init commit of samurai
This commit is contained in:
Cheng-Yen Yang
468
lib/utils/misc.py
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468
lib/utils/misc.py
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@@ -0,0 +1,468 @@
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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"""
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Misc functions, including distributed helpers.
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Mostly copy-paste from torchvision references.
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"""
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import os
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import subprocess
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import time
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from collections import defaultdict, deque
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import datetime
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import pickle
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from typing import Optional, List
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import torch
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import torch.distributed as dist
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from torch import Tensor
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# needed due to empty tensor bug in pytorch and torchvision 0.5
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import torchvision
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vers = torchvision.__version__.split('.')
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if int(vers[0]) <= 0 and int(vers[1]) < 7:
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from torchvision.ops import _new_empty_tensor
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from torchvision.ops.misc import _output_size
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class SmoothedValue(object):
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"""Track a series of values and provide access to smoothed values over a
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window or the global series average.
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"""
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def __init__(self, window_size=20, fmt=None):
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if fmt is None:
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fmt = "{median:.4f} ({global_avg:.4f})"
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self.deque = deque(maxlen=window_size)
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self.total = 0.0
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self.count = 0
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self.fmt = fmt
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def update(self, value, n=1):
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self.deque.append(value)
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self.count += n
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self.total += value * n
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def synchronize_between_processes(self):
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"""
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Warning: does not synchronize the deque!
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"""
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if not is_dist_avail_and_initialized():
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return
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t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
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dist.barrier()
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dist.all_reduce(t)
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t = t.tolist()
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self.count = int(t[0])
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self.total = t[1]
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@property
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def median(self):
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d = torch.tensor(list(self.deque))
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return d.median().item()
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@property
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def avg(self):
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d = torch.tensor(list(self.deque), dtype=torch.float32)
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return d.mean().item()
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@property
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def global_avg(self):
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return self.total / self.count
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@property
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def max(self):
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return max(self.deque)
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@property
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def value(self):
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return self.deque[-1]
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def __str__(self):
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return self.fmt.format(
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median=self.median,
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avg=self.avg,
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global_avg=self.global_avg,
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max=self.max,
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value=self.value)
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def all_gather(data):
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"""
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Run all_gather on arbitrary picklable data (not necessarily tensors)
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Args:
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data: any picklable object
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Returns:
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list[data]: list of data gathered from each rank
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"""
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world_size = get_world_size()
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if world_size == 1:
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return [data]
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# serialized to a Tensor
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buffer = pickle.dumps(data)
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storage = torch.ByteStorage.from_buffer(buffer)
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tensor = torch.ByteTensor(storage).to("cuda")
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# obtain Tensor size of each rank
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local_size = torch.tensor([tensor.numel()], device="cuda")
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size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
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dist.all_gather(size_list, local_size)
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size_list = [int(size.item()) for size in size_list]
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max_size = max(size_list)
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# receiving Tensor from all ranks
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# we pad the tensor because torch all_gather does not support
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# gathering tensors of different shapes
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tensor_list = []
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for _ in size_list:
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tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
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if local_size != max_size:
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padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
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tensor = torch.cat((tensor, padding), dim=0)
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dist.all_gather(tensor_list, tensor)
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data_list = []
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for size, tensor in zip(size_list, tensor_list):
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buffer = tensor.cpu().numpy().tobytes()[:size]
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data_list.append(pickle.loads(buffer))
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return data_list
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def reduce_dict(input_dict, average=True):
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"""
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Args:
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input_dict (dict): all the values will be reduced
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average (bool): whether to do average or sum
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Reduce the values in the dictionary from all processes so that all processes
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have the averaged results. Returns a dict with the same fields as
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input_dict, after reduction.
