pytorch问题记录(1)

问题一

RuntimeError: cuda runtime error (59) : device-side assert triggered at /home/loop/pytorch-master/torch/lib/THC/generic/THCTensorMath.cu:15

参考文章 https://blog.csdn.net/u011276025/article/details/73826562
通过输出label的值发现，error出现的地方label中均有最大值。
所以可以判断为pytorch所设计的分类器的分类label为[0,max-1]，而true ground的标签值为[1,max]
所以可以通过修改label = (label-1).to(opt.device)

问题二

RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 1 and 3 in dimension 1 at /pytorch/aten/src/TH/generic/THTensorMath.cpp:3616

这种情况一般是由于图片并不是三通道造成的，可能是BW图像，也可能是PNG图像图像，所以就可以将图像调整为RGB格式既可。（不过还是要查一下哪张图片在作祟比较好…）

class MyDataset(Dataset):
    def __init__(self, image_paths, transforms=transforms):
        self.image_paths = image_paths
        self.transforms = transforms

    def __getitem__(self, index):
        image = Image.open(self.image_paths[index])
        image = image.convert('RGB')
        if self.transforms:
            image = self.transforms(image)
        return image

问题三

optim.lr_scheduler.ReduceLROnPlateau gives error value cannot be converted to type float without overflow: inf

这个问题是在scheduler对loss进行GPU上优化的错误，问题可能会有converted to type float, int and etc.
所以将loss转到cpu上进行优化即可。

   # after each epoch
   # Note that step should be called after validate()
   self.scheduler.step(loss.cpu().data.numpy())

问题四：

RuntimeError: Error(s) in loading state_dict for Missing key(s) in
state_dict: “fc.weight”, “fc.bias”.

像这种出现丢失key missing key
If you have partial state_dict, which is missing some keys you can do the following:

state = model.state_dict()
state.update(partial)
model.load_state_dict(state)

还有一种多出的key

RuntimeError: Error(s) in loading state_dict for Missing key(s) in
Unexpected key(s) in state_dict: “classifier.0.weight”,

解决办法

# original saved file with DataParallel
state_dict = torch.load('myfile.pth.tar')
# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
    name = k[7:] # remove `module.`
    new_state_dict[name] = v
# load params
model.load_state_dict(new_state_dict)

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