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- # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
- """
- Misc functions, including distributed helpers.
-
- Mostly copy-paste from torchvision references.
- """
- import os
- import subprocess
- import time
- from collections import defaultdict, deque
- import datetime
- import pickle
- from packaging import version
- from typing import Optional, List
-
- import torch
- import torch.distributed as dist
- from torch import Tensor
-
- # needed due to empty tensor bug in pytorch and torchvision 0.5
- import torchvision
- if version.parse(torchvision.__version__) < version.parse('0.7'):
- from torchvision.ops import _new_empty_tensor
- from torchvision.ops.misc import _output_size
-
-
- class SmoothedValue(object):
- """Track a series of values and provide access to smoothed values over a
- window or the global series average.
- """
-
- def __init__(self, window_size=20, fmt=None):
- if fmt is None:
- fmt = "{median:.4f} ({global_avg:.4f})"
- self.deque = deque(maxlen=window_size)
- self.total = 0.0
- self.count = 0
- self.fmt = fmt
-
- def update(self, value, n=1):
- self.deque.append(value)
- self.count += n
- self.total += value * n
-
- def synchronize_between_processes(self):
- """
- Warning: does not synchronize the deque!
- """
- if not is_dist_avail_and_initialized():
- return
- t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
- dist.barrier()
- dist.all_reduce(t)
- t = t.tolist()
- self.count = int(t[0])
- self.total = t[1]
-
- @property
- def median(self):
- d = torch.tensor(list(self.deque))
- return d.median().item()
-
- @property
- def avg(self):
- d = torch.tensor(list(self.deque), dtype=torch.float32)
- return d.mean().item()
-
- @property
- def global_avg(self):
- return self.total / self.count
-
- @property
- def max(self):
- return max(self.deque)
-
- @property
- def value(self):
- return self.deque[-1]
-
- def __str__(self):
- return self.fmt.format(
- median=self.median,
- avg=self.avg,
- global_avg=self.global_avg,
- max=self.max,
- value=self.value)
-
-
- def all_gather(data):
- """
- Run all_gather on arbitrary picklable data (not necessarily tensors)
- Args:
- data: any picklable object
- Returns:
- list[data]: list of data gathered from each rank
- """
- world_size = get_world_size()
- if world_size == 1:
- return [data]
-
- # serialized to a Tensor
- buffer = pickle.dumps(data)
- storage = torch.ByteStorage.from_buffer(buffer)
- tensor = torch.ByteTensor(storage).to("cuda")
-
- # obtain Tensor size of each rank
- local_size = torch.tensor([tensor.numel()], device="cuda")
- size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
- dist.all_gather(size_list, local_size)
- size_list = [int(size.item()) for size in size_list]
- max_size = max(size_list)
-
- # receiving Tensor from all ranks
- # we pad the tensor because torch all_gather does not support
- # gathering tensors of different shapes
- tensor_list = []
- for _ in size_list:
- tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
- if local_size != max_size:
- padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
- tensor = torch.cat((tensor, padding), dim=0)
- dist.all_gather(tensor_list, tensor)
-
- data_list = []
- for size, tensor in zip(size_list, tensor_list):
- buffer = tensor.cpu().numpy().tobytes()[:size]
- data_list.append(pickle.loads(buffer))
-
- return data_list
-
-
- def reduce_dict(input_dict, average=True):
- """
- Args:
- input_dict (dict): all the values will be reduced
- average (bool): whether to do average or sum
- Reduce the values in the dictionary from all processes so that all processes
- have the averaged results. Returns a dict with the same fields as
- input_dict, after reduction.