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"""
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world_size = get_world_size()
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if world_size < 2:
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return input_dict
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with torch.no_grad():
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names = []
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values = []
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# sort the keys so that they are consistent across processes
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for k in sorted(input_dict.keys()):
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names.append(k)
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values.append(input_dict[k])
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values = torch.stack(values, dim=0)
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dist.all_reduce(values)
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if average:
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values /= world_size
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reduced_dict = {k: v for k, v in zip(names, values)}
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return reduced_dict
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class MetricLogger(object):
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def __init__(self, delimiter="\t"):
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self.meters = defaultdict(SmoothedValue)
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self.delimiter = delimiter
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def update(self, **kwargs):
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for k, v in kwargs.items():
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if isinstance(v, torch.Tensor):
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v = v.item()
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assert isinstance(v, (float, int))
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self.meters[k].update(v)
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def __getattr__(self, attr):
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if attr in self.meters:
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return self.meters[attr]
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if attr in self.__dict__:
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return self.__dict__[attr]
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raise AttributeError("'{}' object has no attribute '{}'".format(
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type(self).__name__, attr))
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def __str__(self):
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loss_str = []
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for name, meter in self.meters.items():
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loss_str.append(
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"{}: {}".format(name, str(meter))
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)
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return self.delimiter.join(loss_str)
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def synchronize_between_processes(self):
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for meter in self.meters.values():
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meter.synchronize_between_processes()
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def add_meter(self, name, meter):
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self.meters[name] = meter
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def log_every(self, iterable, print_freq, header=None):
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i = 0
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if not header:
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header = ''
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start_time = time.time()
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end = time.time()
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iter_time = SmoothedValue(fmt='{avg:.4f}')
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data_time = SmoothedValue(fmt='{avg:.4f}')
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space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
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if torch.cuda.is_available():
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log_msg = self.delimiter.join([
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header,
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'[{0' + space_fmt + '}/{1}]',
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'eta: {eta}',
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'{meters}',
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'time: {time}',
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'data: {data}',
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'max mem: {memory:.0f}'
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])
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else:
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log_msg = self.delimiter.join([
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header,
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'[{0' + space_fmt + '}/{1}]',
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'eta: {eta}',
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'{meters}',
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'time: {time}',
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'data: {data}'
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])
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MB = 1024.0 * 1024.0
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for obj in iterable:
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data_time.update(time.time() - end)
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yield obj
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iter_time.update(time.time() - end)
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if i % print_freq == 0 or i == len(iterable) - 1:
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eta_seconds = iter_time.global_avg * (len(iterable) - i)
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eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
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if torch.cuda.is_available():
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print(log_msg.format(
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i, len(iterable), eta=eta_string,
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meters=str(self),
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time=str(iter_time), data=str(data_time),
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memory=torch.cuda.max_memory_allocated() / MB))
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else:
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print(log_msg.format(
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i, len(iterable), eta=eta_string,
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meters=str(self),
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time=str(iter_time), data=str(data_time)))
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i += 1
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end = time.time()
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total_time = time.time() - start_time
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total_time_str = str(datetime.timedelta(seconds=int(total_time)))
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print('{} Total time: {} ({:.4f} s / it)'.format(
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header, total_time_str, total_time / len(iterable)))
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def get_sha():
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cwd = os.path.dirname(os.path.abspath(__file__))
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def _run(command):
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return subprocess.check_output(command, cwd=cwd).decode('ascii').strip()
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sha = 'N/A'
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diff = "clean"
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branch = 'N/A'
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try:
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sha = _run(['git', 'rev-parse', 'HEAD'])
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subprocess.check_output(['git', 'diff'], cwd=cwd)
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diff = _run(['git', 'diff-index', 'HEAD'])
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diff = "has uncommited changes" if diff else "clean"
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branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])
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except Exception:
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pass
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message = f"sha: {sha}, status: {diff}, branch: {branch}"
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return message
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def collate_fn(batch):
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batch = list(zip(*batch))
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batch[0] = nested_tensor_from_tensor_list(batch[0])
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return tuple(batch)
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def _max_by_axis(the_list):
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# type: (List[List[int]]) -> List[int]
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maxes = the_list[0] # get the first one
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for sublist in the_list[1:]: # [h,w,3]
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for index, item in enumerate(sublist): # index: 0,1,2
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maxes[index] = max(maxes[index], item) # compare current max with the other elements in the whole
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return maxes
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class NestedTensor(object):
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def __init__(self, tensors, mask: Optional[Tensor]):
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self.tensors = tensors
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self.mask = mask
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def to(self, device):
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# type: (Device) -> NestedTensor # noqa
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cast_tensor = self.tensors.to(device)
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mask = self.mask
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if mask is not None:
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assert mask is not None
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cast_mask = mask.to(device)
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else:
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cast_mask = None
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return NestedTensor(cast_tensor, cast_mask)
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def decompose(self):
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return self.tensors, self.mask
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def __repr__(self):
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return str(self.tensors)
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def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
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# TODO make this more general
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if tensor_list[0].ndim == 3:
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if torchvision._is_tracing():
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# nested_tensor_from_tensor_list() does not export well to ONNX
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# call _onnx_nested_tensor_from_tensor_list() instead
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return _onnx_nested_tensor_from_tensor_list(tensor_list)
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# TODO make it support different-sized images
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max_size = _max_by_axis([list(img.shape) for img in tensor_list]) # [[3,h1,w1], [3,h2,w2], [3,h3,w3], ...]
