[Fix] Fix unit test in windows (#515)
parent
0fb2b8ca8c
commit
6b1b8a3751
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@ -118,5 +118,10 @@ jobs:
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pip install -r requirements/tests.txt
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pip install openmim
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mim install 'mmcv>=2.0.0rc1'
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- name: Run unittests
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- name: Run CPU unittests
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run: pytest tests/
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if: ${{ matrix.platform == 'cpu' }}
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- name: Run GPU unittests
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# Skip testing distributed related unit tests since the memory of windows CI is limited
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run: pytest tests/ --ignore tests/test_dist --ignore tests/test_optim/test_optimizer/test_optimizer_wrapper.py --ignore tests/test_model/test_wrappers/test_model_wrapper.py
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if: ${{ matrix.platform == 'cu111' }}
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@ -175,7 +175,7 @@ class ImgDataPreprocessor(BaseDataPreprocessor):
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f'RGB or gray image, but got {len(mean)} values')
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assert len(std) == 3 or len(std) == 1, ( # type: ignore
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'`std` should have 1 or 3 values, to be compatible with RGB ' # type: ignore # noqa: E501
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f'or gray image, but got {len(std)} values')
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f'or gray image, but got {len(std)} values') # type: ignore
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self._enable_normalize = True
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self.register_buffer('mean',
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torch.tensor(mean).view(-1, 1, 1), False)
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@ -165,6 +165,11 @@ class InstanceData(BaseDataElement):
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if isinstance(item, list):
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item = np.array(item)
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if isinstance(item, np.ndarray):
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# The default int type of numpy is platform dependent, int32 for
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# windows and int64 for linux. `torch.Tensor` requires the index
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# should be int64, therefore we simply convert it to int64 here.
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# More details in https://github.com/numpy/numpy/issues/9464
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item = item.astype(np.int64) if item.dtype == np.int32 else item
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item = torch.from_numpy(item)
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assert isinstance(
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item, (str, slice, int, torch.LongTensor, torch.cuda.LongTensor,
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@ -4,6 +4,7 @@ import os.path as osp
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import subprocess
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import sys
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from collections import OrderedDict, defaultdict
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from distutils import errors
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import cv2
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import numpy as np
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@ -103,7 +104,7 @@ def collect_env():
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sys.stdout.fileno()) or locale.getpreferredencoding()
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env_info['MSVC'] = cc.decode(encoding).partition('\n')[0].strip()
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env_info['GCC'] = 'n/a'
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except subprocess.CalledProcessError:
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except (subprocess.CalledProcessError, errors.DistutilsPlatformError):
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env_info['GCC'] = 'n/a'
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env_info['PyTorch'] = torch.__version__
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@ -9,7 +9,7 @@ from mmengine.config.utils import (_get_external_cfg_base_path,
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def test_get_external_cfg_base_path(tmp_path):
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package_path = tmp_path
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rel_cfg_path = 'cfg_dir/cfg_file'
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rel_cfg_path = os.path.join('cfg_dir', 'cfg_file')
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with pytest.raises(FileNotFoundError):
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_get_external_cfg_base_path(str(package_path), rel_cfg_path)
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cfg_dir = tmp_path / '.mim' / 'configs' / 'cfg_dir'
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@ -131,25 +131,21 @@ class TestDataUtils(TestCase):
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self.assertEqual(tuple(batch_inputs_0.shape), (2, 1, 3, 5))
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self.assertEqual(tuple(batch_inputs_1.shape), (2, 1, 3, 5))
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self.assertTrue(
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torch.allclose(batch_inputs_0, torch.stack([input1, input1])))
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self.assertTrue(
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torch.allclose(batch_inputs_1, torch.stack([input2, input2])))
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self.assertTrue(
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torch.allclose(batch_value_0,
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torch.stack([torch.tensor(1),
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torch.tensor(1)])))
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self.assertTrue(
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torch.allclose(batch_value_1,
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torch.stack([torch.tensor(2),
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torch.tensor(2)])))
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target1 = torch.stack([torch.tensor(1), torch.tensor(1)])
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target2 = torch.stack([torch.tensor(2), torch.tensor(2)])
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self.assertTrue(
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torch.allclose(batch_array_0,
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torch.stack([torch.tensor(1),
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torch.tensor(1)])))
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torch.allclose(batch_value_0.to(target1.dtype), target1))
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self.assertTrue(
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torch.allclose(batch_array_1,
