Question

在尝试在计算机的CPU而不是GPU中运行代码时遇到RunTimeError。该代码最初来自此GitHub项目-IBD: Interpretable Basis Decomposition for Visual Explanation。这是一个研究项目。我尝试将CUDA设置为false，并在此网站上查看了其他解决方案。

GPU = False               # running on GPU is highly suggested
CLEAN = False             # set to "True" if you want to clean the temporary large files after generating result
APP = "classification"    # Do not change! mode choide: "classification", "imagecap", "vqa". Currently "imagecap" and "vqa" are not supported.
CATAGORIES = ["object", "part"]   # Do not change! concept categories that are chosen to detect: "object", "part", "scene", "material", "texture", "color"

CAM_THRESHOLD = 0.5                 # the threshold used for CAM visualization
FONT_PATH = "components/font.ttc"   # font file path
FONT_SIZE = 26                      # font size
SEG_RESOLUTION = 7                  # the resolution of cam map
BASIS_NUM = 7                       # In decomposition, this is to decide how many concepts are used to interpret the weight vector of a class.

这是错误：

Traceback (most recent call last):
  File "test.py", line 22, in <module>
    model = loadmodel()
  File "/home/joshuayun/Desktop/IBD/loader/model_loader.py", line 48, in loadmodel
    checkpoint = torch.load(settings.MODEL_FILE)
  File "/home/joshuayun/.local/lib/python3.6/site-packages/torch/serialization.py", line 387, in load
    return _load(f, map_location, pickle_module, **pickle_load_args)
  File "/home/joshuayun/.local/lib/python3.6/site-packages/torch/serialization.py", line 574, in _load
    result = unpickler.load()
  File "/home/joshuayun/.local/lib/python3.6/site-packages/torch/serialization.py", line 537, in persistent_load
    deserialized_objects[root_key] = restore_location(obj, location)
  File "/home/joshuayun/.local/lib/python3.6/site-packages/torch/serialization.py", line 119, in default_restore_location
    result = fn(storage, location)
  File "/home/joshuayun/.local/lib/python3.6/site-packages/torch/serialization.py", line 95, in _cuda_deserialize
    device = validate_cuda_device(location)
  File "/home/joshuayun/.local/lib/python3.6/site-packages/torch/serialization.py", line 79, in validate_cuda_device
    raise RuntimeError('Attempting to deserialize object on a CUDA '
RuntimeError: Attempting to deserialize object on a CUDA device but 
  torch.cuda.is_available() is False. If you are running on a CPU-only machine, 
  please use torch.load with map_location='cpu' to map your storages to the CPU.

Answer 1

如果您没有GPU，请对加载模型使用 map_location = torch.device（'cpu'）

my_model = net.load_state_dict(torch.load('classifier.pt', map_location=torch.device('cpu')))

Answer 2

“如果您在仅使用 CPU 的机器上运行，请使用带有 map_location=torch.device('cpu') 的 torch.load 将您的存储映射到 CPU。”

model = torch.load('model/pytorch_resnet50.pth',map_location ='cpu')

Answer 3

给出一个较小的答案。为了解决这个问题，您可以在load()文件中更改名为serialization.py的函数的参数。它存储在：./site-package/torch/serialization.py

写：

def load(f, map_location='cpu', pickle_module=pickle, **pickle_load_args):

代替：

def load(f, map_location=None, pickle_module=pickle, **pickle_load_args):

希望有帮助。

Answer 4

我遇到了同样的问题，而不是修改昨天运行良好的现有代码，首先我检查了我的GPU是否空闲

nvidia-smi

我可以看到，它的利用不足，因此作为传统解决方案，我关闭了笔记本电脑，然后重新启动，它就可以正常工作了。

（我要记住的一点是，它较早就可以工作，并且我没有更改代码中的任何内容，因此一旦重新启动它就可以正常工作，并且我能够使用GPU）

Answer 5

在陈述问题时，提示您正在尝试在非cuda机器上使用cuda-model。请注意错误消息的详细信息-please use torch.load with map_location='cpu' to map your storages to the CPU。当我尝试在仅cpu的计算机上加载（从检查点）预训练的模型时，我遇到了类似的问题。该模型是在cuda机器上训练的，因此无法正确加载。将map_location='cpu'参数添加到load方法后，一切正常。

Answer 6

您可以在加载时使用torch.load的map_location参数重新映射张量位置。

在以下存储库中，在文件“ test.py”中，model = loadmodel（）调用model_loader.py文件以使用torch.load（）加载模型。

这只会映射来自GPU0的存储，请添加map_location：

str

在model_loader.py文件中，在调用torch.load（）函数的任何地方添加map_location = {'cuda：0'：'cpu'}。

Answer 7

出于某种原因，即使您的机器有 GPU，portainer 也会发生这种情况。一个粗略的解决方案是重新启动它。如果您在部署后更改容器的状态（例如，您在容器运行时更改重新启动策略）通常会发生这种情况，这让我认为这是一些更重要的问题。

RuntimeError：尝试在CUDA设备上反序列化对象

7 个答案: