SSD预测错误调试：RuntimeError: index_select(): functions with out=... arguments don‘t support automatic dif
一：错误
运行SSD-pytorch版本时，运行test.py时，遇到这种错误：
RuntimeError: index_select(): functions with out=... arguments don't support automatic differentiation, but one of the arguments requires grad.
二：分析原因
（1）ssd.py中使用detect类创建对象self.detect进行检测时出现错误，寻找detect类
（2）在layers文件夹下找到detection.py文件，找到detect类
在detect类中，Detect类继承自Function类，在pytorch1.3以上版本规定forward方法为静态方法，在进行预测时使用的是1.7版本，所以会出现报错。
三：方法
方法一：更改pyTorch版本，改为1.2版本以下，这种方法不现实
方法二：改代码
首先在ssd.py中修改代码，直接调用detect类的forward函数
if self.phase == "test":
'''
output = self.detect(
loc.view(loc.size(0), -1, 4), # loc preds
self.softmax(conf.view(conf.size(0), -1,
self.num_classes)), # conf preds
self.priors.type(type(x.data)) # default boxes
)
'''
#####改为以下代码
output = self.detect.forward(
loc.view(loc.size(0), -1, 4), # loc preds
self.softmax(conf.view(conf.size(0), -1,
self.num_classes)), # conf preds
self.priors.type(type(x.data))
)然后在box_utils.py文件中的nms函数作修改，不然会出错RuntimeError: index_select(): functions with out=… arguments don’t support automatic differentiation, but one of the arguments requires grad.
count = 0
while idx.numel() > 0:
i = idx[-1] # index of current largest val
# keep.append(i)
keep[count] = i
count += 1
if idx.size(0) == 1:
break
idx = idx[:-1] # remove kept element from view
# load bboxes of next highest vals
'''
torch.index_select(x1, 0, idx, out=xx1)
torch.index_select(y1, 0, idx, out=yy1)
torch.index_select(x2, 0, idx, out=xx2)
torch.index_select(y2, 0, idx, out=yy2)
# store element-wise max with next highest score
xx1 = torch.clamp(xx1, min=x1[i])
yy1 = torch.clamp(yy1, min=y1[i])
xx2 = torch.clamp(xx2, max=x2[i])
yy2 = torch.clamp(yy2, max=y2[i])
w.resize_as_(xx2)
h.resize_as_(yy2)
w = xx2 - xx1
h = yy2 - yy1
# check sizes of xx1 and xx2.. after each iteration
w = torch.clamp(w, min=0.0)
h = torch.clamp(h, min=0.0)
inter = w*h
# IoU = i / (area(a) + area(b) - i)
rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
union = (rem_areas - inter) + area[i]
IoU = inter/union # store result in iou
# keep only elements with an IoU <= overlap
idx = idx[IoU.le(overlap)]
'''
###################添加代码###################
#否者出错RuntimeError: index_select(): functions with out=... arguments don't support automatic differentiation, but one of the arguments requires grad.
idx= torch.autograd.Variable(idx, requires_grad=False)
idx = idx.data
x1 = torch.autograd.Variable(x1, requires_grad=False)
x1 = x1.data
y1 = torch.autograd.Variable(y1, requires_grad=False)
y1 = y1.data
x2 = torch.autograd.Variable(x2, requires_grad=False)
x2 = x2.data
y2 = torch.autograd.Variable(y2, requires_grad=False)
y2 = y2.data
######################################添加代码##############################
torch.index_select(x1, 0, idx, out=xx1)
torch.index_select(y1, 0, idx, out=yy1)
torch.index_select(x2, 0, idx, out=xx2)
torch.index_select(y2, 0, idx, out=yy2)
# store element-wise max with next highest score
xx1 = torch.clamp(xx1, min=x1[i])
yy1 = torch.clamp(yy1, min=y1[i])
xx2 = torch.clamp(xx2, max=x2[i])
yy2 = torch.clamp(yy2, max=y2[i])
w.resize_as_(xx2)
h.resize_as_(yy2)
w = xx2 - xx1
h = yy2 - yy1
# check sizes of xx1 and xx2.. after each iteration
w = torch.clamp(w, min=0.0)
h = torch.clamp(h, min=0.0)
inter = w*h
# IoU = i / (area(a) + area(b) - i)
########################添加代码############################
#否者出错RuntimeError: index_select(): functions with out=... arguments don't support automatic differentiation, but one of the arguments requires grad.
area = torch.autograd.Variable(area, requires_grad=False)
area = area.data
idx= torch.autograd.Variable(idx, requires_grad=False)
idx = idx.data
######################################添加代码###########################
rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
union = (rem_areas - inter) + area[i]
IoU = inter/union # store result in iou
# keep only elements with an IoU <= overlap
'''4.保留iou值小于nms阈值的预测边界框的索引'''
idx = idx[IoU.le(overlap)]#保留交并比小于阈值的预测边界框的id
return keep, count
参考出处
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