[Enhance] enhance runner test case (#631)
* Add runner test cast * Fix unit test * fix unit test * pop None if key does not exist * Fix is_model_wrapper and force register class in test_runner * [Fix] Fix is_model_wrapper * destroy group after ut * register module in testcase * fix as comment * minor refine Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> * fix lint Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>pull/756/head
Mashiro
GitHub
parent
b7aa4dd885
commit
c478bdca27
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@ -3,9 +3,10 @@ from .compare import (assert_allclose, assert_attrs_equal,
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assert_dict_contains_subset, assert_dict_has_keys,
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assert_is_norm_layer, assert_keys_equal,
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assert_params_all_zeros, check_python_script)
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from .runner_test_case import RunnerTestCase
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__all__ = [
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'assert_allclose', 'assert_dict_contains_subset', 'assert_keys_equal',
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'assert_attrs_equal', 'assert_dict_has_keys', 'assert_is_norm_layer',
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'assert_params_all_zeros', 'check_python_script'
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'assert_params_all_zeros', 'check_python_script', 'RunnerTestCase'
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]
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@ -0,0 +1,186 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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import logging
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import os
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import tempfile
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import time
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from unittest import TestCase
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from uuid import uuid4
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import torch
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import torch.nn as nn
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from torch.distributed import destroy_process_group
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from torch.utils.data import Dataset
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import mmengine.hooks # noqa F401
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import mmengine.optim # noqa F401
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from mmengine.config import Config
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from mmengine.dist import is_distributed
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from mmengine.evaluator import BaseMetric
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from mmengine.logging import MessageHub, MMLogger
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from mmengine.model import BaseModel
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from mmengine.registry import DATASETS, METRICS, MODELS, DefaultScope
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from mmengine.runner import Runner
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from mmengine.visualization import Visualizer
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class ToyModel(BaseModel):
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def __init__(self, data_preprocessor=None):
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super().__init__(data_preprocessor=data_preprocessor)
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self.linear1 = nn.Linear(2, 2)
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self.linear2 = nn.Linear(2, 1)
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def forward(self, inputs, data_samples, mode='tensor'):
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if isinstance(inputs, list):
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inputs = torch.stack(inputs)
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if isinstance(data_samples, list):
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data_sample = torch.stack(data_samples)
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outputs = self.linear1(inputs)
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outputs = self.linear2(outputs)
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if mode == 'tensor':
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return outputs
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elif mode == 'loss':
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loss = (data_sample - outputs).sum()
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outputs = dict(loss=loss)
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return outputs
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elif mode == 'predict':
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return outputs
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class ToyDataset(Dataset):
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METAINFO = dict() # type: ignore
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data = torch.randn(12, 2)
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label = torch.ones(12)
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@property
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def metainfo(self):
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return self.METAINFO
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def __len__(self):
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return self.data.size(0)
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def __getitem__(self, index):
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return dict(inputs=self.data[index], data_samples=self.label[index])
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class ToyMetric(BaseMetric):
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def __init__(self, collect_device='cpu', dummy_metrics=None):
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super().__init__(collect_device=collect_device)
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self.dummy_metrics = dummy_metrics
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def process(self, data_batch, predictions):
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result = {'acc': 1}
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self.results.append(result)
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def compute_metrics(self, results):
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return dict(acc=1)
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class RunnerTestCase(TestCase):
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"""A test case to build runner easily.
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`RunnerTestCase` will do the following things:
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1. Registers a toy model, a toy metric, and a toy dataset, which can be
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used to run the `Runner` successfully.
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2. Provides epoch based and iteration based cfg to build runner.
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3. Provides `build_runner` method to build runner easily.
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4. Clean the global variable used by the runner.
