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I am using PyTorch. I am going to use the already trained model on multiple GPUs with CPU. how to do this task?
I tried on Anaconda 3 and pytorch with cpu only i dont have gpu
model = models.get_pose_net(config, is_train=False)
gpus = [int(i) for i in config.GPUS.split(',')]
model = torch.nn.DataParallel(model, device_ids=gpus).cuda()
print('Created model...')
print(model)
checkpoint = torch.load(config.MODEL.RESUME)
model.load_state_dict(checkpoint)
model.eval()
print('Loaded pretrained weights...')
the error i got is
AssertionError Traceback (most recent call last)
<ipython-input-15-bbfcd201d332> in <module>()
2 model = models.get_pose_net(config, is_train=False)
3 gpus = [int(i) for i in config.GPUS.split(',')]
----> 4 model = torch.nn.DataParallel(model, device_ids=gpus).cuda()
5 print('Created model...')
6 print(model)
C:\Users\psl\Anaconda3\lib\site-packages\torch\nn\modules\module.py in cuda(self, device)
258 Module: self
259 """
--> 260 return self._apply(lambda t: t.cuda(device))
261
262 def cpu(self):
C:\Users\psl\Anaconda3\lib\site-packages\torch\nn\modules\module.py in
_apply(self, fn) 185 def _apply(self, fn): 186 for module in self.children(): --> 187 module._apply(fn) 188 189 for param in self._parameters.values():
C:\Users\psl\Anaconda3\lib\site-packages\torch\nn\modules\module.py in _apply(self, fn)
185 def _apply(self, fn):
186 for module in self.children():
--> 187 module._apply(fn)
188
189 for param in self._parameters.values():
C:\Users\psl\Anaconda3\lib\site-packages\torch\nn\modules\module.py in _apply(self, fn)
191 # Tensors stored in modules are graph leaves, and we don't
192 # want to create copy nodes, so we have to unpack the data.
--> 193 param.data = fn(param.data)
194 if param._grad is not None:
195 param._grad.data = fn(param._grad.data)
C:\Users\psl\Anaconda3\lib\site-packages\torch\nn\modules\module.py in <lambda>(t)
258 Module: self
259 """
--> 260 return self._apply(lambda t: t.cuda(device))
261
262 def cpu(self):
C:\Users\psl\Anaconda3\lib\site-packages\torch\cuda\__init__.py in _lazy_init()
159 raise RuntimeError(
160 "Cannot re-initialize CUDA in forked subprocess. " + msg)
--> 161 _check_driver()
162 torch._C._cuda_init()
163 _cudart = _load_cudart()
C:\Users\psl\Anaconda3\lib\site-packages\torch\cuda\__init__.py in _check_driver()
80 Found no NVIDIA driver on your system. Please check that you
81 have an NVIDIA GPU and installed a driver from
---> 82 http://www.nvidia.com/Download/index.aspx""")
83 else:
84 # TODO: directly link to the alternative bin that needs install
AssertionError:
Found no NVIDIA driver on your system. Please check that you
have an NVIDIA GPU and installed a driver from
http://www.nvidia.com/Download/index.aspx
607
When loading a model on a GPU that was trained and saved on CPU, set the map_location argument in the torch. load() function to cuda:device_id . This loads the model to a given GPU device.
To load model weights, you need to create an instance of the same model first, and then load the parameters using load_state_dict() method. be sure to call model. eval() method before inferencing to set the dropout and batch normalization layers to evaluation mode.
A common PyTorch convention is to save these checkpoints using the .tar file extension. To load the models, first initialize the models and optimizers, then load the dictionary locally using torch.load() . From here, you can easily access the saved items by simply querying the dictionary as you would expect.
To force load the saved model onto cpu, use the following command.
torch.load('/path/to/saved/model', map_location='cpu')
In your case change it to
torch.load(config.MODEL.RESUME, map_location='cpu')
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