utils.py

# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Misc functions, including distributed helpers.

Mostly copy-paste from torchvision references.
"""
import io
import os
import time
from collections import defaultdict, deque
import datetime

import torch
import torch.distributed as dist

from pathlib import Path

import logging
from termcolor import colored
import sys

from torch._six import inf
import numpy as np
import math
import multiprocessing

import socket 
import random


def dist_init(port=2333):
    if multiprocessing.get_start_method(allow_none=True) != 'spawn':
        multiprocessing.set_start_method('spawn', force=True)
    
    rank = int(os.environ['SLURM_PROCID'])
    world_size = os.environ['SLURM_NTASKS']
    node_list = os.environ['SLURM_NODELIST']
    num_gpus = torch.cuda.device_count()
    gpu_id = rank % num_gpus
    torch.cuda.set_device(gpu_id)
    
    if '[' in node_list:
        beg = node_list.find('[')
        pos1 = node_list.find('-', beg)
        if pos1 < 0:
            pos1 = 1000
        pos2 = node_list.find(',', beg)
        if pos2 < 0:
            pos2 = 1000
        node_list = node_list[:min(pos1, pos2)].replace('[', '')
    addr = node_list[8:].replace('-', '.')
    
    os.environ['MASTER_PORT'] = port
    os.environ['MASTER_ADDR'] = addr
    os.environ['WORLD_SIZE'] = world_size
    os.environ['RANK'] = str(rank)
    
    torch.distributed.init_process_group(backend='nccl')
    
    return rank, int(world_size), gpu_id

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'])
        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)
    elif 'SLURM_PROCID' in os.environ:
        print('use SLURM_PROCID')
        args.distributed = True
        
        args.rank, args.world_size, args.local_rank = dist_init(port=args.port)
        args.gpu = args.rank % torch.cuda.device_count()
        args.num_gpu = 1
        args.device = 'cuda:%d' % args.local_rank
        args.world_size = torch.distributed.get_world_size()
        args.rank = torch.distributed.get_rank()

    else:
        print('Not using distributed mode')
        args.distributed = False
        return
    torch.distributed.barrier()
    setup_for_distributed(args.rank == 0)


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)

class FormatterNoInfo(logging.Formatter):
    def __init__(self, fmt='%(levelname)s: %(message)s'):
        logging.Formatter.__init__(self, fmt)

    def format(self, record):
        if record.levelno == logging.INFO:
            return str(record.getMessage())
        return logging.Formatter.format(self, record)

def setup_default_logging(default_level=logging.INFO, log_path=''):
    console_handler = logging.StreamHandler()
    console_handler.setFormatter(FormatterNoInfo())
    # logging.root.addHandler(console_handler)
    logging.root.setLevel(default_level)
    if log_path:
        file_handler = logging.handlers.RotatingFileHandler(log_path, maxBytes=(1024 ** 2 * 2), backupCount=3)
        file_formatter = logging.Formatter("%(asctime)s - %(name)20s: [%(levelname)8s] - %(message)s")
        file_handler.setFormatter(file_formatter)
        logging.root.addHandler(file_handler)
        
def get_logger(filename, verbosity=1, name=None):
    level_dict = {0: logging.DEBUG, 1: logging.INFO, 2: logging.WARNING}
    formatter = logging.Formatter(
        "[%(asctime)s][%(filename)s][line:%(lineno)d][%(levelname)s] %(message)s"
    )
    logger = logging.getLogger(name)
    logger.setLevel(level_dict[verbosity])
    fh = logging.FileHandler(filename, "a+")
    fh.setFormatter(formatter)
    logger.addHandler(fh)
    sh = logging.StreamHandler()
    sh.setFormatter(formatter)
    logger.addHandler(sh)
    return logger



def create_logger(output_dir, dist_rank=0, name=''):
    # create logger
    logger = logging.getLogger(name)
    logger.setLevel(logging.INFO)
    logger.propagate = False

    # create formatter
    fmt = '[%(asctime)s %(name)s] (%(filename)s %(lineno)d): %(levelname)s %(message)s'
    color_fmt = colored('[%(asctime)s %(name)s]', 'green') + \
                colored('(%(filename)s %(lineno)d)', 'yellow') + ': %(levelname)s %(message)s'

    # create console handlers for master process
    if dist_rank == 0:
        console_handler = logging.StreamHandler(sys.stdout)
        console_handler.setLevel(logging.DEBUG)
        console_handler.setFormatter(
            logging.Formatter(fmt=color_fmt, datefmt='%Y-%m-%d %H:%M:%S'))
        logger.addHandler(console_handler)

