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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from typing import Dict, List, Optional, Sequence
import torch
from mmengine.dist import get_rank, get_world_size, is_distributed
from mmengine.hooks import Hook
from mmengine.logging import MMLogger
from mmpretrain.registry import HOOKS
from mmpretrain.utils import get_ori_model
@HOOKS.register_module()
class SwAVHook(Hook):
"""Hook for SwAV.
This hook builds the queue in SwAV according to ``epoch_queue_starts``.
The queue will be saved in ``runner.work_dir`` or loaded at start epoch
if the path folder has queues saved before.
Args:
batch_size (int): the batch size per GPU for computing.
epoch_queue_starts (int, optional): from this epoch, starts to use the
queue. Defaults to 15.
crops_for_assign (list[int], optional): list of crops id used for
computing assignments. Defaults to [0, 1].
feat_dim (int, optional): feature dimension of output vector.
Defaults to 128.
queue_length (int, optional): length of the queue (0 for no queue).
Defaults to 0.
interval (int, optional): the interval to save the queue.
Defaults to 1.
frozen_layers_cfg (dict, optional): Dict to config frozen layers.
The key-value pair is layer name and its frozen iters. If frozen,
the layers don't need gradient. Defaults to dict().
"""
def __init__(
self,
batch_size: int,
epoch_queue_starts: Optional[int] = 15,
crops_for_assign: Optional[List[int]] = [0, 1],
feat_dim: Optional[int] = 128,
queue_length: Optional[int] = 0,
interval: Optional[int] = 1,
frozen_layers_cfg: Optional[Dict] = dict()
) -> None:
self.batch_size = batch_size * get_world_size()
self.epoch_queue_starts = epoch_queue_starts
self.crops_for_assign = crops_for_assign
self.feat_dim = feat_dim
self.queue_length = queue_length
self.interval = interval
self.frozen_layers_cfg = frozen_layers_cfg
self.requires_grad = True
self.queue = None
def before_run(self, runner) -> None:
"""Check whether the queues exist locally or not."""
if is_distributed():
self.queue_path = osp.join(runner.work_dir,
'queue' + str(get_rank()) + '.pth')
else:
self.queue_path = osp.join(runner.work_dir, 'queue.pth')
# load the queues if queues exist locally
if osp.isfile(self.queue_path):
self.queue = torch.load(self.queue_path)['queue']
get_ori_model(runner.model).head.loss_module.queue = self.queue
MMLogger.get_current_instance().info(
f'Load queue from file: {self.queue_path}')
# the queue needs to be divisible by the batch size
self.queue_length -= self.queue_length % self.batch_size
def before_train_iter(self,
runner,
batch_idx: int,
data_batch: Optional[Sequence[dict]] = None) -> None:
"""Freeze layers before specific iters according to the config."""
for layer, frozen_iters in self.frozen_layers_cfg.items():
if runner.iter < frozen_iters and self.requires_grad:
self.requires_grad = False
for name, p in get_ori_model(runner.model).named_parameters():
if layer in name:
p.requires_grad = False
elif runner.iter >= frozen_iters and not self.requires_grad:
self.requires_grad = True
for name, p in get_ori_model(runner.model).named_parameters():
if layer in name:
p.requires_grad = True
def before_train_epoch(self, runner) -> None:
"""Check the queues' state."""
# optionally starts a queue
if self.queue_length > 0 \
and runner.epoch >= self.epoch_queue_starts \
and self.queue is None:
self.queue = torch.zeros(
len(self.crops_for_assign),
self.queue_length // runner.world_size,
self.feat_dim,
).cuda()
# set the boolean type of use_the_queue
get_ori_model(runner.model).head.loss_module.queue = self.queue
get_ori_model(runner.model).head.loss_module.use_queue = False
def after_train_epoch(self, runner) -> None:
"""Save the queues locally."""
self.queue = get_ori_model(runner.model).head.loss_module.queue
if self.queue is not None and self.every_n_epochs(
runner, self.interval):
torch.save({'queue': self.queue}, self.queue_path)
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