utils.py

# Copyright (c) Meta Platforms, Inc. and affiliates.

# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.


import os
import math
import time
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict

from pathlib import Path

import torch
import torch.distributed as dist
from torch._six import inf


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 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 v is None:
                continue
            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'
        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():
                    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)))


class WandbLogger(object):
    def __init__(self, args):
        self.args = args

        try:
            import wandb
            self._wandb = wandb
        except ImportError:
            raise ImportError(
                "To use the Weights and Biases Logger please install wandb."
                "Run `pip install wandb` to install it."
            )

        # Initialize a W&B run 
        if self._wandb.run is None:
            self._wandb.init(
                project=args.project,
                config=args
            )

    def log_epoch_metrics(self, metrics, commit=True):
        """
        Log train/test metrics onto W&B.
        """
        # Log number of model parameters as W&B summary
        self._wandb.summary['n_parameters'] = metrics.get('n_parameters', None)
        metrics.pop('n_parameters', None)

        # Log current epoch
        self._wandb.log({'epoch': metrics.get('epoch')}, commit=False)
        metrics.pop('epoch')

        for k, v in metrics.items():
            if 'train' in k:
                self._wandb.log({f'Global Train/{k}': v}, commit=False)
            elif 'test' in k:
                self._wandb.log({f'Global Test/{k}': v}, commit=False)

        self._wandb.log({})

    def log_checkpoints(self):
        output_dir = self.args.output_dir
        model_artifact = self._wandb.Artifact(
            self._wandb.run.id + "_model", type="model"
        )

        model_artifact.add_dir(output_dir)
        self._wandb.log_artifact(model_artifact, aliases=["latest", "best"])

    def set_steps(self):
        # Set global training step
        self._wandb.define_metric('Rank-0 Batch Wise/*', step_metric='Rank-0 Batch Wise/global_train_step')
        # Set epoch-wise step
        self._wandb.define_metric('Global Train/*', step_metric='epoch')
        self._wandb.define_metric('Global Test/*', step_metric='epoch')


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 args.dist_on_itp:
        args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
        args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
        args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
        args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
        os.environ['LOCAL_RANK'] = str(args.gpu)
        os.environ['RANK'] = str(args.rank)
        os.environ['WORLD_SIZE'] = str(args.world_size)
        # ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"]
    elif '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()

        os.environ['RANK'] = str(args.rank)
        os.environ['LOCAL_RANK'] = str(args.gpu)
        os.environ['WORLD_SIZE'] = str(args.world_size)
    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 {}): {}, gpu {}'.format(
        args.rank, args.dist_url, args.gpu), 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)


def load_state_dict(model, state_dict, prefix='', ignore_missing="relative_position_index"):
    missing_keys = []
    unexpected_keys = []
    error_msgs = []
    # copy state_dict so _load_from_state_dict can modify it
    metadata = getattr(state_dict, '_metadata', None)
    state_dict = state_dict.copy()
    if metadata is not None:
        state_dict._metadata = metadata

    def load(module, prefix=''):
        local_metadata = {} if metadata is None else metadata.get(
            prefix[:-1], {})
        module._load_from_state_dict(
            state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
        for name, child in module._modules.items():
            if child is not None:
                load(child, prefix + name + '.')

    load(model, prefix=prefix)

    warn_missing_keys = []
    ignore_missing_keys = []
    for key in missing_keys:
        keep_flag = True
        for ignore_key in ignore_missing.split('|'):
            if ignore_key in key:
                keep_flag = False
                break
        if keep_flag:
            warn_missing_keys.append(key)
        else:
            ignore_missing_keys.append(key)

    missing_keys = warn_missing_keys

    if len(missing_keys) > 0:
        print("Weights of {} not initialized from pretrained model: {}".format(
            model.__class__.__name__, missing_keys))
    if len(unexpected_keys) > 0:
        print("Weights from pretrained model not used in {}: {}".format(
            model.__class__.__name__, unexpected_keys))
    if len(ignore_missing_keys) > 0:
        print("Ignored weights of {} not initialized from pretrained model: {}".format(
            model.__class__.__name__, ignore_missing_keys))
    if len(error_msgs) > 0:
        print('\n'.join(error_msgs))


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 = 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 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


