Spaces:
Runtime error
Runtime error
File size: 12,553 Bytes
b334e29 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 |
import os.path as osp
import warnings
from math import inf
import annotator.uniformer.mmcv as mmcv
import torch.distributed as dist
from annotator.uniformer.mmcv.runner import Hook
from torch.nn.modules.batchnorm import _BatchNorm
from torch.utils.data import DataLoader
from annotator.uniformer.mmdet.utils import get_root_logger
class EvalHook(Hook):
"""Evaluation hook.
Notes:
If new arguments are added for EvalHook, tools/test.py,
tools/analysis_tools/eval_metric.py may be effected.
Attributes:
dataloader (DataLoader): A PyTorch dataloader.
start (int, optional): Evaluation starting epoch. It enables evaluation
before the training starts if ``start`` <= the resuming epoch.
If None, whether to evaluate is merely decided by ``interval``.
Default: None.
interval (int): Evaluation interval (by epochs). Default: 1.
save_best (str, optional): If a metric is specified, it would measure
the best checkpoint during evaluation. The information about best
checkpoint would be save in best.json.
Options are the evaluation metrics to the test dataset. e.g.,
``bbox_mAP``, ``segm_mAP`` for bbox detection and instance
segmentation. ``AR@100`` for proposal recall. If ``save_best`` is
``auto``, the first key will be used. The interval of
``CheckpointHook`` should device EvalHook. Default: None.
rule (str, optional): Comparison rule for best score. If set to None,
it will infer a reasonable rule. Keys such as 'mAP' or 'AR' will
be inferred by 'greater' rule. Keys contain 'loss' will be inferred
by 'less' rule. Options are 'greater', 'less'. Default: None.
**eval_kwargs: Evaluation arguments fed into the evaluate function of
the dataset.
"""
rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y}
init_value_map = {'greater': -inf, 'less': inf}
greater_keys = ['mAP', 'AR']
less_keys = ['loss']
def __init__(self,
dataloader,
start=None,
interval=1,
by_epoch=True,
save_best=None,
rule=None,
**eval_kwargs):
if not isinstance(dataloader, DataLoader):
raise TypeError('dataloader must be a pytorch DataLoader, but got'
f' {type(dataloader)}')
if not interval > 0:
raise ValueError(f'interval must be positive, but got {interval}')
if start is not None and start < 0:
warnings.warn(
f'The evaluation start epoch {start} is smaller than 0, '
f'use 0 instead', UserWarning)
start = 0
self.dataloader = dataloader
self.interval = interval
self.by_epoch = by_epoch
self.start = start
assert isinstance(save_best, str) or save_best is None
self.save_best = save_best
self.eval_kwargs = eval_kwargs
self.initial_epoch_flag = True
self.logger = get_root_logger()
if self.save_best is not None:
self._init_rule(rule, self.save_best)
def _init_rule(self, rule, key_indicator):
"""Initialize rule, key_indicator, comparison_func, and best score.
Args:
rule (str | None): Comparison rule for best score.
key_indicator (str | None): Key indicator to determine the
comparison rule.
"""
if rule not in self.rule_map and rule is not None:
raise KeyError(f'rule must be greater, less or None, '
f'but got {rule}.')
if rule is None:
if key_indicator != 'auto':
if any(key in key_indicator for key in self.greater_keys):
rule = 'greater'
elif any(key in key_indicator for key in self.less_keys):
rule = 'less'
else:
raise ValueError(f'Cannot infer the rule for key '
f'{key_indicator}, thus a specific rule '
f'must be specified.')
self.rule = rule
self.key_indicator = key_indicator
if self.rule is not None:
self.compare_func = self.rule_map[self.rule]
def before_run(self, runner):
if self.save_best is not None:
if runner.meta is None:
warnings.warn('runner.meta is None. Creating a empty one.')
runner.meta = dict()
runner.meta.setdefault('hook_msgs', dict())
def before_train_epoch(self, runner):
"""Evaluate the model only at the start of training."""
if not self.initial_epoch_flag:
return
if self.start is not None and runner.epoch >= self.start:
self.after_train_epoch(runner)
self.initial_epoch_flag = False
def evaluation_flag(self, runner):
"""Judge whether to perform_evaluation after this epoch.
Returns:
bool: The flag indicating whether to perform evaluation.
"""
if self.start is None:
if not self.every_n_epochs(runner, self.interval):
# No evaluation during the interval epochs.
return False
elif (runner.epoch + 1) < self.start:
# No evaluation if start is larger than the current epoch.
return False
else:
# Evaluation only at epochs 3, 5, 7... if start==3 and interval==2
if (runner.epoch + 1 - self.start) % self.interval:
return False
return True
def after_train_epoch(self, runner):
if not self.by_epoch or not self.evaluation_flag(runner):
return
from mmdet.apis import single_gpu_test
results = single_gpu_test(runner.model, self.dataloader, show=False)
key_score = self.evaluate(runner, results)
if self.save_best:
self.save_best_checkpoint(runner, key_score)
def after_train_iter(self, runner):
if self.by_epoch or not self.every_n_iters(runner, self.interval):
return
from mmdet.apis import single_gpu_test
results = single_gpu_test(runner.model, self.dataloader, show=False)
key_score = self.evaluate(runner, results)
if self.save_best:
self.save_best_checkpoint(runner, key_score)
def save_best_checkpoint(self, runner, key_score):
best_score = runner.meta['hook_msgs'].get(
'best_score', self.init_value_map[self.rule])
if self.compare_func(key_score, best_score):
best_score = key_score
runner.meta['hook_msgs']['best_score'] = best_score
last_ckpt = runner.meta['hook_msgs']['last_ckpt']
runner.meta['hook_msgs']['best_ckpt'] = last_ckpt
mmcv.symlink(
last_ckpt,
osp.join(runner.work_dir, f'best_{self.key_indicator}.pth'))
time_stamp = runner.epoch + 1 if self.by_epoch else runner.iter + 1
self.logger.info(f'Now best checkpoint is epoch_{time_stamp}.pth.'
f'Best {self.key_indicator} is {best_score:0.4f}')
def evaluate(self, runner, results):
eval_res = self.dataloader.dataset.evaluate(
results, logger=runner.logger, **self.eval_kwargs)
for name, val in eval_res.items():
runner.log_buffer.output[name] = val
runner.log_buffer.ready = True
if self.save_best is not None:
if self.key_indicator == 'auto':
# infer from eval_results
self._init_rule(self.rule, list(eval_res.keys())[0])
return eval_res[self.key_indicator]
else:
return None
class DistEvalHook(EvalHook):
"""Distributed evaluation hook.
Notes:
If new arguments are added, tools/test.py may be effected.
Attributes:
dataloader (DataLoader): A PyTorch dataloader.
start (int, optional): Evaluation starting epoch. It enables evaluation
before the training starts if ``start`` <= the resuming epoch.
If None, whether to evaluate is merely decided by ``interval``.
Default: None.
interval (int): Evaluation interval (by epochs). Default: 1.
tmpdir (str | None): Temporary directory to save the results of all
processes. Default: None.
gpu_collect (bool): Whether to use gpu or cpu to collect results.
Default: False.
save_best (str, optional): If a metric is specified, it would measure
the best checkpoint during evaluation. The information about best
checkpoint would be save in best.json.
Options are the evaluation metrics to the test dataset. e.g.,
``bbox_mAP``, ``segm_mAP`` for bbox detection and instance
segmentation. ``AR@100`` for proposal recall. If ``save_best`` is
``auto``, the first key will be used. The interval of
``CheckpointHook`` should device EvalHook. Default: None.
rule (str | None): Comparison rule for best score. If set to None,
it will infer a reasonable rule. Default: 'None'.
broadcast_bn_buffer (bool): Whether to broadcast the
buffer(running_mean and running_var) of rank 0 to other rank
before evaluation. Default: True.
**eval_kwargs: Evaluation arguments fed into the evaluate function of
the dataset.
"""
def __init__(self,
dataloader,
start=None,
interval=1,
by_epoch=True,
tmpdir=None,
gpu_collect=False,
save_best=None,
rule=None,
broadcast_bn_buffer=True,
**eval_kwargs):
super().__init__(
dataloader,
start=start,
interval=interval,
by_epoch=by_epoch,
save_best=save_best,
rule=rule,
**eval_kwargs)
self.broadcast_bn_buffer = broadcast_bn_buffer
self.tmpdir = tmpdir
self.gpu_collect = gpu_collect
def _broadcast_bn_buffer(self, runner):
# Synchronization of BatchNorm's buffer (running_mean
# and running_var) is not supported in the DDP of pytorch,
# which may cause the inconsistent performance of models in
# different ranks, so we broadcast BatchNorm's buffers
# of rank 0 to other ranks to avoid this.
if self.broadcast_bn_buffer:
model = runner.model
for name, module in model.named_modules():
if isinstance(module,
_BatchNorm) and module.track_running_stats:
dist.broadcast(module.running_var, 0)
dist.broadcast(module.running_mean, 0)
def after_train_epoch(self, runner):
if not self.by_epoch or not self.evaluation_flag(runner):
return
if self.broadcast_bn_buffer:
self._broadcast_bn_buffer(runner)
from mmdet.apis import multi_gpu_test
tmpdir = self.tmpdir
if tmpdir is None:
tmpdir = osp.join(runner.work_dir, '.eval_hook')
results = multi_gpu_test(
runner.model,
self.dataloader,
tmpdir=tmpdir,
gpu_collect=self.gpu_collect)
if runner.rank == 0:
print('\n')
key_score = self.evaluate(runner, results)
if self.save_best:
self.save_best_checkpoint(runner, key_score)
def after_train_iter(self, runner):
if self.by_epoch or not self.every_n_iters(runner, self.interval):
return
if self.broadcast_bn_buffer:
self._broadcast_bn_buffer(runner)
from mmdet.apis import multi_gpu_test
tmpdir = self.tmpdir
if tmpdir is None:
tmpdir = osp.join(runner.work_dir, '.eval_hook')
results = multi_gpu_test(
runner.model,
self.dataloader,
tmpdir=tmpdir,
gpu_collect=self.gpu_collect)
if runner.rank == 0:
print('\n')
key_score = self.evaluate(runner, results)
if self.save_best:
self.save_best_checkpoint(runner, key_score)
|