    # create file handlers
    file_handler = logging.FileHandler(os.path.join(output_dir, f'log_rank{dist_rank}.txt'), mode='a')
    file_handler.setLevel(logging.DEBUG)
    file_handler.setFormatter(logging.Formatter(fmt=fmt, datefmt='%Y-%m-%d %H:%M:%S'))
    logger.addHandler(file_handler)

    return logger

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,logger=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'
        log_msg = [
            header,
            '[{0' + space_fmt + '}/{1}]',
            'eta: {eta}',
            '{meters}',
            'time: {time}',
            'data: {data}'
        ]
        if torch.cuda.is_available():
            log_msg.append('max mem: {memory:.0f}')
        log_msg = self.delimiter.join(log_msg)
        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():
                    logger.info(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:
                    logger.info(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)))
        logger.info('{} Total time: {} ({:.4f} s / it)'.format(
            header, total_time_str, total_time / len(iterable)))


def _load_checkpoint_for_ema(model_ema, checkpoint):
    """
    Workaround for ModelEma._load_checkpoint to accept an already-loaded object
    """
    mem_file = io.BytesIO()
    torch.save(checkpoint, mem_file)
    mem_file.seek(0)
    model_ema._load_checkpoint(mem_file)


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)



class record_config():
    def __init__(self, args):
        now = datetime.datetime.now().strftime('%Y-%m-%d-%H:%M:%S')
        today = datetime.date.today()

        self.args = args
        self.job_dir = Path(args.output_dir)

        def _make_dir(path):
            if not os.path.exists(path):
                os.makedirs(path)

        _make_dir(self.job_dir)

        config_dir = self.job_dir / 'config.txt'
        #if not os.path.exists(config_dir):
        if args.resume != None:
            with open(config_dir, 'a') as f:
                f.write(now + '\n\n')
                for arg in vars(args):
                    f.write('{}: {}\n'.format(arg, getattr(args, arg)))
                f.write('\n')
        else:
            with open(config_dir, 'w') as f:
                f.write(now + '\n\n')
                for arg in vars(args):
                    f.write('{}: {}\n'.format(arg, getattr(args, arg)))
                f.write('\n')

class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.value = 0
        self.ave = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.value = val
        self.sum += val * n
        self.count += n
        self.ave = self.sum / self.count


def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)

    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.view(1, -1).expand_as(pred))
    correct_k = correct[:1].view(-1).float()

    return correct_k

def ampscaler_get_grad_norm(parameters, norm_type: float = 2.0) -> torch.Tensor:
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    parameters = [p for p in parameters if p.grad is not None]
    norm_type = float(norm_type)
    if len(parameters) == 0:
        return torch.tensor(0.)
    device = parameters[0].grad.device
    if norm_type == inf:
        total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
    else:
        total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(),
                                                        norm_type).to(device) for p in parameters]), norm_type)
    return total_norm


class NativeScalerWithGradNormCount:
    state_dict_key = "amp_scaler"

    def __init__(self):
        self._scaler = torch.cuda.amp.GradScaler()

    def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
        self._scaler.scale(loss).backward(create_graph=create_graph)
        if update_grad:
            if clip_grad is not None:
                assert parameters is not None
                self._scaler.unscale_(optimizer)  # unscale the gradients of optimizer's assigned params in-place
                norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
            else:
                self._scaler.unscale_(optimizer)
                norm = ampscaler_get_grad_norm(parameters)
            self._scaler.step(optimizer)
            self._scaler.update()
        else:
            norm = None
        return norm

    def state_dict(self):
        return self._scaler.state_dict()

    def load_state_dict(self, state_dict):
        self._scaler.load_state_dict(state_dict)



def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0,
                     start_warmup_value=0, warmup_steps=-1):
    warmup_schedule = np.array([])
    warmup_iters = warmup_epochs * niter_per_ep
    if warmup_steps > 0:
        warmup_iters = warmup_steps
    print("Set warmup steps = %d" % warmup_iters)
    if warmup_epochs > 0:
        warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)

    iters = np.arange(epochs * niter_per_ep - warmup_iters)
    schedule = np.array(
        [final_value + 0.5 * (base_value - final_value) * (1 + math.cos(math.pi * i / (len(iters)))) for i in iters])

    schedule = np.concatenate((warmup_schedule, schedule))

    assert len(schedule) == epochs * niter_per_ep
    return schedule



class MultiEpochsDataLoader(torch.utils.data.DataLoader):
    
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._DataLoader__initialized = False
        self.batch_sampler = _RepeatSampler(self.batch_sampler)
        self._DataLoader__initialized = True
        self.iterator = super().__iter__()

    def __len__(self):
        return len(self.batch_sampler.sampler)

    def __iter__(self):
        for i in range(len(self)):
            yield next(self.iterator)


class _RepeatSampler(object):
    """ Sampler that repeats forever.

    Args:
        sampler (Sampler)
    """

    def __init__(self, sampler):
        self.sampler = sampler

    def __iter__(self):
        while True:
            yield from iter(self.sampler)