resize to account for image resizing
gta = np.zeros((num_bboxes, 4))
for bbox_num, bbox in enumerate(img_data['bboxes']):
# get the GT box coordinates, and resize to account for image resizing
gta[bbox_num, 0] = bbox['x1'] * (resized_width / float(width))
gta[bbox_num, 1] = bbox['x2'] * (resized_width / float(width))
gta[bbox_num, 2] = bbox['y1'] * (resized_height / float(height))
gta[bbox_num, 3] = bbox['y2'] * (resized_height / float(height))
首先看一下參數(shù)，C是配置信息，img_data包含一張圖片的路徑，bbox坐標和對應(yīng)的分類（可能一張圖片有多組，即表示圖片里包含多個對象）。后面是圖片的原尺寸和resize之后的尺寸，用于求bbox坐標在resize之后圖片上的坐標，img_length_calc_function是一個方法，基于我們的設(shè)置來從圖片尺寸計算出經(jīng)過網(wǎng)絡(luò)之后特征圖的尺寸。
接下來讀取了幾個參數(shù)，downscale就是從圖片到特征圖的縮放倍數(shù)，anchor_size和anchor_ratios是我們初步選區(qū)大小的參數(shù)，比如3個size和3個ratios，可以組合成9種不同形狀大小的選區(qū)。接下來通過img_.....function這個方法計算出了特征圖的尺寸。
下一步是幾個變量初始化，可以先不看，后面用到的時候再看。因為我們的計算都是基于resize以后的圖像的，所以接下來把bbox中的x1,x2,y1,y2分別通過縮放匹配到resize以后的圖像。這里記做gta，尺寸為(num_of_bbox,4)。
for anchor_size_idx in range(len(anchor_sizes)):
for anchor_ratio_idx in range(n_anchratios):
anchor_x = anchor_sizes[anchor_size_idx] * anchor_ratios[anchor_ratio_idx][0]
anchor_y = anchor_sizes[anchor_size_idx] * anchor_ratios[anchor_ratio_idx][1]
for ix in range(output_width):
# x-coordinates of the current anchor box
x1_anc = downscale * (ix + 0.5) - anchor_x / 2
x2_anc = downscale * (ix + 0.5) + anchor_x / 2
# ignore boxes that go across image boundaries
if x1_anc < 0 or x2_anc > resized_width:
continue
for jy in range(output_height):
# y-coordinates of the current anchor box
y1_anc = downscale * (jy + 0.5) - anchor_y / 2
y2_anc = downscale * (jy + 0.5) + anchor_y / 2
# ignore boxes that go across image boundaries
if y1_anc < 0 or y2_anc > resized_height:
continue
# bbox_type indicates whether an anchor should be a target
bbox_type = 'neg'
# this is the best IOU for the (x,y) coord and the current anchor
# note that this is different from the best IOU for a GT bbox
best_iou_for_loc = 0.0
上面這一段計算了anchor的長寬，然后比較重要的就是把特征圖的每一個點作為一個錨點，通過乘以downscale，映射到圖片的實際尺寸，再結(jié)合anchor的尺寸，忽略掉超出圖片范圍的。一個個大小、比例不一的矩形選框就躍然紙上了。對這些選框進行遍歷，對每個選框進行下面的計算：
# bbox_type indicates whether an anchor should be a target
bbox_type = 'neg'
# this is the best IOU for the (x,y) coord and the current anchor
# note that this is different from the best IOU for a GT bbox
best_iou_for_loc = 0.0
for bbox_num in range(num_bboxes):
# get IOU of the current GT box and the current anchor box
curr_iou = iou([gta[bbox_num, 0], gta[bbox_num, 2], gta[bbox_num, 1], gta[bbox_num, 3]], [x1_anc,y1_anc, x2_anc, y2_anc])
# calculate the regression targets if they will be needed
if curr_iou > best_iou_for_bbox[bbox_num] or curr_iou > C.rpn_max_overlap:
cx = (gta[bbox_num, 0] + gta[bbox_num, 1]) / 2.0
cy = (gta[bbox_num, 2] + gta[bbox_num, 3]) / 2.0
cxa = (x1_anc + x2_anc)/2.0