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"""
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dist_cfg = dict(
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MASTER_ADDR='127.0.0.1',
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MASTER_PORT=29600,
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RANK='0',
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WORLD_SIZE='1',
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LOCAL_RANK='0')
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def setUp(self) -> None:
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self.temp_dir = tempfile.TemporaryDirectory()
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# Prevent from registering module with the same name by other unit
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# test. These registries will be cleared in `tearDown`
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MODELS.register_module(module=ToyModel, force=True)
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METRICS.register_module(module=ToyMetric, force=True)
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DATASETS.register_module(module=ToyDataset, force=True)
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epoch_based_cfg = dict(
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work_dir=self.temp_dir.name,
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model=dict(type='ToyModel'),
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train_dataloader=dict(
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dataset=dict(type='ToyDataset'),
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sampler=dict(type='DefaultSampler', shuffle=True),
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batch_size=3,
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num_workers=0),
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val_dataloader=dict(
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dataset=dict(type='ToyDataset'),
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sampler=dict(type='DefaultSampler', shuffle=False),
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batch_size=3,
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num_workers=0),
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val_evaluator=[dict(type='ToyMetric')],
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test_dataloader=dict(
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dataset=dict(type='ToyDataset'),
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sampler=dict(type='DefaultSampler', shuffle=False),
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batch_size=3,
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num_workers=0),
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test_evaluator=[dict(type='ToyMetric')],
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optim_wrapper=dict(optimizer=dict(type='SGD', lr=0.1)),
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train_cfg=dict(by_epoch=True, max_epochs=2, val_interval=1),
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val_cfg=dict(),
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test_cfg=dict(),
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default_hooks=dict(logger=dict(type='LoggerHook', interval=1)),
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custom_hooks=[],
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env_cfg=dict(dist_cfg=dict(backend='nccl')),
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experiment_name='test1')
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self.epoch_based_cfg = Config(epoch_based_cfg)
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# prepare iter based cfg.
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self.iter_based_cfg: Config = copy.deepcopy(self.epoch_based_cfg)
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self.iter_based_cfg.train_dataloader = dict(
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dataset=dict(type='ToyDataset'),
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sampler=dict(type='InfiniteSampler', shuffle=True),
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batch_size=3,
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num_workers=0)
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self.iter_based_cfg.log_processor = dict(by_epoch=False)
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self.iter_based_cfg.train_cfg = dict(by_epoch=False, max_iters=12)
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self.iter_based_cfg.default_hooks = dict(
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logger=dict(type='LoggerHook', interval=1),
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checkpoint=dict(
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type='CheckpointHook', interval=12, by_epoch=False))
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def tearDown(self):
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# `FileHandler` should be closed in Windows, otherwise we cannot
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# delete the temporary directory
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logging.shutdown()
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MMLogger._instance_dict.clear()
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Visualizer._instance_dict.clear()
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DefaultScope._instance_dict.clear()
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MessageHub._instance_dict.clear()
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MODELS.module_dict.pop('ToyModel', None)
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METRICS.module_dict.pop('ToyMetric', None)
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DATASETS.module_dict.pop('ToyDataset', None)
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self.temp_dir.cleanup()
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if is_distributed():
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destroy_process_group()
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def build_runner(self, cfg: Config):
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cfg.experiment_name = self.experiment_name
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runner = Runner.from_cfg(cfg)
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return runner
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@property
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def experiment_name(self):
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# Since runners could be built too fast to have a unique experiment
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# name(timestamp is the same), here we use uuid to make sure each
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# runner has the unique experiment name.
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return f'{self._testMethodName}_{time.time()} + ' \
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f'{uuid4()}'
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def setup_dist_env(self):
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self.dist_cfg['MASTER_PORT'] += 1
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os.environ['MASTER_PORT'] = str(self.dist_cfg['MASTER_PORT'])
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os.environ['MASTER_ADDR'] = self.dist_cfg['MASTER_ADDR']
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os.environ['RANK'] = self.dist_cfg['RANK']
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os.environ['WORLD_SIZE'] = self.dist_cfg['WORLD_SIZE']
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os.environ['LOCAL_RANK'] = self.dist_cfg['LOCAL_RANK']
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@ -79,7 +79,7 @@ def test_is_model_wrapper():
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pass
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CHILD_REGISTRY.register_module(module=CustomModelWrapper)
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CHILD_REGISTRY.register_module(module=CustomModelWrapper, force=True)
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for wrapper in [
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DistributedDataParallel, MMDistributedDataParallel,
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@ -37,7 +37,6 @@ from mmengine.utils.dl_utils import TORCH_VERSION
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from mmengine.visualization import Visualizer
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@MODELS.register_module()
