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mmengine/tests/test_runner/test_runner.py

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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import logging
import os
import os.path as osp
import random
import shutil
import tempfile
from functools import partial
from unittest import TestCase, skipIf
import numpy as np
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
from torch.optim import SGD, Adam
from torch.utils.data import DataLoader, Dataset
from mmengine.config import Config
from mmengine.dataset import DefaultSampler, pseudo_collate
from mmengine.evaluator import BaseMetric, Evaluator
from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, Hook,
IterTimerHook, LoggerHook, ParamSchedulerHook,
RuntimeInfoHook)
from mmengine.logging import HistoryBuffer, MessageHub, MMLogger
from mmengine.model import BaseDataPreprocessor, BaseModel, ImgDataPreprocessor
from mmengine.optim import (DefaultOptimWrapperConstructor, MultiStepLR,
OptimWrapper, OptimWrapperDict, StepLR)
from mmengine.registry import (DATASETS, EVALUATOR, FUNCTIONS, HOOKS,
LOG_PROCESSORS, LOOPS, METRICS, MODEL_WRAPPERS,
MODELS, OPTIM_WRAPPER_CONSTRUCTORS,
OPTIM_WRAPPERS, PARAM_SCHEDULERS, RUNNERS,
Registry)
from mmengine.runner import (BaseLoop, EpochBasedTrainLoop, IterBasedTrainLoop,
LogProcessor, Runner, TestLoop, ValLoop)
from mmengine.runner.loops import _InfiniteDataloaderIterator
from mmengine.runner.priority import Priority, get_priority
from mmengine.utils import digit_version, is_list_of
from mmengine.utils.dl_utils import TORCH_VERSION
from mmengine.visualization import Visualizer
def skip_test_comile():
if digit_version(torch.__version__) < digit_version('2.0.0'):
return True
# The default compiling backend for PyTorch 2.0, inductor, does not support
# Nvidia graphics cards older than Volta architecture.
# As PyTorch does not provide a public function to confirm the availability
# of the inductor, we check its availability by attempting compilation.
if not torch.cuda.is_available():
return True
try:
model = nn.Sequential(nn.Conv2d(1, 1, 1), nn.BatchNorm2d(1)).cuda()
compiled_model = torch.compile(model)
compiled_model(torch.ones(3, 1, 1, 1).cuda())
except Exception:
return True
else:
return False
SKIP_TEST_COMPILE = skip_test_comile()
class ToyModel(BaseModel):
def __init__(self, data_preprocessor=None):
super().__init__(data_preprocessor=data_preprocessor)
self.linear1 = nn.Linear(2, 2)
self.linear2 = nn.Linear(2, 1)
def forward(self, inputs, data_sample, mode='tensor'):
if isinstance(inputs, list):
inputs = torch.stack(inputs)
if isinstance(data_sample, list):
data_sample = torch.stack(data_sample)
outputs = self.linear1(inputs)
outputs = self.linear2(outputs)
if mode == 'tensor':
return outputs
elif mode == 'loss':
loss = (data_sample - outputs).sum()
outputs = dict(loss=loss)
return outputs
elif mode == 'predict':
return outputs
class ToyModel1(ToyModel):
def __init__(self):
super().__init__()
class ToySyncBNModel(BaseModel):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(3, 8, 2)
self.bn = nn.SyncBatchNorm(8)
def forward(self, inputs, data_sample, mode='tensor'):
data_sample = torch.stack(data_sample)
inputs = torch.stack(inputs)
outputs = self.conv(inputs)
outputs = self.bn(outputs)
if mode == 'tensor':
return outputs
elif mode == 'loss':
loss = (data_sample - outputs).sum()
outputs = dict(loss=loss)
return outputs
elif mode == 'predict':
outputs = dict(log_vars=dict(a=1, b=0.5))
return outputs
class ToyGANModel(BaseModel):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(2, 1)
self.linear2 = nn.Linear(2, 1)
def forward(self, inputs, data_sample, mode='tensor'):
data_sample = torch.stack(data_sample)
inputs = torch.stack(inputs)
output1 = self.linear1(inputs)
output2 = self.linear2(inputs)
if mode == 'tensor':
return output1, output2
elif mode == 'loss':
loss1 = (data_sample - output1).sum()
loss2 = (data_sample - output2).sum()
outputs = dict(linear1=loss1, linear2=loss2)
return outputs
elif mode == 'predict':
return output1, output2
def train_step(self, data, optim_wrapper):
data = self.data_preprocessor(data)
loss = self(**data, mode='loss')
optim_wrapper['linear1'].update_params(loss['linear1'])
optim_wrapper['linear2'].update_params(loss['linear2'])
return loss
class CustomModelWrapper(nn.Module):
def __init__(self, module):
super().__init__()
self.model = module
class ToyMultipleOptimizerConstructor:
def __init__(self, optim_wrapper_cfg, paramwise_cfg=None):
if not isinstance(optim_wrapper_cfg, dict):
raise TypeError('optimizer_cfg should be a dict',
f'but got {type(optim_wrapper_cfg)}')
assert paramwise_cfg is None, (
'parawise_cfg should be set in each optimizer separately')
self.optim_wrapper_cfg = optim_wrapper_cfg
self.constructors = {}
for key, cfg in self.optim_wrapper_cfg.items():
_cfg = cfg.copy()
paramwise_cfg_ = _cfg.pop('paramwise_cfg', None)
self.constructors[key] = DefaultOptimWrapperConstructor(
_cfg, paramwise_cfg_)
def __call__(self, model: nn.Module) -> OptimWrapperDict:
optimizers = {}
while hasattr(model, 'module'):
model = model.module
for key, constructor in self.constructors.items():
module = getattr(model, key)
optimizers[key] = constructor(module)
return OptimWrapperDict(**optimizers)
class ToyDataset(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 ToyDatasetNoMeta(Dataset):
data = torch.randn(12, 2)
label = torch.ones(12)
def __len__(self):
return self.data.size(0)
def __getitem__(self, index):
return dict(inputs=self.data[index], data_sample=self.label[index])
class ToyMetric1(BaseMetric):
def __init__(self, collect_device='cpu', dummy_metrics=None):
super().__init__(collect_device=collect_device)
self.dummy_metrics = dummy_metrics
def process(self, data_batch, predictions):
result = {'acc': 1}
self.results.append(result)
def compute_metrics(self, results):
return dict(acc=1)
class ToyMetric2(BaseMetric):
def __init__(self, collect_device='cpu', dummy_metrics=None):
super().__init__(collect_device=collect_device)
self.dummy_metrics = dummy_metrics
def process(self, data_batch, predictions):
result = {'acc': 1}
self.results.append(result)
def compute_metrics(self, results):
return dict(acc=1)
class ToyOptimWrapper(OptimWrapper):
...
class ToyHook(Hook):
priority = 'Lowest'
def before_train_epoch(self, runner):
pass
class ToyHook2(Hook):
priority = 'Lowest'
def after_train_epoch(self, runner):
pass
class CustomTrainLoop(BaseLoop):
def __init__(self, runner, dataloader, max_epochs):
super().__init__(runner, dataloader)
self._max_epochs = max_epochs
def run(self) -> None:
pass
class CustomValLoop(BaseLoop):
def __init__(self, runner, dataloader, evaluator):
super().__init__(runner, dataloader)
self._runner = runner
if isinstance(evaluator, dict) or is_list_of(evaluator, dict):
self.evaluator = runner.build_evaluator(evaluator) # type: ignore
else:
self.evaluator = evaluator
def run(self) -> None:
pass
class CustomTestLoop(BaseLoop):
def __init__(self, runner, dataloader, evaluator):
super().__init__(runner, dataloader)
self._runner = runner
if isinstance(evaluator, dict) or is_list_of(evaluator, dict):
self.evaluator = runner.build_evaluator(evaluator) # type: ignore
else:
self.evaluator = evaluator
def run(self) -> None:
pass
class CustomLogProcessor(LogProcessor):
def __init__(self, window_size=10, by_epoch=True, custom_cfg=None):
self.window_size = window_size
self.by_epoch = by_epoch
self.custom_cfg = custom_cfg if custom_cfg else []
self._check_custom_cfg()
class CustomRunner(Runner):
def __init__(self,
model,
work_dir,
train_dataloader=None,
val_dataloader=None,
test_dataloader=None,
train_cfg=None,
val_cfg=None,
test_cfg=None,
auto_scale_lr=None,
optim_wrapper=None,
param_scheduler=None,
val_evaluator=None,
test_evaluator=None,
default_hooks=None,
custom_hooks=None,
data_preprocessor=None,
load_from=None,
resume=False,
launcher='none',
env_cfg=dict(dist_cfg=dict(backend='nccl')),
log_processor=None,
log_level='INFO',
visualizer=None,
default_scope=None,
randomness=dict(seed=None),
experiment_name=None,
cfg=None):
pass
def setup_env(self, env_cfg):
pass
class ToyEvaluator(Evaluator):
def __init__(self, metrics):
super().__init__(metrics)
def collate_fn(data_batch):
return pseudo_collate(data_batch)
def custom_collate(data_batch, pad_value):
return pseudo_collate(data_batch)
def custom_worker_init(worker_id):
np.random.seed(worker_id)
random.seed(worker_id)
class TestRunner(TestCase):
def setUp(self):
MODELS.register_module(module=ToyModel, force=True)
MODELS.register_module(module=ToyModel1, force=True)
MODELS.register_module(module=ToySyncBNModel, force=True)
MODELS.register_module(module=ToyGANModel, force=True)
MODEL_WRAPPERS.register_module(module=CustomModelWrapper, force=True)
OPTIM_WRAPPER_CONSTRUCTORS.register_module(
module=ToyMultipleOptimizerConstructor, force=True)
DATASETS.register_module(module=ToyDataset, force=True)
DATASETS.register_module(module=ToyDatasetNoMeta, force=True)
METRICS.register_module(module=ToyMetric1, force=True)
METRICS.register_module(module=ToyMetric2, force=True)
OPTIM_WRAPPERS.register_module(module=ToyOptimWrapper, force=True)
HOOKS.register_module(module=ToyHook, force=True)
HOOKS.register_module(module=ToyHook2, force=True)
LOOPS.register_module(module=CustomTrainLoop, force=True)
LOOPS.register_module(module=CustomValLoop, force=True)
LOOPS.register_module(module=CustomTestLoop, force=True)
LOG_PROCESSORS.register_module(module=CustomLogProcessor, force=True)
RUNNERS.register_module(module=CustomRunner, force=True)
EVALUATOR.register_module(module=ToyEvaluator, force=True)
FUNCTIONS.register_module(module=custom_collate, force=True)
FUNCTIONS.register_module(module=custom_worker_init, force=True)
self.temp_dir = tempfile.mkdtemp()
epoch_based_cfg = dict(
model=dict(type='ToyModel'),
work_dir=self.temp_dir,
train_dataloader=dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=True),
batch_size=3,
num_workers=0),
val_dataloader=dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=False),
batch_size=3,
num_workers=0),
test_dataloader=dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=False),
batch_size=3,
num_workers=0),
auto_scale_lr=dict(base_batch_size=16, enable=False),
optim_wrapper=dict(
type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01)),
