File size: 7,154 Bytes
d19bd3e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | import os
import sys
import glob
import torch
import shutil
import logging
import datetime
from mmcv.runner.hooks import HOOKS
from mmcv.runner.hooks.logger import LoggerHook, TextLoggerHook
from mmcv.runner.dist_utils import master_only
from torch.utils.tensorboard import SummaryWriter
def init_logging(filename=None, debug=False):
logging.root = logging.RootLogger('DEBUG' if debug else 'INFO')
formatter = logging.Formatter('[%(asctime)s][%(levelname)s] - %(message)s')
stream_handler = logging.StreamHandler(sys.stdout)
stream_handler.setFormatter(formatter)
logging.root.addHandler(stream_handler)
if filename is not None:
file_handler = logging.FileHandler(filename)
file_handler.setFormatter(formatter)
logging.root.addHandler(file_handler)
def backup_code(work_dir, verbose=False):
base_dir = os.path.dirname(os.path.abspath(__file__))
for pattern in ['*.py', 'configs/*.py', 'models/*.py', 'loaders/*.py', 'loaders/pipelines/*.py']:
for file in glob.glob(pattern):
src = os.path.join(base_dir, file)
dst = os.path.join(work_dir, 'backup', os.path.dirname(file))
if verbose:
logging.info('Copying %s -> %s' % (os.path.relpath(src), os.path.relpath(dst)))
os.makedirs(dst, exist_ok=True)
shutil.copy2(src, dst)
@HOOKS.register_module()
class MyTextLoggerHook(TextLoggerHook):
def _log_info(self, log_dict, runner):
# print exp name for users to distinguish experiments
# at every ``interval_exp_name`` iterations and the end of each epoch
if runner.meta is not None and 'exp_name' in runner.meta:
if (self.every_n_iters(runner, self.interval_exp_name)) or (
self.by_epoch and self.end_of_epoch(runner)):
exp_info = f'Exp name: {runner.meta["exp_name"]}'
runner.logger.info(exp_info)
# by epoch: Epoch [4][100/1000]
# by iter: Iter [100/100000]
if self.by_epoch:
log_str = f'Epoch [{log_dict["epoch"]}/{runner.max_epochs}]' \
f'[{log_dict["iter"]}/{len(runner.data_loader)}] '
else:
log_str = f'Iter [{log_dict["iter"]}/{runner.max_iters}] '
log_str += 'loss: %.2f, ' % log_dict['loss']
if 'time' in log_dict.keys():
# MOD: skip the first iteration since it's not accurate
if runner.iter == self.start_iter:
time_sec_avg = log_dict['time']
else:
self.time_sec_tot += (log_dict['time'] * self.interval)
time_sec_avg = self.time_sec_tot / (runner.iter - self.start_iter)
eta_sec = time_sec_avg * (runner.max_iters - runner.iter - 1)
eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
log_str += f'eta: {eta_str}, '
log_str += f'time: {log_dict["time"]:.2f}s, ' \
f'data: {log_dict["data_time"] * 1000:.0f}ms, '
# statistic memory
if torch.cuda.is_available():
log_str += f'mem: {log_dict["memory"]}M'
runner.logger.info(log_str)
def log(self, runner):
if 'eval_iter_num' in runner.log_buffer.output:
# this doesn't modify runner.iter and is regardless of by_epoch
cur_iter = runner.log_buffer.output.pop('eval_iter_num')
else:
cur_iter = self.get_iter(runner, inner_iter=True)
log_dict = {
'mode': self.get_mode(runner),
'epoch': self.get_epoch(runner),
'iter': cur_iter
}
# only record lr of the first param group
cur_lr = runner.current_lr()
if isinstance(cur_lr, list):
log_dict['lr'] = cur_lr[0]
else:
assert isinstance(cur_lr, dict)
log_dict['lr'] = {}
for k, lr_ in cur_lr.items():
assert isinstance(lr_, list)
log_dict['lr'].update({k: lr_[0]})
if 'time' in runner.log_buffer.output:
# statistic memory
if torch.cuda.is_available():
log_dict['memory'] = self._get_max_memory(runner)
log_dict = dict(log_dict, **runner.log_buffer.output)
# MOD: disable writing to files
# self._dump_log(log_dict, runner)
self._log_info(log_dict, runner)
return log_dict
def after_train_epoch(self, runner):
if runner.log_buffer.ready:
metrics = self.get_loggable_tags(runner)
runner.logger.info('--- Evaluation Results ---')
runner.logger.info('mAP: %.4f' % metrics['val/pts_bbox_NuScenes/mAP'])
runner.logger.info('mATE: %.4f' % metrics['val/pts_bbox_NuScenes/mATE'])
runner.logger.info('mASE: %.4f' % metrics['val/pts_bbox_NuScenes/mASE'])
runner.logger.info('mAOE: %.4f' % metrics['val/pts_bbox_NuScenes/mAOE'])
runner.logger.info('mAVE: %.4f' % metrics['val/pts_bbox_NuScenes/mAVE'])
runner.logger.info('mAAE: %.4f' % metrics['val/pts_bbox_NuScenes/mAAE'])
runner.logger.info('NDS: %.4f' % metrics['val/pts_bbox_NuScenes/NDS'])
@HOOKS.register_module()
class MyTensorboardLoggerHook(LoggerHook):
def __init__(self, log_dir=None, interval=10, ignore_last=True, reset_flag=False, by_epoch=True):
super(MyTensorboardLoggerHook, self).__init__(
interval, ignore_last, reset_flag, by_epoch)
self.log_dir = log_dir
@master_only
def before_run(self, runner):
super(MyTensorboardLoggerHook, self).before_run(runner)
if self.log_dir is None:
self.log_dir = runner.work_dir
self.writer = SummaryWriter(self.log_dir)
@master_only
def log(self, runner):
tags = self.get_loggable_tags(runner)
for key, value in tags.items():
# MOD: merge into the 'train' group
if key == 'learning_rate':
key = 'train/learning_rate'
# MOD: skip momentum
ignore = False
if key == 'momentum':
ignore = True
# MOD: skip intermediate losses
for i in range(5):
if key[:13] == 'train/d%d.loss' % i:
ignore = True
if key[:3] == 'val':
metric_name = key[22:]
if metric_name in ['mAP', 'mATE', 'mASE', 'mAOE', 'mAVE', 'mAAE', 'NDS']:
key = 'val/' + metric_name
else:
ignore = True
if self.get_mode(runner) == 'train' and key[:5] != 'train':
ignore = True
if self.get_mode(runner) != 'train' and key[:3] != 'val':
ignore = True
if ignore:
continue
if key[:5] == 'train':
self.writer.add_scalar(key, value, self.get_iter(runner))
elif key[:3] == 'val':
self.writer.add_scalar(key, value, self.get_epoch(runner))
@master_only
def after_run(self, runner):
self.writer.close()
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