utils.py

# --------------------------------------------------------
# --------------------------------------------------------
# Based on BEiT, timm, DINO and DeiT code bases
# https://github.com/microsoft/unilm/tree/master/beit
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit
# https://github.com/facebookresearch/dino
# --------------------------------------------------------'
import datetime
import io
import json
import math
import os
import random
import subprocess
import time
from collections import defaultdict, deque
from pathlib import Path

import numpy as np
import torch
import torch.distributed as dist
from tensorboardX import SummaryWriter
from timm.utils import get_state_dict
from torch import inf
from torch.utils.data._utils.collate import default_collate


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 min(self):
        return min(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,
            min=self.min,
            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} ({min:.4f} -- {max:.4f})')
        data_time = SmoothedValue(fmt='{avg:.4f} ({min:.4f} -- {max:.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 TensorboardLogger(object):

    def __init__(self, log_dir):
        self.writer = SummaryWriter(logdir=log_dir)
        self.step = 0

    def set_step(self, step=None):
        if step is not None:
            self.step = step
        else:
            self.step += 1

    def update(self, head='scalar', step=None, **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.writer.add_scalar(head + "/" + k, v,
                                   self.step if step is None else step)

    def flush(self):
        self.writer.flush()


def seed_worker(worker_id):
    worker_seed = torch.initial_seed() % 2**32
    np.random.seed(worker_seed)
    random.seed(worker_seed)


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)


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 'SLURM_PROCID' in os.environ:
        args.rank = int(os.environ['SLURM_PROCID'])
        args.gpu = int(os.environ['SLURM_LOCALID'])
        args.world_size = int(os.environ['SLURM_NTASKS'])
        os.environ['RANK'] = str(args.rank)
        os.environ['LOCAL_RANK'] = str(args.gpu)
        os.environ['WORLD_SIZE'] = str(args.world_size)

        node_list = os.environ['SLURM_NODELIST']
        addr = subprocess.getoutput(
            f'scontrol show hostname {node_list} | head -n1')
        if 'MASTER_ADDR' not in os.environ:
            os.environ['MASTER_ADDR'] = addr
    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'])
    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.cuda.empty_cache()
    torch.distributed.barrier()
    assert torch.distributed.is_initialized()
    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)
    if loss_scaler is not None:
        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)
    else:
        client_state = {'epoch': epoch}
        if model_ema is not None:
            client_state['model_ema'] = get_state_dict(model_ema)
        model.save_checkpoint(
            save_dir=args.output_dir,
            tag="checkpoint-%s" % epoch_name,
            client_state=client_state)


def auto_load_model(args,
                    model,
                    model_without_ddp,
                    optimizer,
                    loss_scaler,
                    model_ema=None):
    output_dir = Path(args.output_dir)
    if loss_scaler is not None:
        # torch.amp
        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'])
                args.start_epoch = checkpoint['epoch'] + 1
                if hasattr(args, 'model_ema') and args.model_ema:
                    _load_checkpoint_for_ema(model_ema,
                                             checkpoint['model_ema'])
                if 'scaler' in checkpoint:
                    loss_scaler.load_state_dict(checkpoint['scaler'])
                print("With optim & sched!")
    else:
        # deepspeed, only support '--auto_resume'.
        if args.auto_resume:
            import glob
            all_checkpoints = glob.glob(
                os.path.join(output_dir, 'checkpoint-*'))
            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' % latest_ckpt)
                print("Auto resume checkpoint: %d" % latest_ckpt)
                _, client_states = model.load_checkpoint(
                    args.output_dir, tag='checkpoint-%d' % latest_ckpt)
                if 'epoch' in client_states:
                    args.start_epoch = client_states['epoch'] + 1
                if model_ema is not None:
                    if args.model_ema:
                        _load_checkpoint_for_ema(model_ema,
                                                 client_states['model_ema'])


def create_ds_config(args):
    args.deepspeed_config = os.path.join(args.output_dir,
                                         "deepspeed_config.json")
    with open(args.deepspeed_config, mode="w") as writer:
        ds_config = {
            "train_batch_size":
            args.batch_size * args.update_freq * get_world_size(),
            "train_micro_batch_size_per_gpu":
            args.batch_size,
            "steps_per_print":
            1000,
            "gradient_clipping":
            0.0 if args.clip_grad is None else args.clip_grad,
            "optimizer": {
                "type": "Adam",
                "adam_w_mode": True,
                "params": {
                    "lr": args.lr,
                    "weight_decay": args.weight_decay,
                    "bias_correction": True,
                    "betas": [0.9, 0.999],
                    "eps": 1e-8
                }
            },
            "fp16": {
                "enabled": True,
                "loss_scale": 0,
                "initial_scale_power": 7,
                "loss_scale_window": 128
            }
        }

        writer.write(json.dumps(ds_config, indent=2))


def multiple_samples_collate(batch, fold=False):
    """
    Collate function for repeated augmentation. Each instance in the batch has
    more than one sample.
    Args:
        batch (tuple or list): data batch to collate.
    Returns:
        (tuple): collated data batch.
    """
    inputs, labels, video_idx, extra_data = zip(*batch)
    inputs = [item for sublist in inputs for item in sublist]
    labels = [item for sublist in labels for item in sublist]
    video_idx = [item for sublist in video_idx for item in sublist]
    inputs, labels, video_idx, extra_data = (
        default_collate(inputs),
        default_collate(labels),
        default_collate(video_idx),
        default_collate(extra_data),
    )
    if fold:
        return [inputs], labels, video_idx, extra_data
    else:
        return inputs, labels, video_idx, extra_data


def multiple_pretrain_samples_collate(batch, fold=False):
    """
    Collate function for repeated augmentation. Each instance in the batch has
    more than one sample.
    Args:
        batch (tuple or list): data batch to collate.
    Returns:
        (tuple): collated data batch.
    """
    process_data, encoder_mask, decoder_mask = zip(*batch)

    process_data = [item for sublist in process_data for item in sublist]
    encoder_mask = [item for sublist in encoder_mask for item in sublist]
    decoder_mask = [item for sublist in decoder_mask for item in sublist]
    process_data, encoder_mask, decoder_mask = (
        default_collate(process_data),
        default_collate(encoder_mask),
        default_collate(decoder_mask),
    )
    if fold:
        return [process_data], encoder_mask, decoder_mask
    else:
        return process_data, encoder_mask, decoder_mask