在上一篇里我们实现了forward函数.得到了prediction.此时预测出了特别多的box以及各种class probability,现在我们要从中过滤出我们最终的预测box.

理解了yolov3的输出的格式及每一个位置的含义,并不难理解源码.我在阅读源码的过程中主要的困难在于对pytorch不熟悉,所以在这篇文章里,关于其中涉及的一些pytorch中的函数的用法我都已经用加粗标示了并且给出了相应的链接,测试代码等.

obj score threshold

我们设置一个obj score thershold,超过这个值的才认为是有效的.

conf_mask = (prediction[:,:,4] > confidence).float().unsqueeze(2)

prediction = prediction*conf_mask

prediction是1*boxnum*boxattr

prediction[:,:,4]是1*boxnum 元素值为boxattr的index=4的那个值.

torch中的Tensor index和numpy是类似的,参看下列代码输出

import torch

x = torch.Tensor(1,3,10) # Create an un-initialized Tensor of size 2x3

print(x)

print(x.shape) # Print out the Tensor

y = x[:,:,4]

print(y)

print(y.shape)

z = x[:,:,4:6]

print(z)

print(z.shape)

print((y>0.5).float().unsqueeze(2))

#### 输出如下

tensor([[[2.5226e-18, 1.6898e-04, 1.0413e-11, 7.7198e-10, 1.0549e-08,

4.0516e-11, 1.0681e-05, 2.9575e-18, 6.7333e+22, 1.7591e+22],

[1.7184e+25, 4.3222e+27, 6.1972e-04, 7.2443e+22, 1.7728e+28,

7.0367e+22, 5.9018e-10, 2.6540e-09, 1.2972e-11, 5.3370e-08],

[2.7001e-06, 2.6801e-09, 4.1292e-05, 2.1511e+23, 3.2770e-09,

2.5125e-18, 7.7052e+31, 1.9447e+31, 5.0207e+28, 1.1492e-38]]])

torch.Size([1, 3, 10])

tensor([[1.0549e-08, 1.7728e+28, 3.2770e-09]])

torch.Size([1, 3])

tensor([[[1.0549e-08, 4.0516e-11],

[1.7728e+28, 7.0367e+22],

[3.2770e-09, 2.5125e-18]]])

torch.Size([1, 3, 2])

tensor([[[0.],

[0.],

[0.]]])

Squeeze and unsqueeze 降低维度,升高维度.

t = torch.ones(2,1,2,1) # Size 2x1x2x1

r = torch.squeeze(t) # Size 2x2

r = torch.squeeze(t, 1) # Squeeze dimension 1: Size 2x2x1

# Un-squeeze a dimension

x = torch.Tensor([1, 2, 3])

r = torch.unsqueeze(x, 0) # Size: 1x3 表示在第0个维度添加1维

r = torch.unsqueeze(x, 1) # Size: 3x1 表示在第1个维度添加1维

这样prediction中objscore

nms

#得到box坐标(top-left corner x, top-left corner y, right-bottom corner x, right-bottom corner y)

box_corner = prediction.new(prediction.shape)

box_corner[:,:,0] = (prediction[:,:,0] - prediction[:,:,2]/2)

box_corner[:,:,1] = (prediction[:,:,1] - prediction[:,:,3]/2)

box_corner[:,:,2] = (prediction[:,:,0] + prediction[:,:,2]/2)

box_corner[:,:,3] = (prediction[:,:,1] + prediction[:,:,3]/2)

prediction[:,:,:4] = box_corner[:,:,:4]

原始的prediction中boxattr存放的是x,y,w,h,...,不方便我们处理,我们将其转换成(top-left corner x, top-left corner y, right-bottom corner x, right-bottom corner y)

接下来我们挨个处理每一张图片对应的feature map.

batch_size = prediction.size(0)

write = False

for ind in range(batch_size):