param_scheduler=dict(type='MultiStepLR', milestones=[1, 2]),
val_evaluator=dict(type='ToyMetric1'),
test_evaluator=dict(type='ToyMetric1'),
train_cfg=dict(
by_epoch=True, max_epochs=3, val_interval=1, val_begin=1),
val_cfg=dict(),
test_cfg=dict(),
custom_hooks=[],
default_hooks=dict(
runtime_info=dict(type='RuntimeInfoHook'),
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(
type='CheckpointHook', interval=1, by_epoch=True),
sampler_seed=dict(type='DistSamplerSeedHook')),
data_preprocessor=None,
launcher='none',
env_cfg=dict(dist_cfg=dict(backend='nccl')),
)
self.epoch_based_cfg = Config(epoch_based_cfg)
self.iter_based_cfg = copy.deepcopy(self.epoch_based_cfg)
self.iter_based_cfg.train_dataloader = dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='InfiniteSampler', shuffle=True),
batch_size=3,
num_workers=0)
self.iter_based_cfg.train_cfg = dict(by_epoch=False, max_iters=12)
self.iter_based_cfg.default_hooks = dict(
runtime_info=dict(type='RuntimeInfoHook'),
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1, by_epoch=False),
sampler_seed=dict(type='DistSamplerSeedHook'))
def tearDown(self):
# `FileHandler` should be closed in Windows, otherwise we cannot
# delete the temporary directory
MODELS.module_dict.pop('ToyModel')
MODELS.module_dict.pop('ToyModel1')
MODELS.module_dict.pop('ToySyncBNModel')
MODELS.module_dict.pop('ToyGANModel')
MODEL_WRAPPERS.module_dict.pop('CustomModelWrapper')
OPTIM_WRAPPER_CONSTRUCTORS.module_dict.pop(
'ToyMultipleOptimizerConstructor')
OPTIM_WRAPPERS.module_dict.pop('ToyOptimWrapper')
DATASETS.module_dict.pop('ToyDataset')
DATASETS.module_dict.pop('ToyDatasetNoMeta')
METRICS.module_dict.pop('ToyMetric1')
METRICS.module_dict.pop('ToyMetric2')
HOOKS.module_dict.pop('ToyHook')
HOOKS.module_dict.pop('ToyHook2')
LOOPS.module_dict.pop('CustomTrainLoop')
LOOPS.module_dict.pop('CustomValLoop')
LOOPS.module_dict.pop('CustomTestLoop')
LOG_PROCESSORS.module_dict.pop('CustomLogProcessor')
RUNNERS.module_dict.pop('CustomRunner')
EVALUATOR.module_dict.pop('ToyEvaluator')
FUNCTIONS.module_dict.pop('custom_collate')
FUNCTIONS.module_dict.pop('custom_worker_init')
logging.shutdown()
MMLogger._instance_dict.clear()
shutil.rmtree(self.temp_dir)
def test_init(self):
# 1. test arguments
# 1.1 train_dataloader, train_cfg, optimizer and param_scheduler
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init1'
cfg.pop('train_cfg')
with self.assertRaisesRegex(ValueError, 'either all None or not None'):
Runner(**cfg)
# all of training related configs are None and param_scheduler should
# also be None
cfg.experiment_name = 'test_init2'
cfg.pop('train_dataloader')
cfg.pop('optim_wrapper')
cfg.pop('param_scheduler')
runner = Runner(**cfg)
self.assertIsInstance(runner, Runner)
# all of training related configs are not None
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init3'
runner = Runner(**cfg)
self.assertIsInstance(runner, Runner)
# all of training related configs are not None and param_scheduler
# can be None
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init4'
cfg.pop('param_scheduler')
runner = Runner(**cfg)
self.assertIsInstance(runner, Runner)
self.assertEqual(runner.param_schedulers, None)
# param_scheduler should be None when optimizer is None
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init5'
cfg.pop('train_cfg')
cfg.pop('train_dataloader')
cfg.pop('optim_wrapper')
with self.assertRaisesRegex(ValueError, 'should be None'):
runner = Runner(**cfg)
# 1.2 val_dataloader, val_evaluator, val_cfg
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init6'
cfg.pop('val_cfg')
with self.assertRaisesRegex(ValueError, 'either all None or not None'):
Runner(**cfg)
cfg.experiment_name = 'test_init7'
cfg.pop('val_dataloader')
cfg.pop('val_evaluator')
runner = Runner(**cfg)
self.assertIsInstance(runner, Runner)
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init8'
runner = Runner(**cfg)
self.assertIsInstance(runner, Runner)
# 1.3 test_dataloader, test_evaluator and test_cfg
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init9'
cfg.pop('test_cfg')
with self.assertRaisesRegex(ValueError, 'either all None or not None'):
runner = Runner(**cfg)
cfg.experiment_name = 'test_init10'
cfg.pop('test_dataloader')
cfg.pop('test_evaluator')
runner = Runner(**cfg)
self.assertIsInstance(runner, Runner)
# 1.4 test env params
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init11'
runner = Runner(**cfg)
self.assertFalse(runner.distributed)
self.assertFalse(runner.deterministic)
# 1.5 message_hub, logger and visualizer
# they are all not specified
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init12'
runner = Runner(**cfg)
self.assertIsInstance(runner.logger, MMLogger)
self.assertIsInstance(runner.message_hub, MessageHub)
self.assertIsInstance(runner.visualizer, Visualizer)
# they are all specified
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init13'
cfg.log_level = 'INFO'
cfg.visualizer = None
runner = Runner(**cfg)
self.assertIsInstance(runner.logger, MMLogger)
self.assertIsInstance(runner.message_hub, MessageHub)
self.assertIsInstance(runner.visualizer, Visualizer)
assert runner.distributed is False
assert runner.seed is not None
assert runner.work_dir == self.temp_dir
# 2 model should be initialized
self.assertIsInstance(runner.model,
(nn.Module, DistributedDataParallel))
self.assertEqual(runner.model_name, 'ToyModel')
# 3. test lazy initialization
self.assertIsInstance(runner._train_dataloader, dict)
self.assertIsInstance(runner._val_dataloader, dict)
self.assertIsInstance(runner._test_dataloader, dict)
self.assertIsInstance(runner.optim_wrapper, dict)
self.assertIsInstance(runner.param_schedulers, dict)
# After calling runner.train(),
# train_dataloader and val_loader should be initialized but
# test_dataloader should also be dict
runner.train()
self.assertIsInstance(runner._train_loop, BaseLoop)
self.assertIsInstance(runner.train_dataloader, DataLoader)
self.assertIsInstance(runner.optim_wrapper, OptimWrapper)
self.assertIsInstance(runner.param_schedulers[0], MultiStepLR)
self.assertIsInstance(runner._val_loop, BaseLoop)
self.assertIsInstance(runner._val_loop.dataloader, DataLoader)
self.assertIsInstance(runner._val_loop.evaluator, Evaluator)
# After calling runner.test(), test_dataloader should be initialized
self.assertIsInstance(runner._test_loop, dict)
runner.test()
self.assertIsInstance(runner._test_loop, BaseLoop)
self.assertIsInstance(runner._test_loop.dataloader, DataLoader)
self.assertIsInstance(runner._test_loop.evaluator, Evaluator)
# 4. initialize runner with objects rather than config
model = ToyModel()
optim_wrapper = OptimWrapper(SGD(
model.parameters(),
lr=0.01,
))
toy_hook = ToyHook()
toy_hook2 = ToyHook2()
train_dataloader = DataLoader(ToyDataset(), collate_fn=collate_fn)
val_dataloader = DataLoader(ToyDataset(), collate_fn=collate_fn)
test_dataloader = DataLoader(ToyDataset(), collate_fn=collate_fn)
runner = Runner(
model=model,
work_dir=self.temp_dir,
train_cfg=dict(
by_epoch=True, max_epochs=3, val_interval=1, val_begin=1),
train_dataloader=train_dataloader,
optim_wrapper=optim_wrapper,
param_scheduler=MultiStepLR(optim_wrapper, milestones=[1, 2]),
val_cfg=dict(),
val_dataloader=val_dataloader,
val_evaluator=[ToyMetric1()],
test_cfg=dict(),
test_dataloader=test_dataloader,
test_evaluator=[ToyMetric1()],
default_hooks=dict(param_scheduler=toy_hook),
custom_hooks=[toy_hook2],
experiment_name='test_init14')
runner.train()
runner.test()
# 5. Test building multiple runners. In Windows, nccl could not be
# available, and this test will be skipped.
if torch.cuda.is_available() and torch.distributed.is_nccl_available():
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init15'
cfg.launcher = 'pytorch'
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '29600'
os.environ['RANK'] = '0'
os.environ['WORLD_SIZE'] = '1'
os.environ['LOCAL_RANK'] = '0'
Runner(**cfg)
cfg.experiment_name = 'test_init16'
Runner(**cfg)
# 6.1 Test initializing with empty scheduler.
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init17'
cfg.param_scheduler = None
runner = Runner(**cfg)
self.assertIsNone(runner.param_schedulers)
# 6.2 Test initializing single scheduler.
cfg.experiment_name = 'test_init18'
cfg.param_scheduler = dict(type='MultiStepLR', milestones=[1, 2])
Runner(**cfg)
# 6.3 Test initializing list of scheduler.
cfg.param_scheduler = [
dict(type='MultiStepLR', milestones=[1, 2]),
dict(type='MultiStepLR', milestones=[2, 3])
]
cfg.experiment_name = 'test_init19'
Runner(**cfg)
# 6.4 Test initializing 2 schedulers for 2 optimizers.
cfg.param_scheduler = dict(
linear1=dict(type='MultiStepLR', milestones=[1, 2]),
linear2=dict(type='MultiStepLR', milestones=[1, 2]),
)
cfg.experiment_name = 'test_init20'
Runner(**cfg)
# 6.5 Test initializing 2 schedulers for 2 optimizers.
cfg.param_scheduler = dict(
linear1=[dict(type='MultiStepLR', milestones=[1, 2])],
linear2=[dict(type='MultiStepLR', milestones=[1, 2])],
)
cfg.experiment_name = 'test_init21'
Runner(**cfg)
# 6.6 Test initializing with `_ParameterScheduler`.
optimizer = SGD(nn.Linear(1, 1).parameters(), lr=0.1)
cfg.param_scheduler = MultiStepLR(
milestones=[1, 2], optimizer=optimizer)
cfg.experiment_name = 'test_init22'
Runner(**cfg)
# 6.7 Test initializing with list of `_ParameterScheduler`.
cfg.param_scheduler = [
MultiStepLR(milestones=[1, 2], optimizer=optimizer)
]
cfg.experiment_name = 'test_init23'
Runner(**cfg)
# 6.8 Test initializing with 2 `_ParameterScheduler` for 2 optimizers.
cfg.param_scheduler = dict(
linear1=MultiStepLR(milestones=[1, 2], optimizer=optimizer),
linear2=MultiStepLR(milestones=[1, 2], optimizer=optimizer))
cfg.experiment_name = 'test_init24'
Runner(**cfg)
# 6.9 Test initializing with 2 list of `_ParameterScheduler` for 2
# optimizers.
cfg.param_scheduler = dict(
linear1=[MultiStepLR(milestones=[1, 2], optimizer=optimizer)],
linear2=[MultiStepLR(milestones=[1, 2], optimizer=optimizer)])
cfg.experiment_name = 'test_init25'
Runner(**cfg)
# 6.10 Test initializing with error type scheduler.
cfg.param_scheduler = dict(linear1='error_type')
cfg.experiment_name = 'test_init26'
with self.assertRaisesRegex(AssertionError, 'Each value of'):
Runner(**cfg)
cfg.param_scheduler = 'error_type'
cfg.experiment_name = 'test_init27'
with self.assertRaisesRegex(TypeError,
'`param_scheduler` should be a'):
Runner(**cfg)
def test_dump_config(self):
