本文整理匯總了Python中torch.utils.data.dataloader.numpy_type_map方法的典型用法代碼示例。如果您正苦於以下問題：Python dataloader.numpy_type_map方法的具體用法？Python dataloader.numpy_type_map怎麽用？Python dataloader.numpy_type_map使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類torch.utils.data.dataloader的用法示例。
在下文中一共展示了dataloader.numpy_type_map方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: gather
# 需要導入模塊: from torch.utils.data import dataloader [as 別名]
# 或者: from torch.utils.data.dataloader import numpy_type_map [as 別名]
def gather(outputs, target_device, dim=0):
r"""
Gathers variables from different GPUs on a specified device
(-1 means the CPU).
"""
error_msg = "outputs must contain tensors, numbers, dicts or lists; found {}"
def gather_map(outputs):
out = outputs[0]
elem_type = type(out)
if isinstance(out, Variable):
return Gather.apply(target_device, dim, *outputs)
if out is None:
return None
if isinstance(out, collections.Sequence):
return type(out)(map(gather_map, zip(*outputs)))
elif isinstance(out, collections.Mapping):
return {key: gather_map([d[key] for d in outputs]) for key in out}
elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
and elem_type.__name__ != 'string_':
elem = out
if elem_type.__name__ == 'ndarray':
# array of string classes and object
if re.search('[SaUO]', elem.dtype.str) is not None:
raise TypeError(error_msg.format(elem.dtype))
return Variable(torch.from_numpy(np.concatenate(outputs, dim)))
if elem.shape == (): # scalars
py_type = float if elem.dtype.name.startswith('float') else int
return Variable(numpy_type_map[elem.dtype.name](list(map(py_type, outputs))))
elif isinstance(out, int_classes):
return Variable(torch.LongTensor(outputs))
elif isinstance(out, float):
return Variable(torch.DoubleTensor(outputs))
elif isinstance(out, string_classes):
return outputs
raise TypeError((error_msg.format(elem_type)))
# Recursive function calls like this create reference cycles.
# Setting the function to None clears the refcycle.
try:
return gather_map(outputs)
finally:
gather_map = None 示例2: concat_collate
# 需要導入模塊: from torch.utils.data import dataloader [as 別名]
# 或者: from torch.utils.data.dataloader import numpy_type_map [as 別名]
def concat_collate(batch):
# type: (List[torch.Tensor]) -> torch.Tensor
"""
Puts each data field into a tensor stacking along the first dimension.
This is different to the default pytorch collate that stacks samples rather than
concatenating them.
:param batch: the input batch to be collated.
"""
error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
elem_type = type(batch[0])
if isinstance(batch[0], torch.Tensor):
out = None
if _use_shared_memory:
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum([x.numel() for x in batch])
storage = batch[0].storage()._new_shared(numel)
out = batch[0].new(storage)
return torch.cat(batch, 0, out=out)
elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
and elem_type.__name__ != 'string_':
elem = batch[0]
if elem_type.__name__ == 'ndarray':
# array of string classes and object
if re.search('[SaUO]', elem.dtype.str) is not None:
raise TypeError(error_msg.format(elem.dtype))
return torch.cat([torch.from_numpy(b) for b in batch], 0)
if elem.shape == (): # scalars
py_type = float if elem.dtype.name.startswith('float') else int
return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
elif isinstance(batch[0], int_classes):
return torch.LongTensor(batch)
elif isinstance(batch[0], float):
return torch.DoubleTensor(batch)
elif isinstance(batch[0], string_classes):
return batch
elif isinstance(batch[0], collections.Mapping):
return {key: concat_collate([d[key] for d in batch]) for key in batch[0]}
elif isinstance(batch[0], collections.Sequence):
transposed = zip(*batch)
return [concat_collate(samples) for samples in transposed]
raise TypeError((error_msg.format(type(batch[0])))) 示例3: collate
# 需要導入模塊: from torch.utils.data import dataloader [as 別名]
# 或者: from torch.utils.data.dataloader import numpy_type_map [as 別名]
def collate(batch):
r"""Puts each data field into a tensor with outer dimension batch size"""
error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
elem_type = type(batch[0])
if isinstance(batch[0], torch.Tensor):
out = None
if _use_shared_memory:
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum([x.numel() for x in batch])
storage = batch[0].storage()._new_shared(numel)
out = batch[0].new(storage)
return torch.stack(batch, 0, out=out)
elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
and elem_type.__name__ != 'string_':
elem = batch[0]
if elem_type.__name__ == 'ndarray':
# array of string classes and object
if re.search('[SaUO]', elem.dtype.str) is not None:
raise TypeError(error_msg.format(elem.dtype))
return torch.stack([torch.from_numpy(b) for b in batch], 0)
if elem.shape == (): # scalars
py_type = float if elem.dtype.name.startswith('float') else int
return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
elif isinstance(batch[0], int_classes):
return torch.LongTensor(batch)
elif isinstance(batch[0], float):
return torch.DoubleTensor(batch)
elif isinstance(batch[0], string_classes):
return batch
elif isinstance(batch[0], collections.Mapping):
res = {key: collate([d[key] for d in batch]) for key in batch[0] if key!='instance_mask'}
if 'instance_mask' in batch[0]:
max_obj = max([d['instance_mask'].shape[0] for d in batch])
instance_mask = torch.zeros(len(batch),max_obj,*(batch[0]['instance_mask'].shape[1:]))
for i in range(len(batch)):
num_obj = batch[i]['instance_mask'].shape[0]
instance_mask[i,:num_obj] = torch.from_numpy(batch[i]['instance_mask'])
res.update({'instance_mask':instance_mask})
return res
elif isinstance(batch[0], collections.Sequence):
transposed = zip(*batch)
return [collate(samples) for samples in transposed]
raise TypeError((error_msg.format(type(batch[0]))))