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mmpretrain.engine.hooks.precise_bn_hook 源代码

# Copyright (c) OpenMMLab. All rights reserved.
# Adapted from https://github.com/facebookresearch/pycls/blob/f8cd962737e33ce9e19b3083a33551da95c2d9c0/pycls/core/net.py  # noqa: E501
# Original licence: Copyright (c) 2019 Facebook, Inc under the Apache License 2.0  # noqa: E501

import itertools
import logging
from typing import List, Optional, Sequence, Union

import mmengine
import torch
import torch.nn as nn
from mmengine.hooks import Hook
from mmengine.logging import print_log
from mmengine.model import is_model_wrapper
from mmengine.runner import EpochBasedTrainLoop, IterBasedTrainLoop, Runner
from mmengine.utils import ProgressBar
from torch.functional import Tensor
from torch.nn import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.modules.instancenorm import _InstanceNorm
from torch.utils.data import DataLoader

from mmpretrain.registry import HOOKS

DATA_BATCH = Optional[Sequence[dict]]


def scaled_all_reduce(tensors: List[Tensor], num_gpus: int) -> List[Tensor]:
    """Performs the scaled all_reduce operation on the provided tensors.

    The input tensors are modified in-place. Currently supports only the sum
    reduction operator. The reduced values are scaled by the inverse size of
    the process group.

    Args:
        tensors (List[torch.Tensor]): The tensors to process.
        num_gpus (int): The number of gpus to use
    Returns:
        List[torch.Tensor]: The processed tensors.
    """
    # There is no need for reduction in the single-proc case
    if num_gpus == 1:
        return tensors
    # Queue the reductions
    reductions = []
    for tensor in tensors:
        reduction = torch.distributed.all_reduce(tensor, async_op=True)
        reductions.append(reduction)
    # Wait for reductions to finish
    for reduction in reductions:
        reduction.wait()
    # Scale the results
    for tensor in tensors:
        tensor.mul_(1.0 / num_gpus)
    return tensors


@torch.no_grad()
def update_bn_stats(
        model: nn.Module,
        loader: DataLoader,
        num_samples: int = 8192,
        logger: Optional[Union[logging.Logger, str]] = None) -> None:
    """Computes precise BN stats on training data.

    Args:
        model (nn.module): The model whose bn stats will be recomputed.
        loader (DataLoader): PyTorch dataloader._dataloader
        num_samples (int): The number of samples to update the bn stats.
            Defaults to 8192.
        logger (logging.Logger or str, optional): If the type of logger is
        ``logging.Logger``, we directly use logger to log messages.
            Some special loggers are:
            - "silent": No message will be printed.
            - "current": Use latest created logger to log message.
            - other str: Instance name of logger. The corresponding logger
            will log message if it has been created, otherwise will raise a
            `ValueError`.
            - None: The `print()` method will be used to print log messages.
    """
    if is_model_wrapper(model):
        model = model.module

    # get dist info
    rank, world_size = mmengine.dist.get_dist_info()
    # Compute the number of mini-batches to use, if the size of dataloader is
    # less than num_iters, use all the samples in dataloader.
    num_iter = num_samples // (loader.batch_size * world_size)
    num_iter = min(num_iter, len(loader))
    # Retrieve the BN layers
    bn_layers = [
        m for m in model.modules()
        if m.training and isinstance(m, (_BatchNorm))
    ]
    if len(bn_layers) == 0:
        print_log('No BN found in model', logger=logger, level=logging.WARNING)
        return
    print_log(
        f'{len(bn_layers)} BN found, run {num_iter} iters...', logger=logger)

    # Finds all the other norm layers with training=True.
    other_norm_layers = [
        m for m in model.modules()
        if m.training and isinstance(m, (_InstanceNorm, GroupNorm))
    ]
    if len(other_norm_layers) > 0:
        print_log(
            'IN/GN stats will not be updated in PreciseHook.',
            logger=logger,
            level=logging.INFO)

