1. 问题一：pytroch-ssd : RuntimeError: Expected a 'cuda' device type for generator but found 'cpu'
Solution: File "C:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\sampler.py", line 133, in __iter__ yield from torch.randperm(n, generator=generator).tolist() . Click the link of the error report, navigate to the sampler.py file, find the location shown in Figure 1, modify lines 121 and 122, and change generator = troch.Generator() to generator = torch.Generator(device= 39;cuda').
figure 1
2. Question 2: IndexError: Caught IndexError in DataLoader worker process 0.
Solution: Set num_works to 0, parser.add_argument('--num_workers', default=0, type=int,help='Number of workers used in dataloading')
3. Question 3: When there is only one category, pay attention to
When there is only one category, you need to modify it to NUM_CLASSES= [('people')], and add a square bracket outside the brackets in the category.
4. 问题四：IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed
Solution: It means that there are no tags in some xml files, that is, there is no object, so such xml files need to be deleted.
5. 问题五：IndexError: invalid index of a 0-dim tensor. Use tensor.item() in Python or tensor.item＜T＞()
解决：由于pytorch版本不一致导致的问题，将所有类似报错中的.data[0]改为.item()
# 把data[0]全部改为.item()
self.losses[loss name] = losses [loss name] .data[0]
self.losses[loss name] = losses [loss name] .item()6. Question 6: StopIteration
Solution: Change 165 lines of code in train.py
images, targets = next(batch_iterator)Change to:
try:
images, targets = next(batch_iterator)
except StopIteration:
batch_iterator = iter(data_loader)
images, targets = next(batch_iterator)
7. Question 7: During training, LOSS is nan
Solution: You can try changing the learning rate in train.py from 0.001 to 1e-4
8. Question 8: When running eval.py, an error is reported at args = parser.parse_args().pytest errors may occur during the running process
Solution: Change the test_net in the last three lines of the eval.py file to set_net, and change the specific implementation of the method to set_net. When you right-click to run eval.py, it will change from the original run pytest in eval.py to run eval
9. Question 9: When running eval.py, an error is reported: TypeError: can only concatenate str(not "int") to str
Solution: Modify the code in eval.py
# 原代码
# dataset = MaizeDetection(args.voc_root, [('2007', set_type)],
# BaseTransform(300, dataset_mean),
# MaizeAnnotationTransform())
# 修改后
dataset = MaizeDetection(args.voc_root, set_type,
BaseTransform(300, dataset_mean),
MaizeAnnotationTransform())10. 问题十：RuntimeError: Legacy autograd function with non-static forward method is deprecated. Please use new-style autograd function with static forward method. (Example:
Solution: First modify the contents in detection.py according to http://t.csdnimg.cn/nQ4NQ, and then make the following modifications in ssd.py
# 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
# )
#修改为
if self.phase == "test":
output = self.detect.apply(self.num_classes, 0, 200, 0.01, 0.45,
loc.view(loc.size(0), -1, 4), # loc preds
self.softmax(conf.view(-1,
self.num_classes)), # conf preds
self.priors.type(type(x.data)) # default boxes
)
11. 问题十一：UserWarning: An output with one or more elements was resized since it had shape [80], which does not match the required output shape [84].
12. 问题十二：DeprecationWarning: elementwise comparison failed; this will raise an error in the future. if dets == []:
Solution: Replace if dets == []: with if len(dets) == 0:.
13. Question 13: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`.
Solution: Replace the np.bool that appears with bool.
Summary:During the experiment using pytorch-ssd, the data set format was converted in my ownhttp://t.csdnimg. cn/Xahl7The third part of this article; this article is just to record some errors encountered during the experiment and their solutions...
References:
SSD trains its own data set (pytorch version)_ssd trains its own data set pytorch-CSDN blog