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torch.stack([torch.tensor(2),
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torch.tensor(2)])))
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torch.allclose(batch_value_1.to(target2.dtype), target2))
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self.assertTrue(
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torch.allclose(batch_array_0.to(target1.dtype), target1))
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self.assertTrue(
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torch.allclose(batch_array_1.to(target2.dtype), target2))
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@ -46,8 +46,8 @@ class TestCheckpointHook:
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checkpoint_hook = CheckpointHook(
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interval=1, by_epoch=True, out_dir='test_dir')
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checkpoint_hook.before_train(runner)
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assert checkpoint_hook.out_dir == (
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f'test_dir/{osp.basename(work_dir)}')
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assert checkpoint_hook.out_dir == osp.join(
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'test_dir', osp.join(osp.basename(work_dir)))
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runner.message_hub = MessageHub.get_instance('test_before_train')
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# no 'best_ckpt_path' in runtime_info
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@ -297,20 +297,20 @@ class TestCheckpointHook:
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checkpoint_hook.after_train_epoch(runner)
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assert (runner.epoch + 1) % 2 == 0
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assert 'last_ckpt' in runner.message_hub.runtime_info and \
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runner.message_hub.get_info('last_ckpt') == (
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f'{work_dir}/epoch_10.pth')
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runner.message_hub.get_info('last_ckpt') == \
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osp.join(work_dir, 'epoch_10.pth')
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last_ckpt_path = osp.join(work_dir, 'last_checkpoint')
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assert osp.isfile(last_ckpt_path)
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with open(last_ckpt_path) as f:
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filepath = f.read()
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assert filepath == f'{work_dir}/epoch_10.pth'
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assert filepath == osp.join(work_dir, 'epoch_10.pth')
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# epoch can not be evenly divided by 2
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runner.epoch = 10
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checkpoint_hook.after_train_epoch(runner)
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assert 'last_ckpt' in runner.message_hub.runtime_info and \
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runner.message_hub.get_info('last_ckpt') == (
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f'{work_dir}/epoch_10.pth')
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runner.message_hub.get_info('last_ckpt') == \
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osp.join(work_dir, 'epoch_10.pth')
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# by epoch is False
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runner.epoch = 9
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@ -351,20 +351,20 @@ class TestCheckpointHook:
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checkpoint_hook.after_train_iter(runner, batch_idx=batch_idx)
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assert (runner.iter + 1) % 2 == 0
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assert 'last_ckpt' in runner.message_hub.runtime_info and \
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runner.message_hub.get_info('last_ckpt') == (
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f'{work_dir}/iter_10.pth')
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runner.message_hub.get_info('last_ckpt') == \
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osp.join(work_dir, 'iter_10.pth')
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# epoch can not be evenly divided by 2
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runner.iter = 10
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checkpoint_hook.after_train_epoch(runner)
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assert 'last_ckpt' in runner.message_hub.runtime_info and \
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runner.message_hub.get_info('last_ckpt') == (
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f'{work_dir}/iter_10.pth')
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runner.message_hub.get_info('last_ckpt') == \
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osp.join(work_dir, 'iter_10.pth')
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# max_keep_ckpts > 0
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runner.iter = 9
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runner.work_dir = work_dir
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os.system(f'touch {work_dir}/iter_8.pth')
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os.system(f'touch {osp.join(work_dir, "iter_8.pth")}')
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checkpoint_hook = CheckpointHook(
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interval=2, by_epoch=False, max_keep_ckpts=1)
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checkpoint_hook.before_train(runner)
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@ -1,4 +1,5 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import logging
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import os.path as osp
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import tempfile
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from unittest import TestCase
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@ -10,6 +11,7 @@ from torch.utils.data import Dataset
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from mmengine.evaluator import Evaluator
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from mmengine.hooks import EMAHook
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from mmengine.logging import MMLogger
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from mmengine.model import BaseModel, ExponentialMovingAverage
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from mmengine.optim import OptimWrapper
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from mmengine.registry import DATASETS, MODEL_WRAPPERS
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@ -89,6 +91,10 @@ class TestEMAHook(TestCase):
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self.temp_dir = tempfile.TemporaryDirectory()
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def tearDown(self):
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# `FileHandler` should be closed in Windows, otherwise we cannot
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# delete the temporary directory
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logging.shutdown()
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MMLogger._instance_dict.clear()