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class ToyModel(BaseModel):
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def __init__(self, data_preprocessor=None):
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@ -63,14 +62,12 @@ class ToyModel(BaseModel):
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return outputs
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@MODELS.register_module()
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class ToyModel1(ToyModel):
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def __init__(self):
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super().__init__()
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@MODELS.register_module()
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class ToySyncBNModel(BaseModel):
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def __init__(self):
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@ -95,7 +92,6 @@ class ToySyncBNModel(BaseModel):
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return outputs
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@MODELS.register_module()
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class ToyGANModel(BaseModel):
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def __init__(self):
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@ -127,7 +123,6 @@ class ToyGANModel(BaseModel):
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return loss
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@MODEL_WRAPPERS.register_module()
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class CustomModelWrapper(nn.Module):
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def __init__(self, module):
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@ -135,7 +130,6 @@ class CustomModelWrapper(nn.Module):
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self.model = module
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@OPTIM_WRAPPER_CONSTRUCTORS.register_module()
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class ToyMultipleOptimizerConstructor:
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def __init__(self, optim_wrapper_cfg, paramwise_cfg=None):
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@ -163,7 +157,6 @@ class ToyMultipleOptimizerConstructor:
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return OptimWrapperDict(**optimizers)
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@DATASETS.register_module()
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class ToyDataset(Dataset):
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METAINFO = dict() # type: ignore
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data = torch.randn(12, 2)
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@ -180,7 +173,6 @@ class ToyDataset(Dataset):
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return dict(inputs=self.data[index], data_sample=self.label[index])
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@DATASETS.register_module()
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class ToyDatasetNoMeta(Dataset):
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data = torch.randn(12, 2)
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label = torch.ones(12)
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@ -192,7 +184,6 @@ class ToyDatasetNoMeta(Dataset):
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return dict(inputs=self.data[index], data_sample=self.label[index])
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@METRICS.register_module()
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class ToyMetric1(BaseMetric):
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def __init__(self, collect_device='cpu', dummy_metrics=None):
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@ -207,7 +198,6 @@ class ToyMetric1(BaseMetric):
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return dict(acc=1)
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@METRICS.register_module()
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class ToyMetric2(BaseMetric):
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def __init__(self, collect_device='cpu', dummy_metrics=None):
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@ -222,12 +212,10 @@ class ToyMetric2(BaseMetric):
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return dict(acc=1)
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@OPTIM_WRAPPERS.register_module()
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class ToyOptimWrapper(OptimWrapper):
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...
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@HOOKS.register_module()
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class ToyHook(Hook):
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priority = 'Lowest'
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@ -235,7 +223,6 @@ class ToyHook(Hook):
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pass
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@HOOKS.register_module()
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class ToyHook2(Hook):
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priority = 'Lowest'
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@ -243,7 +230,6 @@ class ToyHook2(Hook):
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pass
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@LOOPS.register_module()
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class CustomTrainLoop(BaseLoop):
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def __init__(self, runner, dataloader, max_epochs):
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@ -254,7 +240,6 @@ class CustomTrainLoop(BaseLoop):
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pass
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@LOOPS.register_module()
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class CustomValLoop(BaseLoop):
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def __init__(self, runner, dataloader, evaluator):
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@ -270,7 +255,6 @@ class CustomValLoop(BaseLoop):
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pass
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@LOOPS.register_module()
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class CustomTestLoop(BaseLoop):
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def __init__(self, runner, dataloader, evaluator):
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@ -286,7 +270,6 @@ class CustomTestLoop(BaseLoop):
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pass
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@LOG_PROCESSORS.register_module()
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class CustomLogProcessor(LogProcessor):
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def __init__(self, window_size=10, by_epoch=True, custom_cfg=None):
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@ -296,7 +279,6 @@ class CustomLogProcessor(LogProcessor):
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self._check_custom_cfg()
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@RUNNERS.register_module()
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class CustomRunner(Runner):
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def __init__(self,
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@ -333,7 +315,6 @@ class CustomRunner(Runner):
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pass
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@EVALUATOR.register_module()
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class ToyEvaluator(Evaluator):
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def __init__(self, metrics):
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@ -344,7 +325,6 @@ def collate_fn(data_batch):
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return pseudo_collate(data_batch)
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@COLLATE_FUNCTIONS.register_module()
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def custom_collate(data_batch, pad_value):
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return pseudo_collate(data_batch)
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@ -352,6 +332,28 @@ def custom_collate(data_batch, pad_value):
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class TestRunner(TestCase):
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def setUp(self):
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MODELS.register_module(module=ToyModel, force=True)
