import collections
import torch
from torch._six import string_classes
def _to_hours_mins_secs(time_taken):
mins, secs = divmod(time_taken, 60)
hours, mins = divmod(mins, 60)
return hours, mins, secs
def convert_tensor(input_, device=None):
if torch.is_tensor(input_):
if device:
input_ = input_.to(device=device)
return input_
elif isinstance(input_, string_classes):
return input_
elif isinstance(input_, collections.Mapping):
return {k: convert_tensor(sample, device=device) for k, sample in input_.items()}
elif isinstance(input_, collections.Sequence):
return [convert_tensor(sample, device=device) for sample in input_]
else:
raise TypeError(("input must contain tensors, dicts or lists; found {}"
.format(type(input_))))
def to_onehot(indices, num_classes):
onehot = torch.zeros(indices.size(0), num_classes, device=indices.device)
return onehot.scatter_(1, indices.unsqueeze(1), 1)
