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Add runner type (#118) 

* Add runner_type option

* pre-commit

* Fix max_iters

* Add by_epoch to EvalHook

* Add test_eval_hook for epoch runner

* Remove runner-type arg from tools/train

* Add missing every_n_iters check for epoch mode

* Bump mmcv min version

* Use build_runner

* Use interval in tests

* Update test_eval_hook.py

* Use every_n_epochs instead of every_n_iters. Update DistEvalHook

* Add test_dist_eval_hook_epoch

* Fix tests

* Add DeprecationWarning

* Update docs

* Replace DeprecationWarning with UserWarning
pull/1801/head
David de la Iglesia Castro 2020-10-25 23:18:46 +01:00 committed by GitHub
parent 464ffb77ad
commit 969c7fd8e4
9 changed files with 134 additions and 20 deletions
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2
configs/_base_/schedules/schedule_160k.py
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@ -4,6 +4,6 @@ optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
total_iters = 160000
runner = dict(type='IterBasedRunner', max_iters=160000)
checkpoint_config = dict(by_epoch=False, interval=16000)
evaluation = dict(interval=16000, metric='mIoU')

2
configs/_base_/schedules/schedule_20k.py
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@ -4,6 +4,6 @@ optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
total_iters = 20000
runner = dict(type='IterBasedRunner', max_iters=20000)
checkpoint_config = dict(by_epoch=False, interval=2000)
evaluation = dict(interval=2000, metric='mIoU')

2
configs/_base_/schedules/schedule_40k.py
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@ -4,6 +4,6 @@ optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
total_iters = 40000
runner = dict(type='IterBasedRunner', max_iters=40000)
checkpoint_config = dict(by_epoch=False, interval=4000)
evaluation = dict(interval=4000, metric='mIoU')

2
configs/_base_/schedules/schedule_80k.py
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@ -4,6 +4,6 @@ optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
total_iters = 80000
runner = dict(type='IterBasedRunner', max_iters=80000)
checkpoint_config = dict(by_epoch=False, interval=8000)
evaluation = dict(interval=8000, metric='mIoU')

6
docs/config.md
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@ -226,7 +226,7 @@ dist_params = dict(backend='nccl') # Parameters to setup distributed training,
log_level = 'INFO' # The level of logging.
load_from = None # load models as a pre-trained model from a given path. This will not resume training.
resume_from = None # Resume checkpoints from a given path, the training will be resumed from the iteration when the checkpoint's is saved.
workflow = [('train', 1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 40000 iterations according to the total_iters.
workflow = [('train', 1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 40000 iterations according to the `runner.max_iters`.
cudnn_benchmark = True # Whether use cudnn_benchmark to speed up, which is fast for fixed input size.
optimizer = dict( # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch
type='SGD', # Type of optimizers, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py#L13 for more details
@ -239,7 +239,9 @@ lr_config = dict(
power=0.9, # The power of polynomial decay.
min_lr=0.0001, # The minimum learning rate to stable the training.
by_epoch=False) # Whethe count by epoch or not.
total_iters = 40000 # Total number of iterations.
runner = dict(
type='IterBasedRunner', # Type of runner to use (i.e. IterBasedRunner or EpochBasedRunner)
max_iters=40000) # Total number of iterations. For EpochBasedRunner use `max_epochs`
checkpoint_config = dict( # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.
by_epoch=False, # Whethe count by epoch or not.
interval=4000) # The save interval.

2
mmseg/__init__.py
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@ -2,7 +2,7 @@ import mmcv
from .version import __version__, version_info
MMCV_MIN = '1.1.2'
MMCV_MIN = '1.1.4'
MMCV_MAX = '1.2.0'

28
mmseg/apis/train.py
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@ -1,9 +1,10 @@
import random
import warnings
import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import IterBasedRunner, build_optimizer
from mmcv.runner import build_optimizer, build_runner
from mmseg.core import DistEvalHook, EvalHook
from mmseg.datasets import build_dataloader, build_dataset
@ -70,13 +71,21 @@ def train_segmentor(model,
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = IterBasedRunner(
model=model,
batch_processor=None,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta)
if cfg.get('runner') is None:
cfg.runner = {'type': 'IterBasedRunner', 'max_iters': cfg.total_iters}
warnings.warn(
'config is now expected to have a `runner` section, '
'please set `runner` in your config.', UserWarning)
runner = build_runner(
cfg.runner,
default_args=dict(
model=model,
batch_processor=None,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta))
# register hooks
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
@ -96,6 +105,7 @@ def train_segmentor(model,
dist=distributed,
shuffle=False)
eval_cfg = cfg.get('evaluation', {})
eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
eval_hook = DistEvalHook if distributed else EvalHook
runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
@ -103,4 +113,4 @@ def train_segmentor(model,
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_iters)
runner.run(data_loaders, cfg.workflow)

