utils.py

# Copyright (c) 2018-2019, NVIDIA CORPORATION
# Copyright (c) 2017-      Facebook, Inc
#
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# modification, are permitted provided that the following conditions are met:
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# * Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
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#   this software without specific prior written permission.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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import math
import os
import numpy as np
import torch
import shutil
import signal
import torch.distributed as dist


class Checkpointer:
    def __init__(self, last_filename, checkpoint_dir="./", keep_last_n=0):
        self.last_filename = last_filename
        self.checkpoints = []
        self.checkpoint_dir = checkpoint_dir
        self.keep_last_n = keep_last_n

    def cleanup(self):
        to_delete = self.checkpoints[: -self.keep_last_n]
        self.checkpoints = self.checkpoints[-self.keep_last_n :]
        for f in to_delete:
            full_path = os.path.join(self.checkpoint_dir, f)
            os.remove(full_path)

    def get_full_path(self, filename):
        return os.path.join(self.checkpoint_dir, filename)

    def save_checkpoint(
        self,
        state,
        is_best,
        filename,
    ):
        if torch.distributed.is_initialized() and torch.distributed.get_rank() != 0:
            assert False

        full_path = self.get_full_path(filename)

        print("SAVING {}".format(full_path))
        torch.save(state, full_path)
        self.checkpoints.append(filename)

        shutil.copyfile(
            full_path, self.get_full_path(self.last_filename)
        )

        if is_best:
            shutil.copyfile(
                full_path, self.get_full_path("model_best.pth.tar")
            )

        self.cleanup()


def timed_generator(gen):
    start = time.time()
    for g in gen:
        end = time.time()
        t = end - start
        yield g, t
        start = time.time()


def timed_function(f):
    def _timed_function(*args, **kwargs):
        start = time.time()
        ret = f(*args, **kwargs)
        return ret, time.time() - start

    return _timed_function


def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    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].float().sum()
        res.append(correct_k.mul_(100.0 / batch_size))
    return res


def reduce_tensor(tensor):
    rt = tensor.clone().detach()
    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
    rt /= (
        torch.distributed.get_world_size() if torch.distributed.is_initialized() else 1
    )
    return rt


def first_n(n, generator):
    for i, d in zip(range(n), generator):
        yield d


class TimeoutHandler:
    def __init__(self, sig=signal.SIGTERM):
        self.sig = sig
        self.device = torch.device("cuda")

    @property
    def interrupted(self):
        if not dist.is_initialized():
            return self._interrupted

        interrupted = torch.tensor(self._interrupted).int().to(self.device)
        dist.broadcast(interrupted, 0)
        interrupted = bool(interrupted.item())
        return interrupted

    def __enter__(self):
        self._interrupted = False
        self.released = False
        self.original_handler = signal.getsignal(self.sig)

        def master_handler(signum, frame):
            self.release()
            self._interrupted = True
            print(f"Received SIGTERM")

        def ignoring_handler(signum, frame):
            self.release()
            print("Received SIGTERM, ignoring")

        rank = dist.get_rank() if dist.is_initialized() else 0
        if rank == 0:
            signal.signal(self.sig, master_handler)
        else:
            signal.signal(self.sig, ignoring_handler)
        return self

    def __exit__(self, type, value, tb):
        self.release()

    def release(self):
        if self.released:
            return False
        signal.signal(self.sig, self.original_handler)
        self.released = True
        return True


def calc_ips(batch_size, time):
    world_size = (
        torch.distributed.get_world_size() if torch.distributed.is_initialized() else 1
    )
    tbs = world_size * batch_size
    return tbs / time