import os.path as osp
from mmcv.runner import Hook
from torch.utils.data import DataLoader
[docs]class EvalHook(Hook):
"""Evaluation hook.
Attributes:
dataloader (DataLoader): A PyTorch dataloader.
interval (int): Evaluation interval (by epochs). Default: 1.
"""
def __init__(self, dataloader, interval=1, **eval_kwargs):
if not isinstance(dataloader, DataLoader):
raise TypeError('dataloader must be a pytorch DataLoader, but got'
f' {type(dataloader)}')
self.dataloader = dataloader
self.interval = interval
self.eval_kwargs = eval_kwargs
def after_train_epoch(self, runner):
if not self.every_n_epochs(runner, self.interval):
return
from mmdet.apis import single_gpu_test
results = single_gpu_test(runner.model, self.dataloader, show=False)
self.evaluate(runner, results)
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
[docs]class DistEvalHook(EvalHook):
"""Distributed evaluation hook.
Attributes:
dataloader (DataLoader): A PyTorch dataloader.
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.
"""
def __init__(self,
dataloader,
interval=1,
gpu_collect=False,
**eval_kwargs):
if not isinstance(dataloader, DataLoader):
raise TypeError('dataloader must be a pytorch DataLoader, but got '
f'{type(dataloader)}')
self.dataloader = dataloader
self.interval = interval
self.gpu_collect = gpu_collect
self.eval_kwargs = eval_kwargs
def after_train_epoch(self, runner):
if not self.every_n_epochs(runner, self.interval):
return
from mmdet.apis import multi_gpu_test
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)