[Refactor] Refactor entry scripts `train.py` and `test.py`.
mzr1996
parent
65f3b2221d
commit
efaa93aae1
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@ -10,6 +10,7 @@ from .dataset_wrappers import (ClassBalancedDataset, ConcatDataset,
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from .imagenet import ImageNet, ImageNet21k
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from .mnist import MNIST, FashionMNIST
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from .multi_label import MultiLabelDataset
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from .pipelines import * # noqa: F401,F403
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from .samplers import DistributedSampler, RepeatAugSampler
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from .voc import VOC
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@ -1,8 +1,9 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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from .collect_env import collect_env
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from .logger import get_root_logger, load_json_log
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from .setup_env import setup_multi_processes
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from .setup_env import register_all_modules, setup_multi_processes
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__all__ = [
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'collect_env', 'get_root_logger', 'load_json_log', 'setup_multi_processes'
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'collect_env', 'get_root_logger', 'load_json_log', 'setup_multi_processes',
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'register_all_modules'
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]
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@ -1,10 +1,12 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import datetime
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import os
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import platform
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import warnings
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import cv2
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import torch.multiprocessing as mp
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from mmengine import DefaultScope
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def setup_multi_processes(cfg):
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@ -45,3 +47,35 @@ def setup_multi_processes(cfg):
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f'overloaded, please further tune the variable for optimal '
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f'performance in your application as needed.')
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os.environ['MKL_NUM_THREADS'] = str(mkl_num_threads)
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def register_all_modules(init_default_scope: bool = True) -> None:
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"""Register all modules in mmcls into the registries.
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Args:
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init_default_scope (bool): Whether initialize the mmcls default scope.
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If True, the global default scope will be set to `mmcls`, and all
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registries will build modules from mmcls's registry node. To
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understand more about the registry, please refer to
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https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/registry.md
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Defaults to True.
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""" # noqa
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import mmcls.core # noqa: F401,F403
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import mmcls.datasets # noqa: F401,F403
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import mmcls.metrics # noqa: F401,F403
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import mmcls.models # noqa: F401,F403
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if not init_default_scope:
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return
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current_scope = DefaultScope.get_current_instance()
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if current_scope is None:
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DefaultScope.get_instance('mmcls', scope_name='mmcls')
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elif current_scope.scope_name != 'mmcls':
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warnings.warn(f'The current default scope "{current_scope.scope_name}"'
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' is not "mmcls", `register_all_modules` will force the '
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'current default scope to be "mmcls". If this is not '
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'expected, please set `init_default_scope=False`.')
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# avoid name conflict
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new_instance_name = f'mmcls-{datetime.datetime.now()}'
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DefaultScope.get_instance(new_instance_name, scope_name='mmcls')
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@ -1,12 +1,47 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import datetime
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import multiprocessing as mp
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import os
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import platform
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import sys
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from unittest import TestCase
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import cv2
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from mmcv import Config
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from mmengine import DefaultScope
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from mmcls.utils import setup_multi_processes
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from mmcls.utils import register_all_modules, setup_multi_processes
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class TestSetupEnv(TestCase):
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def test_register_all_modules(self):
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from mmcls.registry import DATASETS
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# not init default scope
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sys.modules.pop('mmcls.datasets', None)
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sys.modules.pop('mmcls.datasets.custom', None)
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DATASETS._module_dict.pop('CustomDataset', None)
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self.assertFalse('CustomDataset' in DATASETS.module_dict)
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register_all_modules(init_default_scope=False)
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self.assertTrue('CustomDataset' in DATASETS.module_dict)
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# init default scope
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sys.modules.pop('mmcls.datasets')
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sys.modules.pop('mmcls.datasets.custom')
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DATASETS._module_dict.pop('CustomDataset', None)
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self.assertFalse('CustomDataset' in DATASETS.module_dict)
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register_all_modules(init_default_scope=True)
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self.assertTrue('CustomDataset' in DATASETS.module_dict)
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self.assertEqual(DefaultScope.get_current_instance().scope_name,
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'mmcls')
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# init default scope when another scope is init
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name = f'test-{datetime.datetime.now()}'
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DefaultScope.get_instance(name, scope_name='test')
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with self.assertWarnsRegex(
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Warning, 'The current default scope "test" is not "mmcls"'):
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register_all_modules(init_default_scope=True)
