# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
from unittest.mock import Mock, patch
import pytest
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from mmengine.evaluator import BaseMetric
from mmengine.hooks import CheckpointHook
from mmengine.logging import MessageHub
from mmengine.model import BaseModel
from mmengine.optim import OptimWrapper
from mmengine.runner import Runner
class ToyModel(BaseModel):
def __init__(self):
super().__init__()
self.linear = nn.Linear(2, 1)
def forward(self, inputs, data_sample, mode='tensor'):
labels = torch.stack(data_sample)
inputs = torch.stack(inputs)
outputs = self.linear(inputs)
if mode == 'tensor':
return outputs
elif mode == 'loss':
loss = (labels - outputs).sum()
outputs = dict(loss=loss)
return outputs
else:
return outputs
class DummyDataset(Dataset):
METAINFO = dict() # type: ignore
data = torch.randn(12, 2)
label = torch.ones(12)
@property
def metainfo(self):
return self.METAINFO
def __len__(self):
return self.data.size(0)
def __getitem__(self, index):
return dict(inputs=self.data[index], data_sample=self.label[index])
class TriangleMetric(BaseMetric):
default_prefix: str = 'test'
def __init__(self, length):
super().__init__()
self.length = length
self.best_idx = length // 2
self.cur_idx = 0
def process(self, *args, **kwargs):
self.results.append(0)
def compute_metrics(self, *args, **kwargs):
self.cur_idx += 1
acc = 1.0 - abs(self.cur_idx - self.best_idx) / self.length
return dict(acc=acc)
class MockPetrel:
_allow_symlink = False
def __init__(self):
pass
@property
def name(self):
return self.__class__.__name__
@property
def allow_symlink(self):
return self._allow_symlink
prefix_to_backends = {'s3': MockPetrel}
class TestCheckpointHook:
@patch('mmengine.fileio.file_client.FileClient._prefix_to_backends',
prefix_to_backends)
def test_before_train(self, tmp_path):
runner = Mock()
work_dir = str(tmp_path)
runner.work_dir = work_dir
# the out_dir of the checkpoint hook is None
checkpoint_hook = CheckpointHook(interval=1, by_epoch=True)
checkpoint_hook.before_train(runner)
assert checkpoint_hook.out_dir == runner.work_dir
# the out_dir of the checkpoint hook is not None
checkpoint_hook = CheckpointHook(
interval=1, by_epoch=True, out_dir='test_dir')
checkpoint_hook.before_train(runner)
assert checkpoint_hook.out_dir == osp.join(
'test_dir', osp.join(osp.basename(work_dir)))
runner.message_hub = MessageHub.get_instance('test_before_train')
# no 'best_ckpt_path' in runtime_info
checkpoint_hook = CheckpointHook(interval=1, save_best=['acc', 'mIoU'])
checkpoint_hook.before_train(runner)
assert checkpoint_hook.best_ckpt_path_dict == dict(acc=None, mIoU=None)
assert not hasattr(checkpoint_hook, 'best_ckpt_path')
# only one 'best_ckpt_path' in runtime_info
runner.message_hub.update_info('best_ckpt_acc', 'best_acc')
checkpoint_hook.before_train(runner)
assert checkpoint_hook.best_ckpt_path_dict == dict(
acc='best_acc', mIoU=None)
# no 'best_ckpt_path' in runtime_info
checkpoint_hook = CheckpointHook(interval=1, save_best='acc')
checkpoint_hook.before_train(runner)
assert checkpoint_hook.best_ckpt_path is None
assert not hasattr(checkpoint_hook, 'best_ckpt_path_dict')
# 'best_ckpt_path' in runtime_info
runner.message_hub.update_info('best_ckpt', 'best_ckpt')
checkpoint_hook.before_train(runner)
assert checkpoint_hook.best_ckpt_path == 'best_ckpt'
def test_after_val_epoch(self, tmp_path):
runner = Mock()
runner.work_dir = tmp_path
runner.epoch = 9
runner.model = Mock()
runner.message_hub = MessageHub.get_instance('test_after_val_epoch')
with pytest.raises(ValueError):
# key_indicator must be valid when rule_map is None
CheckpointHook(interval=2, by_epoch=True, save_best='unsupport')
with pytest.raises(KeyError):
# rule must be in keys of rule_map
CheckpointHook(
interval=2, by_epoch=True, save_best='auto', rule='unsupport')
# if eval_res is an empty dict, print a warning information
with pytest.warns(UserWarning) as record_warnings:
eval_hook = CheckpointHook(
interval=2, by_epoch=True, save_best='auto')
eval_hook._get_metric_score(None, None)
# Since there will be many warnings thrown, we just need to check
# if the expected exceptions are thrown
expected_message = (
'Since `eval_res` is an empty dict, the behavior to '
'save the best checkpoint will be skipped in this '
'evaluation.')