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

def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler, model_ema=None):
    output_dir = Path(args.output_dir)
    epoch_name = str(epoch)
    checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name)]
    for checkpoint_path in checkpoint_paths:
        to_save = {
            'model': model_without_ddp.state_dict(),
            'optimizer': optimizer.state_dict(),
            'epoch': epoch,
            'scaler': loss_scaler.state_dict(),
            'args': args,
        }

        if model_ema is not None:
            to_save['model_ema'] = get_state_dict(model_ema)

        save_on_master(to_save, checkpoint_path)
    
    if is_main_process() and isinstance(epoch, int):
        to_del = epoch - args.save_ckpt_num * args.save_ckpt_freq
        old_ckpt = output_dir / ('checkpoint-%s.pth' % to_del)
        if os.path.exists(old_ckpt):
            os.remove(old_ckpt)


def auto_load_model(args, model, model_without_ddp, optimizer, loss_scaler, model_ema=None):
    output_dir = Path(args.output_dir)
    if args.auto_resume and len(args.resume) == 0:
        import glob
        all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*.pth'))
        latest_ckpt = -1
        for ckpt in all_checkpoints:
            t = ckpt.split('-')[-1].split('.')[0]
            if t.isdigit():
                latest_ckpt = max(int(t), latest_ckpt)
        if latest_ckpt >= 0:
            args.resume = os.path.join(output_dir, 'checkpoint-%d.pth' % latest_ckpt)
        print("Auto resume checkpoint: %s" % args.resume)

    if args.resume:
        if args.resume.startswith('https'):
            checkpoint = torch.hub.load_state_dict_from_url(
                args.resume, map_location='cpu', check_hash=True)
        else:
            checkpoint = torch.load(args.resume, map_location='cpu')
        model_without_ddp.load_state_dict(checkpoint['model'])
        print("Resume checkpoint %s" % args.resume)
        if 'optimizer' in checkpoint and 'epoch' in checkpoint:
            optimizer.load_state_dict(checkpoint['optimizer'])
            if not isinstance(checkpoint['epoch'], str): # does not support resuming with 'best', 'best-ema'
                args.start_epoch = checkpoint['epoch'] + 1
            else:
                assert args.eval, 'Does not support resuming with checkpoint-best'
            if hasattr(args, 'model_ema') and args.model_ema:
                if 'model_ema' in checkpoint.keys():
                    model_ema.ema.load_state_dict(checkpoint['model_ema'])
                else:
                    model_ema.ema.load_state_dict(checkpoint['model'])
            if 'scaler' in checkpoint:
                loss_scaler.load_state_dict(checkpoint['scaler'])
            print("With optim & sched!")




def _check_bn(module, flag):
    if issubclass(module.__class__, torch.nn.modules.batchnorm._BatchNorm):
        flag[0] = True


def check_bn(model):
    flag = [False]
    model.apply(lambda module: _check_bn(module, flag))
    return flag[0]


def reset_bn(module):
    if issubclass(module.__class__, torch.nn.modules.batchnorm._BatchNorm):
        module.running_mean = torch.zeros_like(module.running_mean)
        module.running_var = torch.ones_like(module.running_var)


def _get_momenta(module, momenta):
    if issubclass(module.__class__, torch.nn.modules.batchnorm._BatchNorm):
        momenta[module] = module.momentum


def _set_momenta(module, momenta):
    if issubclass(module.__class__, torch.nn.modules.batchnorm._BatchNorm):
        module.momentum = momenta[module]


def bn_update(loader, model, use_amp=False):
    """
        BatchNorm buffers update (if any).
        Performs 1 epochs to estimate buffers average using train dataset.
        :param loader: train dataset loader for buffers average estimation.
        :param model: model being update
        :return: None
    """
    if not check_bn(model):
        return
    model.train()
    momenta = {}
    model.apply(reset_bn)
    model.apply(lambda module: _get_momenta(module, momenta))
    n = 0
    for input, _ in loader:
        input = input.cuda()
        b = input.data.size(0)

        momentum = b / (n + b)
        for module in momenta.keys():
            module.momentum = momentum

        if use_amp:
            with torch.cuda.amp.autocast():
                model(input)
        else:
            model(input)

        n += b

    model.apply(lambda module: _set_momenta(module, momenta))