# dump config from dict.
cfg = copy.deepcopy(self.epoch_based_cfg)
for idx, cfg in enumerate((cfg, cfg._cfg_dict)):
cfg.experiment_name = f'test_dump{idx}'
runner = Runner.from_cfg(cfg=cfg)
assert osp.exists(
osp.join(runner.work_dir, f'{runner.timestamp}.py'))
# dump config from file.
with tempfile.TemporaryDirectory() as temp_config_dir:
# Set `delete=Flase` and close the file to make it
# work in Windows.
temp_config_file = tempfile.NamedTemporaryFile(
dir=temp_config_dir, suffix='.py', delete=False)
temp_config_file.close()
file_cfg = Config(
self.epoch_based_cfg._cfg_dict,
filename=temp_config_file.name)
file_cfg.experiment_name = f'test_dump2{idx}'
runner = Runner.from_cfg(cfg=file_cfg)
assert osp.exists(
osp.join(runner.work_dir,
osp.basename(temp_config_file.name)))
def test_from_cfg(self):
runner = Runner.from_cfg(cfg=self.epoch_based_cfg)
self.assertIsInstance(runner, Runner)
def test_setup_env(self):
# TODO
pass
def test_build_logger(self):
self.epoch_based_cfg.experiment_name = 'test_build_logger1'
runner = Runner.from_cfg(self.epoch_based_cfg)
self.assertIsInstance(runner.logger, MMLogger)
self.assertEqual(runner.experiment_name, runner.logger.instance_name)
# input is a dict
logger = runner.build_logger(name='test_build_logger2')
self.assertIsInstance(logger, MMLogger)
self.assertEqual(logger.instance_name, 'test_build_logger2')
# input is a dict but does not contain name key
runner._experiment_name = 'test_build_logger3'
logger = runner.build_logger()
self.assertIsInstance(logger, MMLogger)
self.assertEqual(logger.instance_name, 'test_build_logger3')
def test_build_message_hub(self):
self.epoch_based_cfg.experiment_name = 'test_build_message_hub1'
runner = Runner.from_cfg(self.epoch_based_cfg)
self.assertIsInstance(runner.message_hub, MessageHub)
self.assertEqual(runner.message_hub.instance_name,
runner.experiment_name)
# input is a dict
message_hub_cfg = dict(name='test_build_message_hub2')
message_hub = runner.build_message_hub(message_hub_cfg)
self.assertIsInstance(message_hub, MessageHub)
self.assertEqual(message_hub.instance_name, 'test_build_message_hub2')
# input is a dict but does not contain name key
runner._experiment_name = 'test_build_message_hub3'
message_hub_cfg = dict()
message_hub = runner.build_message_hub(message_hub_cfg)
self.assertIsInstance(message_hub, MessageHub)
self.assertEqual(message_hub.instance_name, 'test_build_message_hub3')
# input is not a valid type
with self.assertRaisesRegex(TypeError, 'message_hub should be'):
runner.build_message_hub('invalid-type')
def test_build_visualizer(self):
self.epoch_based_cfg.experiment_name = 'test_build_visualizer1'
runner = Runner.from_cfg(self.epoch_based_cfg)
self.assertIsInstance(runner.visualizer, Visualizer)
self.assertEqual(runner.experiment_name,
runner.visualizer.instance_name)
# input is a Visualizer object
self.assertEqual(
id(runner.build_visualizer(runner.visualizer)),
id(runner.visualizer))
# input is a dict
visualizer_cfg = dict(type='Visualizer', name='test_build_visualizer2')
visualizer = runner.build_visualizer(visualizer_cfg)
self.assertIsInstance(visualizer, Visualizer)
self.assertEqual(visualizer.instance_name, 'test_build_visualizer2')
# input is a dict but does not contain name key
runner._experiment_name = 'test_build_visualizer3'
visualizer_cfg = None
visualizer = runner.build_visualizer(visualizer_cfg)
self.assertIsInstance(visualizer, Visualizer)
self.assertEqual(visualizer.instance_name, 'test_build_visualizer3')
# input is not a valid type
with self.assertRaisesRegex(TypeError, 'visualizer should be'):
runner.build_visualizer('invalid-type')
def test_default_scope(self):
TOY_SCHEDULERS = Registry(
'parameter scheduler', parent=PARAM_SCHEDULERS, scope='toy')
@TOY_SCHEDULERS.register_module(force=True)
class ToyScheduler(MultiStepLR):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.epoch_based_cfg.param_scheduler = dict(
type='ToyScheduler', milestones=[1, 2])
self.epoch_based_cfg.default_scope = 'toy'
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_default_scope'
runner = Runner.from_cfg(cfg)
runner.train()
self.assertIsInstance(runner.param_schedulers[0], ToyScheduler)
def test_build_model(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_model1'
runner = Runner.from_cfg(cfg)
self.assertIsInstance(runner.model, ToyModel)
self.assertIsInstance(runner.model.data_preprocessor,
BaseDataPreprocessor)
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_data_preprocessor'
cfg.data_preprocessor = dict(type='ImgDataPreprocessor')
runner = Runner.from_cfg(cfg)
# data_preprocessor is passed to used if no `data_preprocessor`
# in model config.
self.assertIsInstance(runner.model.data_preprocessor,
ImgDataPreprocessor)
# input should be a nn.Module object or dict
with self.assertRaisesRegex(TypeError, 'model should be'):
runner.build_model('invalid-type')
# input is a nn.Module object
_model = ToyModel1()
model = runner.build_model(_model)
self.assertEqual(id(model), id(_model))
# input is a dict
model = runner.build_model(dict(type='ToyModel1'))
self.assertIsInstance(model, ToyModel1)
def test_wrap_model(self):
# revert sync batchnorm
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_revert_syncbn'
cfg.model = dict(type='ToySyncBNModel')
runner = Runner.from_cfg(cfg)
self.assertIsInstance(runner.model, BaseModel)
assert not isinstance(runner.model.bn, nn.SyncBatchNorm)
# custom model wrapper
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_wrap_model'
cfg.model_wrapper_cfg = dict(type='CustomModelWrapper')
runner = Runner.from_cfg(cfg)
self.assertIsInstance(runner.model, BaseModel)
# Test with ddp wrapper
if torch.cuda.is_available() and torch.distributed.is_nccl_available():
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '29515'
os.environ['RANK'] = str(0)
os.environ['WORLD_SIZE'] = str(1)
cfg.launcher = 'pytorch'
cfg.experiment_name = 'test_wrap_model1'
runner = Runner.from_cfg(cfg)
self.assertIsInstance(runner.model, CustomModelWrapper)
# Test cfg.sync_bn = 'torch', when model does not have BN layer
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.launcher = 'pytorch'
cfg.experiment_name = 'test_wrap_model2'
cfg.sync_bn = 'torch'
cfg.model_wrapper_cfg = dict(type='CustomModelWrapper')
runner.from_cfg(cfg)
@MODELS.register_module(force=True)
class ToyBN(BaseModel):
def __init__(self):
super().__init__()
self.bn = nn.BatchNorm2d(2)
def forward(self, *args, **kwargs):
pass
cfg.model = dict(type='ToyBN')
cfg.experiment_name = 'test_data_preprocessor2'
runner = Runner.from_cfg(cfg)
self.assertIsInstance(runner.model.model.bn,
torch.nn.SyncBatchNorm)
cfg.sync_bn = 'unknown'
cfg.experiment_name = 'test_data_preprocessor3'
with self.assertRaises(ValueError):
Runner.from_cfg(cfg)
def test_scale_lr(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_scale_lr'
runner = Runner.from_cfg(cfg)
# When no base_batch_size in auto_scale_lr, an
# assertion error will raise.
auto_scale_lr = dict(enable=True)
optim_wrapper = OptimWrapper(SGD(runner.model.parameters(), lr=0.01))
with self.assertRaises(AssertionError):
runner.scale_lr(optim_wrapper, auto_scale_lr)
# When auto_scale_lr is None or enable is False, the lr will
# not be linearly scaled.
auto_scale_lr = dict(base_batch_size=16, enable=False)
optim_wrapper = OptimWrapper(SGD(runner.model.parameters(), lr=0.01))
runner.scale_lr(optim_wrapper)
self.assertEqual(optim_wrapper.optimizer.param_groups[0]['lr'], 0.01)
runner.scale_lr(optim_wrapper, auto_scale_lr)
self.assertEqual(optim_wrapper.optimizer.param_groups[0]['lr'], 0.01)
# When auto_scale_lr is correct and enable is True, the lr will
# be linearly scaled.
auto_scale_lr = dict(base_batch_size=16, enable=True)
real_bs = runner.world_size * cfg.train_dataloader['batch_size']
optim_wrapper = OptimWrapper(SGD(runner.model.parameters(), lr=0.01))
runner.scale_lr(optim_wrapper, auto_scale_lr)
self.assertEqual(optim_wrapper.optimizer.param_groups[0]['lr'],
0.01 * (real_bs / 16))
# Test when optim_wrapper is an OptimWrapperDict
optim_wrapper = OptimWrapper(SGD(runner.model.parameters(), lr=0.01))
wrapper_dict = OptimWrapperDict(wrapper=optim_wrapper)
runner.scale_lr(wrapper_dict, auto_scale_lr)
scaled_lr = wrapper_dict['wrapper'].optimizer.param_groups[0]['lr']
self.assertEqual(scaled_lr, 0.01 * (real_bs / 16))
def test_build_optim_wrapper(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_optim_wrapper'
runner = Runner.from_cfg(cfg)
# input should be an Optimizer object or dict
with self.assertRaisesRegex(TypeError, 'optimizer wrapper should be'):
runner.build_optim_wrapper('invalid-type')
# 1. test one optimizer
# 1.1 input is an Optimizer object
optimizer = SGD(runner.model.parameters(), lr=0.01)
optim_wrapper = OptimWrapper(optimizer)
optim_wrapper = runner.build_optim_wrapper(optim_wrapper)
self.assertEqual(id(optimizer), id(optim_wrapper.optimizer))
# 1.2 input is a dict
optim_wrapper = runner.build_optim_wrapper(
dict(type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01)))
self.assertIsInstance(optim_wrapper, OptimWrapper)
# 1.3 use default OptimWrapper type.
optim_wrapper = runner.build_optim_wrapper(
dict(optimizer=dict(type='SGD', lr=0.01)))
self.assertIsInstance(optim_wrapper, OptimWrapper)
# 2. test multiple optmizers
# 2.1 input is a dict which contains multiple optimizer objects
optimizer1 = SGD(runner.model.linear1.parameters(), lr=0.01)
optim_wrapper1 = OptimWrapper(optimizer1)
optimizer2 = Adam(runner.model.linear2.parameters(), lr=0.02)
optim_wrapper2 = OptimWrapper(optimizer2)
optim_wrapper_cfg = dict(key1=optim_wrapper1, key2=optim_wrapper2)
optim_wrapper = runner.build_optim_wrapper(optim_wrapper_cfg)
self.assertIsInstance(optim_wrapper, OptimWrapperDict)
self.assertIsInstance(optim_wrapper['key1'].optimizer, SGD)
self.assertIsInstance(optim_wrapper['key2'].optimizer, Adam)
# 2.2 each item mush be an optimizer object when "type" and
# "constructor" are not in optimizer
optimizer1 = SGD(runner.model.linear1.parameters(), lr=0.01)
optim_wrapper1 = OptimWrapper(optimizer1)
optim_wrapper2 = dict(
type='OptimWrapper', optimizer=dict(type='Adam', lr=0.01))
optim_cfg = dict(key1=optim_wrapper1, key2=optim_wrapper2)
with self.assertRaisesRegex(ValueError,
'each item mush be an optimizer object'):
runner.build_optim_wrapper(optim_cfg)
# 2.3 input is a dict which contains multiple configs
optim_wrapper_cfg = dict(
linear1=dict(
type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01)),
linear2=dict(
type='OptimWrapper', optimizer=dict(type='Adam', lr=0.02)),
constructor='ToyMultipleOptimizerConstructor')
optim_wrapper = runner.build_optim_wrapper(optim_wrapper_cfg)
self.assertIsInstance(optim_wrapper, OptimWrapperDict)
self.assertIsInstance(optim_wrapper['linear1'].optimizer, SGD)
self.assertIsInstance(optim_wrapper['linear2'].optimizer, Adam)