for warning in record_warnings:
if str(warning.message) == expected_message:
break
else:
assert False
# test error when number of rules and metrics are not same
with pytest.raises(AssertionError) as assert_error:
CheckpointHook(
interval=1,
save_best=['mIoU', 'acc'],
rule=['greater', 'greater', 'less'],
by_epoch=True)
error_message = ('Number of "rule" must be 1 or the same as number of '
'"save_best", but got 3.')
assert error_message in str(assert_error.value)
# if save_best is None,no best_ckpt meta should be stored
eval_hook = CheckpointHook(interval=2, by_epoch=True, save_best=None)
eval_hook.before_train(runner)
eval_hook.after_val_epoch(runner, None)
assert 'best_score' not in runner.message_hub.runtime_info
assert 'best_ckpt' not in runner.message_hub.runtime_info
# when `save_best` is set to `auto`, first metric will be used.
metrics = {'acc': 0.5, 'map': 0.3}
eval_hook = CheckpointHook(interval=2, by_epoch=True, save_best='auto')
eval_hook.before_train(runner)
eval_hook.after_val_epoch(runner, metrics)
best_ckpt_name = 'best_acc_epoch_9.pth'
best_ckpt_path = eval_hook.file_client.join_path(
eval_hook.out_dir, best_ckpt_name)
assert eval_hook.key_indicators == ['acc']
assert eval_hook.rules == ['greater']
assert 'best_score' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_score') == 0.5
assert 'best_ckpt' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_ckpt') == best_ckpt_path
# # when `save_best` is set to `acc`, it should update greater value
eval_hook = CheckpointHook(interval=2, by_epoch=True, save_best='acc')
eval_hook.before_train(runner)
metrics['acc'] = 0.8
eval_hook.after_val_epoch(runner, metrics)
assert 'best_score' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_score') == 0.8
# # when `save_best` is set to `loss`, it should update less value
eval_hook = CheckpointHook(interval=2, by_epoch=True, save_best='loss')
eval_hook.before_train(runner)
metrics['loss'] = 0.8
eval_hook.after_val_epoch(runner, metrics)
metrics['loss'] = 0.5
eval_hook.after_val_epoch(runner, metrics)
assert 'best_score' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_score') == 0.5
# when `rule` is set to `less`,then it should update less value
# no matter what `save_best` is
eval_hook = CheckpointHook(
interval=2, by_epoch=True, save_best='acc', rule='less')
eval_hook.before_train(runner)
metrics['acc'] = 0.3
eval_hook.after_val_epoch(runner, metrics)
assert 'best_score' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_score') == 0.3
# # when `rule` is set to `greater`,then it should update greater value
# # no matter what `save_best` is
eval_hook = CheckpointHook(
interval=2, by_epoch=True, save_best='loss', rule='greater')
eval_hook.before_train(runner)
metrics['loss'] = 1.0
eval_hook.after_val_epoch(runner, metrics)
assert 'best_score' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_score') == 1.0
# test multi `save_best` with one rule
eval_hook = CheckpointHook(
interval=2, save_best=['acc', 'mIoU'], rule='greater')
assert eval_hook.key_indicators == ['acc', 'mIoU']
assert eval_hook.rules == ['greater', 'greater']
# test multi `save_best` with multi rules
eval_hook = CheckpointHook(
interval=2, save_best=['FID', 'IS'], rule=['less', 'greater'])
assert eval_hook.key_indicators == ['FID', 'IS']
assert eval_hook.rules == ['less', 'greater']
# test multi `save_best` with default rule
eval_hook = CheckpointHook(interval=2, save_best=['acc', 'mIoU'])
assert eval_hook.key_indicators == ['acc', 'mIoU']