- """
- world_size = get_world_size()
- if world_size < 2:
- return input_dict
- with torch.no_grad():
- names = []
- values = []
- # sort the keys so that they are consistent across processes
- for k in sorted(input_dict.keys()):
- names.append(k)
- values.append(input_dict[k])
- values = torch.stack(values, dim=0)
- dist.all_reduce(values)
- if average:
- values /= world_size
- reduced_dict = {k: v for k, v in zip(names, values)}
- return reduced_dict
-
-
- class MetricLogger(object):
- def __init__(self, delimiter="\t"):
- self.meters = defaultdict(SmoothedValue)
- self.delimiter = delimiter
-
- def update(self, **kwargs):
- for k, v in kwargs.items():
- if isinstance(v, torch.Tensor):
- v = v.item()
- assert isinstance(v, (float, int))
- self.meters[k].update(v)
-
- def __getattr__(self, attr):
- if attr in self.meters:
- return self.meters[attr]
- if attr in self.__dict__:
- return self.__dict__[attr]
- raise AttributeError("'{}' object has no attribute '{}'".format(
- type(self).__name__, attr))
-
- def __str__(self):
- loss_str = []
- for name, meter in self.meters.items():
- loss_str.append(
- "{}: {}".format(name, str(meter))
- )
- return self.delimiter.join(loss_str)
-
- def synchronize_between_processes(self):
- for meter in self.meters.values():
- meter.synchronize_between_processes()
-
- def add_meter(self, name, meter):
- self.meters[name] = meter
-
- def log_every(self, iterable, print_freq, header=None):
- i = 0
- if not header:
- header = ''
- start_time = time.time()
- end = time.time()
- iter_time = SmoothedValue(fmt='{avg:.4f}')
- data_time = SmoothedValue(fmt='{avg:.4f}')
- space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
- if torch.cuda.is_available():
- log_msg = self.delimiter.join([
- header,
- '[{0' + space_fmt + '}/{1}]',
- 'eta: {eta}',
- '{meters}',
- 'time: {time}',
- 'data: {data}',
- 'max mem: {memory:.0f}'
- ])
- else:
- log_msg = self.delimiter.join([
- header,
- '[{0' + space_fmt + '}/{1}]',
- 'eta: {eta}',
- '{meters}',
- 'time: {time}',
- 'data: {data}'
- ])
- MB = 1024.0 * 1024.0
- for obj in iterable:
- data_time.update(time.time() - end)
- yield obj
- iter_time.update(time.time() - end)
- if i % print_freq == 0 or i == len(iterable) - 1:
- eta_seconds = iter_time.global_avg * (len(iterable) - i)
- eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
- if torch.cuda.is_available():
- print(log_msg.format(
- i, len(iterable), eta=eta_string,
- meters=str(self),
- time=str(iter_time), data=str(data_time),
- memory=torch.cuda.max_memory_allocated() / MB))
- else:
- print(log_msg.format(
- i, len(iterable), eta=eta_string,
- meters=str(self),
- time=str(iter_time), data=str(data_time)))
- i += 1
- end = time.time()
- total_time = time.time() - start_time
- total_time_str = str(datetime.timedelta(seconds=int(total_time)))
- print('{} Total time: {} ({:.4f} s / it)'.format(
- header, total_time_str, total_time / len(iterable)))
-
-
- def get_sha():
- cwd = os.path.dirname(os.path.abspath(__file__))
-
- def _run(command):
- return subprocess.check_output(command, cwd=cwd).decode('ascii').strip()
- sha = 'N/A'
- diff = "clean"
- branch = 'N/A'
- try:
- sha = _run(['git', 'rev-parse', 'HEAD'])
- subprocess.check_output(['git', 'diff'], cwd=cwd)
- diff = _run(['git', 'diff-index', 'HEAD'])
- diff = "has uncommited changes" if diff else "clean"
- branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])
- except Exception:
- pass
- message = f"sha: {sha}, status: {diff}, branch: {branch}"
- return message
-
-
- def collate_fn(batch):
- batch = list(zip(*batch))
- batch[0] = nested_tensor_from_tensor_list(batch[0])
- return tuple(batch)
-
-
- def _max_by_axis(the_list):
- # type: (List[List[int]]) -> List[int]
- maxes = the_list[0]
- for sublist in the_list[1:]:
- for index, item in enumerate(sublist):
- maxes[index] = max(maxes[index], item)
- return maxes
-
-
- class NestedTensor(object):
- def __init__(self, tensors, mask: Optional[Tensor]):
- self.tensors = tensors
- self.mask = mask
-
- def to(self, device):
- # type: (Device) -> NestedTensor # noqa
- cast_tensor = self.tensors.to(device)
- mask = self.mask
- if mask is not None:
- assert mask is not None
- cast_mask = mask.to(device)
- else:
- cast_mask = None
- return NestedTensor(cast_tensor, cast_mask)
-
- def decompose(self):
- return self.tensors, self.mask
-
- def __repr__(self):
- return str(self.tensors)
-
-
- def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
- # TODO make this more general
- if tensor_list[0].ndim == 3:
- if torchvision._is_tracing():
- # nested_tensor_from_tensor_list() does not export well to ONNX
- # call _onnx_nested_tensor_from_tensor_list() instead
- return _onnx_nested_tensor_from_tensor_list(tensor_list)
-
- # TODO make it support different-sized images
- max_size = _max_by_axis([list(img.shape) for img in tensor_list])
- # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
- batch_shape = [len(tensor_list)] + max_size
- b, c, h, w = batch_shape
- dtype = tensor_list[0].dtype
- device = tensor_list[0].device
- tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
- mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
- for img, pad_img, m in zip(tensor_list, tensor, mask):
- pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
- m[: img.shape[1], :img.shape[2]] = False
- else:
- raise ValueError('not supported')
- return NestedTensor(tensor, mask)
-
-
- # _onnx_nested_tensor_from_tensor_list() is an implementation of
- # nested_tensor_from_tensor_list() that is supported by ONNX tracing.
