追溯 pytorch 中已弃用的警告
Tracing back deprecated warning in pytorch
我正在使用这里的代码在我的数据上训练 yolov3:
https://github.com/cfotache/pytorch_custom_yolo_training/
但是我收到了这个烦人的弃用警告
Warning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. (expandTensors at /pytorch/aten/src/ATen/native/IndexingUtils.h:20)
我尝试使用 python3 -W ignore train.py
我尝试添加:
import warnings
warnings.filterwarnings('ignore')
但警告仍然存在。
我在 Whosebug 上找到了这段代码,它在警告时打印堆栈,
import traceback
import warnings
import sys
def warn_with_traceback(message, category, filename, lineno, file=None, line=None):
log = file if hasattr(file,'write') else sys.stderr
traceback.print_stack(file=log)
log.write(warnings.formatwarning(message, category, filename, lineno, line))
warnings.showwarning = warn_with_traceback
这是我得到的:
File "/content/pytorch_custom_yolo_training/train.py", line 102, in <module>
loss = model(imgs, targets)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/content/pytorch_custom_yolo_training/models.py", line 267, in forward
x, *losses = module[0](x, targets)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/content/pytorch_custom_yolo_training/models.py", line 203, in forward
loss_x = self.mse_loss(x[mask], tx[mask])
File "/usr/lib/python3.6/warnings.py", line 99, in _showwarnmsg
msg.file, msg.line)
File "/content/pytorch_custom_yolo_training/train.py", line 29, in warn_with_traceback
traceback.print_stack(file=log)
/pytorch/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
转到堆栈中提到的文件和函数,我没有找到任何 uint8
。
我该如何解决问题甚至停止收到这些警告?
找到问题了。
行:loss_x = self.mse_loss(x[mask], tx[mask])
mask
变量是一个 ByteTensor
,已弃用。只是将其替换为 BoolTensor
这适用于我的情况:添加
obj_mask = obj_mask.type(torch.BoolTensor)
noobj_mask = noobj_mask.type(torch.BoolTensor)
之前
loss_x = self.mse_loss(x[obj_mask], tx[obj_mask])
在in models.py
.
这个很好用
obj_mask = obj_mask.bool()
noobj_mask = noobj_mask.bool()
我正在使用这里的代码在我的数据上训练 yolov3: https://github.com/cfotache/pytorch_custom_yolo_training/
但是我收到了这个烦人的弃用警告
Warning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. (expandTensors at /pytorch/aten/src/ATen/native/IndexingUtils.h:20)
我尝试使用 python3 -W ignore train.py
我尝试添加:
import warnings
warnings.filterwarnings('ignore')
但警告仍然存在。
我在 Whosebug 上找到了这段代码,它在警告时打印堆栈,
import traceback
import warnings
import sys
def warn_with_traceback(message, category, filename, lineno, file=None, line=None):
log = file if hasattr(file,'write') else sys.stderr
traceback.print_stack(file=log)
log.write(warnings.formatwarning(message, category, filename, lineno, line))
warnings.showwarning = warn_with_traceback
这是我得到的:
File "/content/pytorch_custom_yolo_training/train.py", line 102, in <module>
loss = model(imgs, targets)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/content/pytorch_custom_yolo_training/models.py", line 267, in forward
x, *losses = module[0](x, targets)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/content/pytorch_custom_yolo_training/models.py", line 203, in forward
loss_x = self.mse_loss(x[mask], tx[mask])
File "/usr/lib/python3.6/warnings.py", line 99, in _showwarnmsg
msg.file, msg.line)
File "/content/pytorch_custom_yolo_training/train.py", line 29, in warn_with_traceback
traceback.print_stack(file=log)
/pytorch/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
转到堆栈中提到的文件和函数,我没有找到任何 uint8
。
我该如何解决问题甚至停止收到这些警告?
找到问题了。
行:loss_x = self.mse_loss(x[mask], tx[mask])
mask
变量是一个 ByteTensor
,已弃用。只是将其替换为 BoolTensor
这适用于我的情况:添加
obj_mask = obj_mask.type(torch.BoolTensor)
noobj_mask = noobj_mask.type(torch.BoolTensor)
之前
loss_x = self.mse_loss(x[obj_mask], tx[obj_mask])
在in models.py
.
这个很好用
obj_mask = obj_mask.bool()
noobj_mask = noobj_mask.bool()