# 2.4 input is a dict which contains optimizer instance.
model = nn.Linear(1, 1)
optimizer = SGD(model.parameters(), lr=0.1)
optim_wrapper_cfg = dict(optimizer=optimizer)
optim_wrapper = runner.build_optim_wrapper(optim_wrapper_cfg)
self.assertIsInstance(optim_wrapper, OptimWrapper)
self.assertIs(optim_wrapper.optimizer, optimizer)
# Specify the type of optimizer wrapper
model = nn.Linear(1, 1)
optimizer = SGD(model.parameters(), lr=0.1)
optim_wrapper_cfg = dict(
optimizer=optimizer, type='ToyOptimWrapper', accumulative_counts=2)
optim_wrapper = runner.build_optim_wrapper(optim_wrapper_cfg)
self.assertIsInstance(optim_wrapper, ToyOptimWrapper)
self.assertIs(optim_wrapper.optimizer, optimizer)
self.assertEqual(optim_wrapper._accumulative_counts, 2)
def test_build_param_scheduler(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_param_scheduler'
runner = Runner.from_cfg(cfg)
# `build_optim_wrapper` should be called before
# `build_param_scheduler`
cfg = dict(type='MultiStepLR', milestones=[1, 2])
runner.optim_wrapper = dict(
key1=dict(
type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01)),
key2=dict(
type='OptimWrapper', optimizer=dict(type='Adam', lr=0.02)),
)
with self.assertRaisesRegex(AssertionError, 'should be called before'):
runner.build_param_scheduler(cfg)
runner.optim_wrapper = runner.build_optim_wrapper(
dict(type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01)))
param_schedulers = runner.build_param_scheduler(cfg)
self.assertIsInstance(param_schedulers, list)
self.assertEqual(len(param_schedulers), 1)
self.assertIsInstance(param_schedulers[0], MultiStepLR)
# 1. test one optimizer and one parameter scheduler
# 1.1 input is a ParamScheduler object
param_scheduler = MultiStepLR(runner.optim_wrapper, milestones=[1, 2])
param_schedulers = runner.build_param_scheduler(param_scheduler)
self.assertEqual(len(param_schedulers), 1)
self.assertEqual(id(param_schedulers[0]), id(param_scheduler))
# 1.2 input is a dict
param_schedulers = runner.build_param_scheduler(param_scheduler)
self.assertEqual(len(param_schedulers), 1)
self.assertIsInstance(param_schedulers[0], MultiStepLR)
# 2. test one optimizer and list of parameter schedulers
# 2.1 input is a list of dict
cfg = [
dict(type='MultiStepLR', milestones=[1, 2]),
dict(type='StepLR', step_size=1)
]
param_schedulers = runner.build_param_scheduler(cfg)
self.assertEqual(len(param_schedulers), 2)
self.assertIsInstance(param_schedulers[0], MultiStepLR)
self.assertIsInstance(param_schedulers[1], StepLR)
# 2.2 input is a list and some items are ParamScheduler objects
cfg = [param_scheduler, dict(type='StepLR', step_size=1)]
param_schedulers = runner.build_param_scheduler(cfg)
self.assertEqual(len(param_schedulers), 2)
self.assertIsInstance(param_schedulers[0], MultiStepLR)
self.assertIsInstance(param_schedulers[1], StepLR)
# 3. test multiple optimizers and list of parameter schedulers
optimizer1 = SGD(runner.model.linear1.parameters(), lr=0.01)
optim_wrapper1 = OptimWrapper(optimizer1)
optimizer2 = Adam(runner.model.linear2.parameters(), lr=0.02)
optim_wrapper2 = OptimWrapper(optimizer2)
optim_wrapper_cfg = dict(key1=optim_wrapper1, key2=optim_wrapper2)
runner.optim_wrapper = runner.build_optim_wrapper(optim_wrapper_cfg)
cfg = [
dict(type='MultiStepLR', milestones=[1, 2]),
dict(type='StepLR', step_size=1)
]
param_schedulers = runner.build_param_scheduler(cfg)
print(param_schedulers)
self.assertIsInstance(param_schedulers, dict)
self.assertEqual(len(param_schedulers), 2)
self.assertEqual(len(param_schedulers['key1']), 2)
self.assertEqual(len(param_schedulers['key2']), 2)
# 4. test multiple optimizers and multiple parameter shceduers
cfg = dict(
key1=dict(type='MultiStepLR', milestones=[1, 2]),
key2=[
dict(type='MultiStepLR', milestones=[1, 2]),
dict(type='StepLR', step_size=1)
])
param_schedulers = runner.build_param_scheduler(cfg)
self.assertIsInstance(param_schedulers, dict)
self.assertEqual(len(param_schedulers), 2)
self.assertEqual(len(param_schedulers['key1']), 1)
self.assertEqual(len(param_schedulers['key2']), 2)
# 5. test converting epoch-based scheduler to iter-based
runner.optim_wrapper = runner.build_optim_wrapper(
dict(type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01)))
# 5.1 train loop should be built before converting scheduler
cfg = dict(
type='MultiStepLR', milestones=[1, 2], convert_to_iter_based=True)
# 5.2 convert epoch-based to iter-based scheduler
cfg = dict(
type='MultiStepLR',
milestones=[1, 2],
begin=1,
end=7,
convert_to_iter_based=True)
runner._train_loop = runner.build_train_loop(runner.train_loop)
param_schedulers = runner.build_param_scheduler(cfg)
self.assertFalse(param_schedulers[0].by_epoch)
self.assertEqual(param_schedulers[0].begin, 4)
self.assertEqual(param_schedulers[0].end, 28)
# 6. test set default end of schedulers
cfg = dict(type='MultiStepLR', milestones=[1, 2], begin=1)
param_schedulers = runner.build_param_scheduler(cfg)
self.assertTrue(param_schedulers[0].by_epoch)
self.assertEqual(param_schedulers[0].begin, 1)
# runner.max_epochs = 3
self.assertEqual(param_schedulers[0].end, 3)
cfg = dict(
type='MultiStepLR',
milestones=[1, 2],
begin=1,
convert_to_iter_based=True)
param_schedulers = runner.build_param_scheduler(cfg)
self.assertFalse(param_schedulers[0].by_epoch)
self.assertEqual(param_schedulers[0].begin, 4)
# runner.max_iters = 3*4
self.assertEqual(param_schedulers[0].end, 12)
def test_build_evaluator(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_evaluator'
runner = Runner.from_cfg(cfg)
# input is a BaseEvaluator or ComposedEvaluator object
evaluator = Evaluator(ToyMetric1())
self.assertEqual(id(runner.build_evaluator(evaluator)), id(evaluator))
evaluator = Evaluator([ToyMetric1(), ToyMetric2()])
self.assertEqual(id(runner.build_evaluator(evaluator)), id(evaluator))
# input is a dict
evaluator = dict(type='ToyMetric1')
self.assertIsInstance(runner.build_evaluator(evaluator), Evaluator)
# input is a list of dict
evaluator = [dict(type='ToyMetric1'), dict(type='ToyMetric2')]
self.assertIsInstance(runner.build_evaluator(evaluator), Evaluator)
# input is a list of built metric.
metric = [ToyMetric1(), ToyMetric2()]
_evaluator = runner.build_evaluator(metric)
self.assertIs(_evaluator.metrics[0], metric[0])
self.assertIs(_evaluator.metrics[1], metric[1])
# test collect device
evaluator = [
dict(type='ToyMetric1', collect_device='cpu'),
dict(type='ToyMetric2', collect_device='gpu')
]
_evaluator = runner.build_evaluator(evaluator)
self.assertEqual(_evaluator.metrics[0].collect_device, 'cpu')
self.assertEqual(_evaluator.metrics[1].collect_device, 'gpu')
# test build a customize evaluator
evaluator = dict(
type='ToyEvaluator',
metrics=[
dict(type='ToyMetric1', collect_device='cpu'),
dict(type='ToyMetric2', collect_device='gpu')
])
_evaluator = runner.build_evaluator(evaluator)
self.assertIsInstance(runner.build_evaluator(evaluator), ToyEvaluator)
self.assertEqual(_evaluator.metrics[0].collect_device, 'cpu')
self.assertEqual(_evaluator.metrics[1].collect_device, 'gpu')
# test evaluator must be a Evaluator instance
with self.assertRaisesRegex(TypeError, 'evaluator should be'):
_evaluator = runner.build_evaluator(ToyMetric1())
def test_build_dataloader(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_dataloader'
runner = Runner.from_cfg(cfg)
cfg = dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=True),
batch_size=1,
num_workers=0)
seed = np.random.randint(2**31)
dataloader = runner.build_dataloader(cfg, seed=seed)
self.assertIsInstance(dataloader, DataLoader)
self.assertIsInstance(dataloader.dataset, ToyDataset)
self.assertIsInstance(dataloader.sampler, DefaultSampler)
self.assertEqual(dataloader.sampler.seed, seed)
# diff_rank_seed is True
dataloader = runner.build_dataloader(
cfg, seed=seed, diff_rank_seed=True)
self.assertNotEqual(dataloader.sampler.seed, seed)
# custom worker_init_fn
cfg = dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=True),
worker_init_fn=dict(type='custom_worker_init'),
batch_size=1,
num_workers=2)
dataloader = runner.build_dataloader(cfg)
self.assertIs(dataloader.worker_init_fn.func, custom_worker_init)
# collate_fn is a dict
cfg = dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=True),
worker_init_fn=dict(type='custom_worker_init'),
batch_size=1,
num_workers=2,
collate_fn=dict(type='pseudo_collate'))
dataloader = runner.build_dataloader(cfg)
self.assertIsInstance(dataloader.collate_fn, partial)
# collate_fn is a callable object
def custom_collate(data_batch):
return data_batch
cfg = dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=True),
worker_init_fn=dict(type='custom_worker_init'),
batch_size=1,
num_workers=2,
collate_fn=custom_collate)
dataloader = runner.build_dataloader(cfg)
self.assertIs(dataloader.collate_fn, custom_collate)
# collate_fn is a invalid value
with self.assertRaisesRegex(
TypeError, 'collate_fn should be a dict or callable object'):
cfg = dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=True),
worker_init_fn=dict(type='custom_worker_init'),
batch_size=1,
num_workers=2,
collate_fn='collate_fn')
dataloader = runner.build_dataloader(cfg)
self.assertIsInstance(dataloader.collate_fn, partial)
# num_batch_per_epoch is not None
cfg = dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='DefaultSampler', shuffle=True),
collate_fn=dict(type='default_collate'),
batch_size=3,
num_workers=2,
num_batch_per_epoch=2)
dataloader = runner.build_dataloader(cfg)
self.assertEqual(len(dataloader.dataset), 6)
def test_build_train_loop(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_train_loop'
runner = Runner.from_cfg(cfg)
# input should be a Loop object or dict
with self.assertRaisesRegex(TypeError, 'should be'):
runner.build_train_loop('invalid-type')
# Only one of type or by_epoch can exist in cfg
cfg = dict(type='EpochBasedTrainLoop', by_epoch=True, max_epochs=3)
with self.assertRaisesRegex(RuntimeError, 'Only one'):
runner.build_train_loop(cfg)
# input is a dict and contains type key
cfg = dict(type='EpochBasedTrainLoop', max_epochs=3)
loop = runner.build_train_loop(cfg)
self.assertIsInstance(loop, EpochBasedTrainLoop)
cfg = dict(type='IterBasedTrainLoop', max_iters=3)
loop = runner.build_train_loop(cfg)
self.assertIsInstance(loop, IterBasedTrainLoop)
# input is a dict and does not contain type key
cfg = dict(by_epoch=True, max_epochs=3)
loop = runner.build_train_loop(cfg)
self.assertIsInstance(loop, EpochBasedTrainLoop)
cfg = dict(by_epoch=False, max_iters=3)
loop = runner.build_train_loop(cfg)
