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mmclassification/mmcls/engine/hooks/precise_bn_hook.py

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# 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 mmcls.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):
batch_inputs, data_samples = model.data_preprocessor(data, False)
model(batch_inputs, data_samples)
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]
@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)
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)
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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