# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import shutil
import tempfile
from unittest import TestCase
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
from torch.optim import SGD
from torch.utils.data import DataLoader, Dataset
from mmengine.config import Config
from mmengine.data import DefaultSampler
from mmengine.evaluator import BaseMetric, Evaluator
from mmengine.hooks import (Hook, IterTimerHook, LoggerHook, OptimizerHook,
ParamSchedulerHook)
from mmengine.hooks.checkpoint_hook import CheckpointHook
from mmengine.logging import MessageHub, MMLogger
from mmengine.optim.scheduler import MultiStepLR, StepLR
from mmengine.registry import (DATASETS, HOOKS, LOOPS, METRICS, MODEL_WRAPPERS,
MODELS, PARAM_SCHEDULERS, Registry)
from mmengine.runner import (BaseLoop, EpochBasedTrainLoop, IterBasedTrainLoop,
Runner, TestLoop, ValLoop)
from mmengine.runner.priority import Priority, get_priority
from mmengine.utils import is_list_of
from mmengine.visualization.writer import ComposedWriter
@MODELS.register_module()
class ToyModel(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(2, 1)
def forward(self, data_batch, return_loss=False):
inputs, labels = zip(*data_batch)
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
inputs = torch.stack(inputs).to(device)
labels = torch.stack(labels).to(device)
outputs = self.linear(inputs)
if return_loss:
loss = (labels - outputs).sum()
outputs = dict(loss=loss, log_vars=dict(loss=loss.item()))
return outputs
else:
outputs = dict(log_vars=dict(a=1, b=0.5))
return outputs
@MODELS.register_module()
class ToyModel1(ToyModel):
def __init__(self):
super().__init__()
@MODEL_WRAPPERS.register_module()
class CustomModelWrapper(nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
@DATASETS.register_module()
class ToyDataset(Dataset):
META = dict() # type: ignore
data = torch.randn(12, 2)
label = torch.ones(12)
def __len__(self):
return self.data.size(0)
def __getitem__(self, index):
return self.data[index], self.label[index]
@METRICS.register_module()
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_samples, predictions):
result = {'acc': 1}
self.results.append(result)
def compute_metrics(self, results):
return dict(acc=1)
@METRICS.register_module()
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_samples, predictions):
result = {'acc': 1}
self.results.append(result)
def compute_metrics(self, results):
return dict(acc=1)
@HOOKS.register_module()
class ToyHook(Hook):
priority = 'Lowest'
def before_train_epoch(self, runner):
pass
@HOOKS.register_module()
class ToyHook2(Hook):
priority = 'Lowest'
def after_train_epoch(self, runner):
pass
@LOOPS.register_module()
class CustomTrainLoop(BaseLoop):
def __init__(self, runner, dataloader, max_epochs):
super().__init__(runner, dataloader)
self._max_epochs = max_epochs
def run(self) -> None:
pass
@LOOPS.register_module()
class CustomValLoop(BaseLoop):
def __init__(self, runner, dataloader, evaluator, interval=1):
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
@LOOPS.register_module()
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
def collate_fn(data_batch):
return data_batch
class TestRunner(TestCase):
def setUp(self):
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),
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_cfg=dict(interval=1),
test_cfg=dict(),
custom_hooks=[],
default_hooks=dict(
timer=dict(type='IterTimerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
logger=dict(type='LoggerHook'),
optimizer=dict(type='OptimizerHook', grad_clip=None),
param_scheduler=dict(type='ParamSchedulerHook')),
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(
timer=dict(type='IterTimerHook'),
checkpoint=dict(type='CheckpointHook', interval=1, by_epoch=False),
logger=dict(type='LoggerHook', by_epoch=False),
optimizer=dict(type='OptimizerHook', grad_clip=None),
param_scheduler=dict(type='ParamSchedulerHook'))
def tearDown(self):
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('optimizer')
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)
# 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('optimizer')
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 writer
# 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.writer, ComposedWriter)
# they are all specified
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_init13'
cfg.log_level = 'INFO'
cfg.writer = dict(name='test_writer')
runner = Runner(**cfg)
self.assertIsInstance(runner.logger, MMLogger)
self.assertIsInstance(runner.message_hub, MessageHub)
self.assertIsInstance(runner.writer, ComposedWriter)
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.optimizer, dict)
self.assertIsInstance(runner.param_schedulers[0], 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_loop.dataloader, DataLoader)