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MODELS.register_module(module=ToyModel1, force=True)
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MODELS.register_module(module=ToySyncBNModel, force=True)
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MODELS.register_module(module=ToyGANModel, force=True)
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MODEL_WRAPPERS.register_module(module=CustomModelWrapper, force=True)
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OPTIM_WRAPPER_CONSTRUCTORS.register_module(
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module=ToyMultipleOptimizerConstructor, force=True)
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DATASETS.register_module(module=ToyDataset, force=True)
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DATASETS.register_module(module=ToyDatasetNoMeta, force=True)
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METRICS.register_module(module=ToyMetric1, force=True)
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METRICS.register_module(module=ToyMetric2, force=True)
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OPTIM_WRAPPERS.register_module(module=ToyOptimWrapper, force=True)
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HOOKS.register_module(module=ToyHook, force=True)
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HOOKS.register_module(module=ToyHook2, force=True)
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LOOPS.register_module(module=CustomTrainLoop, force=True)
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LOOPS.register_module(module=CustomValLoop, force=True)
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LOOPS.register_module(module=CustomTestLoop, force=True)
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LOG_PROCESSORS.register_module(module=CustomLogProcessor, force=True)
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RUNNERS.register_module(module=CustomRunner, force=True)
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EVALUATOR.register_module(module=ToyEvaluator, force=True)
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COLLATE_FUNCTIONS.register_module(module=custom_collate, force=True)
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self.temp_dir = tempfile.mkdtemp()
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epoch_based_cfg = dict(
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model=dict(type='ToyModel'),
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@ -413,6 +415,28 @@ class TestRunner(TestCase):
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def tearDown(self):
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# `FileHandler` should be closed in Windows, otherwise we cannot
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# delete the temporary directory
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MODELS.module_dict.pop('ToyModel')
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MODELS.module_dict.pop('ToyModel1')
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MODELS.module_dict.pop('ToySyncBNModel')
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MODELS.module_dict.pop('ToyGANModel')
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MODEL_WRAPPERS.module_dict.pop('CustomModelWrapper')
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OPTIM_WRAPPER_CONSTRUCTORS.module_dict.pop(
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'ToyMultipleOptimizerConstructor')
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OPTIM_WRAPPERS.module_dict.pop('ToyOptimWrapper')
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DATASETS.module_dict.pop('ToyDataset')
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DATASETS.module_dict.pop('ToyDatasetNoMeta')
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METRICS.module_dict.pop('ToyMetric1')
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METRICS.module_dict.pop('ToyMetric2')
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HOOKS.module_dict.pop('ToyHook')
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HOOKS.module_dict.pop('ToyHook2')
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LOOPS.module_dict.pop('CustomTrainLoop')
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LOOPS.module_dict.pop('CustomValLoop')
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LOOPS.module_dict.pop('CustomTestLoop')
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LOG_PROCESSORS.module_dict.pop('CustomLogProcessor')
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RUNNERS.module_dict.pop('CustomRunner')
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EVALUATOR.module_dict.pop('ToyEvaluator')
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COLLATE_FUNCTIONS.module_dict.pop('custom_collate')
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logging.shutdown()
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MMLogger._instance_dict.clear()
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shutil.rmtree(self.temp_dir)
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@ -782,7 +806,7 @@ class TestRunner(TestCase):
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TOY_SCHEDULERS = Registry(
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'parameter scheduler', parent=PARAM_SCHEDULERS, scope='toy')
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@TOY_SCHEDULERS.register_module()
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@TOY_SCHEDULERS.register_module(force=True)
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class ToyScheduler(MultiStepLR):
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def __init__(self, *args, **kwargs):
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@ -863,7 +887,7 @@ class TestRunner(TestCase):
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cfg.model_wrapper_cfg = dict(type='CustomModelWrapper')
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runner.from_cfg(cfg)
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@MODELS.register_module()
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@MODELS.register_module(force=True)
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class ToyBN(BaseModel):
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def __init__(self):
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@ -1349,7 +1373,7 @@ class TestRunner(TestCase):
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val_epoch_results = []
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val_epoch_targets = [i for i in range(2, 4)]
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||||
@HOOKS.register_module()
|
||||
@HOOKS.register_module(force=True)
|
||||
class TestEpochHook(Hook):
|
||||
|
||||
def before_train_epoch(self, runner):
|
||||
|
|
@ -1394,7 +1418,7 @@ class TestRunner(TestCase):
|
|||
val_iter_targets = [i for i in range(4, 12)]
|
||||
val_batch_idx_targets = [i for i in range(4)] * 2
|
||||
|
||||
@HOOKS.register_module()
|
||||
@HOOKS.register_module(force=True)
|
||||
class TestIterHook(Hook):
|
||||
|
||||
def before_train_epoch(self, runner):
|
||||
|
|
@ -1487,7 +1511,7 @@ class TestRunner(TestCase):
|
|||
val_interval_results = []
|
||||
val_interval_targets = [5] * 10 + [2] * 2
|
||||
|
||||
@HOOKS.register_module()
|
||||
@HOOKS.register_module(force=True)
|
||||
class TestIterDynamicIntervalHook(Hook):
|
||||
|
||||
def before_val(self, runner):
|
||||
|
|
@ -1524,7 +1548,7 @@ class TestRunner(TestCase):
|
|||
val_interval_results = []
|
||||
val_interval_targets = [5] * 10 + [2] * 2
|
||||
|
||||
@HOOKS.register_module()
|
||||
@HOOKS.register_module(force=True)
|
||||
class TestEpochDynamicIntervalHook(Hook):
|
||||
|
||||
def before_val_epoch(self, runner):
|
||||
|
|
@ -1553,7 +1577,7 @@ class TestRunner(TestCase):
|
|||
self.assertEqual(result, target)
|
||||
|
||||
# 7. test init weights
|
||||
@MODELS.register_module()
|
||||
@MODELS.register_module(force=True)
|
||||
class ToyModel2(ToyModel):
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -1654,7 +1678,7 @@ class TestRunner(TestCase):
|
|||
runner.train()
|
||||
|
||||
# 12.1 Test train with model, which does not inherit from BaseModel
|
||||
@MODELS.register_module()
|
||||
@MODELS.register_module(force=True)
|
||||
class ToyModel3(nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -1901,7 +1925,7 @@ class TestRunner(TestCase):
|
|||
|
||||
def test_custom_loop(self):
|
||||
# test custom loop with additional hook
|
||||
@LOOPS.register_module()
|
||||
@LOOPS.register_module(force=True)
|
||||
class CustomTrainLoop2(IterBasedTrainLoop):
|
||||
"""Custom train loop with additional warmup stage."""
