1、nn.Conv2D()输入参数数据格式不对
报错：TypeError: new() received an invalid combination of arguments - got (float, int, int, int), but expected one of:
完整报错：
File "G:/python/project/model/A2net.py", line 36, in <module>
model = A2Block(64)
File "G:/python/project/model/A2net.py", line 15, in __init__
self.dimension_reduction = nn.Conv2d(in_channels=inplanes, out_channels=inplanes/2, kernel_size=1, stride=1)
File "C:\Users\MSY\Anaconda3\lib\site-packages\torch\nn\modules\conv.py", line 297, in __init__
False, _pair(0), groups, bias)
File "C:\Users\MSY\Anaconda3\lib\site-packages\torch\nn\modules\conv.py", line 33, in __init__
out_channels, in_channels // groups, *kernel_size))
TypeError: new() received an invalid combination of arguments - got (float, int, int, int), but expected one of:
* (torch.device device)
* (torch.Storage storage)
* (Tensor other)
* (tuple of ints size, torch.device device)
* (object data, torch.device device)
问题定位：定位到报错行为：
self.dimension_reduction = nn.Conv2d(in_channels=inplanes, out_channels=inplanes/2, kernel_size=1, stride=1)
问题分析: 根据报错信息，是说本行代码包含有float的数据类型，通过分析可以看到，只有inplanes/2可能是float类型，由此想到在python3中n/2是带有小数点的，应该为n//2为整数。(由于一个粗心，报错一个如此尴尬的bug)
问题解决：将输出通道数inplanes/2改为inplanes//2完美解决。
2、make.sh 编译NMS遇到问题
报错:OSError: The CUDA lib64 path could not be located in /usr/lib64
完整报错：
Traceback (most recent call last):
File "build.py", line 59, in <module>
CUDA = locate_cuda()
File "build.py", line 54, in locate_cuda
raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v))
OSError: The CUDA lib64 path could not be located in /usr/lib64
问题定位：打开build.py（某些项目为setup.py）找到
cudaconfig = {'home': home, 'nvcc': nvcc,
'include': pjoin(home, 'include'),
'lib64': pjoin(home, 'lib64')}
问题分析：lib引用的问题
问题解决：将home, 'lib64'中的lib64改为lib完美解决
3、one of the variables needed for gradient computation has been modified by an inplace operation
报错：one of the variables needed for gradient computation has been modified by an inplace operation
完整报错：
Traceback (most recent call last):
File "train_test.py", line 454, in <module>
train()
File "train_test.py", line 327, in train
loss.backward()
File "/home/miao/anaconda3/lib/python3.6/site-packages/torch/tensor.py", line 93, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/home/miao/anaconda3/lib/python3.6/site-packages/torch/autograd/__init__.py", line 90, in backward
allow_unreachable=True) # allow_unreachable flag
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation
问题定位：这个bug报错并没有报明显的错误位置是最难过的，wwwwww~~~~~
问题分析：此问题是在测试运行网上关于一版Pytorch版本的SSD代码时，出现的，根据网上的错误解释，应该时Pytorch0.4版本和0.3版本的某些不一致造成的。该问题的常用解决方法时：
1：如果使用的是pytorch0.4.0版本，回退到pytorch0.3.0版本
2：如果有inreplace参数，设为False
3：报错原因是pytorch0.4.0版本后tensor不支持inplace操作了，所以把所有inplace错作去掉。
后在博客modified by an inplace operation中似乎找到了合适的答案.简单来说：x += 1 这种改成 x = x+1 原因：x+=1的值会直接在原值上面做更新，是inplace=True的情况，而后一种是先让x+1然后赋值给x,属于inplace=False
但是由于自己的代码较多，开始很难具体定位到哪个错误的位置，后来使用Beyond Compare(一款很棒的软件，强推~~~)与网上一版正确的代码比较，发现了错误。
x /= norm #（原本的错误代码）
In-place的具体解释可以参考。pytorch 学习笔记（二十二）：关于 inplace operation
问题解决: 将x /= norm #改为x = x / norm
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