self.assertIsInstance(runner.optimizer, SGD)
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()
optimizer = 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),
train_dataloader=train_dataloader,
optimizer=optimizer,
param_scheduler=MultiStepLR(optimizer, milestones=[1, 2]),
val_cfg=dict(interval=1),
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 `dump_config`
# TODO
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_writer(self):
self.epoch_based_cfg.experiment_name = 'test_build_writer1'
runner = Runner.from_cfg(self.epoch_based_cfg)
self.assertIsInstance(runner.writer, ComposedWriter)
self.assertEqual(runner.experiment_name, runner.writer.instance_name)
# input is a ComposedWriter object
self.assertEqual(
id(runner.build_writer(runner.writer)), id(runner.writer))
# input is a dict
writer_cfg = dict(name='test_build_writer2')
writer = runner.build_writer(writer_cfg)
self.assertIsInstance(writer, ComposedWriter)
self.assertEqual(writer.instance_name, 'test_build_writer2')
# input is a dict but does not contain name key
runner._experiment_name = 'test_build_writer3'
writer_cfg = dict()
writer = runner.build_writer(writer_cfg)
self.assertIsInstance(writer, ComposedWriter)
self.assertEqual(writer.instance_name, 'test_build_writer3')
# input is not a valid type
with self.assertRaisesRegex(TypeError, 'writer should be'):
runner.build_writer('invalid-type')
def test_default_scope(self):
TOY_SCHEDULERS = Registry(
'parameter scheduler', parent=PARAM_SCHEDULERS, scope='toy')
@TOY_SCHEDULERS.register_module()
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_model'
runner = Runner.from_cfg(cfg)
self.assertIsInstance(runner.model, ToyModel)
# 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)
# test init weights
@MODELS.register_module()
class ToyModel2(ToyModel):
def __init__(self):
super().__init__()
self.initiailzed = False
def init_weights(self):
self.initiailzed = True
model = runner.build_model(dict(type='ToyModel2'))
self.assertTrue(model.initiailzed)
# test init weights with model object
_model = ToyModel2()
model = runner.build_model(_model)
self.assertFalse(model.initiailzed)
def test_wrap_model(self):
# TODO: test on distributed environment
# 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, CustomModelWrapper)
def test_build_optimizer(self):
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_build_optimizer'
runner = Runner.from_cfg(cfg)
# input should be an Optimizer object or dict
with self.assertRaisesRegex(TypeError, 'optimizer should be'):
runner.build_optimizer('invalid-type')
# input is an Optimizer object
_optimizer = SGD(runner.model.parameters(), lr=0.01)
optimizer = runner.build_optimizer(_optimizer)
self.assertEqual(id(_optimizer), id(optimizer))
# input is a dict
optimizer = runner.build_optimizer(dict(type='SGD', lr=0.01))
self.assertIsInstance(optimizer, SGD)
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_optimizer` should be called before `build_param_scheduler`
cfg = dict(type='MultiStepLR', milestones=[1, 2])
runner.optimizer = None
with self.assertRaisesRegex(RuntimeError, 'should be called before'):
runner.build_param_scheduler(cfg)
runner.optimizer = runner.build_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)
# input is a ParamScheduler object
param_scheduler = MultiStepLR(runner.optimizer, milestones=[1, 2])
param_schedulers = runner.build_param_scheduler(param_scheduler)
self.assertEqual(id(param_schedulers[0]), id(param_scheduler))
# 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)
# 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)
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)
# 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')
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)
dataloader = runner.build_dataloader(cfg)
self.assertIsInstance(dataloader, DataLoader)
self.assertIsInstance(dataloader.dataset, ToyDataset)
self.assertIsInstance(dataloader.sampler, DefaultSampler)
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))
# 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', interval=1)
loop = runner.build_test_loop(cfg)
self.assertIsInstance(loop, ValLoop)
# input is a dict but does not contain type key
cfg = dict(interval=1)
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', interval=1)
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_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('optimizer')
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
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
@HOOKS.register_module()
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)
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_train2'
cfg.custom_hooks = [dict(type='TestEpochHook', priority=50)]
