test_runner.py 8.46 KB
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# Copyright (c) OpenMMLab. All rights reserved.
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import logging
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import os
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import os.path as osp
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import platform
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import random
import string
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import tempfile
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import pytest
import torch
import torch.nn as nn
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from mmcv.parallel import MMDataParallel
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from mmcv.runner import (RUNNERS, EpochBasedRunner, IterBasedRunner,
                         build_runner)
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from mmcv.runner.hooks import IterTimerHook
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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


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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):
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    with pytest.warns(UserWarning):
        # batch_processor is deprecated
        model = OldStyleModel()
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        def batch_processor():
            pass
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        _ = runner_class(model, batch_processor, logger=logging.getLogger())
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    with pytest.raises(TypeError):
        # batch_processor must be callable
        model = OldStyleModel()
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        _ = runner_class(model, batch_processor=0, logger=logging.getLogger())
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    with pytest.raises(TypeError):
        # optimizer must be a optimizer or a dict of optimizers
        model = Model()
        optimizer = 'NotAOptimizer'
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        _ = runner_class(
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            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')
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        _ = runner_class(
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            model, optimizer=optimizers, logger=logging.getLogger())

    with pytest.raises(TypeError):
        # logger must be a logging.Logger
        model = Model()
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        _ = runner_class(model, logger=None)
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    with pytest.raises(TypeError):
        # meta must be a dict or None
        model = Model()
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        _ = runner_class(model, logger=logging.getLogger(), meta=['list'])
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    with pytest.raises(AssertionError):
        # model must implement the method train_step()
        model = OldStyleModel()
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        _ = runner_class(model, logger=logging.getLogger())
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    with pytest.raises(TypeError):
        # work_dir must be a str or None
        model = Model()
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        _ = runner_class(model, work_dir=1, logger=logging.getLogger())
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    with pytest.raises(RuntimeError):
        # batch_processor and train_step() cannot be both set

        def batch_processor():
            pass

        model = Model()
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        _ = runner_class(model, batch_processor, logger=logging.getLogger())
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    # 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)
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    _ = runner_class(model, work_dir=work_dir, logger=logging.getLogger())
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    assert osp.isdir(work_dir)
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    _ = runner_class(model, work_dir=work_dir, logger=logging.getLogger())
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    assert osp.isdir(work_dir)
    os.removedirs(work_dir)


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@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_runner_with_parallel(runner_class):
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    def batch_processor():
        pass

    model = MMDataParallel(OldStyleModel())
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    _ = runner_class(model, batch_processor, logger=logging.getLogger())
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    model = MMDataParallel(Model())
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    _ = runner_class(model, logger=logging.getLogger())
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    with pytest.raises(RuntimeError):
        # batch_processor and train_step() cannot be both set

        def batch_processor():
            pass

        model = MMDataParallel(Model())
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        _ = runner_class(model, batch_processor, logger=logging.getLogger())
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@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_save_checkpoint(runner_class):
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    model = Model()
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    runner = runner_class(model=model, logger=logging.getLogger())
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    with pytest.raises(TypeError):
        # meta should be None or dict
        runner.save_checkpoint('.', meta=list())

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    with tempfile.TemporaryDirectory() as root:
        runner.save_checkpoint(root)

        latest_path = osp.join(root, 'latest.pth')
        assert osp.exists(latest_path)
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        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)
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        if platform.system() != 'Windows':
            assert osp.realpath(latest_path) == osp.realpath(first_ckp_path)
        else:
            # use copy instead of symlink on windows
            pass
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        torch.load(latest_path)
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@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_build_lr_momentum_hook(runner_class):
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    model = Model()
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    runner = runner_class(model=model, logger=logging.getLogger())
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    # test policy that is already title
    lr_config = dict(
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        policy='CosineAnnealing',
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        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(
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        policy='CosineAnnealing',
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        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
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@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)