self.assertIsInstance(loop, IterBasedTrainLoop)
# input is a Loop object
self.assertEqual(id(runner.build_train_loop(loop)), id(loop))
# param_schedulers can be None
cfg = dict(type='EpochBasedTrainLoop', max_epochs=3)
runner.param_schedulers = None
loop = runner.build_train_loop(cfg)
self.assertIsInstance(loop, EpochBasedTrainLoop)
# test custom training loop
cfg = dict(type='CustomTrainLoop', max_epochs=3)
loop = runner.build_train_loop(cfg)
self.assertIsInstance(loop, CustomTrainLoop)
def test_build_val_loop(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_val_loop'
runner = Runner.from_cfg(cfg)
# input should be a Loop object or dict
with self.assertRaisesRegex(TypeError, 'should be'):
runner.build_test_loop('invalid-type')
# input is a dict and contains type key
cfg = dict(type='ValLoop')
loop = runner.build_test_loop(cfg)
self.assertIsInstance(loop, ValLoop)
# input is a dict but does not contain type key
cfg = dict()
loop = runner.build_val_loop(cfg)
self.assertIsInstance(loop, ValLoop)
# input is a Loop object
self.assertEqual(id(runner.build_val_loop(loop)), id(loop))
# test custom validation loop
cfg = dict(type='CustomValLoop')
loop = runner.build_val_loop(cfg)
self.assertIsInstance(loop, CustomValLoop)
def test_build_test_loop(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_test_loop'
runner = Runner.from_cfg(cfg)
# input should be a Loop object or dict
with self.assertRaisesRegex(TypeError, 'should be'):
runner.build_test_loop('invalid-type')
# input is a dict and contains type key
cfg = dict(type='TestLoop')
loop = runner.build_test_loop(cfg)
self.assertIsInstance(loop, TestLoop)
# input is a dict but does not contain type key
cfg = dict()
loop = runner.build_test_loop(cfg)
self.assertIsInstance(loop, TestLoop)
# input is a Loop object
self.assertEqual(id(runner.build_test_loop(loop)), id(loop))
# test custom validation loop
cfg = dict(type='CustomTestLoop')
loop = runner.build_val_loop(cfg)
self.assertIsInstance(loop, CustomTestLoop)
def test_build_log_processor(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_log_processor'
runner = Runner.from_cfg(cfg)
# input should be a LogProcessor object or dict
with self.assertRaisesRegex(TypeError, 'should be'):
runner.build_log_processor('invalid-type')
# input is a dict and contains type key
cfg = dict(type='LogProcessor')
log_processor = runner.build_log_processor(cfg)
self.assertIsInstance(log_processor, LogProcessor)
# input is a dict but does not contain type key
cfg = dict()
log_processor = runner.build_log_processor(cfg)
self.assertIsInstance(log_processor, LogProcessor)
# input is a LogProcessor object
self.assertEqual(
id(runner.build_log_processor(log_processor)), id(log_processor))
# test custom validation log_processor
cfg = dict(type='CustomLogProcessor')
log_processor = runner.build_log_processor(cfg)
self.assertIsInstance(log_processor, CustomLogProcessor)
def test_train(self):
# 1. test `self.train_loop` is None
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_train1'
cfg.pop('train_dataloader')
cfg.pop('train_cfg')
cfg.pop('optim_wrapper')
cfg.pop('param_scheduler')
runner = Runner.from_cfg(cfg)
with self.assertRaisesRegex(RuntimeError, 'should not be None'):
runner.train()
# 2. test iter and epoch counter of EpochBasedTrainLoop and timing of
# running ValLoop
epoch_results = []
epoch_targets = [i for i in range(3)]
iter_results = []
iter_targets = [i for i in range(4 * 3)]
batch_idx_results = []
batch_idx_targets = [i for i in range(4)] * 3 # train and val
val_epoch_results = []
val_epoch_targets = [i for i in range(2, 4)]
@HOOKS.register_module(force=True)
class TestEpochHook(Hook):
def before_train_epoch(self, runner):
epoch_results.append(runner.epoch)
def before_train_iter(self, runner, batch_idx, data_batch=None):
iter_results.append(runner.iter)
batch_idx_results.append(batch_idx)
def before_val_epoch(self, runner):
val_epoch_results.append(runner.epoch)
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_train2'
cfg.custom_hooks = [dict(type='TestEpochHook', priority=50)]
cfg.train_cfg = dict(by_epoch=True, max_epochs=3, val_begin=2)
runner = Runner.from_cfg(cfg)
runner.train()
self.assertEqual(runner.optim_wrapper._inner_count, 12)
self.assertEqual(runner.optim_wrapper._max_counts, 12)
assert isinstance(runner.train_loop, EpochBasedTrainLoop)
for result, target, in zip(epoch_results, epoch_targets):
self.assertEqual(result, target)
for result, target, in zip(iter_results, iter_targets):
self.assertEqual(result, target)
for result, target, in zip(batch_idx_results, batch_idx_targets):
self.assertEqual(result, target)
for result, target, in zip(val_epoch_results, val_epoch_targets):
self.assertEqual(result, target)
# 3. test iter and epoch counter of IterBasedTrainLoop and timing of
# running ValLoop
epoch_results = []
iter_results = []
batch_idx_results = []
val_iter_results = []
val_batch_idx_results = []
iter_targets = [i for i in range(12)]
batch_idx_targets = [i for i in range(12)]
val_iter_targets = [i for i in range(4, 12)]
val_batch_idx_targets = [i for i in range(4)] * 2
@HOOKS.register_module(force=True)
class TestIterHook(Hook):
def before_train_epoch(self, runner):
epoch_results.append(runner.epoch)
def before_train_iter(self, runner, batch_idx, data_batch=None):
iter_results.append(runner.iter)
batch_idx_results.append(batch_idx)
def before_val_iter(self, runner, batch_idx, data_batch=None):
val_epoch_results.append(runner.iter)
val_batch_idx_results.append(batch_idx)
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_train3'
cfg.custom_hooks = [dict(type='TestIterHook', priority=50)]
cfg.train_cfg = dict(
by_epoch=False, max_iters=12, val_interval=4, val_begin=4)
runner = Runner.from_cfg(cfg)
runner.train()
self.assertEqual(runner.optim_wrapper._inner_count, 12)
self.assertEqual(runner.optim_wrapper._max_counts, 12)
assert isinstance(runner.train_loop, IterBasedTrainLoop)
self.assertEqual(len(epoch_results), 1)
self.assertEqual(epoch_results[0], 0)
self.assertEqual(runner.val_interval, 4)
self.assertEqual(runner.val_begin, 4)
for result, target, in zip(iter_results, iter_targets):
self.assertEqual(result, target)
for result, target, in zip(batch_idx_results, batch_idx_targets):
self.assertEqual(result, target)
for result, target, in zip(val_iter_results, val_iter_targets):
self.assertEqual(result, target)
for result, target, in zip(val_batch_idx_results,
val_batch_idx_targets):
self.assertEqual(result, target)
# 4. test iter and epoch counter of IterBasedTrainLoop and timing of
# running ValLoop without InfiniteSampler
epoch_results = []
iter_results = []
batch_idx_results = []
val_iter_results = []
val_batch_idx_results = []
iter_targets = [i for i in range(12)]
batch_idx_targets = [i for i in range(12)]
val_iter_targets = [i for i in range(4, 12)]
val_batch_idx_targets = [i for i in range(4)] * 2
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_train4'
cfg.train_dataloader.sampler = dict(
type='DefaultSampler', shuffle=True)
cfg.custom_hooks = [dict(type='TestIterHook', priority=50)]
cfg.train_cfg = dict(
by_epoch=False, max_iters=12, val_interval=4, val_begin=4)
runner = Runner.from_cfg(cfg)
# Warning should be raised since the sampler is not InfiniteSampler.
with self.assertLogs(MMLogger.get_current_instance(), level='WARNING'):
runner.train()
assert isinstance(runner.train_loop, IterBasedTrainLoop)
assert isinstance(runner.train_loop.dataloader_iterator,
_InfiniteDataloaderIterator)
self.assertEqual(len(epoch_results), 1)
self.assertEqual(epoch_results[0], 0)
self.assertEqual(runner.val_interval, 4)
self.assertEqual(runner.val_begin, 4)
for result, target, in zip(iter_results, iter_targets):
self.assertEqual(result, target)
for result, target, in zip(batch_idx_results, batch_idx_targets):
self.assertEqual(result, target)
for result, target, in zip(val_iter_results, val_iter_targets):
self.assertEqual(result, target)
for result, target, in zip(val_batch_idx_results,
val_batch_idx_targets):
self.assertEqual(result, target)
# 5.1 test dynamic interval in IterBasedTrainLoop
max_iters = 12
interval = 5
dynamic_intervals = [(11, 2)]
iter_results = []
iter_targets = [5, 10, 12]
val_interval_results = []
val_interval_targets = [5] * 10 + [2] * 2
@HOOKS.register_module(force=True)
class TestIterDynamicIntervalHook(Hook):
def before_val(self, runner):
iter_results.append(runner.iter)
def before_train_iter(self, runner, batch_idx, data_batch=None):
val_interval_results.append(runner.train_loop.val_interval)
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_train5'
cfg.train_dataloader.sampler = dict(
type='DefaultSampler', shuffle=True)
cfg.custom_hooks = [
dict(type='TestIterDynamicIntervalHook', priority=50)
]
cfg.train_cfg = dict(
by_epoch=False,
max_iters=max_iters,
val_interval=interval,
dynamic_intervals=dynamic_intervals)
runner = Runner.from_cfg(cfg)
runner.train()
for result, target, in zip(iter_results, iter_targets):
self.assertEqual(result, target)
for result, target, in zip(val_interval_results, val_interval_targets):
self.assertEqual(result, target)
# 5.2 test dynamic interval in EpochBasedTrainLoop
max_epochs = 12
interval = 5
dynamic_intervals = [(11, 2)]
epoch_results = []
epoch_targets = [5, 10, 12]
val_interval_results = []
val_interval_targets = [5] * 10 + [2] * 2
@HOOKS.register_module(force=True)
class TestEpochDynamicIntervalHook(Hook):
def before_val_epoch(self, runner):
epoch_results.append(runner.epoch)
def before_train_epoch(self, runner):
val_interval_results.append(runner.train_loop.val_interval)
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_train6'
cfg.train_dataloader.sampler = dict(
type='DefaultSampler', shuffle=True)
cfg.custom_hooks = [
dict(type='TestEpochDynamicIntervalHook', priority=50)
]
cfg.train_cfg = dict(
by_epoch=True,
max_epochs=max_epochs,
val_interval=interval,
dynamic_intervals=dynamic_intervals)
runner = Runner.from_cfg(cfg)
runner.train()
for result, target, in zip(epoch_results, epoch_targets):
self.assertEqual(result, target)
for result, target, in zip(val_interval_results, val_interval_targets):
self.assertEqual(result, target)
# 7. test init weights
@MODELS.register_module(force=True)
class ToyModel2(ToyModel):
def __init__(self):
super().__init__()
self.initiailzed = False
def init_weights(self):
self.initiailzed = True
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_train7'
runner = Runner.from_cfg(cfg)
model = ToyModel2()
runner.model = model
runner.train()
self.assertTrue(model.initiailzed)
