292 lines
12 KiB
Python
292 lines
12 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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from unittest.mock import MagicMock, patch
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import numpy as np
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import pytest
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import torch
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from mmengine.logging import HistoryBuffer, MessageHub, MMLogger
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from mmengine.runner import LogProcessor
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class TestLogProcessor:
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def test_init(self):
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log_processor = LogProcessor(
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window_size=10, by_epoch=True, custom_cfg=None)
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assert log_processor.by_epoch
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assert log_processor.window_size == 10
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assert log_processor.custom_cfg == []
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def test_check_custom_cfg(self):
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# ``by_epoch==False`` and `window_size='epoch'` in log config will
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# raise AssertionError.
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custom_cfg = [dict(data_src='loss', window_size='epoch')]
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with pytest.raises(AssertionError):
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LogProcessor(by_epoch=False, custom_cfg=custom_cfg)
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# Duplicate log_name will raise AssertionError.
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custom_cfg = [
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dict(data_src='loss', log_name='loss_1'),
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dict(data_src='loss', log_name='loss_1')
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]
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with pytest.raises(AssertionError):
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LogProcessor(custom_cfg=custom_cfg)
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# Overwrite loss item twice will raise AssertionError.
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custom_cfg = [dict(data_src='loss'), dict(data_src='loss')]
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with pytest.raises(AssertionError):
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LogProcessor(custom_cfg=custom_cfg)
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custom_cfg = [
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dict(data_src='loss_cls', window_size=100, method_name='min'),
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dict(data_src='loss', log_name='loss_min', method_name='max'),
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dict(data_src='loss', log_name='loss_max', method_name='max')
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]
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LogProcessor(custom_cfg=custom_cfg)
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def test_parse_windows_size(self):
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log_processor = LogProcessor()
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# Test parse 'epoch' window_size.
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custom_cfg = [dict(data_src='loss_cls', window_size='epoch')]
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custom_cfg = log_processor._parse_windows_size(self.runner, 1,
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custom_cfg)
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assert custom_cfg[0]['window_size'] == 2
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# Test parse 'global' window_size.
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custom_cfg = [dict(data_src='loss_cls', window_size='global')]
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custom_cfg = log_processor._parse_windows_size(self.runner, 1,
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custom_cfg)
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assert custom_cfg[0]['window_size'] == 11
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# Test parse int window_size
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custom_cfg = [dict(data_src='loss_cls', window_size=100)]
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custom_cfg = log_processor._parse_windows_size(self.runner, 1,
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custom_cfg)
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assert custom_cfg[0]['window_size'] == 100
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# Invalid type window_size will raise TypeError.
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custom_cfg = [dict(data_src='loss_cls', window_size=[])]
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with pytest.raises(TypeError):
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log_processor._parse_windows_size(self.runner, 1, custom_cfg)
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@pytest.mark.parametrize(
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'by_epoch,mode,log_with_hierarchy',
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([True, 'train', True], [True, 'train', False], [False, 'train', True],
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[False, 'train', False], [True, 'val', True], [True, 'val', False],
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[False, 'val', True], [False, 'val', False], [True, 'test', True],
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[True, 'test', False], [False, 'test', True], [False, 'test', False]))
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def test_get_log_after_iter(self, by_epoch, mode, log_with_hierarchy):
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# Prepare LoggerHook
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log_processor = LogProcessor(
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by_epoch=by_epoch, log_with_hierarchy=log_with_hierarchy)
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log_processor._get_max_memory = MagicMock(return_value='100')
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eta = 40
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self.runner.message_hub.update_info('eta', eta)
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# Prepare training information.
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if mode == 'train':
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train_logs = dict(lr=0.1, time=1.0, data_time=1.0, loss_cls=1.0)
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else:
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train_logs = dict(time=1.0, data_time=1.0, loss_cls=1.0)
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log_processor._collect_scalars = \
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lambda *args, **kwargs: copy.deepcopy(train_logs)
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_, out = log_processor.get_log_after_iter(self.runner, 1, mode)
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# Verify that the correct context have been logged.