33
mmseg/core/evaluation/eval_hooks.py
View File

@ -12,17 +12,27 @@ class EvalHook(Hook):
interval (int): Evaluation interval (by epochs). Default: 1.
"""
def __init__(self, dataloader, interval=1, **eval_kwargs):
def __init__(self, dataloader, interval=1, by_epoch=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.by_epoch = by_epoch
self.eval_kwargs = eval_kwargs
def after_train_iter(self, runner):
"""After train epoch hook."""
if not self.every_n_iters(runner, self.interval):
if self.by_epoch or not self.every_n_iters(runner, self.interval):
return
from mmseg.apis import single_gpu_test
runner.log_buffer.clear()
results = single_gpu_test(runner.model, self.dataloader, show=False)
self.evaluate(runner, results)
def after_train_epoch(self, runner):
"""After train epoch hook."""
if not self.by_epoch or not self.every_n_epochs(runner, self.interval):
return
from mmseg.apis import single_gpu_test
runner.log_buffer.clear()
@ -54,6 +64,7 @@ class DistEvalHook(EvalHook):
dataloader,
interval=1,
gpu_collect=False,
by_epoch=False,
**eval_kwargs):
if not isinstance(dataloader, DataLoader):
raise TypeError(
@ -62,11 +73,27 @@ class DistEvalHook(EvalHook):
self.dataloader = dataloader
self.interval = interval
self.gpu_collect = gpu_collect
self.by_epoch = by_epoch
self.eval_kwargs = eval_kwargs
def after_train_iter(self, runner):
"""After train epoch hook."""
if not self.every_n_iters(runner, self.interval):
if self.by_epoch or not self.every_n_iters(runner, self.interval):
return
from 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)
def after_train_epoch(self, runner):
"""After train epoch hook."""
if not self.by_epoch or not self.every_n_epochs(runner, self.interval):
return
from mmseg.apis import multi_gpu_test
runner.log_buffer.clear()

77
tests/test_eval_hook.py
View File

@ -38,7 +38,7 @@ class ExampleModel(nn.Module):
return dict(loss=loss)
def test_eval_hook():
def test_iter_eval_hook():
with pytest.raises(TypeError):
test_dataset = ExampleModel()
data_loader = [
@ -75,6 +75,43 @@ def test_eval_hook():
logger=runner.logger)
def test_epoch_eval_hook():
with pytest.raises(TypeError):
test_dataset = ExampleModel()
data_loader = [
DataLoader(
test_dataset,
batch_size=1,
sampler=None,
num_worker=0,
shuffle=False)
]
EvalHook(data_loader, by_epoch=True)
test_dataset = ExampleDataset()
test_dataset.evaluate = MagicMock(return_value=dict(test='success'))
loader = DataLoader(test_dataset, batch_size=1)
model = ExampleModel()
data_loader = DataLoader(
test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False)
optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer = obj_from_dict(optim_cfg, torch.optim,
dict(params=model.parameters()))
# test EvalHook with interval
with tempfile.TemporaryDirectory() as tmpdir:
eval_hook = EvalHook(data_loader, by_epoch=True, interval=2)
runner = mmcv.runner.EpochBasedRunner(
model=model,
optimizer=optimizer,
work_dir=tmpdir,
logger=logging.getLogger())
runner.register_hook(eval_hook)
runner.run([loader], [('train', 1)], 2)
test_dataset.evaluate.assert_called_once_with([torch.tensor([1])],
logger=runner.logger)
def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
results = single_gpu_test(model, data_loader)
return results
@ -116,3 +153,41 @@ def test_dist_eval_hook():
runner.run([loader], [('train', 1)], 1)
test_dataset.evaluate.assert_called_with([torch.tensor([1])],
logger=runner.logger)
@patch('mmseg.apis.multi_gpu_test', multi_gpu_test)
def test_dist_eval_hook_epoch():
with pytest.raises(TypeError):
test_dataset = ExampleModel()
data_loader = [
DataLoader(
test_dataset,
batch_size=1,
sampler=None,
num_worker=0,
shuffle=False)
]
DistEvalHook(data_loader)
test_dataset = ExampleDataset()
test_dataset.evaluate = MagicMock(return_value=dict(test='success'))
loader = DataLoader(test_dataset, batch_size=1)
model = ExampleModel()
data_loader = DataLoader(
test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False)
optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer = obj_from_dict(optim_cfg, torch.optim,
dict(params=model.parameters()))
# test DistEvalHook
with tempfile.TemporaryDirectory() as tmpdir:
eval_hook = DistEvalHook(data_loader, by_epoch=True, interval=2)
runner = mmcv.runner.EpochBasedRunner(
model=model,
optimizer=optimizer,
work_dir=tmpdir,
logger=logging.getLogger())
runner.register_hook(eval_hook)
runner.run([loader], [('train', 1)], 2)
test_dataset.evaluate.assert_called_with([torch.tensor([1])],
logger=runner.logger)