# 8.1 test train with multiple optimizer and single list of schedulers.
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_train8'
cfg.param_scheduler = dict(type='MultiStepLR', milestones=[1, 2])
cfg.optim_wrapper = dict(
linear1=dict(
type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01)),
linear2=dict(
type='OptimWrapper', optimizer=dict(type='Adam', lr=0.02)),
constructor='ToyMultipleOptimizerConstructor')
cfg.model = dict(type='ToyGANModel')
runner = runner.from_cfg(cfg)
runner.train()
# 8.1 Test train with multiple optimizer and single schedulers.
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_train8.1.1'
cfg.param_scheduler = dict(type='MultiStepLR', milestones=[1, 2])
cfg.optim_wrapper = dict(
linear1=dict(
type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01)),
linear2=dict(
type='OptimWrapper', optimizer=dict(type='Adam', lr=0.02)),
constructor='ToyMultipleOptimizerConstructor')
cfg.model = dict(type='ToyGANModel')
runner = runner.from_cfg(cfg)
runner.train()
# Test list like single scheduler.
cfg.experiment_name = 'test_train8.1.2'
cfg.param_scheduler = [dict(type='MultiStepLR', milestones=[1, 2])]
runner = runner.from_cfg(cfg)
runner.train()
# 8.2 Test train with multiple optimizer and multiple schedulers.
cfg.experiment_name = 'test_train8.2.1'
cfg.param_scheduler = dict(
linear1=dict(type='MultiStepLR', milestones=[1, 2]),
linear2=dict(type='MultiStepLR', milestones=[1, 2]),
)
runner = runner.from_cfg(cfg)
runner.train()
cfg.experiment_name = 'test_train8.2.2'
cfg.param_scheduler = dict(
linear1=[dict(type='MultiStepLR', milestones=[1, 2])],
linear2=[dict(type='MultiStepLR', milestones=[1, 2])],
)
runner = runner.from_cfg(cfg)
runner.train()
# 9 Test training with a dataset without metainfo
cfg.experiment_name = 'test_train9'
cfg = copy.deepcopy(cfg)
cfg.train_dataloader.dataset = dict(type='ToyDatasetNoMeta')
runner = runner.from_cfg(cfg)
runner.train()
# 10.1 Test build dataloader with default collate function
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_train10.1'
cfg.train_dataloader.update(collate_fn=dict(type='default_collate'))
runner = Runner.from_cfg(cfg)
runner.train()
# 10.2 Test build dataloader with custom collate function
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_train10.2'
cfg.train_dataloader.update(
collate_fn=dict(type='custom_collate', pad_value=100))
runner = Runner.from_cfg(cfg)
runner.train()
# 10.3 Test build dataloader with custom worker_init function
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_train10.3'
cfg.train_dataloader.update(
worker_init_fn=dict(type='custom_worker_init'))
runner = Runner.from_cfg(cfg)
runner.train()
# 11 test build dataloader without default arguments of collate
# function.
with self.assertRaises(TypeError):
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_train11'
cfg.train_dataloader.update(collate_fn=dict(type='custom_collate'))
runner = Runner.from_cfg(cfg)
runner.train()
# 12.1 Test train with model, which does not inherit from BaseModel
@MODELS.register_module(force=True)
class ToyModel3(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(1, 1)
def train_step(self, *args, **kwargs):
return dict(loss=torch.tensor(1))
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.pop('val_cfg')
cfg.pop('val_dataloader')
cfg.pop('val_evaluator')
cfg.model = dict(type='ToyModel3')
cfg.experiment_name = 'test_train12.1'
runner = Runner.from_cfg(cfg)
runner.train()
# 12.2 Test val_step should be implemented if val_cfg is not None
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.model = dict(type='ToyModel3')
cfg.experiment_name = 'test_train12.2'
runner = Runner.from_cfg(cfg)
with self.assertRaisesRegex(AssertionError, 'If you want to validate'):
runner.train()
# 13 Test the logs will be printed when the length of
# train_dataloader is smaller than the interval set in LoggerHook
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_train13'
cfg.default_hooks = dict(logger=dict(type='LoggerHook', interval=5))
runner = Runner.from_cfg(cfg)
runner.train()
with open(runner.logger._log_file) as f:
log = f.read()
self.assertIn('Epoch(train) [1][4/4]', log)
# 14. test_loop will not be built
for cfg in (self.epoch_based_cfg, self.iter_based_cfg):
cfg = copy.deepcopy(cfg)
cfg.experiment_name = 'test_train14'
runner = Runner.from_cfg(cfg)
runner.train()
self.assertIsInstance(runner._train_loop, BaseLoop)
self.assertIsInstance(runner._val_loop, BaseLoop)
self.assertIsInstance(runner._test_loop, dict)
# 15. test num_batch_per_epoch
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_train15'
cfg.train_dataloader['num_batch_per_epoch'] = 2
cfg.train_cfg = dict(
by_epoch=True,
max_epochs=3,
)
runner = Runner.from_cfg(cfg)
runner.train()
self.assertEqual(runner.iter, 3 * 2)
@skipIf(
SKIP_TEST_COMPILE,
reason='torch.compile is not valid, please install PyTorch>=2.0.0')
def test_train_with_compile(self):
# 1. test with simple configuration
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_train_compile_simple'
cfg.compile = True
runner = Runner.from_cfg(cfg)
runner.train()
# 2. test with advanced configuration
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_train_compile_advanced'
cfg.compile = dict(backend='inductor', mode='default')
runner = Runner.from_cfg(cfg)
runner.train()
runner._maybe_compile('train_step')
# PyTorch 2.0.0 could close the FileHandler after calling of
# ``torch.compile``. So we need to test our file handler still works.
with open(osp.join(f'{runner.log_dir}',
f'{runner.timestamp}.log')) as f:
last_line = f.readlines()[-1]
self.assertTrue(last_line.endswith('please be patient.\n'))
def test_val(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_val1'
cfg.pop('val_dataloader')
cfg.pop('val_cfg')
cfg.pop('val_evaluator')
runner = Runner.from_cfg(cfg)
with self.assertRaisesRegex(RuntimeError, 'should not be None'):
runner.val()
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_val2'
runner = Runner.from_cfg(cfg)
runner.val()
# test run val without train and test components
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_individually_val'
cfg.pop('train_dataloader')
cfg.pop('train_cfg')
cfg.pop('optim_wrapper')
cfg.pop('param_scheduler')
cfg.pop('test_dataloader')
cfg.pop('test_cfg')
cfg.pop('test_evaluator')
runner = Runner.from_cfg(cfg)
# Test default fp32 `autocast` context.
predictions = []
def get_outputs_callback(module, inputs, outputs):
predictions.append(outputs)
runner.model.register_forward_hook(get_outputs_callback)
runner.val()
self.assertEqual(predictions[0].dtype, torch.float32)
predictions.clear()
# Test fp16 `autocast` context.
cfg.experiment_name = 'test_val3'
cfg.val_cfg = dict(fp16=True)
runner = Runner.from_cfg(cfg)
runner.model.register_forward_hook(get_outputs_callback)
if (digit_version(TORCH_VERSION) < digit_version('1.10.0')
and not torch.cuda.is_available()):
with self.assertRaisesRegex(RuntimeError, 'If pytorch versions'):
runner.val()
else:
runner.val()
self.assertIn(predictions[0].dtype,
(torch.float16, torch.bfloat16))
# train_loop and test_loop will not be built
for cfg in (self.epoch_based_cfg, self.iter_based_cfg):
cfg = copy.deepcopy(cfg)
cfg.experiment_name = 'test_val4'
runner = Runner.from_cfg(cfg)
runner.val()
self.assertIsInstance(runner._train_loop, dict)
self.assertIsInstance(runner._test_loop, dict)
# test num_batch_per_epoch
val_result = 0
@HOOKS.register_module(force=True)
class TestIterHook(Hook):
def __init__(self):
self.val_iter = 0
def after_val_iter(self,
runner,
batch_idx,
data_batch=None,
outputs=None):
self.val_iter += 1
nonlocal val_result
val_result = self.val_iter
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.custom_hooks = [dict(type='TestIterHook', priority=50)]
cfg.val_dataloader['num_batch_per_epoch'] = 2
runner = Runner.from_cfg(cfg)
runner.val()
self.assertEqual(val_result, 2)
@skipIf(
SKIP_TEST_COMPILE,
reason='torch.compile is not valid, please install PyTorch>=2.0.0')
def test_val_with_compile(self):
# 1. test with simple configuration
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_val_compile_simple'
cfg.compile = True
runner = Runner.from_cfg(cfg)
runner.val()
# 2. test with advanced configuration
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_val_compile_advanced'
cfg.compile = dict(backend='inductor', mode='default')
runner = Runner.from_cfg(cfg)
runner.val()
def test_test(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_test1'
cfg.pop('test_dataloader')
cfg.pop('test_cfg')
cfg.pop('test_evaluator')
runner = Runner.from_cfg(cfg)
with self.assertRaisesRegex(RuntimeError, 'should not be None'):
runner.test()
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_test2'
runner = Runner.from_cfg(cfg)
runner.test()
# Test run test without building train loop.
self.assertIsInstance(runner._train_loop, dict)
# test run test without train and test components
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_individually_test'
cfg.pop('train_dataloader')
cfg.pop('train_cfg')
cfg.pop('optim_wrapper')
cfg.pop('param_scheduler')
cfg.pop('val_dataloader')
cfg.pop('val_cfg')
cfg.pop('val_evaluator')
runner = Runner.from_cfg(cfg)
# Test default fp32 `autocast` context.
predictions = []
def get_outputs_callback(module, inputs, outputs):
predictions.append(outputs)
runner.model.register_forward_hook(get_outputs_callback)
runner.test()
self.assertEqual(predictions[0].dtype, torch.float32)
predictions.clear()
