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# Copyright (c) OpenMMLab. All rights reserved.
import logging
import os
import os.path as osp
import platform
import random
import string
import tempfile
import pytest
import torch
import torch.nn as nn
from mmcv.parallel import MMDataParallel
from mmcv.runner import (RUNNERS, EpochBasedRunner, IterBasedRunner,
build_runner)
from mmcv.runner.hooks import IterTimerHook
class OldStyleModel(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(3, 3, 1)
class Model(OldStyleModel):
def train_step(self):
pass
def val_step(self):
pass
def test_build_runner():
temp_root = tempfile.gettempdir()
dir_name = ''.join(
[random.choice(string.ascii_letters) for _ in range(10)])
default_args = dict(
model=Model(),
work_dir=osp.join(temp_root, dir_name),
logger=logging.getLogger())
cfg = dict(type='EpochBasedRunner', max_epochs=1)
runner = build_runner(cfg, default_args=default_args)
assert runner._max_epochs == 1
cfg = dict(type='IterBasedRunner', max_iters=1)
runner = build_runner(cfg, default_args=default_args)
assert runner._max_iters == 1
with pytest.raises(ValueError, match='Only one of'):
cfg = dict(type='IterBasedRunner', max_epochs=1, max_iters=1)
runner = build_runner(cfg, default_args=default_args)
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_epoch_based_runner(runner_class):
with pytest.warns(DeprecationWarning):
# batch_processor is deprecated
model = OldStyleModel()
def batch_processor():
pass
_ = runner_class(model, batch_processor, logger=logging.getLogger())
with pytest.raises(TypeError):
# batch_processor must be callable
model = OldStyleModel()
_ = runner_class(model, batch_processor=0, logger=logging.getLogger())
with pytest.raises(TypeError):
# optimizer must be a optimizer or a dict of optimizers
model = Model()
optimizer = 'NotAOptimizer'
_ = runner_class(
model, optimizer=optimizer, logger=logging.getLogger())
with pytest.raises(TypeError):
# optimizer must be a optimizer or a dict of optimizers
model = Model()
optimizers = dict(optim1=torch.optim.Adam(), optim2='NotAOptimizer')
_ = runner_class(
model, optimizer=optimizers, logger=logging.getLogger())
with pytest.raises(TypeError):
# logger must be a logging.Logger
model = Model()
_ = runner_class(model, logger=None)
with pytest.raises(TypeError):
# meta must be a dict or None
model = Model()
_ = runner_class(model, logger=logging.getLogger(), meta=['list'])
with pytest.raises(AssertionError):
# model must implement the method train_step()
model = OldStyleModel()
_ = runner_class(model, logger=logging.getLogger())
with pytest.raises(TypeError):
# work_dir must be a str or None
model = Model()
_ = runner_class(model, work_dir=1, logger=logging.getLogger())
with pytest.raises(RuntimeError):
# batch_processor and train_step() cannot be both set
def batch_processor():
pass
model = Model()
_ = runner_class(model, batch_processor, logger=logging.getLogger())
# test work_dir
model = Model()
temp_root = tempfile.gettempdir()
dir_name = ''.join(
[random.choice(string.ascii_letters) for _ in range(10)])
work_dir = osp.join(temp_root, dir_name)
_ = runner_class(model, work_dir=work_dir, logger=logging.getLogger())
assert osp.isdir(work_dir)
_ = runner_class(model, work_dir=work_dir, logger=logging.getLogger())
assert osp.isdir(work_dir)
os.removedirs(work_dir)
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_runner_with_parallel(runner_class):
def batch_processor():
pass
model = MMDataParallel(OldStyleModel())
_ = runner_class(model, batch_processor, logger=logging.getLogger())
model = MMDataParallel(Model())
_ = runner_class(model, logger=logging.getLogger())
with pytest.raises(RuntimeError):
# batch_processor and train_step() cannot be both set
def batch_processor():
pass
model = MMDataParallel(Model())
_ = runner_class(model, batch_processor, logger=logging.getLogger())
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_save_checkpoint(runner_class):
model = Model()
runner = runner_class(model=model, logger=logging.getLogger())
with pytest.raises(TypeError):
# meta should be None or dict
runner.save_checkpoint('.', meta=list())
with tempfile.TemporaryDirectory() as root:
runner.save_checkpoint(root)
latest_path = osp.join(root, 'latest.pth')
assert osp.exists(latest_path)
if isinstance(runner, EpochBasedRunner):
first_ckp_path = osp.join(root, 'epoch_1.pth')
elif isinstance(runner, IterBasedRunner):
first_ckp_path = osp.join(root, 'iter_1.pth')
assert osp.exists(first_ckp_path)
if platform.system() != 'Windows':
assert osp.realpath(latest_path) == osp.realpath(first_ckp_path)
else:
# use copy instead of symlink on windows
pass
torch.load(latest_path)
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_build_lr_momentum_hook(runner_class):
model = Model()
runner = runner_class(model=model, logger=logging.getLogger())
# test policy that is already title
lr_config = dict(
policy='CosineAnnealing',
by_epoch=False,
min_lr_ratio=0,
warmup_iters=2,
warmup_ratio=0.9)
runner.register_lr_hook(lr_config)
assert len(runner.hooks) == 1
# test policy that is already title
lr_config = dict(
policy='Cyclic',
by_epoch=False,
target_ratio=(10, 1),
cyclic_times=1,
step_ratio_up=0.4)
runner.register_lr_hook(lr_config)
assert len(runner.hooks) == 2
# test policy that is not title
lr_config = dict(
policy='cyclic',
by_epoch=False,
target_ratio=(0.85 / 0.95, 1),
cyclic_times=1,
step_ratio_up=0.4)
runner.register_lr_hook(lr_config)
assert len(runner.hooks) == 3
# test policy that is title
lr_config = dict(
policy='Step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[8, 11])
runner.register_lr_hook(lr_config)
assert len(runner.hooks) == 4
# test policy that is not title
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[8, 11])
runner.register_lr_hook(lr_config)
assert len(runner.hooks) == 5
# test policy that is already title
mom_config = dict(
policy='CosineAnnealing',
min_momentum_ratio=0.99 / 0.95,
by_epoch=False,
warmup_iters=2,
warmup_ratio=0.9 / 0.95)
runner.register_momentum_hook(mom_config)
assert len(runner.hooks) == 6
# test policy that is already title
mom_config = dict(
policy='Cyclic',
by_epoch=False,
target_ratio=(0.85 / 0.95, 1),
cyclic_times=1,
step_ratio_up=0.4)
runner.register_momentum_hook(mom_config)
assert len(runner.hooks) == 7
# test policy that is already title
mom_config = dict(
policy='cyclic',
by_epoch=False,
target_ratio=(0.85 / 0.95, 1),
cyclic_times=1,
step_ratio_up=0.4)
runner.register_momentum_hook(mom_config)
assert len(runner.hooks) == 8
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_register_timer_hook(runner_class):
model = Model()
runner = runner_class(model=model, logger=logging.getLogger())
# test register None
timer_config = None
runner.register_timer_hook(timer_config)
assert len(runner.hooks) == 0
# test register IterTimerHook with config
timer_config = dict(type='IterTimerHook')
runner.register_timer_hook(timer_config)
assert len(runner.hooks) == 1
assert isinstance(runner.hooks[0], IterTimerHook)
# test register IterTimerHook
timer_config = IterTimerHook()
runner.register_timer_hook(timer_config)
assert len(runner.hooks) == 2
assert isinstance(runner.hooks[1], IterTimerHook)
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