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self.temp_dir.cleanup()
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def test_ema_hook(self):
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@ -43,7 +43,10 @@ class TestLogger:
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'rank0.pkg3', logger_name='logger_test', log_level='INFO')
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assert logger.name == 'logger_test'
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assert logger.instance_name == 'rank0.pkg3'
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# `FileHandler` should be closed in Windows, otherwise we cannot
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# delete the temporary directory
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logging.shutdown()
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MMLogger._instance_dict.clear()
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@patch('mmengine.logging.logger._get_rank', lambda: 1)
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def test_init_rank1(self, tmp_path):
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@ -62,7 +65,10 @@ class TestLogger:
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assert logger.handlers[1].level == logging.INFO
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assert len(logger.handlers) == 2
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assert os.path.exists(log_path)
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# `FileHandler` should be closed in Windows, otherwise we cannot
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# delete the temporary directory
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logging.shutdown()
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MMLogger._instance_dict.clear()
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@pytest.mark.parametrize('log_level',
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[logging.WARNING, logging.INFO, logging.DEBUG])
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@ -92,7 +98,10 @@ class TestLogger:
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f' - mmengine - {loglevl_name} - '
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f'welcome\n', log_text)
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assert match is not None
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# `FileHandler` should be closed in Windows, otherwise we cannot
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# delete the temporary directory
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logging.shutdown()
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MMLogger._instance_dict.clear()
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def test_error_format(self, capsys):
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# test error level log can output file path, function name and
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@ -100,7 +109,10 @@ class TestLogger:
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logger = MMLogger.get_instance('test_error', log_level='INFO')
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logger.error('welcome')
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lineno = sys._getframe().f_lineno - 1
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file_path = __file__
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# replace \ for windows:
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# origin: c:\\a\\b\\c.py
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# replaced: c:\\\\a\\\\b\\\\c.py for re.match.
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file_path = __file__.replace('\\', '\\\\')
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function_name = sys._getframe().f_code.co_name
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pattern = self.stream_handler_regex_time + \
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r' - mmengine - (.*)ERROR(.*) - ' \
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@ -1,4 +1,5 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import logging
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from unittest import TestCase
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import torch
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@ -106,6 +107,12 @@ class TestBaseModule(TestCase):
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conv1d=dict(type='FooConv1d')))
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self.model = build_from_cfg(self.model_cfg, FOOMODELS)
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self.logger = MMLogger.get_instance(self._testMethodName)
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def tearDown(self) -> None:
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logging.shutdown()
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MMLogger._instance_dict.clear()
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return super().tearDown()
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def test_is_init(self):
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assert self.BaseModule.is_init is False
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@ -194,6 +201,10 @@ class TestBaseModule(TestCase):
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model2.init_weights()
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assert len(os.listdir(dump_dir)) == 1
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assert os.stat(log_path).st_size != 0
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# `FileHandler` should be closed in Windows, otherwise we cannot
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# delete the temporary directory
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logging.shutdown()
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MMLogger._instance_dict.clear()
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shutil.rmtree(dump_dir)
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@ -1,5 +1,6 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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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 shutil
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@ -410,6 +411,10 @@ class TestRunner(TestCase):
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sampler_seed=dict(type='DistSamplerSeedHook'))
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def tearDown(self):
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# `FileHandler` should be closed in Windows, otherwise we cannot
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# delete the temporary directory
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logging.shutdown()
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MMLogger._instance_dict.clear()
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shutil.rmtree(self.temp_dir)
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def test_init(self):
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@ -579,8 +584,9 @@ class TestRunner(TestCase):
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runner.train()
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runner.test()
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# 5. Test building multiple runners
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if torch.cuda.is_available():
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# 5. Test building multiple runners. In Windows, nccl could not be
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# available, and this test will be skipped.