#image_pred.shape=boxnum\*boxattr

image_pred = prediction[ind] #image Tensor box_num*box_attr

#confidence threshholding

#NMS

#返回每一行的最大值,及最大值所在的列.

max_conf, max_conf_score = torch.max(image_pred[:,5:5+ num_classes], 1)

#升级成和image_pred同样的维度

max_conf = max_conf.float().unsqueeze(1)

max_conf_score = max_conf_score.float().unsqueeze(1)

seq = (image_pred[:,:5], max_conf, max_conf_score)

#沿着列的方向拼接. 现在image_pred变成boxnum\*7

image_pred = torch.cat(seq, 1)

这里涉及到torch.max的用法,参见https://blog.csdn.net/Z_lbj/article/details/79766690

torch.max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor)

按维度dim 返回最大值.可以这么记忆,沿着第dim维度比较.torch.max(0)即沿着行的方向比较,即得到每列的最大值.

假设input是二维矩阵,即行*列,行是第0维,列是第一维.

torch.max(a,0) 返回每一列中最大值的那个元素，且返回索引(返回最大元素在这一列的行索引)

torch.max(a,1) 返回每一行中最大值的那个元素，且返回其索引(返回最大元素在这一行的列索引)

c=torch.Tensor([[1,2,3],[6,5,4]])

print(c)

a,b=torch.max(c,1)

print(a)

print(b)

##输出如下:

tensor([[1., 2., 3.],

[6., 5., 4.]])

tensor([3., 6.])

tensor([2, 0])

torch.cat(tensors, dim=0, out=None) → Tensor

>>> x = torch.randn(2, 3)

>>> x

tensor([[ 0.6580, -1.0969, -0.4614],

[-0.1034, -0.5790, 0.1497]])

>>> torch.cat((x, x, x), 0)

tensor([[ 0.6580, -1.0969, -0.4614],

[-0.1034, -0.5790, 0.1497],

[ 0.6580, -1.0969, -0.4614],

[-0.1034, -0.5790, 0.1497],

[ 0.6580, -1.0969, -0.4614],

[-0.1034, -0.5790, 0.1497]])

>>> torch.cat((x, x, x), 1)

tensor([[ 0.6580, -1.0969, -0.4614, 0.6580, -1.0969, -0.4614, 0.6580,

-1.0969, -0.4614],

[-0.1034, -0.5790, 0.1497, -0.1034, -0.5790, 0.1497, -0.1034,

-0.5790, 0.1497]])

接下来我们只处理obj_score非0的数据(obj_score

non_zero_ind = (torch.nonzero(image_pred[:,4]))

try:

image_pred_ = image_pred[non_zero_ind.squeeze(),:].view(-1,7)

except:

continue

#For PyTorch 0.4 compatibility

#Since the above code with not raise exception for no detection

#as scalars are supported in PyTorch 0.4

if image_pred_.shape[0] == 0:

continue

ok,接下来我们对每一种class做nms.

首先取到我们有哪些类别

#Get the various classes detected in the image

img_classes = unique(image_pred_[:,-1]) # -1 index holds the class index

然后依次对每一种类别做处理

for cls in img_classes:

#perform NMS

#get the detections with one particular class

#取出当前class为当前class且class prob!=0的行

cls_mask = image_pred_*(image_pred_[:,-1] == cls).float().unsqueeze(1)

class_mask_ind = torch.nonzero(cls_mask[:,-2]).squeeze()

image_pred_class = image_pred_[class_mask_ind].view(-1,7)

#sort the detections such that the entry with the maximum objectness

#confidence is at the top

#按照obj score从高到低做排序

conf_sort_index = torch.sort(image_pred_class[:,4], descending = True )[1]

image_pred_class = image_pred_class[conf_sort_index]

idx = image_pred_class.size(0) #Number of detections

for i in range(idx):

#Get the IOUs of all boxes that come after the one we are looking at

#in the loop

try:

#计算第i个和其后每一行的的iou

ious = bbox_iou(image_pred_class[i].unsqueeze(0), image_pred_class[i+1:])

except ValueError:

break

except IndexError:

break

#Zero out all the detections that have IoU > treshhold

#把与第i行iou>nms_conf的认为是同一个目标的box,将其转成0

iou_mask = (ious < nms_conf).float().unsqueeze(1)

image_pred_class[i+1:] *= iou_mask

#把iou>nms_conf的移除掉

non_zero_ind = torch.nonzero(image_pred_class[:,4]).squeeze()

image_pred_class = image_pred_class[non_zero_ind].view(-1,7)

batch_ind = image_pred_class.new(image_pred_class.size(0), 1).fill_(ind) #Repeat the batch_id for as many detections of the class cls in the image

seq = batch_ind, image_pred_class

其中计算iou的代码如下,不多解释了.iou=交叠面积/总面积

def bbox_iou(box1, box2):

"""

Returns the IoU of two bounding boxes

"""

#Get the coordinates of bounding boxes

b1_x1, b1_y1, b1_x2, b1_y2 = box1[:,0], box1[:,1], box1[:,2], box1[:,3]

b2_x1, b2_y1, b2_x2, b2_y2 = box2[:,0], box2[:,1], box2[:,2], box2[:,3]

#get the corrdinates of the intersection rectangle

inter_rect_x1 = torch.max(b1_x1, b2_x1)

inter_rect_y1 = torch.max(b1_y1, b2_y1)

inter_rect_x2 = torch.min(b1_x2, b2_x2)

inter_rect_y2 = torch.min(b1_y2, b2_y2)

#Intersection area

inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(inter_rect_y2 - inter_rect_y1 + 1, min=0)

#Union Area

b1_area = (b1_x2 - b1_x1 + 1)*(b1_y2 - b1_y1 + 1)

b2_area = (b2_x2 - b2_x1 + 1)*(b2_y2 - b2_y1 + 1)

iou = inter_area / (b1_area + b2_area - inter_area)

return iou

tensor index操作用法如下:

image_pred_ = torch.Tensor([[1,2,3,4,9],[5,6,7,8,9]])

#print(image_pred_[:,-1] == 9)

has_9 = (image_pred_[:,-1] == 9)

print(has_9)

###执行顺序是(image_pred_[:,-1] == 9).float().unsqueeze(1) 再做tensor乘法

cls_mask = image_pred_*(image_pred_[:,-1] == 9).float().unsqueeze(1)

print(cls_mask)

class_mask_ind = torch.nonzero(cls_mask[:,-2]).squeeze()

image_pred_class = image_pred_[class_mask_ind]

输出如下:

tensor([1, 1], dtype=torch.uint8)

tensor([[1., 2., 3., 4., 9.],

[5., 6., 7., 8., 9.]])

torch.sort用法如下:

d=torch.Tensor([[1,2,3],[6,5,4]])

e=d[:,2]

print(e)

print(torch.sort(e))

输出

tensor([3., 4.])

torch.return_types.sort(

values=tensor([3., 4.]),

indices=tensor([0, 1]))

总结一下我们做nms的流程

每一个image,会预测出N个detetction信息,包括4+1+C(4个坐标信息,1个obj score以及C个class probability)

首先过滤掉obj_score < confidence的行

每一行只取class probability最高的作为预测出来的类别

将所有的预测按照obj_score从大到小排序

循环每一种类别,开始做nms

比较第一个box与其后所有box的iou,删除iou>threshold的box,即剔除所有相似box

比较下一个box与其后所有box的iou,删除所有与该box相似的box

不断重复上述过程,直至不再有相似box

至此,实现了当前处理的类别的多个box均是独一无二的box.

write_results最终的返回值是一个n*8的tensor,其中8是(batch_index,4个坐标,1个objscore,1个class prob,一个class index)

def write_results(prediction, confidence, num_classes, nms_conf = 0.4):

print("prediction.shape=",prediction.shape)

