diff --git a/.circleci/config.yml b/.circleci/config.yml
new file mode 100644
index 0000000000000000000000000000000000000000..8a8acb510a7ebe6e73ae5e97c6b2be391b75bc56
--- /dev/null
+++ b/.circleci/config.yml
@@ -0,0 +1,122 @@
+version: 2.1
+
+jobs:
+ lint:
+ docker:
+ - image: cimg/python:3.7.4
+ steps:
+ - checkout
+ - run:
+ name: Install pre-commit hook
+ command: |
+ sudo apt-add-repository ppa:brightbox/ruby-ng -y
+ sudo apt-get update
+ sudo apt-get install -y ruby2.7
+ pip install pre-commit
+ pre-commit install
+ - run:
+ name: Linting
+ command: pre-commit run --all-files
+ - run:
+ name: Check docstring coverage
+ command: |
+ pip install interrogate
+ interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 80 mmengine
+
+ build_cpu:
+ parameters:
+ # The python version must match available image tags in
+ # https://circleci.com/developer/images/image/cimg/python
+ python:
+ type: string
+ default: "3.7.4"
+ torch:
+ type: string
+ torchvision:
+ type: string
+ docker:
+ - image: cimg/python:<< parameters.python >>
+ resource_class: large
+ steps:
+ - checkout
+ - run:
+ name: Upgrade pip
+ command: |
+ python -V
+ python -m pip install pip --upgrade
+ python -m pip --version
+ - run:
+ name: Install PyTorch
+ command: python -m pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
+ - run:
+ name: Install mmcv-full
+ command: python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.8.0/index.html
+ - run:
+ name: Install mmengine dependencies
+ command: python -m pip install -r requirements.txt
+ - run:
+ name: Build and install
+ command: python -m pip install -e .
+ - run:
+ name: Run unit tests
+ command: python -m pytest tests/
+
+ build_cu102:
+ machine:
+ image: ubuntu-1604-cuda-10.1:201909-23 # the actual version of cuda is 10.2
+ resource_class: gpu.nvidia.small
+ steps:
+ - checkout
+ - run:
+ # https://github.com/pytorch/vision/issues/2921
+ name: Install dependency of torchvision when using pyenv
+ command: sudo apt-get install -y liblzma-dev
+ - run:
+ # python3.7 should be re-installed due to the issue https://github.com/pytorch/vision/issues/2921
+ name: Select python3.7
+ command: |
+ pyenv uninstall -f 3.7.0
+ pyenv install 3.7.0
+ pyenv global 3.7.0
+ - run:
+ name: Upgrade pip
+ command: |
+ python -V
+ python -m pip install pip --upgrade
+ python -m pip --version
+ - run:
+ name: Install PyTorch
+ command: python -m pip install torch==1.8.1+cu102 torchvision==0.9.1+cu102 -f https://download.pytorch.org/whl/torch_stable.html
+ - run:
+ name: Install mmcv-full
+ command: python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html
+ - run:
+ name: Install mmengine dependencies
+ command: python -m pip install -r requirements.txt
+ - run:
+ name: Build and install
+ command: python -m pip install -e .
+ - run:
+ name: Run unit tests
+ command: |
+ python -m coverage run --branch --source mmengine -m pytest tests/
+ python -m coverage xml
+ python -m coverage report -m
+
+workflows:
+ unit_tests:
+ jobs:
+ - lint
+ - build_cpu:
+ name: build_cpu_th1.8_py3.7
+ torch: 1.8.0
+ torchvision: 0.9.0
+ requires:
+ - lint
+ - hold:
+ type: approval # <<< This key-value pair will set your workflow to a status of "On Hold"
+ requires:
+ - build_cpu_th1.8_py3.7
+ - build_cu102:
+ requires:
+ - hold
diff --git a/docs/zh_cn/tutorials/registry.md b/docs/zh_cn/tutorials/registry.md
index 2febb6c287bf51aa423112804ee701df85aedf98..09a3805881d69653bb116ed388a0ddcd4f14d81d 100644
--- a/docs/zh_cn/tutorials/registry.md
+++ b/docs/zh_cn/tutorials/registry.md
@@ -311,11 +311,15 @@ from mmcls.models import MODELS
model = MODELS.build(cfg=dict(type='mmdet.RetinaNet'))
```
-调用兄弟节点的模å—需è¦æŒ‡å®šåœ¨ `type` ä¸æŒ‡å®š `scope` å‰ç¼€ï¼Œå¦‚æžœä¸æƒ³æŒ‡å®šï¼Œæˆ‘们å¯ä»¥å°† `build` 方法ä¸çš„ `default_scope` å‚数设置为 'mmdet'，它会将 `default_scope` 对应的 `registry` ä½œä¸ºå½“å‰ `Registry` 并调用 `build` 方法。
+调用éžæœ¬èŠ‚点的模å—需è¦æŒ‡å®šåœ¨ `type` ä¸æŒ‡å®š `scope` å‰ç¼€ï¼Œå¦‚æžœä¸æƒ³æŒ‡å®šï¼Œæˆ‘们å¯ä»¥åˆ›å»ºä¸€ä¸ªå…¨å±€å˜é‡ `default_scope` 并将 `scope_name` 设置为 'mmdet'，`Registry` 会将 `scope_name` 对应的 `registry` ä½œä¸ºå½“å‰ `Registry` 并调用 `build` 方法。
```python
-from mmcls.models import MODELS
-model = MODELS.build(cfg=dict(type='RetinaNet'), default_scope='mmdet')
+from mmengine.registry import DefaultScope, MODELS
+
+# 调用注册在 mmdet ä¸çš„ RetinaNet
+default_scope = DefaultScope.get_instance(
+ 'my_experiment', scope_name='mmdet')
+model = MODELS.build(cfg=dict(type='RetinaNet'))
```
注册器除了支æŒä¸¤å±‚结构，三层甚至更多层结构也是支æŒçš„。
@@ -325,7 +329,7 @@ model = MODELS.build(cfg=dict(type='RetinaNet'), default_scope='mmdet')
`DetPlus` ä¸å®šä¹‰äº†æ¨¡å— `MetaNet`，
```python
-from mmengine.model import Registry
+from mmengine.registry import Registry
from mmdet.model import MODELS as MMDET_MODELS
MODELS = Registry('model', parent=MMDET_MODELS, scope='det_plus')
@@ -354,6 +358,10 @@ model = MODELS.build(cfg=dict(type='mmcls.ResNet'))
from mmcls.models import MODELS
# 需è¦æ³¨æ„å‰ç¼€çš„顺åºï¼Œ'detplus.mmdet.ResNet' 是ä¸æ£ç¡®çš„
model = MODELS.build(cfg=dict(type='mmdet.detplus.MetaNet'))
-# 当然，更简å•çš„方法是直接设置 default_scope
+
+# 如果希望默认从 detplus 构建模型，设置å¯ä»¥ default_scope
+from mmengine.registry import DefaultScope
+default_scope = DefaultScope.get_instance(
+ 'my_experiment', scope_name='detplus')
model = MODELS.build(cfg=dict(type='MetaNet', default_scope='detplus'))
```
diff --git a/mmengine/evaluator/builder.py b/mmengine/evaluator/builder.py
index fcc8003157072c097c225d904832978936dc5109..40fa03a3f240f6aff95942982df985d7ef5fafea 100644
--- a/mmengine/evaluator/builder.py
+++ b/mmengine/evaluator/builder.py
@@ -1,5 +1,5 @@
# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Optional, Union
+from typing import Union
from ..registry import EVALUATORS
from .base import BaseEvaluator
@@ -7,9 +7,7 @@ from .composed_evaluator import ComposedEvaluator
def build_evaluator(
- cfg: Union[dict, list],
- default_scope: Optional[str] = None
-) -> Union[BaseEvaluator, ComposedEvaluator]:
+ cfg: Union[dict, list]) -> Union[BaseEvaluator, ComposedEvaluator]:
"""Build function of evaluator.
When the evaluator config is a list, it will automatically build composed
@@ -18,16 +16,12 @@ def build_evaluator(
Args:
cfg (dict | list): Config of evaluator. When the config is a list, it
will automatically build composed evaluators.
- default_scope (str, optional): The ``default_scope`` is used to
- reset the current registry. Defaults to None.
Returns:
BaseEvaluator or ComposedEvaluator: The built evaluator.
"""
if isinstance(cfg, list):
- evaluators = [
- EVALUATORS.build(_cfg, default_scope=default_scope) for _cfg in cfg
- ]
+ evaluators = [EVALUATORS.build(_cfg) for _cfg in cfg]
return ComposedEvaluator(evaluators=evaluators)
else:
- return EVALUATORS.build(cfg, default_scope=default_scope)
+ return EVALUATORS.build(cfg)
diff --git a/mmengine/optim/optimizer/builder.py b/mmengine/optim/optimizer/builder.py
index a3e1612d81c00b71bf93641f314eee0ec6854b93..31350f6fbb63a8745e8097f6c3ec1f2a646f8725 100644
--- a/mmengine/optim/optimizer/builder.py
+++ b/mmengine/optim/optimizer/builder.py
@@ -1,7 +1,7 @@
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import inspect
-from typing import List, Optional
+from typing import List
import torch
import torch.nn as nn
@@ -30,10 +30,7 @@ def register_torch_optimizers() -> List[str]:
TORCH_OPTIMIZERS = register_torch_optimizers()
-def build_optimizer(
- model: nn.Module,
- cfg: dict,
- default_scope: Optional[str] = None) -> torch.optim.Optimizer:
+def build_optimizer(model: nn.Module, cfg: dict) -> torch.optim.Optimizer:
"""Build function of optimizer.
If ``constructor`` is set in the ``cfg``, this method will build an
@@ -58,7 +55,6 @@ def build_optimizer(
dict(
type=constructor_type,
optimizer_cfg=optimizer_cfg,
- paramwise_cfg=paramwise_cfg),
- default_scope=default_scope)
- optimizer = optim_constructor(model, default_scope=default_scope)
+ paramwise_cfg=paramwise_cfg))
+ optimizer = optim_constructor(model)
return optimizer
diff --git a/mmengine/optim/optimizer/default_constructor.py b/mmengine/optim/optimizer/default_constructor.py
index 18b9db478f3c88b464a3e5bdddb37d78c2790780..f46cd2080b6186c2a0b49b896ec4dd0a8bd2330a 100644
--- a/mmengine/optim/optimizer/default_constructor.py
+++ b/mmengine/optim/optimizer/default_constructor.py
@@ -241,9 +241,7 @@ class DefaultOptimizerConstructor:
prefix=child_prefix,
is_dcn_module=is_dcn_module)
- def __call__(self,
- model: nn.Module,
- default_scope: Optional[str] = None) -> torch.optim.Optimizer:
+ def __call__(self, model: nn.Module) -> torch.optim.Optimizer:
if hasattr(model, 'module'):
model = model.module
@@ -251,11 +249,11 @@ class DefaultOptimizerConstructor:
# if no paramwise option is specified, just use the global setting
if not self.paramwise_cfg:
optimizer_cfg['params'] = model.parameters()
- return OPTIMIZERS.build(optimizer_cfg, default_scope=default_scope)
+ return OPTIMIZERS.build(optimizer_cfg)
# set param-wise lr and weight decay recursively
params: List = []
self.add_params(params, model)
optimizer_cfg['params'] = params
- return OPTIMIZERS.build(optimizer_cfg, default_scope=default_scope)
+ return OPTIMIZERS.build(optimizer_cfg)
diff --git a/mmengine/registry/default_scope.py b/mmengine/registry/default_scope.py
index 204ac43d10007c3d037eb4154504fe1cb4305da5..dc2256f44781746061542eff5f0cc3860e41af8a 100644
--- a/mmengine/registry/default_scope.py
+++ b/mmengine/registry/default_scope.py
@@ -25,8 +25,6 @@ class DefaultScope(ManagerMixin):
>>> DefaultScope.get_instance('task', scope_name='mmdet')
>>> # Get default scope globally.
>>> scope_name = DefaultScope.get_instance('task').scope_name
- >>> # build model from cfg.
- >>> model = MODELS.build(model_cfg, default_scope=scope_name)
"""
def __init__(self, name: str, scope_name: str):
diff --git a/mmengine/registry/registry.py b/mmengine/registry/registry.py
index f0e59a7e01b1b57a57f9b1d8e7a655512692be55..3ee7d4d62a367f260a53c51470c9f9c72f7e14f8 100644
--- a/mmengine/registry/registry.py
+++ b/mmengine/registry/registry.py
@@ -7,6 +7,7 @@ from typing import Any, Dict, List, Optional, Tuple, Type, Union
from ..config import Config, ConfigDict
from ..utils import is_seq_of
+from .default_scope import DefaultScope
def build_from_cfg(
@@ -354,19 +355,13 @@ class Registry:
return None
- def build(self,
- *args,
- default_scope: Optional[str] = None,
- **kwargs) -> Any:
+ def build(self, *args, **kwargs) -> Any:
"""Build an instance.
- Build an instance by calling :attr:`build_func`. If
- :attr:`default_scope` is given, :meth:`build` will firstly get the
- responding registry and then call its own :meth:`build`.
-
- Args:
- default_scope (str, optional): The ``default_scope`` is used to
- reset the current registry. Defaults to None.
+ Build an instance by calling :attr:`build_func`. If the global
+ variable default scope (:obj:`DefaultScope`) exists ,
+ :meth:`build` will firstly get the responding registry and then call
+ its own :meth:`build`.
Examples:
>>> from mmengine import Registry
@@ -379,9 +374,11 @@ class Registry:
>>> cfg = dict(type='ResNet', depth=50)
>>> model = MODELS.build(cfg)
"""
+ # get the global default scope
+ default_scope = DefaultScope.get_current_instance()
if default_scope is not None:
root = self._get_root_registry()
- registry = root._search_child(default_scope)
+ registry = root._search_child(default_scope.scope_name)
if registry is None:
# if `default_scope` can not be found, fallback to use self
warnings.warn(
diff --git a/mmengine/runner/runner.py b/mmengine/runner/runner.py
index 5cb15fada5194611540751b55b1c732c48535ce0..fea3dff2f3e34d0166626ad7fd5f3a679563ad42 100644
--- a/mmengine/runner/runner.py
+++ b/mmengine/runner/runner.py
@@ -675,8 +675,7 @@ class Runner:
if isinstance(model, nn.Module):
return model
elif isinstance(model, dict):
- return MODELS.build(
- model, default_scope=self.default_scope.scope_name)
+ return MODELS.build(model)
else:
raise TypeError('model should be a nn.Module object or dict, '
f'but got {model}')
@@ -726,9 +725,7 @@ class Runner:
model = model.cuda()
else:
model = MODEL_WRAPPERS.build(
- model_wrapper_cfg,
- default_scope=self.default_scope.scope_name,
- default_args=dict(model=self.model))
+ model_wrapper_cfg, default_args=dict(model=self.model))
return model
@@ -750,10 +747,7 @@ class Runner:
if isinstance(optimizer, Optimizer):
return optimizer
elif isinstance(optimizer, dict):
- optimizer = build_optimizer(
- self.model,
- optimizer,
- default_scope=self.default_scope.scope_name)
+ optimizer = build_optimizer(self.model, optimizer)
return optimizer
else:
raise TypeError('optimizer should be an Optimizer object or dict, '
@@ -801,7 +795,6 @@ class Runner:
param_schedulers.append(
PARAM_SCHEDULERS.build(
_scheduler,
- default_scope=self.default_scope.scope_name,
default_args=dict(optimizer=self.optimizer)))
else:
raise TypeError(
@@ -837,9 +830,7 @@ class Runner:
if isinstance(evaluator, (BaseEvaluator, ComposedEvaluator)):
return evaluator
elif isinstance(evaluator, dict) or is_list_of(evaluator, dict):
- return build_evaluator(
- evaluator,
- default_scope=self.default_scope.scope_name) # type: ignore
+ return build_evaluator(evaluator) # type: ignore
else:
raise TypeError(
'evaluator should be one of dict, list of dict, BaseEvaluator '
@@ -880,8 +871,7 @@ class Runner:
# build dataset
dataset_cfg = dataloader_cfg.pop('dataset')
if isinstance(dataset_cfg, dict):
- dataset = DATASETS.build(
- dataset_cfg, default_scope=self.default_scope.scope_name)
+ dataset = DATASETS.build(dataset_cfg)
else:
# fallback to raise error in dataloader
# if `dataset_cfg` is not a valid type
@@ -891,9 +881,7 @@ class Runner:
sampler_cfg = dataloader_cfg.pop('sampler')
if isinstance(sampler_cfg, dict):
sampler = DATA_SAMPLERS.build(
- sampler_cfg,
- default_scope=self.default_scope.scope_name,
- default_args=dict(dataset=dataset))
+ sampler_cfg, default_args=dict(dataset=dataset))
else:
# fallback to raise error in dataloader
# if `sampler_cfg` is not a valid type
@@ -961,7 +949,6 @@ class Runner:
if 'type' in loop_cfg:
loop = LOOPS.build(
loop_cfg,
- default_scope=self.default_scope.scope_name,
default_args=dict(
runner=self, dataloader=self.train_dataloader))
else:
@@ -1012,7 +999,6 @@ class Runner:
if 'type' in loop_cfg:
loop = LOOPS.build(
loop_cfg,
- default_scope=self.default_scope.scope_name,
default_args=dict(
runner=self,
dataloader=self.val_dataloader,
@@ -1059,7 +1045,6 @@ class Runner:
if 'type' in loop_cfg:
loop = LOOPS.build(
loop_cfg,
- default_scope=self.default_scope.scope_name,
default_args=dict(
runner=self,
dataloader=self.test_dataloader,
diff --git a/tests/test_dist/test_dist.py b/tests/test_dist/test_dist.py
index 3dccb075cc74692ce3aa002b87688a2f2e324cea..14d3dec40f8eb0e93deeae4a952e3794e541f022 100644
--- a/tests/test_dist/test_dist.py
+++ b/tests/test_dist/test_dist.py
@@ -6,6 +6,7 @@ from unittest.mock import patch
import pytest
import torch
+import torch.distributed as torch_dist
import torch.multiprocessing as mp
import mmengine.dist as dist
@@ -108,9 +109,16 @@ def init_process(rank, world_size, functions, backend='gloo'):
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '29505'
os.environ['RANK'] = str(rank)
- dist.init_dist('pytorch', backend, rank=rank, world_size=world_size)
- device = 'cpu' if backend == 'gloo' else 'cuda'
+ if backend == 'nccl':
+ num_gpus = torch.cuda.device_count()
+ torch.cuda.set_device(rank % num_gpus)
+ device = 'cuda'
+ else:
+ device = 'cpu'
+
+ torch_dist.init_process_group(
+ backend=backend, rank=rank, world_size=world_size)
for func in functions:
func(device)
diff --git a/tests/test_dist/test_utils.py b/tests/test_dist/test_utils.py
index e099c8792cd8bb8cb2f1416eaaec919790ed9c13..b4b74d4526d325c9620793a3ff916575d8b6e963 100644
--- a/tests/test_dist/test_utils.py
+++ b/tests/test_dist/test_utils.py
@@ -55,7 +55,13 @@ def init_process(rank, world_size, functions, backend='gloo'):
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '29501'
os.environ['RANK'] = str(rank)
- dist.init_dist('pytorch', backend, rank=rank, world_size=world_size)
+
+ if backend == 'nccl':
+ num_gpus = torch.cuda.device_count()
+ torch.cuda.set_device(rank % num_gpus)
+
+ torch_dist.init_process_group(
+ backend=backend, rank=rank, world_size=world_size)
dist.init_local_group(0, world_size)
for func in functions:
diff --git a/tests/test_evaluator/test_base_evaluator.py b/tests/test_evaluator/test_base_evaluator.py
index bed31b1f21ae825ca0f8558075554a39375a5477..042d2fb8a0a1298f7dec93e59c276718349f7368 100644
--- a/tests/test_evaluator/test_base_evaluator.py
+++ b/tests/test_evaluator/test_base_evaluator.py
@@ -79,10 +79,9 @@ def generate_test_results(size, batch_size, pred, label):
bs_residual = size % batch_size
for i in range(num_batch):
bs = bs_residual if i == num_batch - 1 else batch_size
- data_batch = [(np.zeros(
- (3, 10, 10)), BaseDataElement(data={'label': label}))
+ data_batch = [(np.zeros((3, 10, 10)), BaseDataElement(label=label))
for _ in range(bs)]
- predictions = [BaseDataElement(data={'pred': pred}) for _ in range(bs)]
+ predictions = [BaseDataElement(pred=pred) for _ in range(bs)]
yield (data_batch, predictions)
diff --git a/tests/test_registry/test_registry.py b/tests/test_registry/test_registry.py
index 344762d7327b8afcf20846a6acdba09296b4deb0..76f1d7ce9b2b926f65c32b65eff471619f8a6c5f 100644
--- a/tests/test_registry/test_registry.py
+++ b/tests/test_registry/test_registry.py
@@ -1,8 +1,10 @@
# Copyright (c) OpenMMLab. All rights reserved.
+import time
+
import pytest
from mmengine.config import Config, ConfigDict # type: ignore
-from mmengine.registry import Registry, build_from_cfg
+from mmengine.registry import DefaultScope, Registry, build_from_cfg
class TestRegistry:
@@ -342,11 +344,15 @@ class TestRegistry:
# test `default_scope`
# switch the current registry to another registry
- dog = LITTLE_HOUNDS.build(b_cfg, default_scope='mid_hound')
+ DefaultScope.get_instance(
+ f'test-{time.time()}', scope_name='mid_hound')
+ dog = LITTLE_HOUNDS.build(b_cfg)
assert isinstance(dog, Beagle)
# `default_scope` can not be found
- dog = MID_HOUNDS.build(b_cfg, default_scope='scope-not-found')
+ DefaultScope.get_instance(
+ f'test2-{time.time()}', scope_name='scope-not-found')
+ dog = MID_HOUNDS.build(b_cfg)
assert isinstance(dog, Beagle)
def test_repr(self):