cya = (y1_anc + y2_anc)/2.0
tx = (cx - cxa) / (x2_anc - x1_anc)
ty = (cy - cya) / (y2_anc - y1_anc)
tw = np.log((gta[bbox_num, 1] - gta[bbox_num, 0]) / (x2_anc - x1_anc))
th = np.log((gta[bbox_num, 3] - gta[bbox_num, 2]) / (y2_anc - y1_anc))
定義了兩個變量，bbox_type和best_iou_for_loc，后面會用到。計算了anchor與gta的交集，比較簡單，就不展開說了。然后就是如果交集大于best_iou_for_bbox[bbox_num]或者大于我們設(shè)定的閾值，就會去計算gta和anchor的中心點坐標，再通過中心點坐標和bbox坐標，計算出x,y,w,h四個值的梯度值（不知道這么理解對不對）。為什么要計算這個梯度呢？因為RPN計算出來的區(qū)域不一定是很準確的，從只有9個尺寸的anchor也可以推測出來，因此我們在預(yù)測時還會進行一次回歸計算，而不是直接使用這個區(qū)域的坐標。
接下來是根據(jù)anchor的表現(xiàn)對其進行標注。
if img_data['bboxes'][bbox_num]['class'] != 'bg':
# all GT boxes should be mapped to an anchor box, so we keep track of which anchor box was best
if curr_iou > best_iou_for_bbox[bbox_num]:
best_anchor_for_bbox[bbox_num] = [jy, ix, anchor_ratio_idx, anchor_size_idx]
best_iou_for_bbox[bbox_num] = curr_iou
best_x_for_bbox[bbox_num,:] = [x1_anc, x2_anc, y1_anc, y2_anc]
best_dx_for_bbox[bbox_num,:] = [tx, ty, tw, th]
# we set the anchor to positive if the IOU is >0.7 (it does not matter if there was another better box, it just indicates overlap)
if curr_iou > C.rpn_max_overlap:
bbox_type = 'pos'
num_anchors_for_bbox[bbox_num] += 1
# we update the regression layer target if this IOU is the best for the current (x,y) and anchor position
if curr_iou > best_iou_for_loc:
best_iou_for_loc = curr_iou
best_regr = (tx, ty, tw, th)
# if the IOU is >0.3 and <0.7, it is ambiguous and no included in the objective
if C.rpn_min_overlap < curr_iou < C.rpn_max_overlap:
# gray zone between neg and pos
if bbox_type != 'pos':
bbox_type = 'neutral'# turn on or off outputs depending on IOUs
if bbox_type == 'neg':
y_is_box_valid[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 1
y_rpn_overlap[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 0elif bbox_type == 'neutral':
y_is_box_valid[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 0
y_rpn_overlap[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 0elif bbox_type == 'pos':
y_is_box_valid[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 1
y_rpn_overlap[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 1
start = 4 * (anchor_ratio_idx + n_anchratios * anchor_size_idx)y_rpn_regr[jy, ix, start:start+4] = best_regr
前提是這個bbox的class不是'bg'，即背景。如果交集大于這個bbox的最佳值，則進行一系列更新。如果交集大于我們設(shè)定的閾值，則定義為一個positive的anchor，即存在與之重合度比較高的bbox，同時該bbox的num_anchors加1。如果交集剛好也大于best_iou_for_loc，則將best_regr設(shè)為當前的梯度值。這里best_iou_for_loc指的是該anchor下的最佳交集，我的理解就是一個anchor如果能匹配到1個以上的bbox為pos，那我們?nèi)est_iou_for_loc下的梯度，要知道這一步我們只要找到最佳的選區(qū)就行了，并不管選區(qū)里是哪個class。如果剛好處于最大和最小閾值之間，那我們不確定它是背景還是對象，將其定義為neutral，即中性。
接下來根據(jù)bbox_type對本anchor進行打標，y_is_box_valid和y_rpn_overlap分別定義了這個anchor是否可用和是否包含對象。
for idx in range(num_anchors_for_bbox.shape[0]):
if num_anchors_for_bbox[idx] == 0:
# no box with an IOU greater than zero ...
if best_anchor_for_bbox[idx, 0] == -1:
continue
y_is_box_valid[
best_anchor_for_bbox[idx,0], best_anchor_for_bbox[idx,1], best_anchor_for_bbox[idx,2] + n_anchratios *
best_anchor_for_bbox[idx,3]] = 1
y_rpn_overlap[
best_anchor_for_bbox[idx,0], best_anchor_for_bbox[idx,1], best_anchor_for_bbox[idx,2] + n_anchratios *
best_anchor_for_bbox[idx,3]] = 1
start = 4 * (best_anchor_for_bbox[idx,2] + n_anchratios * best_anchor_for_bbox[idx,3])
y_rpn_regr[
best_anchor_for_bbox[idx,0], best_anchor_for_bbox[idx,1], start:start+4] = best_dx_for_bbox[idx,:]
這里又出現(xiàn)了一個問題，很多bbox可能找不到心儀的anchor，那這些訓練數(shù)據(jù)就沒法利用了，因此我們用一個折中的辦法來保證每個bbox至少有一個anchor與之對應(yīng)。下面是具體的方法，比較簡單，對于沒有對應(yīng)anchor的bbox，在中性anchor里挑最好的，當然前提是你不能跟我完全不相交，那就太過分了。。
y_rpn_overlap = np.transpose(y_rpn_overlap, (2, 0, 1))
y_rpn_overlap = np.expand_dims(y_rpn_overlap, axis=0)
y_is_box_valid = np.transpose(y_is_box_valid, (2, 0, 1))
y_is_box_valid = np.expand_dims(y_is_box_valid, axis=0)
y_rpn_regr = np.transpose(y_rpn_regr, (2, 0, 1))
y_rpn_regr = np.expand_dims(y_rpn_regr, axis=0)
pos_locs = np.where(np.logical_and(y_rpn_overlap[0, :, :, :] == 1, y_is_box_valid[0, :, :, :] == 1))
neg_locs = np.where(np.logical_and(y_rpn_overlap[0, :, :, :] == 0, y_is_box_valid[0, :, :, :] == 1))
num_pos = len(pos_locs[0])
接下來通過numpy大法進行了一系列操作，對pos和neg的anchor進行了定位。
num_regions = 256
if len(pos_locs[0]) > num_regions/2:
val_locs = random.sample(range(len(pos_locs[0])), len(pos_locs[0]) - num_regions/2)
y_is_box_valid[0, pos_locs[0][val_locs], pos_locs[1][val_locs], pos_locs[2][val_locs]] = 0
num_pos = num_regions/2
if len(neg_locs[0]) + num_pos > num_regions:
val_locs = random.sample(range(len(neg_locs[0])), len(neg_locs[0]) - num_pos)
y_is_box_valid[0, neg_locs[0][val_locs], neg_locs[1][val_locs], neg_locs[2][val_locs]] = 0
因為negtive的anchor肯定遠多于postive的，因此在這里設(shè)定了regions數(shù)量的最大值，并對pos和neg的樣本進行了均勻的取樣。
y_rpn_cls = np.concatenate([y_is_box_valid, y_rpn_overlap], axis=1)
y_rpn_regr = np.concatenate([np.repeat(y_rpn_overlap, 4, axis=1), y_rpn_regr], axis=1)
return np.copy(y_rpn_cls), np.copy(y_rpn_regr)
最后，得到了兩個返回值y_rpn_cls,y_rpn_regr。分別用于確定anchor是否包含物體，和回歸梯度。
再來看一下網(wǎng)絡(luò)中RPN層的結(jié)構(gòu)：
def rpn(base_layers,num_anchors):
x = Convolution2D(512, (3, 3), padding='same', activation='relu', kernel_initializer='normal', name='rpn_conv1')(base_layers)
x_class = Convolution2D(num_anchors, (1, 1), activation='sigmoid', kernel_initializer='uniform', name='rpn_out_class')(x)
x_regr = Convolution2D(num_anchors * 4, (1, 1), activation='linear', kernel_initializer='zero', name='rpn_out_regress')(x)
return [x_class, x_regr, base_layers]
通過1*1的窗口在特征圖上滑過，生成了num_anchors數(shù)量的channel,每個channel包含特征圖（w*h）個sigmoid激活值，表明該anchor是否可用，與我們剛剛計算的y_rpn_cls對應(yīng)。同樣的方法，得到x_regr與剛剛計算的y_rpn_regr對應(yīng)。
得到了region proposals，接下來另一個重要的思想就是ROI，可將不同shape的特征圖轉(zhuǎn)化為固定shape，送到全連接層進行最終的預(yù)測。等我學習完了再更新。由于自己也是學習過程，可能很多地方的理解有誤差，歡迎指正～
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