    # Initialize BN stats storage for computing
    # mean(mean(batch)) and mean(var(batch))
    running_means = [torch.zeros_like(bn.running_mean) for bn in bn_layers]
    running_vars = [torch.zeros_like(bn.running_var) for bn in bn_layers]
    # Remember momentum values
    momentums = [bn.momentum for bn in bn_layers]
    # Set momentum to 1.0 to compute BN stats that reflect the current batch
    for bn in bn_layers:
        bn.momentum = 1.0
    # Average the BN stats for each BN layer over the batches
    if rank == 0:
        prog_bar = ProgressBar(num_iter)

    for data in itertools.islice(loader, num_iter):
        data = model.data_preprocessor(data, False)
        model(**data)

        for i, bn in enumerate(bn_layers):
            running_means[i] += bn.running_mean / num_iter
            running_vars[i] += bn.running_var / num_iter
        if rank == 0:
            prog_bar.update()

    # Sync BN stats across GPUs (no reduction if 1 GPU used)
    running_means = scaled_all_reduce(running_means, world_size)
    running_vars = scaled_all_reduce(running_vars, world_size)
    # Set BN stats and restore original momentum values
    for i, bn in enumerate(bn_layers):
        bn.running_mean = running_means[i]
        bn.running_var = running_vars[i]
        bn.momentum = momentums[i]


[文档]@HOOKS.register_module() class PreciseBNHook(Hook): """Precise BN hook. Recompute and update the batch norm stats to make them more precise. During training both BN stats and the weight are changing after every iteration, so the running average can not precisely reflect the actual stats of the current model. With this hook, the BN stats are recomputed with fixed weights, to make the running average more precise. Specifically, it computes the true average of per-batch mean/variance instead of the running average. See Sec. 3 of the paper `Rethinking Batch in BatchNorm <https://arxiv.org/abs/2105.07576>` for details. This hook will update BN stats, so it should be executed before ``CheckpointHook`` and ``EMAHook``, generally set its priority to "ABOVE_NORMAL". Args: num_samples (int): The number of samples to update the bn stats. Defaults to 8192. interval (int): Perform precise bn interval. If the train loop is `EpochBasedTrainLoop` or `by_epoch=True`, its unit is 'epoch'; if the train loop is `IterBasedTrainLoop` or `by_epoch=False`, its unit is 'iter'. Defaults to 1. """ def __init__(self, num_samples: int = 8192, interval: int = 1) -> None: assert interval > 0 and num_samples > 0, "'interval' and " \ "'num_samples' must be bigger than 0." self.interval = interval self.num_samples = num_samples def _perform_precise_bn(self, runner: Runner) -> None: """perform precise bn.""" print_log( f'Running Precise BN for {self.num_samples} samples...', logger=runner.logger) update_bn_stats( runner.model, runner.train_loop.dataloader, self.num_samples, logger=runner.logger) print_log('Finish Precise BN, BN stats updated.', logger=runner.logger)
[文档] def after_train_epoch(self, runner: Runner) -> None: """Calculate prcise BN and broadcast BN stats across GPUs. Args: runner (obj:`Runner`): The runner of the training process. """ # if use `EpochBasedTrainLoop``, do perform precise every # `self.interval` epochs. if isinstance(runner.train_loop, EpochBasedTrainLoop) and self.every_n_epochs( runner, self.interval): self._perform_precise_bn(runner)
[文档] def after_train_iter(self, runner, batch_idx: int, data_batch: DATA_BATCH = None, outputs: Optional[dict] = None) -> None: """Calculate prcise BN and broadcast BN stats across GPUs. Args: runner (obj:`Runner`): The runner of the training process. batch_idx (int): The index of the current batch in the train loop. data_batch (Sequence[dict], optional): Data from dataloader. Defaults to None. """ # if use `IterBasedTrainLoop``, do perform precise every # `self.interval` iters. if isinstance(runner.train_loop, IterBasedTrainLoop) and self.every_n_train_iters( runner, self.interval): self._perform_precise_bn(runner)
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