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[Fix]: fix log processor to log average time and grad norm (#292)

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Mashiro 2022-06-17 10:54:20 +08:00 committed by GitHub
parent 7b55c5bdbf
commit 7129a98e36
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2 changed files with 23 additions and 11 deletions
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3
mmengine/logging/log_processor.py
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@ -260,7 +260,8 @@ class LogProcessor:
mode_history_scalars[key] = log_buffer
for key in mode_history_scalars:
# Update the latest learning rate and smoothed time logs.
if key.startswith('loss'):
if key.startswith('loss') or key in ('time', 'data_time',
'grad_norm'):
tag[key] = mode_history_scalars[key].mean(self.window_size)
else:
# Default statistic method is current.

31
tests/test_logging/test_log_processor.py
View File

@ -2,10 +2,11 @@
import copy
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
import torch
from mmengine.logging import LogProcessor, MessageHub, MMLogger
from mmengine.logging import HistoryBuffer, LogProcessor, MessageHub, MMLogger
class TestLogProcessor:
@ -154,17 +155,27 @@ class TestLogProcessor:
assert out == 'Iter(val) [10/10] accuracy: 0.9000'
def test_collect_scalars(self):
history_count = np.ones(100)
time_scalars = np.random.randn(100)
loss_cls_scalars = np.random.randn(100)
lr_scalars = np.random.randn(100)
metric_scalars = np.random.randn(100)
history_time_buffer = HistoryBuffer(time_scalars, history_count)
histroy_loss_cls = HistoryBuffer(loss_cls_scalars, history_count)
history_lr_buffer = HistoryBuffer(lr_scalars, history_count)
history_metric_buffer = HistoryBuffer(metric_scalars, history_count)
custom_cfg = [
dict(data_src='time', method_name='mean', window_size=100),
dict(data_src='time', method_name='max', log_name='time_max')
]
logger_hook = LogProcessor(custom_cfg=custom_cfg)
# Collect with prefix.
log_scalars = {
'train/time': MagicMock(),
'lr': MagicMock(),
'train/loss_cls': MagicMock(),
'val/metric': MagicMock()
'train/time': history_time_buffer,
'lr': history_lr_buffer,
'train/loss_cls': histroy_loss_cls,
'val/metric': history_metric_buffer
}
self.runner.message_hub._log_scalars = log_scalars
tag = logger_hook._collect_scalars(
@ -172,14 +183,14 @@ class TestLogProcessor:
# Test training key in tag.
assert list(tag.keys()) == ['time', 'loss_cls', 'time_max']
# Test statistics lr with `current`, loss and time with 'mean'
log_scalars['train/time'].statistics.assert_called_with(
method_name='max')
log_scalars['train/loss_cls'].mean.assert_called()
assert tag['time'] == time_scalars[-10:].mean()
assert tag['time_max'] == time_scalars.max()
assert tag['loss_cls'] == loss_cls_scalars[-10:].mean()
tag = logger_hook._collect_scalars(
copy.deepcopy(custom_cfg), self.runner, mode='val')
assert list(tag.keys()) == ['metric']
log_scalars['val/metric'].current.assert_called()
assert tag['metric'] == metric_scalars[-1]
@patch('torch.cuda.max_memory_allocated', MagicMock())
@patch('torch.cuda.reset_peak_memory_stats', MagicMock())