#将obj_score < confidence的行置为0

conf_mask = (prediction[:,:,4] > confidence).float().unsqueeze(2)

prediction = prediction*conf_mask

#得到box坐标(top-left corner x, top-left corner y, right-bottom corner x, right-bottom corner y)

box_corner = prediction.new(prediction.shape)

box_corner[:,:,0] = (prediction[:,:,0] - prediction[:,:,2]/2)

box_corner[:,:,1] = (prediction[:,:,1] - prediction[:,:,3]/2)

box_corner[:,:,2] = (prediction[:,:,0] + prediction[:,:,2]/2)

box_corner[:,:,3] = (prediction[:,:,1] + prediction[:,:,3]/2)

#修改prediction第三个维度的前四列

prediction[:,:,:4] = box_corner[:,:,:4]

batch_size = prediction.size(0)

write = False

for ind in range(batch_size):

#image_pred.shape=boxnum\*boxattr

image_pred = prediction[ind] #image Tensor

#confidence threshholding

#NMS

##取出每一行的class score最大的一个

max_conf_score,max_conf = torch.max(image_pred[:,5:5+ num_classes], 1)

max_conf = max_conf.float().unsqueeze(1)

max_conf_score = max_conf_score.float().unsqueeze(1)

seq = (image_pred[:,:5], max_conf_score, max_conf)

image_pred = torch.cat(seq, 1) #现在变成7列,分别为左上角x,左上角y,右下角x,右下角y,obj score,最大probabilty,相应的class index

print(image_pred.shape)

non_zero_ind = (torch.nonzero(image_pred[:,4]))

try:

image_pred_ = image_pred[non_zero_ind.squeeze(),:].view(-1,7)

except:

continue

#For PyTorch 0.4 compatibility

#Since the above code with not raise exception for no detection

#as scalars are supported in PyTorch 0.4

if image_pred_.shape[0] == 0:

continue

#Get the various classes detected in the image

img_classes = unique(image_pred_[:,-1]) # -1 index holds the class index

for cls in img_classes:

#perform NMS

#get the detections with one particular class

#取出当前class为当前class且class prob!=0的行

cls_mask = image_pred_*(image_pred_[:,-1] == cls).float().unsqueeze(1)

class_mask_ind = torch.nonzero(cls_mask[:,-2]).squeeze()

image_pred_class = image_pred_[class_mask_ind].view(-1,7)

#sort the detections such that the entry with the maximum objectness

#confidence is at the top

#按照obj score从高到低做排序

conf_sort_index = torch.sort(image_pred_class[:,4], descending = True )[1]

image_pred_class = image_pred_class[conf_sort_index]

idx = image_pred_class.size(0) #Number of detections

for i in range(idx):

#Get the IOUs of all boxes that come after the one we are looking at

#in the loop

try:

#计算第i个和其后每一行的的iou

ious = bbox_iou(image_pred_class[i].unsqueeze(0), image_pred_class[i+1:])

except ValueError:

break

except IndexError:

break

#Zero out all the detections that have IoU > treshhold

#把与第i行iou>nms_conf的认为是同一个目标的box,将其转成0

iou_mask = (ious < nms_conf).float().unsqueeze(1)

image_pred_class[i+1:] *= iou_mask

#把iou>nms_conf的移除掉

non_zero_ind = torch.nonzero(image_pred_class[:,4]).squeeze()

image_pred_class = image_pred_class[non_zero_ind].view(-1,7)

batch_ind = image_pred_class.new(image_pred_class.size(0), 1).fill_(ind) #Repeat the batch_id for as many detections of the class cls in the image

seq = batch_ind, image_pred_class

if not write:

output = torch.cat(seq,1) #沿着列方向,shape 1*8

write = True

else:

out = torch.cat(seq,1)

output = torch.cat((output,out)) #沿着行方向 shape n*8

try:

return output

except:

return 0