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cur_loop = log_processor._get_cur_loop(self.runner, mode)
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if by_epoch:
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if mode in ['train', 'val']:
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cur_epoch = log_processor._get_epoch(self.runner, mode)
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log_str = (f'Epoch({mode}) [{cur_epoch}][ 2/'
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f'{len(cur_loop.dataloader)}] ')
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else:
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log_str = (f'Epoch({mode}) [2/{len(cur_loop.dataloader)}] ')
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if mode == 'train':
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log_str += f"lr: {train_logs['lr']:.4e} "
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else:
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log_str += ' '
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log_str += (f'eta: 0:00:40 '
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f"time: {train_logs['time']:.4f} "
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f"data_time: {train_logs['data_time']:.4f} ")
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if torch.cuda.is_available():
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log_str += 'memory: 100 '
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if mode == 'train':
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log_str += f"loss_cls: {train_logs['loss_cls']:.4f}"
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assert out == log_str
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else:
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if mode == 'train':
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max_iters = self.runner.max_iters
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log_str = f'Iter({mode}) [11/{max_iters}] '
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elif mode == 'val':
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max_iters = len(cur_loop.dataloader)
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log_str = f'Iter({mode}) [ 2/{max_iters}] '
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else:
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max_iters = len(cur_loop.dataloader)
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log_str = f'Iter({mode}) [2/{max_iters}] '
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if mode == 'train':
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log_str += f"lr: {train_logs['lr']:.4e} "
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else:
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log_str += ' '
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log_str += (f'eta: 0:00:40 '
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f"time: {train_logs['time']:.4f} "
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f"data_time: {train_logs['data_time']:.4f} ")
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if torch.cuda.is_available():
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log_str += 'memory: 100 '
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if mode == 'train':
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log_str += f"loss_cls: {train_logs['loss_cls']:.4f}"
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assert out == log_str
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@pytest.mark.parametrize(
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'by_epoch,mode,log_with_hierarchy',
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([True, 'val', True], [True, 'val', False], [False, 'val', True],
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[False, 'val', False], [True, 'test', True], [False, 'test', False]))
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def test_log_val(self, by_epoch, mode, log_with_hierarchy):
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# Prepare LoggerHook
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log_processor = LogProcessor(
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by_epoch=by_epoch, log_with_hierarchy=log_with_hierarchy)
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# Prepare validation information.
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scalar_logs = dict(accuracy=0.9, data_time=1.0)
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non_scalar_logs = dict(
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recall={
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'cat': 1,
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'dog': 0
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}, cm=torch.tensor([1, 2, 3]))
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log_processor._collect_scalars = MagicMock(return_value=scalar_logs)
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log_processor._collect_non_scalars = MagicMock(
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return_value=non_scalar_logs)
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_, out = log_processor.get_log_after_epoch(self.runner, 2, mode)
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expect_metric_str = ("accuracy: 0.9000 recall: {'cat': 1, 'dog': 0} "
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'cm: \ntensor([1, 2, 3])\n data_time: 1.0000')
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if by_epoch:
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if mode == 'test':
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assert out == 'Epoch(test) [5/5] ' + expect_metric_str
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else:
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assert out == 'Epoch(val) [1][10/10] ' + expect_metric_str
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else:
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if mode == 'test':
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assert out == 'Iter(test) [5/5] ' + expect_metric_str
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else:
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assert out == 'Iter(val) [10/10] ' + expect_metric_str
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def test_collect_scalars(self):
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history_count = np.ones(100)
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time_scalars = np.random.randn(100)
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loss_cls_scalars = np.random.randn(100)
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lr_scalars = np.random.randn(100)
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metric_scalars = np.random.randn(100)
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history_time_buffer = HistoryBuffer(time_scalars, history_count)
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histroy_loss_cls = HistoryBuffer(loss_cls_scalars, history_count)
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history_lr_buffer = HistoryBuffer(lr_scalars, history_count)
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history_metric_buffer = HistoryBuffer(metric_scalars, history_count)
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custom_cfg = [
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dict(data_src='time', method_name='max', log_name='time_max')
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]
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logger_hook = LogProcessor(custom_cfg=custom_cfg)
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# Collect with prefix.