runner = Runner.from_cfg(cfg)
runner.train()
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)
# 3. test iter and epoch counter of IterBasedTrainLoop
epoch_results = []
iter_results = []
batch_idx_results = []
iter_targets = [i for i in range(12)]
batch_idx_targets = [i for i in range(12)]
@HOOKS.register_module()
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)
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_train3'
cfg.custom_hooks = [dict(type='TestIterHook', priority=50)]
cfg.val_cfg = dict(interval=4)
runner = Runner.from_cfg(cfg)
runner.train()
assert isinstance(runner.train_loop, IterBasedTrainLoop)
self.assertEqual(len(epoch_results), 1)
self.assertEqual(epoch_results[0], 0)
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)
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()
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()
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
optimizer_hook = OptimizerHook()
runner.register_hook(optimizer_hook)
self.assertEqual(len(runner._hooks), 2)
# The priority of `OptimizerHook` is `HIGH` which is greater than
# `IterTimerHook`, so the first item of `_hooks` should be
# `OptimizerHook`
self.assertTrue(isinstance(runner._hooks[0], OptimizerHook))
self.assertEqual(
get_priority(runner._hooks[0].priority), get_priority('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 five hooks by default
runner.register_default_hooks()
self.assertEqual(len(runner._hooks), 5)
# the forth registered hook should be `ParamSchedulerHook`
self.assertTrue(isinstance(runner._hooks[3], ParamSchedulerHook))
runner._hooks = []
# remove `ParamSchedulerHook` from default hooks
runner.register_default_hooks(hooks=dict(timer=None))
self.assertEqual(len(runner._hooks), 4)
# `ParamSchedulerHook` was popped so the forth is `CheckpointHook`
self.assertTrue(isinstance(runner._hooks[3], CheckpointHook))
# add a new default hook
runner._hooks = []
runner.register_default_hooks(hooks=dict(ToyHook=dict(type='ToyHook')))
self.assertEqual(len(runner._hooks), 6)
self.assertTrue(isinstance(runner._hooks[5], 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), 5)
custom_hooks = [dict(type='ToyHook')]
runner.register_custom_hooks(custom_hooks)
self.assertEqual(len(runner._hooks), 6)
self.assertTrue(isinstance(runner._hooks[5], 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)
# five default hooks + custom hook (ToyHook)
self.assertEqual(len(runner._hooks), 6)
self.assertTrue(isinstance(runner._hooks[5], ToyHook))
def test_custom_loop(self):
# test custom loop with additional hook
@LOOPS.register_module()
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)
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)
self.runner.outputs = self.runner.model(
data_batch, return_loss=True)
self.runner.call_hook(
'after_warmup_iter',
data_batch=data_batch,
outputs=self.runner.outputs)
before_warmup_iter_results = []
after_warmup_iter_results = []
@HOOKS.register_module()
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 called by `CheckpointHook`
path = osp.join(self.temp_dir, 'epoch_3.pth')
self.assertTrue(osp.exists(path))
self.assertTrue(osp.exists(osp.join(self.temp_dir, 'latest.pth')))
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']['hook_msgs']['last_ckpt'], path)
assert isinstance(ckpt['optimizer'], dict)
assert isinstance(ckpt['param_schedulers'], list)
# 1.2 test `load_checkpoint`
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint2'
runner = Runner.from_cfg(cfg)
runner.load_checkpoint(path)
self.assertEqual(runner.epoch, 0)
self.assertEqual(runner.iter, 0)
self.assertTrue(runner._has_loaded)
# 1.3 test `resume`
cfg = copy.deepcopy(self.epoch_based_cfg)
cfg.experiment_name = 'test_checkpoint3'
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.optimizer, SGD)
self.assertIsInstance(runner.param_schedulers[0], MultiStepLR)
# 2. test iter based
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint4'
runner = Runner.from_cfg(cfg)
runner.train()
# 2.1 test `save_checkpoint` which called by `CheckpointHook`
path = osp.join(self.temp_dir, 'iter_12.pth')
self.assertTrue(osp.exists(path))
self.assertTrue(osp.exists(osp.join(self.temp_dir, 'latest.pth')))
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)
# self.assertEqual(ckpt['meta']['hook_msgs']['last_ckpt'], path)
assert isinstance(ckpt['optimizer'], dict)
assert isinstance(ckpt['param_schedulers'], list)
# 2.2 test `load_checkpoint`
cfg = copy.deepcopy(self.iter_based_cfg)
cfg.experiment_name = 'test_checkpoint5'
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_checkpoint6'
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.optimizer, SGD)
self.assertIsInstance(runner.param_schedulers[0], MultiStepLR)