- @torch.jit.unused
- def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
- max_size = []
- for i in range(tensor_list[0].dim()):
- max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64)
- max_size.append(max_size_i)
- max_size = tuple(max_size)
-
- # work around for
- # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
- # m[: img.shape[1], :img.shape[2]] = False
- # which is not yet supported in onnx
- padded_imgs = []
- padded_masks = []
- for img in tensor_list:
- padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
- padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
- padded_imgs.append(padded_img)
-
- m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
- padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
- padded_masks.append(padded_mask.to(torch.bool))
-
- tensor = torch.stack(padded_imgs)
- mask = torch.stack(padded_masks)
-
- return NestedTensor(tensor, mask=mask)
-
-
- def setup_for_distributed(is_master):
- """
- This function disables printing when not in master process
- """
- import builtins as __builtin__
- builtin_print = __builtin__.print
-
- def print(*args, **kwargs):
- force = kwargs.pop('force', False)
- if is_master or force:
- builtin_print(*args, **kwargs)
-
- __builtin__.print = print
-
-
- def is_dist_avail_and_initialized():
- if not dist.is_available():
- return False
- if not dist.is_initialized():
- return False
- return True
-
-
- def get_world_size():
- if not is_dist_avail_and_initialized():
- return 1
- return dist.get_world_size()
-
-
- def get_rank():
- if not is_dist_avail_and_initialized():
- return 0
- return dist.get_rank()
-
-
- def is_main_process():
- return get_rank() == 0
-
-
- def save_on_master(*args, **kwargs):
- if is_main_process():
- torch.save(*args, **kwargs)
-
-
- def init_distributed_mode(args):
- if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
- args.rank = int(os.environ["RANK"])
- args.world_size = int(os.environ['WORLD_SIZE'])
- args.gpu = int(os.environ['LOCAL_RANK'])
- elif 'SLURM_PROCID' in os.environ:
- args.rank = int(os.environ['SLURM_PROCID'])
- args.gpu = args.rank % torch.cuda.device_count()
- else:
- print('Not using distributed mode')
- args.distributed = False
- return
-
- args.distributed = True
-
- torch.cuda.set_device(args.gpu)
- args.dist_backend = 'nccl'
- print('| distributed init (rank {}): {}'.format(
- args.rank, args.dist_url), flush=True)
- torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
- world_size=args.world_size, rank=args.rank)
- torch.distributed.barrier()
- setup_for_distributed(args.rank == 0)
-
-
- @torch.no_grad()
- def accuracy(output, target, topk=(1,)):
- """Computes the precision@k for the specified values of k"""
- if target.numel() == 0:
- return [torch.zeros([], device=output.device)]
- maxk = max(topk)
- batch_size = target.size(0)
-
- _, pred = output.topk(maxk, 1, True, True)
- pred = pred.t()
- correct = pred.eq(target.view(1, -1).expand_as(pred))
-
- res = []
- for k in topk:
- correct_k = correct[:k].view(-1).float().sum(0)
- res.append(correct_k.mul_(100.0 / batch_size))
- return res
-
-
- def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
- # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
- """
- Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
- This will eventually be supported natively by PyTorch, and this
- class can go away.
- """
- if version.parse(torchvision.__version__) < version.parse('0.7'):
- if input.numel() > 0:
- return torch.nn.functional.interpolate(
- input, size, scale_factor, mode, align_corners
- )
-
- output_shape = _output_size(2, input, size, scale_factor)
- output_shape = list(input.shape[:-2]) + list(output_shape)
- return _new_empty_tensor(input, output_shape)
- else:
- return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
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