assert eval_hook.rules == ['greater', 'greater']
runner.message_hub = MessageHub.get_instance(
'test_after_val_epoch_save_multi_best')
eval_hook.before_train(runner)
metrics = dict(acc=0.5, mIoU=0.6)
eval_hook.after_val_epoch(runner, metrics)
best_acc_name = 'best_acc_epoch_9.pth'
best_acc_path = eval_hook.file_client.join_path(
eval_hook.out_dir, best_acc_name)
best_mIoU_name = 'best_mIoU_epoch_9.pth'
best_mIoU_path = eval_hook.file_client.join_path(
eval_hook.out_dir, best_mIoU_name)
assert 'best_score_acc' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_score_acc') == 0.5
assert 'best_score_mIoU' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_score_mIoU') == 0.6
assert 'best_ckpt_acc' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_ckpt_acc') == best_acc_path
assert 'best_ckpt_mIoU' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_ckpt_mIoU') == best_mIoU_path
# test behavior when by_epoch is False
runner = Mock()
runner.work_dir = tmp_path
runner.iter = 9
runner.model = Mock()
runner.message_hub = MessageHub.get_instance(
'test_after_val_epoch_by_epoch_is_false')
# check best ckpt name and best score
metrics = {'acc': 0.5, 'map': 0.3}
eval_hook = CheckpointHook(
interval=2, by_epoch=False, save_best='acc', rule='greater')
eval_hook.before_train(runner)
eval_hook.after_val_epoch(runner, metrics)
assert eval_hook.key_indicators == ['acc']
assert eval_hook.rules == ['greater']
best_ckpt_name = 'best_acc_iter_9.pth'
best_ckpt_path = eval_hook.file_client.join_path(
eval_hook.out_dir, best_ckpt_name)
assert 'best_ckpt' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_ckpt') == best_ckpt_path
assert 'best_score' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_score') == 0.5
# check best score updating
metrics['acc'] = 0.666
eval_hook.after_val_epoch(runner, metrics)
best_ckpt_name = 'best_acc_iter_9.pth'
best_ckpt_path = eval_hook.file_client.join_path(
eval_hook.out_dir, best_ckpt_name)
assert 'best_ckpt' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_ckpt') == best_ckpt_path
assert 'best_score' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_score') == 0.666
# error when 'auto' in `save_best` list
with pytest.raises(AssertionError):
CheckpointHook(interval=2, save_best=['auto', 'acc'])
# error when one `save_best` with multi `rule`
with pytest.raises(AssertionError):
CheckpointHook(
interval=2, save_best='acc', rule=['greater', 'less'])
# check best checkpoint name with `by_epoch` is False
eval_hook = CheckpointHook(
interval=2, by_epoch=False, save_best=['acc', 'mIoU'])
assert eval_hook.key_indicators == ['acc', 'mIoU']
assert eval_hook.rules == ['greater', 'greater']
runner.message_hub = MessageHub.get_instance(
'test_after_val_epoch_save_multi_best_by_epoch_is_false')
eval_hook.before_train(runner)
metrics = dict(acc=0.5, mIoU=0.6)
eval_hook.after_val_epoch(runner, metrics)
best_acc_name = 'best_acc_iter_9.pth'
best_acc_path = eval_hook.file_client.join_path(
eval_hook.out_dir, best_acc_name)
best_mIoU_name = 'best_mIoU_iter_9.pth'
best_mIoU_path = eval_hook.file_client.join_path(
eval_hook.out_dir, best_mIoU_name)
assert 'best_score_acc' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_score_acc') == 0.5
assert 'best_score_mIoU' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_score_mIoU') == 0.6
assert 'best_ckpt_acc' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_ckpt_acc') == best_acc_path
assert 'best_ckpt_mIoU' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('best_ckpt_mIoU') == best_mIoU_path