# Test fp16 `autocast` context.
cfg.experiment_name = 'test_test3'
cfg.test_cfg = dict(fp16=True)
runner = Runner.from_cfg(cfg)
runner.model.register_forward_hook(get_outputs_callback)
if (digit_version(TORCH_VERSION) < digit_version('1.10.0')
and not torch.cuda.is_available()):
with self.assertRaisesRegex(RuntimeError, 'If pytorch versions'):
runner.test()
else:
runner.test()
self.assertIn(predictions[0].dtype,
(torch.float16, torch.bfloat16))
# train_loop and val_loop will not be built
for cfg in (self.epoch_based_cfg, self.iter_based_cfg):
cfg = copy.deepcopy(cfg)
cfg.experiment_name = 'test_test4'
runner = Runner.from_cfg(cfg)
runner.test()
self.assertIsInstance(runner._train_loop, dict)
self.assertIsInstance(runner._val_loop, dict)
# test num_batch_per_epoch
test_result = 0
@HOOKS.register_module(force=True)
class TestIterHook(Hook):
def __init__(self):
self.test_iter = 0
def after_test_iter(self,
runner,
batch_idx,
data_batch=None,
outputs=None):
self.test_iter += 1
nonlocal test_result
test_result = self.test_iter
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.custom_hooks = [dict(type='TestIterHook', priority=50)]
cfg.test_dataloader['num_batch_per_epoch'] = 2
runner = Runner.from_cfg(cfg)
runner.test()
self.assertEqual(test_result, 2)
@skipIf(
SKIP_TEST_COMPILE,
reason='torch.compile is not valid, please install PyTorch>=2.0.0')
def test_test_with_compile(self):
# 1. test with simple configuration
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_test_compile_simple'
cfg.compile = True
runner = Runner.from_cfg(cfg)
runner.test()
# 2. test with advanced configuration
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_test_compile_advanced'
cfg.compile = dict(backend='inductor', mode='default')
runner = Runner.from_cfg(cfg)
runner.test()
def test_register_hook(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_register_hook'
runner = Runner.from_cfg(cfg)
runner._hooks = []
# 1. test `hook` parameter
# 1.1 `hook` should be either a Hook object or dict
with self.assertRaisesRegex(
TypeError, 'hook should be an instance of Hook or dict'):
runner.register_hook(['string'])
# 1.2 `hook` is a dict
timer_cfg = dict(type='IterTimerHook')
runner.register_hook(timer_cfg)
self.assertEqual(len(runner._hooks), 1)
self.assertTrue(isinstance(runner._hooks[0], IterTimerHook))
# default priority of `IterTimerHook` is 'NORMAL'
self.assertEqual(
get_priority(runner._hooks[0].priority), get_priority('NORMAL'))
runner._hooks = []
# 1.2.1 `hook` is a dict and contains `priority` field
# set the priority of `IterTimerHook` as 'BELOW_NORMAL'
timer_cfg = dict(type='IterTimerHook', priority='BELOW_NORMAL')
runner.register_hook(timer_cfg)
self.assertEqual(len(runner._hooks), 1)
self.assertTrue(isinstance(runner._hooks[0], IterTimerHook))
self.assertEqual(
get_priority(runner._hooks[0].priority),
get_priority('BELOW_NORMAL'))
# 1.3 `hook` is a hook object
runtime_info_hook = RuntimeInfoHook()
runner.register_hook(runtime_info_hook)
self.assertEqual(len(runner._hooks), 2)
# The priority of `runtime_info_hook` is `HIGH` which is greater than
# `IterTimerHook`, so the first item of `_hooks` should be
# `runtime_info_hook`
self.assertTrue(isinstance(runner._hooks[0], RuntimeInfoHook))
self.assertEqual(
get_priority(runner._hooks[0].priority), get_priority('VERY_HIGH'))
# 2. test `priority` parameter
# `priority` argument is not None and it will be set as priority of
# hook
param_scheduler_cfg = dict(type='ParamSchedulerHook', priority='LOW')
runner.register_hook(param_scheduler_cfg, priority='VERY_LOW')
self.assertEqual(len(runner._hooks), 3)
self.assertTrue(isinstance(runner._hooks[2], ParamSchedulerHook))
self.assertEqual(
get_priority(runner._hooks[2].priority), get_priority('VERY_LOW'))
# `priority` is Priority
logger_cfg = dict(type='LoggerHook', priority='BELOW_NORMAL')
runner.register_hook(logger_cfg, priority=Priority.VERY_LOW)
self.assertEqual(len(runner._hooks), 4)
self.assertTrue(isinstance(runner._hooks[3], LoggerHook))
self.assertEqual(
get_priority(runner._hooks[3].priority), get_priority('VERY_LOW'))
def test_default_hooks(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_default_hooks'
runner = Runner.from_cfg(cfg)
runner._hooks = []
# register 7 hooks by default
runner.register_default_hooks()
self.assertEqual(len(runner._hooks), 6)
# the third registered hook should be `DistSamplerSeedHook`
self.assertTrue(isinstance(runner._hooks[2], DistSamplerSeedHook))
# the fifth registered hook should be `ParamSchedulerHook`
self.assertTrue(isinstance(runner._hooks[4], ParamSchedulerHook))
runner._hooks = []
# remove `ParamSchedulerHook` from default hooks
runner.register_default_hooks(hooks=dict(timer=None))
self.assertEqual(len(runner._hooks), 5)
# `ParamSchedulerHook` was popped so the fifth is `CheckpointHook`
self.assertTrue(isinstance(runner._hooks[4], CheckpointHook))
# add a new default hook
runner._hooks = []
runner.register_default_hooks(hooks=dict(ToyHook=dict(type='ToyHook')))
self.assertEqual(len(runner._hooks), 7)
self.assertTrue(isinstance(runner._hooks[6], ToyHook))
def test_custom_hooks(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_custom_hooks'
runner = Runner.from_cfg(cfg)
self.assertEqual(len(runner._hooks), 6)
custom_hooks = [dict(type='ToyHook')]
runner.register_custom_hooks(custom_hooks)
self.assertEqual(len(runner._hooks), 7)
self.assertTrue(isinstance(runner._hooks[6], ToyHook))
def test_register_hooks(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_register_hooks'
runner = Runner.from_cfg(cfg)
runner._hooks = []
custom_hooks = [dict(type='ToyHook')]
runner.register_hooks(custom_hooks=custom_hooks)
# six default hooks + custom hook (ToyHook)
self.assertEqual(len(runner._hooks), 7)
self.assertTrue(isinstance(runner._hooks[6], ToyHook))
def test_custom_loop(self):
# test custom loop with additional hook
@LOOPS.register_module(force=True)
class CustomTrainLoop2(IterBasedTrainLoop):
"""Custom train loop with additional warmup stage."""
def __init__(self, runner, dataloader, max_iters, warmup_loader,
max_warmup_iters):
super().__init__(
runner=runner, dataloader=dataloader, max_iters=max_iters)
self.warmup_loader = self.runner.build_dataloader(
warmup_loader)
self.max_warmup_iters = max_warmup_iters
def run(self):
self.runner.call_hook('before_train')
self.runner.cur_dataloader = self.warmup_loader
for idx, data_batch in enumerate(self.warmup_loader, 1):
self.warmup_iter(data_batch)
if idx == self.max_warmup_iters:
break
self.runner.cur_dataloader = self.warmup_loader
self.runner.call_hook('before_train_epoch')
while self.runner.iter < self._max_iters:
data_batch = next(self.dataloader_iterator)
self.run_iter(data_batch)
self.runner.call_hook('after_train_epoch')
self.runner.call_hook('after_train')
def warmup_iter(self, data_batch):
self.runner.call_hook(
'before_warmup_iter', data_batch=data_batch)
train_logs = self.runner.model.train_step(
data_batch, self.runner.optim_wrapper)
self.runner.message_hub.update_info('train_logs', train_logs)
self.runner.call_hook(
'after_warmup_iter', data_batch=data_batch)
before_warmup_iter_results = []
after_warmup_iter_results = []
@HOOKS.register_module(force=True)
class TestWarmupHook(Hook):
"""Test custom train loop."""
def before_warmup_iter(self, runner, data_batch=None):
before_warmup_iter_results.append('before')
def after_warmup_iter(self, runner, data_batch=None, outputs=None):
after_warmup_iter_results.append('after')
self.iter_based_cfg.train_cfg = dict(
type='CustomTrainLoop2',
max_iters=10,
warmup_loader=dict(
dataset=dict(type='ToyDataset'),
sampler=dict(type='InfiniteSampler', shuffle=True),
batch_size=1,
num_workers=0),
max_warmup_iters=5)
self.iter_based_cfg.custom_hooks = [
dict(type='TestWarmupHook', priority=50)
]
self.iter_based_cfg.experiment_name = 'test_custom_loop'
runner = Runner.from_cfg(self.iter_based_cfg)
runner.train()
self.assertIsInstance(runner.train_loop, CustomTrainLoop2)
# test custom hook triggered as expected
self.assertEqual(len(before_warmup_iter_results), 5)
self.assertEqual(len(after_warmup_iter_results), 5)
for before, after in zip(before_warmup_iter_results,
after_warmup_iter_results):
self.assertEqual(before, 'before')
self.assertEqual(after, 'after')
def test_checkpoint(self):
# 1. test epoch based
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint1'
runner = Runner.from_cfg(cfg)
runner.train()
# 1.1 test `save_checkpoint` which is called by `CheckpointHook`
path = osp.join(self.temp_dir, 'epoch_3.pth')
self.assertTrue(osp.exists(path))
self.assertFalse(osp.exists(osp.join(self.temp_dir, 'epoch_4.pth')))
ckpt = torch.load(path)
self.assertEqual(ckpt['meta']['epoch'], 3)
self.assertEqual(ckpt['meta']['iter'], 12)
self.assertEqual(ckpt['meta']['experiment_name'],
runner.experiment_name)
self.assertEqual(ckpt['meta']['seed'], runner.seed)
assert isinstance(ckpt['optimizer'], dict)
assert isinstance(ckpt['param_schedulers'], list)
self.assertIsInstance(ckpt['message_hub'], dict)
message_hub = MessageHub.get_instance('test_ckpt')
message_hub.load_state_dict(ckpt['message_hub'])
self.assertEqual(message_hub.get_info('epoch'), 2)
self.assertEqual(message_hub.get_info('iter'), 11)
# 1.2 test `load_checkpoint`
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint2'
cfg.optim_wrapper = dict(type='SGD', lr=0.2)
cfg.param_scheduler = dict(type='MultiStepLR', milestones=[1, 2, 3])
runner = Runner.from_cfg(cfg)
runner.load_checkpoint(path)
self.assertEqual(runner.epoch, 0)
self.assertEqual(runner.iter, 0)
self.assertTrue(runner._has_loaded)
# load checkpoint will not initialize optimizer and param_schedulers
# objects
self.assertIsInstance(runner.optim_wrapper, dict)
self.assertIsInstance(runner.param_schedulers, dict)
# 1.3.1 test `resume`
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint3'
cfg.optim_wrapper = dict(
type='OptimWrapper', optimizer=dict(type='SGD', lr=0.2))
cfg.param_scheduler = dict(type='MultiStepLR', milestones=[1, 2, 3])
runner = Runner.from_cfg(cfg)
runner.resume(path)
self.assertEqual(runner.epoch, 3)
self.assertEqual(runner.iter, 12)
self.assertTrue(runner._has_loaded)
self.assertIsInstance(runner.optim_wrapper.optimizer, SGD)
self.assertIsInstance(runner.optim_wrapper.optimizer, SGD)
self.assertEqual(runner.optim_wrapper.param_groups[0]['lr'], 0.0001)
self.assertIsInstance(runner.param_schedulers[0], MultiStepLR)
self.assertEqual(runner.param_schedulers[0].milestones, {1: 1, 2: 1})