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log_scalars = {
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'train/time': history_time_buffer,
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'lr': history_lr_buffer,
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'train/loss_cls': histroy_loss_cls,
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'val/metric': history_metric_buffer
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}
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self.runner.message_hub._log_scalars = log_scalars
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tag = logger_hook._collect_scalars(
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copy.deepcopy(custom_cfg), self.runner, mode='train')
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# Test training key in tag.
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assert list(tag.keys()) == ['time', 'loss_cls', 'time_max']
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# Test statistics lr with `current`, loss and time with 'mean'
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assert tag['time'] == time_scalars[-10:].mean()
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assert tag['time_max'] == time_scalars.max()
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assert tag['loss_cls'] == loss_cls_scalars[-10:].mean()
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tag = logger_hook._collect_scalars(
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copy.deepcopy(custom_cfg), self.runner, mode='val')
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assert list(tag.keys()) == ['metric']
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assert tag['metric'] == metric_scalars[-1]
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def test_collect_non_scalars(self):
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metric1 = np.random.rand(10)
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metric2 = torch.tensor(10)
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log_processor = LogProcessor()
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# Collect with prefix.
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log_infos = {'test/metric1': metric1, 'test/metric2': metric2}
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self.runner.message_hub._runtime_info = log_infos
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tag = log_processor._collect_non_scalars(self.runner, mode='test')
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# Test training key in tag.
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assert list(tag.keys()) == ['metric1', 'metric2']
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# Test statistics lr with `current`, loss and time with 'mean'
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assert tag['metric1'] is metric1
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assert tag['metric2'] is metric2
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@patch('torch.cuda.max_memory_allocated', MagicMock())
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@patch('torch.cuda.reset_peak_memory_stats', MagicMock())
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def test_get_max_memory(self):
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logger_hook = LogProcessor()
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runner = MagicMock()
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runner.world_size = 1
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runner.model = torch.nn.Linear(1, 1)
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logger_hook._get_max_memory(runner)
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torch.cuda.max_memory_allocated.assert_called()
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torch.cuda.reset_peak_memory_stats.assert_called()
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def test_get_iter(self):
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log_processor = LogProcessor()
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# Get global iter when `inner_iter=False`
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iter = log_processor._get_iter(self.runner)
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assert iter == 11
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# Get inner iter
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iter = log_processor._get_iter(self.runner, 1)
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assert iter == 2
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# Still get global iter when `logger_hook.by_epoch==False`
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log_processor.by_epoch = False
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iter = log_processor._get_iter(self.runner, 1)
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assert iter == 11
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def test_get_epoch(self):
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log_processor = LogProcessor()
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epoch = log_processor._get_epoch(self.runner, 'train')
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assert epoch == 2
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epoch = log_processor._get_epoch(self.runner, 'val')
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assert epoch == 1
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with pytest.raises(ValueError):
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log_processor._get_epoch(self.runner, 'test')
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def test_get_cur_loop(self):
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log_processor = LogProcessor()
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loop = log_processor._get_cur_loop(self.runner, 'train')
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assert len(loop.dataloader) == 20
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loop = log_processor._get_cur_loop(self.runner, 'val')
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assert len(loop.dataloader) == 10
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loop = log_processor._get_cur_loop(self.runner, 'test')
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assert len(loop.dataloader) == 5
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def setup_method(self):
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runner = MagicMock()
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runner.epoch = 1
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runner.max_epochs = 10
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runner.iter = 10
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runner.max_iters = 50
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runner.train_dataloader = [0] * 20
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runner.val_dataloader = [0] * 10
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runner.test_dataloader = [0] * 5
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runner.train_loop.dataloader = [0] * 20
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runner.val_loop.dataloader = [0] * 10
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runner.test_loop.dataloader = [0] * 5
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logger = MMLogger.get_instance('log_processor_test')
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runner.logger = logger
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message_hub = MessageHub.get_instance('log_processor_test')
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for i in range(10):
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message_hub.update_scalar('train/loss', 10 - i)
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for i in range(10):
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message_hub.update_scalar('val/acc', i * 0.1)
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runner.message_hub = message_hub
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self.runner = runner
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