# after_val_epoch should not save last_checkpoint.
assert not osp.isfile(osp.join(runner.work_dir, 'last_checkpoint'))
def test_after_train_epoch(self, tmp_path):
runner = Mock()
work_dir = str(tmp_path)
runner.work_dir = tmp_path
runner.epoch = 9
runner.model = Mock()
runner.message_hub = MessageHub.get_instance('test_after_train_epoch')
# by epoch is True
checkpoint_hook = CheckpointHook(interval=2, by_epoch=True)
checkpoint_hook.before_train(runner)
checkpoint_hook.after_train_epoch(runner)
assert (runner.epoch + 1) % 2 == 0
assert 'last_ckpt' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('last_ckpt') == \
osp.join(work_dir, 'epoch_10.pth')
last_ckpt_path = osp.join(work_dir, 'last_checkpoint')
assert osp.isfile(last_ckpt_path)
with open(last_ckpt_path) as f:
filepath = f.read()
assert filepath == osp.join(work_dir, 'epoch_10.pth')
# epoch can not be evenly divided by 2
runner.epoch = 10
checkpoint_hook.after_train_epoch(runner)
assert 'last_ckpt' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('last_ckpt') == \
osp.join(work_dir, 'epoch_10.pth')
# by epoch is False
runner.epoch = 9
runner.message_hub = MessageHub.get_instance('test_after_train_epoch1')
checkpoint_hook = CheckpointHook(interval=2, by_epoch=False)
checkpoint_hook.before_train(runner)
checkpoint_hook.after_train_epoch(runner)
assert 'last_ckpt' not in runner.message_hub.runtime_info
# # max_keep_ckpts > 0
runner.work_dir = work_dir
os.system(f'touch {work_dir}/epoch_8.pth')
checkpoint_hook = CheckpointHook(
interval=2, by_epoch=True, max_keep_ckpts=1)
checkpoint_hook.before_train(runner)
checkpoint_hook.after_train_epoch(runner)
assert (runner.epoch + 1) % 2 == 0
assert not os.path.exists(f'{work_dir}/epoch_8.pth')
def test_after_train_iter(self, tmp_path):
work_dir = str(tmp_path)
runner = Mock()
runner.work_dir = str(work_dir)
runner.iter = 9
batch_idx = 9
runner.model = Mock()
runner.message_hub = MessageHub.get_instance('test_after_train_iter')
# by epoch is True
checkpoint_hook = CheckpointHook(interval=2, by_epoch=True)
checkpoint_hook.before_train(runner)
checkpoint_hook.after_train_iter(runner, batch_idx=batch_idx)
assert 'last_ckpt' not in runner.message_hub.runtime_info
# by epoch is False
checkpoint_hook = CheckpointHook(interval=2, by_epoch=False)
checkpoint_hook.before_train(runner)
checkpoint_hook.after_train_iter(runner, batch_idx=batch_idx)
assert (runner.iter + 1) % 2 == 0
assert 'last_ckpt' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('last_ckpt') == \
osp.join(work_dir, 'iter_10.pth')
# epoch can not be evenly divided by 2
runner.iter = 10
checkpoint_hook.after_train_epoch(runner)
assert 'last_ckpt' in runner.message_hub.runtime_info and \
runner.message_hub.get_info('last_ckpt') == \
osp.join(work_dir, 'iter_10.pth')
# max_keep_ckpts > 0
runner.iter = 9
runner.work_dir = work_dir
os.system(f'touch {osp.join(work_dir, "iter_8.pth")}')
checkpoint_hook = CheckpointHook(
interval=2, by_epoch=False, max_keep_ckpts=1)
checkpoint_hook.before_train(runner)
checkpoint_hook.after_train_iter(runner, batch_idx=batch_idx)
assert not os.path.exists(f'{work_dir}/iter_8.pth')
def test_with_runner(self, tmp_path):
max_epoch = 10
work_dir = osp.join(str(tmp_path), 'runner_test')
tmpl = '{}.pth'
save_interval = 2
checkpoint_cfg = dict(
type='CheckpointHook',
interval=save_interval,
filename_tmpl=tmpl,
by_epoch=True)
runner = Runner(
model=ToyModel(),
work_dir=work_dir,
train_dataloader=dict(
dataset=DummyDataset(),
sampler=dict(type='DefaultSampler', shuffle=True),
batch_size=3,
num_workers=0),
val_dataloader=dict(
dataset=DummyDataset(),
sampler=dict(type='DefaultSampler', shuffle=False),
batch_size=3,
num_workers=0),
val_evaluator=dict(type=TriangleMetric, length=max_epoch),
optim_wrapper=OptimWrapper(
torch.optim.Adam(ToyModel().parameters())),
train_cfg=dict(
by_epoch=True, max_epochs=max_epoch, val_interval=1),
val_cfg=dict(),
default_hooks=dict(checkpoint=checkpoint_cfg))
runner.train()
for epoch in range(max_epoch):
if epoch % save_interval != 0 or epoch == 0:
continue
path = osp.join(work_dir, tmpl.format(epoch))
assert osp.isfile(path=path)