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|
|
| import os |
| import torch |
| from torch.autograd import Variable |
| import errno |
| import torch.distributed as dist |
| import math |
| from functools import reduce |
| def make_folder(path, version): |
| if not os.path.exists(os.path.join(path, version)): |
| print(os.path.join(path, version)) |
| os.makedirs(os.path.join(path, version)) |
|
|
|
|
| def tensor2var(x, grad=False): |
| if torch.cuda.is_available(): |
| x = x.cuda() |
| return Variable(x, requires_grad=grad) |
|
|
| def var2tensor(x): |
| return x.data.cpu() |
|
|
| def var2numpy(x): |
| return x.data.cpu().numpy() |
|
|
| def denorm(x): |
| out = (x + 1) / 2 |
| return out.clamp_(0, 1) |
|
|
| def mkdir_p(dirname): |
| """ Like "mkdir -p", make a dir recursively, but do nothing if the dir exists |
| Args: |
| dirname(str): |
| """ |
| assert dirname is not None |
| if dirname == '' or os.path.isdir(dirname): |
| return |
| try: |
| os.makedirs(dirname) |
| except OSError as e: |
| if e.errno != errno.EEXIST: |
| raise e |
|
|
|
|
| def skipShardSplit(aList, drop_last=False, num_replicas=None, rank=None): |
| if not isinstance(aList, list) and not isinstance(aList, tuple): |
| aList = List |
|
|
| if num_replicas is None: |
| num_replicas = dist.get_world_size() if dist.is_initialized() else 1 |
| if rank is None: |
| rank = dist.get_rank() if dist.is_initialized() else 0 |
|
|
| num_replicas = num_replicas |
| rank = rank |
| drop_last = drop_last |
|
|
| if drop_last: |
| aList = aList[0: (len(aList) // num_replicas) * num_replicas] |
|
|
| |
| aList = aList[rank::num_replicas] |
|
|
| return aList |
|
|
| def mixb2a(a,b): |
| if len(b) > len(a): |
| a,b = b,a |
| if len(b) == 0: |
| return a |
| chunk_num = (len(b)) |
| a_chunk = splitIntoChunk(a, chunk_num) |
| b_chunk = list(map(lambda x:[x],b)) |
| return reduce(lambda x, y: x+y, [_a+_b for _a,_b in zip(a_chunk, b_chunk)]) |
|
|
| def splitIntoChunk(aList, chunk_num): |
| return [aList[math.ceil(k * (len(aList) / chunk_num)):math.ceil((k + 1) * (len(aList) / chunk_num)):] for k in range(chunk_num)] |
