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import os.path as osp
from annotator.uniformer.mmcv.runner import DistEvalHook as _DistEvalHook
from annotator.uniformer.mmcv.runner import EvalHook as _EvalHook
class EvalHook(_EvalHook):
"""Single GPU EvalHook, with efficient test support.
Args:
by_epoch (bool): Determine perform evaluation by epoch or by iteration.
If set to True, it will perform by epoch. Otherwise, by iteration.
Default: False.
efficient_test (bool): Whether save the results as local numpy files to
save CPU memory during evaluation. Default: False.
Returns:
list: The prediction results.
"""
greater_keys = ['mIoU', 'mAcc', 'aAcc']
def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs):
super().__init__(*args, by_epoch=by_epoch, **kwargs)
self.efficient_test = efficient_test
def after_train_iter(self, runner):
"""After train epoch hook.
Override default ``single_gpu_test``.
"""
if self.by_epoch or not self.every_n_iters(runner, self.interval):
return
from annotator.uniformer.mmseg.apis import single_gpu_test
runner.log_buffer.clear()
results = single_gpu_test(
runner.model,
self.dataloader,
show=False,
efficient_test=self.efficient_test)
self.evaluate(runner, results)
def after_train_epoch(self, runner):
"""After train epoch hook.
Override default ``single_gpu_test``.
"""
if not self.by_epoch or not self.every_n_epochs(runner, self.interval):
return
from annotator.uniformer.mmseg.apis import single_gpu_test
runner.log_buffer.clear()
results = single_gpu_test(runner.model, self.dataloader, show=False)
self.evaluate(runner, results)
class DistEvalHook(_DistEvalHook):
"""Distributed EvalHook, with efficient test support.
Args:
by_epoch (bool): Determine perform evaluation by epoch or by iteration.
If set to True, it will perform by epoch. Otherwise, by iteration.
Default: False.
efficient_test (bool): Whether save the results as local numpy files to
save CPU memory during evaluation. Default: False.
Returns:
list: The prediction results.
"""
greater_keys = ['mIoU', 'mAcc', 'aAcc']
def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs):
super().__init__(*args, by_epoch=by_epoch, **kwargs)
self.efficient_test = efficient_test
def after_train_iter(self, runner):
"""After train epoch hook.
Override default ``multi_gpu_test``.
"""
if self.by_epoch or not self.every_n_iters(runner, self.interval):
return
from annotator.uniformer.mmseg.apis import multi_gpu_test
runner.log_buffer.clear()
results = multi_gpu_test(
runner.model,
self.dataloader,
tmpdir=osp.join(runner.work_dir, '.eval_hook'),
gpu_collect=self.gpu_collect,
efficient_test=self.efficient_test)
if runner.rank == 0:
print('\n')
self.evaluate(runner, results)
def after_train_epoch(self, runner):
"""After train epoch hook.
Override default ``multi_gpu_test``.
"""
if not self.by_epoch or not self.every_n_epochs(runner, self.interval):
return
from annotator.uniformer.mmseg.apis import multi_gpu_test
runner.log_buffer.clear()
results = multi_gpu_test(
runner.model,
self.dataloader,
tmpdir=osp.join(runner.work_dir, '.eval_hook'),
gpu_collect=self.gpu_collect)
if runner.rank == 0:
print('\n')
self.evaluate(runner, results)
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