self.assertIsInstance(runner.message_hub, MessageHub)
self.assertEqual(runner.message_hub.get_info('epoch'), 2)
self.assertEqual(runner.message_hub.get_info('iter'), 11)
self.assertEqual(MessageHub.get_current_instance().get_info('epoch'),
2)
self.assertEqual(MessageHub.get_current_instance().get_info('iter'),
11)
# 1.3.2 test resume with unmatched dataset_meta
ckpt_modified = copy.deepcopy(ckpt)
ckpt_modified['meta']['dataset_meta'] = {'CLASSES': ['cat', 'dog']}
# ckpt_modified['meta']['seed'] = 123
path_modified = osp.join(self.temp_dir, 'modified.pth')
torch.save(ckpt_modified, path_modified)
# Warning should be raised since dataset_meta is not matched
with self.assertLogs(MMLogger.get_current_instance(), level='WARNING'):
runner.resume(path_modified)
# 1.3.3 test resume with unmatched seed
ckpt_modified = copy.deepcopy(ckpt)
ckpt_modified['meta']['seed'] = 123
path_modified = osp.join(self.temp_dir, 'modified.pth')
torch.save(ckpt_modified, path_modified)
# Warning should be raised since seed is not matched
with self.assertLogs(MMLogger.get_current_instance(), level='WARNING'):
runner.resume(path_modified)
# 1.3.3 test resume with no seed and dataset meta
ckpt_modified = copy.deepcopy(ckpt)
ckpt_modified['meta'].pop('seed')
ckpt_modified['meta'].pop('dataset_meta')
path_modified = osp.join(self.temp_dir, 'modified.pth')
torch.save(ckpt_modified, path_modified)
runner.resume(path_modified)
# 1.4 test auto resume
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint4'
cfg.resume = True
runner = Runner.from_cfg(cfg)
runner.load_or_resume()
self.assertEqual(runner.epoch, 3)
self.assertEqual(runner.iter, 12)
self.assertTrue(runner._has_loaded)
self.assertIsInstance(runner.optim_wrapper.optimizer, SGD)
self.assertIsInstance(runner.param_schedulers[0], MultiStepLR)
# 1.5 test resume from a specified checkpoint
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint5'
cfg.resume = True
cfg.load_from = osp.join(self.temp_dir, 'epoch_1.pth')
runner = Runner.from_cfg(cfg)
runner.load_or_resume()
self.assertEqual(runner.epoch, 1)
self.assertEqual(runner.iter, 4)
self.assertTrue(runner._has_loaded)
self.assertIsInstance(runner.optim_wrapper.optimizer, SGD)
self.assertIsInstance(runner.param_schedulers[0], MultiStepLR)
# 1.6 multiple optimizers
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint6'
cfg.optim_wrapper = dict(
linear1=dict(
type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01)),
linear2=dict(
type='OptimWrapper', optimizer=dict(type='Adam', lr=0.02)),
constructor='ToyMultipleOptimizerConstructor')
cfg.model = dict(type='ToyGANModel')
# disable OptimizerHook because it only works with one optimizer
runner = Runner.from_cfg(cfg)
runner.train()
path = osp.join(self.temp_dir, 'epoch_3.pth')
self.assertTrue(osp.exists(path))
self.assertEqual(runner.optim_wrapper['linear1'].param_groups[0]['lr'],
0.0001)
self.assertIsInstance(runner.optim_wrapper['linear2'].optimizer, Adam)
self.assertEqual(runner.optim_wrapper['linear2'].param_groups[0]['lr'],
0.0002)
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint7'
cfg.optim_wrapper = dict(
linear1=dict(
type='OptimWrapper', optimizer=dict(type='SGD', lr=0.2)),
linear2=dict(
type='OptimWrapper', optimizer=dict(type='Adam', lr=0.03)),
constructor='ToyMultipleOptimizerConstructor')
cfg.model = dict(type='ToyGANModel')
cfg.param_scheduler = dict(type='MultiStepLR', milestones=[1, 2, 3])
runner = Runner.from_cfg(cfg)
runner.resume(path)
self.assertIsInstance(runner.optim_wrapper, OptimWrapperDict)
self.assertIsInstance(runner.optim_wrapper['linear1'].optimizer, SGD)
self.assertEqual(runner.optim_wrapper['linear1'].param_groups[0]['lr'],
0.0001)
self.assertIsInstance(runner.optim_wrapper['linear2'].optimizer, Adam)
self.assertEqual(runner.optim_wrapper['linear2'].param_groups[0]['lr'],
0.0002)
self.assertIsInstance(runner.param_schedulers, dict)
self.assertEqual(len(runner.param_schedulers['linear1']), 1)
self.assertIsInstance(runner.param_schedulers['linear1'][0],
MultiStepLR)
self.assertEqual(runner.param_schedulers['linear1'][0].milestones, {
1: 1,
2: 1
})
self.assertEqual(len(runner.param_schedulers['linear2']), 1)
self.assertIsInstance(runner.param_schedulers['linear2'][0],
MultiStepLR)
self.assertEqual(runner.param_schedulers['linear2'][0].milestones, {
1: 1,
2: 1
})
# 2. test iter based
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint8'
runner = Runner.from_cfg(cfg)
runner.train()
# 2.1.1 test `save_checkpoint` which is called by `CheckpointHook`
path = osp.join(self.temp_dir, 'iter_12.pth')
self.assertTrue(osp.exists(path))
self.assertFalse(osp.exists(osp.join(self.temp_dir, 'epoch_13.pth')))
ckpt = torch.load(path)
self.assertEqual(ckpt['meta']['epoch'], 0)
self.assertEqual(ckpt['meta']['iter'], 12)
assert isinstance(ckpt['optimizer'], dict)
assert isinstance(ckpt['param_schedulers'], list)
self.assertIsInstance(ckpt['message_hub'], dict)
message_hub.load_state_dict(ckpt['message_hub'])
self.assertEqual(message_hub.get_info('epoch'), 0)
self.assertEqual(message_hub.get_info('iter'), 11)
# 2.1.2 check class attribute _statistic_methods can be saved
HistoryBuffer._statistics_methods.clear()
ckpt = torch.load(path)
self.assertIn('min', HistoryBuffer._statistics_methods)
# 2.2 test `load_checkpoint`
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint9'
runner = Runner.from_cfg(cfg)
runner.load_checkpoint(path)
self.assertEqual(runner.epoch, 0)
self.assertEqual(runner.iter, 0)
self.assertTrue(runner._has_loaded)
# 2.3 test `resume`
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint10'
runner = Runner.from_cfg(cfg)
runner.resume(path)
self.assertEqual(runner.epoch, 0)
self.assertEqual(runner.iter, 12)
self.assertTrue(runner._has_loaded)
self.assertIsInstance(runner.optim_wrapper.optimizer, SGD)
self.assertIsInstance(runner.param_schedulers[0], MultiStepLR)
self.assertEqual(runner.message_hub.get_info('epoch'), 0)
self.assertEqual(runner.message_hub.get_info('iter'), 11)
# 2.4 test auto resume
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint11'
cfg.resume = True
runner = Runner.from_cfg(cfg)
runner.load_or_resume()
self.assertEqual(runner.epoch, 0)
self.assertEqual(runner.iter, 12)
self.assertTrue(runner._has_loaded)
self.assertIsInstance(runner.optim_wrapper.optimizer, SGD)
self.assertIsInstance(runner.param_schedulers[0], MultiStepLR)
# 2.5 test resume from a specified checkpoint
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint12'
cfg.resume = True
cfg.load_from = osp.join(self.temp_dir, 'iter_3.pth')
runner = Runner.from_cfg(cfg)
runner.load_or_resume()
self.assertEqual(runner.epoch, 0)
self.assertEqual(runner.iter, 3)
self.assertTrue(runner._has_loaded)
self.assertIsInstance(runner.optim_wrapper.optimizer, SGD)
self.assertIsInstance(runner.param_schedulers[0], MultiStepLR)
# 2.6 test resumed message_hub has the history value.
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint13'
cfg.resume = True
cfg.load_from = osp.join(self.temp_dir, 'iter_3.pth')
runner = Runner.from_cfg(cfg)
runner.load_or_resume()
assert len(runner.message_hub.log_scalars['train/lr'].data[1]) == 3
assert len(MessageHub.get_current_instance().log_scalars['train/lr'].
data[1]) == 3
# 2.7.1 test `resume` 2 optimizers and 1 scheduler list.
path = osp.join(self.temp_dir, 'epoch_3.pth')
optim_cfg = dict(
linear1=dict(
type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01)),
linear2=dict(
type='OptimWrapper', optimizer=dict(type='Adam', lr=0.02)),
constructor='ToyMultipleOptimizerConstructor')
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint14'
cfg.optim_wrapper = optim_cfg
cfg.param_scheduler = dict(type='MultiStepLR', milestones=[1, 2, 3])
cfg.model = dict(type='ToyGANModel')
resumed_cfg = copy.deepcopy(cfg)
runner = Runner.from_cfg(cfg)
runner.train()
resumed_cfg.experiment_name = 'test_checkpoint15'
runner = Runner.from_cfg(resumed_cfg)
runner.resume(path)
self.assertEqual(len(runner.param_schedulers['linear1']), 1)
self.assertEqual(len(runner.param_schedulers['linear2']), 1)
self.assertIsInstance(runner.param_schedulers['linear1'][0],
MultiStepLR)
self.assertIsInstance(runner.param_schedulers['linear2'][0],
MultiStepLR)
# 2.7.2 test `resume` 2 optimizers and 2 scheduler list.
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint16'
cfg.optim_wrapper = optim_cfg
cfg.param_scheduler = dict(
linear1=dict(type='MultiStepLR', milestones=[1, 2, 3]),
linear2=dict(type='StepLR', gamma=0.1, step_size=3))
cfg.model = dict(type='ToyGANModel')
resumed_cfg = copy.deepcopy(cfg)
runner = Runner.from_cfg(cfg)
runner.train()
resumed_cfg.experiment_name = 'test_checkpoint17'
runner = Runner.from_cfg(resumed_cfg)
runner.resume(path)
self.assertEqual(len(runner.param_schedulers['linear1']), 1)
self.assertEqual(len(runner.param_schedulers['linear2']), 1)
self.assertIsInstance(runner.param_schedulers['linear1'][0],
MultiStepLR)
self.assertIsInstance(runner.param_schedulers['linear2'][0], StepLR)
# 2.7.3 test `resume` 2 optimizers and 0 scheduler list.
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint18'
cfg.optim_wrapper = optim_cfg
cfg.model = dict(type='ToyGANModel')
cfg.param_scheduler = None
resumed_cfg = copy.deepcopy(cfg)
runner = Runner.from_cfg(cfg)
runner.train()
resumed_cfg.experiment_name = 'test_checkpoint19'
runner = Runner.from_cfg(resumed_cfg)
runner.resume(path)
self.assertIsNone(runner.param_schedulers)
def test_build_runner(self):
# No need to test other cases which have been tested in
# `test_build_from_cfg`
# test custom runner
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_runner1'
cfg.runner_type = 'CustomRunner'
assert isinstance(RUNNERS.build(cfg), CustomRunner)
# test default runner
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_runner2'
assert isinstance(RUNNERS.build(cfg), Runner)
def test_get_hooks_info(self):
# test get_hooks_info() function
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_get_hooks_info_from_test_runner_py'
cfg.runner_type = 'Runner'
runner = RUNNERS.build(cfg)
self.assertIsInstance(runner, Runner)
target_str = ('after_train_iter:\n'
'(VERY_HIGH ) RuntimeInfoHook \n'
'(NORMAL ) IterTimerHook \n'
'(BELOW_NORMAL) LoggerHook \n'
'(LOW ) ParamSchedulerHook \n'
'(VERY_LOW ) CheckpointHook \n')
self.assertIn(target_str, runner.get_hooks_info(),
'target string is not in logged hooks information.')
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