(tuple, param_ids)) torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised: TypeError: 'int' object is not iterable You can suppress this exception and fall back to eager by setting: import torch._dynamo torch._dynamo.config.suppress_errors = True During handling of the above ...
garymmchanged the title[onnx] Use '.repeat_interleave' will raise a error. 'torch._C.Value' object is not iterable.Oct 15, 2021 garymmaddedonnx-triagedtriaged by ONNX teamand removedonnx-needs-infoneeds information from the author / reporter before ONNX team can take actionlabelsOct 15...
In addition, this method will only cast the floating point parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When non_blocking is set, it tries to convert/move asynchronously with respect to ...
classSampler(object):r"""Base class for all Samplers. Every Sampler subclass has to provide an :meth:`__iter__` method, providing a way to iterate over indices of dataset elements, and a :meth:`__len__` method that returns the length of the returned iterators. .. note:: The :meth...
而后使用dataloader进行包裹,需要注意的是,dataloader是非必须的,例如我们通过其它的方法(method1)产生了batch 版的数据集(比如我们使用tf.data已经把原始的数据分batch了),则只使用dataset也是可以的,只要注意__getitem__的时候将数据转化为torch.tensor即可。 但是对于一些框架而言,例如pytorch-lightning是直接支持dataloade...
Should be an object returned from a call to state_dict(). state_dict()[source] Returns the state of the scheduler as a dict. It contains an entry for every variable in self.__dict__ which is not the optimizer. The learning rate lambda functions will only be saved if they are ...
RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the ‘spawn’ start method 原因:dataloader里面不能出现.cuda(),只能一直在cpu上运行。这是CUDA的基本局限,只能避开,目前无法解决。 解决方案:把num_workers设成1,或者去掉.cuda() ...
class _LRScheduler(object): def __init__(self, optimizer, last_epoch=-1, verbose=False): # Attach optimizer if not isinstance(optimizer, Optimizer): raise TypeError('{} is not an Optimizer'.format( type(optimizer).__name__)) self.optimizer = optimizer # Initialize epoch and base learnin...
return self.batch_sampler is not None @property def _index_sampler(self): if self._auto_collation: return self.batch_sampler else: return self.sampler class _BaseDataLoaderIter(object): ... def _reset(self, loader, first_iter=False): ...
classDataLoader(object): r"""Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. Arguments: dataset (Dataset): datasetfromwhich to load the data. batch_size (int, optional): how many samples per batch to load ...