|
||||
|
||||
|
|
@ -1942,7 +1966,7 @@ class TestRunner(TestCase):
|
|||
before_warmup_iter_results = []
|
||||
after_warmup_iter_results = []
|
||||
|
||||
@HOOKS.register_module()
|
||||
@HOOKS.register_module(force=True)
|
||||
class TestWarmupHook(Hook):
|
||||
"""test custom train loop."""
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,58 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
import os
|
||||
|
||||
from mmengine import Config
|
||||
from mmengine.logging import MessageHub, MMLogger
|
||||
from mmengine.registry import DefaultScope
|
||||
from mmengine.testing import RunnerTestCase
|
||||
from mmengine.visualization import Visualizer
|
||||
|
||||
|
||||
class TestRunnerTestCase(RunnerTestCase):
|
||||
|
||||
def test_setup(self):
|
||||
self.assertIsInstance(self.epoch_based_cfg, Config)
|
||||
self.assertIsInstance(self.iter_based_cfg, Config)
|
||||
self.assertIn('MASTER_ADDR', self.dist_cfg)
|
||||
self.assertIn('MASTER_PORT', self.dist_cfg)
|
||||
self.assertIn('RANK', self.dist_cfg)
|
||||
self.assertIn('WORLD_SIZE', self.dist_cfg)
|
||||
self.assertIn('LOCAL_RANK', self.dist_cfg)
|
||||
|
||||
def test_tearDown(self):
|
||||
self.tearDown()
|
||||
self.assertEqual(MMLogger._instance_dict, {})
|
||||
self.assertEqual(MessageHub._instance_dict, {})
|
||||
self.assertEqual(Visualizer._instance_dict, {})
|
||||
self.assertEqual(DefaultScope._instance_dict, {})
|
||||
# tearDown should not be called twice.
|
||||
self.tearDown = super(RunnerTestCase, self).tearDown
|
||||
|
||||
def test_build_runner(self):
|
||||
runner = self.build_runner(self.epoch_based_cfg)
|
||||
runner.train()
|
||||
runner.val()
|
||||
runner.test()
|
||||
|
||||
runner = self.build_runner(self.iter_based_cfg)
|
||||
runner.train()
|
||||
runner.val()
|
||||
runner.test()
|
||||
|
||||
def test_experiment_name(self):
|
||||
runner1 = self.build_runner(self.epoch_based_cfg)
|
||||
runner2 = self.build_runner(self.epoch_based_cfg)
|
||||
self.assertNotEqual(runner1.experiment_name, runner2.experiment_name)
|
||||
|
||||
def test_init_dist(self):
|
||||
self.setup_dist_env()
|
||||
self.assertEqual(
|
||||
str(self.dist_cfg['MASTER_PORT']), os.environ['MASTER_PORT'])
|
||||
self.assertEqual(self.dist_cfg['MASTER_ADDR'],
|
||||
os.environ['MASTER_ADDR'])
|
||||
self.assertEqual(self.dist_cfg['RANK'], os.environ['RANK'])
|
||||
self.assertEqual(self.dist_cfg['LOCAL_RANK'], os.environ['LOCAL_RANK'])
|
||||
self.assertEqual(self.dist_cfg['WORLD_SIZE'], os.environ['WORLD_SIZE'])
|
||||
fisrt_port = os.environ['MASTER_ADDR']
|
||||
self.setup_dist_env()
|
||||
self.assertNotEqual(fisrt_port, os.environ['MASTER_PORT'])
|
||||
Loading…
Reference in New Issue