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# min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
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batch_shape = [len(tensor_list)] + max_size # ()
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b, c, h, w = batch_shape
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dtype = tensor_list[0].dtype
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device = tensor_list[0].device
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tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
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mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
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for img, pad_img, m in zip(tensor_list, tensor, mask):
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pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) # copy valid regions of the images to the largest padded base.
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m[: img.shape[1], :img.shape[2]] = False
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else:
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raise ValueError('not supported')
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return NestedTensor(tensor, mask)
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# _onnx_nested_tensor_from_tensor_list() is an implementation of
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# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
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@torch.jit.unused
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def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
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max_size = []
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for i in range(tensor_list[0].dim()):
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max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64)
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max_size.append(max_size_i)
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max_size = tuple(max_size)
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# work around for
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# pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
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# m[: img.shape[1], :img.shape[2]] = False
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# which is not yet supported in onnx
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padded_imgs = []
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padded_masks = []
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for img in tensor_list:
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padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
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padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
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padded_imgs.append(padded_img)
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m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
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padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
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padded_masks.append(padded_mask.to(torch.bool))
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tensor = torch.stack(padded_imgs)
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mask = torch.stack(padded_masks)
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return NestedTensor(tensor, mask=mask)
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def setup_for_distributed(is_master):
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"""
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This function disables printing when not in master process
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"""
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import builtins as __builtin__
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builtin_print = __builtin__.print
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def print(*args, **kwargs):
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force = kwargs.pop('force', False)
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if is_master or force:
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builtin_print(*args, **kwargs)
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__builtin__.print = print
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def is_dist_avail_and_initialized():
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if not dist.is_available():
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return False
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if not dist.is_initialized():
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return False
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return True
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def get_world_size():
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if not is_dist_avail_and_initialized():
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return 1
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return dist.get_world_size()
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def get_rank():
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if not is_dist_avail_and_initialized():
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return 0
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return dist.get_rank()
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def is_main_process():
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return get_rank() == 0
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def save_on_master(*args, **kwargs):
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if is_main_process():
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torch.save(*args, **kwargs)
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def init_distributed_mode(args):
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if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
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args.rank = int(os.environ["RANK"])
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args.world_size = int(os.environ['WORLD_SIZE'])
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args.gpu = int(os.environ['LOCAL_RANK'])
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elif 'SLURM_PROCID' in os.environ:
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args.rank = int(os.environ['SLURM_PROCID'])
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args.gpu = args.rank % torch.cuda.device_count()
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else:
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print('Not using distributed mode')
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args.distributed = False
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return
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args.distributed = True
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torch.cuda.set_device(args.gpu)
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args.dist_backend = 'nccl'
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print('| distributed init (rank {}): {}'.format(
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args.rank, args.dist_url), flush=True)
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torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
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world_size=args.world_size, rank=args.rank)
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torch.distributed.barrier()
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setup_for_distributed(args.rank == 0)
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@torch.no_grad()
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def accuracy(output, target, topk=(1,)):
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"""Computes the precision@k for the specified values of k"""
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if target.numel() == 0:
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return [torch.zeros([], device=output.device)]
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maxk = max(topk)
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batch_size = target.size(0)
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_, pred = output.topk(maxk, 1, True, True)
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pred = pred.t()
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correct = pred.eq(target.view(1, -1).expand_as(pred))
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res = []
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for k in topk:
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correct_k = correct[:k].view(-1).float().sum(0)
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res.append(correct_k.mul_(100.0 / batch_size))
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return res
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def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
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# type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
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"""
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Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
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This will eventually be supported natively by PyTorch, and this
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class can go away.
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"""
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if float(torchvision.__version__[:3]) < 0.7:
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||||
if input.numel() > 0:
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return torch.nn.functional.interpolate(
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input, size, scale_factor, mode, align_corners
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)
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output_shape = _output_size(2, input, size, scale_factor)
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output_shape = list(input.shape[:-2]) + list(output_shape)
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return _new_empty_tensor(input, output_shape)
|
||||
else:
|
||||
return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
|
||||
Reference in New Issue
Block a user