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if torch.cuda.is_available() and torch.distributed.is_nccl_available():
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cfg = copy.deepcopy(self.epoch_based_cfg)
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cfg.experiment_name = 'test_init15'
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cfg.launcher = 'pytorch'
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@ -589,9 +595,9 @@ class TestRunner(TestCase):
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os.environ['RANK'] = '0'
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os.environ['WORLD_SIZE'] = '1'
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os.environ['LOCAL_RANK'] = '0'
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runner = Runner(**cfg)
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Runner(**cfg)
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cfg.experiment_name = 'test_init16'
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runner = Runner(**cfg)
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Runner(**cfg)
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# 6.1 Test initializing with empty scheduler.
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cfg = copy.deepcopy(self.epoch_based_cfg)
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@ -680,8 +686,11 @@ class TestRunner(TestCase):
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osp.join(runner.work_dir, f'{runner.timestamp}.py'))
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# dump config from file.
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with tempfile.TemporaryDirectory() as temp_config_dir:
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# Set `delete=Flase` and close the file to make it
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# work in Windows.
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temp_config_file = tempfile.NamedTemporaryFile(
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dir=temp_config_dir, suffix='.py')
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dir=temp_config_dir, suffix='.py', delete=False)
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temp_config_file.close()
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file_cfg = Config(
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self.epoch_based_cfg._cfg_dict,
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filename=temp_config_file.name)
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@ -834,7 +843,7 @@ class TestRunner(TestCase):
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cfg.model_wrapper_cfg = dict(type='CustomModelWrapper')
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runner = Runner.from_cfg(cfg)
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self.assertIsInstance(runner.model, BaseModel)
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if torch.cuda.is_available():
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if torch.cuda.is_available() and torch.distributed.is_nccl_available():
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os.environ['MASTER_ADDR'] = '127.0.0.1'
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os.environ['MASTER_PORT'] = '29515'
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os.environ['RANK'] = str(0)
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@ -2,22 +2,12 @@
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import sys
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from unittest import TestCase
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import torch.cuda
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import mmengine
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from mmengine.utils.dl_utils import collect_env
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from mmengine.utils.dl_utils.parrots_wrapper import _get_cuda_home
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class TestCollectEnv(TestCase):
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def test_get_cuda_home(self):
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CUDA_HOME = _get_cuda_home()
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if torch.version.cuda is not None:
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self.assertIsNotNone(CUDA_HOME)
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else:
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self.assertIsNone(CUDA_HOME)
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def test_collect_env(self):
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env_info = collect_env()
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expected_keys = [
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@ -31,9 +21,6 @@ class TestCollectEnv(TestCase):
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for key in ['CUDA_HOME', 'NVCC']:
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assert key in env_info
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if sys.platform == 'win32':
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assert 'MSVC' in env_info
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assert env_info['sys.platform'] == sys.platform
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assert env_info['Python'] == sys.version.replace('\n', '')
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assert env_info['MMEngine'] == mmengine.__version__
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@ -18,10 +18,12 @@ def test_timer_init():
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def test_timer_run():
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timer = mmengine.Timer()
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time.sleep(1)
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assert abs(timer.since_start() - 1) < 1e-2
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# In Windows, the error could be larger than 20ms. More details in
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# https://stackoverflow.com/questions/11657734/sleep-for-exact-time-in-python. # noqa: E501
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assert abs(timer.since_start() - 1) < 3e-2
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time.sleep(1)
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assert abs(timer.since_last_check() - 1) < 1e-2
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assert abs(timer.since_start() - 2) < 1e-2
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assert abs(timer.since_last_check() - 1) < 3e-2
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assert abs(timer.since_start() - 2) < 3e-2
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timer = mmengine.Timer(False)
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with pytest.raises(mmengine.TimerError):
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timer.since_start()
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@ -33,7 +35,9 @@ def test_timer_context(capsys):
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with mmengine.Timer():
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time.sleep(1)
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out, _ = capsys.readouterr()
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assert abs(float(out) - 1) < 1e-2
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# In Windows, the error could be larger than 20ms. More details in
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# https://stackoverflow.com/questions/11657734/sleep-for-exact-time-in-python. # noqa: E501
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assert abs(float(out) - 1) < 3e-2
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with mmengine.Timer(print_tmpl='time: {:.1f}s'):
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time.sleep(1)
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out, _ = capsys.readouterr()
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