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def test_setup_multi_processes():
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235
tools/test.py
235
tools/test.py
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@ -1,55 +1,22 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os
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import warnings
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from numbers import Number
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import os.path as osp
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import mmcv
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import numpy as np
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import torch
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from mmcv import DictAction
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from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
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from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
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wrap_fp16_model)
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from mmengine.config import Config, DictAction
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from mmengine.runner import Runner
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from mmcls.apis import multi_gpu_test, single_gpu_test
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from mmcls.datasets import build_dataloader, build_dataset
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from mmcls.models import build_classifier
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from mmcls.utils import get_root_logger, setup_multi_processes
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from mmcls.utils import register_all_modules
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def parse_args():
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parser = argparse.ArgumentParser(description='mmcls test model')
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parser = argparse.ArgumentParser(
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description='MMCLS test (and eval) a model')
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parser.add_argument('config', help='test config file path')
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parser.add_argument('checkpoint', help='checkpoint file')
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parser.add_argument('--out', help='output result file')
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out_options = ['class_scores', 'pred_score', 'pred_label', 'pred_class']
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parser.add_argument(
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'--out-items',
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nargs='+',
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default=['all'],
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choices=out_options + ['none', 'all'],
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help='Besides metrics, what items will be included in the output '
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f'result file. You can choose some of ({", ".join(out_options)}), '
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'or use "all" to include all above, or use "none" to disable all of '
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'above. Defaults to output all.',
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metavar='')
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parser.add_argument(
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'--metrics',
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type=str,
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nargs='+',
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help='evaluation metrics, which depends on the dataset, e.g., '
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'"accuracy", "precision", "recall", "f1_score", "support" for single '
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'label dataset, and "mAP", "CP", "CR", "CF1", "OP", "OR", "OF1" for '
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'multi-label dataset')
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parser.add_argument('--show', action='store_true', help='show results')
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parser.add_argument(
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'--show-dir', help='directory where painted images will be saved')
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parser.add_argument(
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'--gpu-collect',
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action='store_true',
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help='whether to use gpu to collect results')
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parser.add_argument('--tmpdir', help='tmp dir for writing some results')
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'--work-dir',
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help='the directory to save the file containing evaluation metrics')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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@ -60,193 +27,47 @@ def parse_args():
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
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'Note that the quotation marks are necessary and that no white space '
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'is allowed.')
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parser.add_argument(
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'--metric-options',
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nargs='+',
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action=DictAction,
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default={},
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help='custom options for evaluation, the key-value pair in xxx=yyy '
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'format will be parsed as a dict metric_options for dataset.evaluate()'
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' function.')
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parser.add_argument(
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'--show-options',
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nargs='+',
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action=DictAction,
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help='custom options for show_result. key-value pair in xxx=yyy.'
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'Check available options in `model.show_result`.')
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parser.add_argument(
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'--gpu-ids',
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type=int,
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nargs='+',
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help='(Deprecated, please use --gpu-id) ids of gpus to use '
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'(only applicable to non-distributed testing)')
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parser.add_argument(
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'--gpu-id',
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type=int,
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default=0,
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help='id of gpu to use '
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'(only applicable to non-distributed testing)')
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parser.add_argument(
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'--launcher',
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choices=['none', 'pytorch', 'slurm', 'mpi'],
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default='none',
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help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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parser.add_argument(
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'--device',
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choices=['cpu', 'cuda', 'ipu'],
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default='cuda',
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help='device used for testing')
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args = parser.parse_args()
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if 'LOCAL_RANK' not in os.environ:
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os.environ['LOCAL_RANK'] = str(args.local_rank)
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assert args.metrics or args.out, \
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'Please specify at least one of output path and evaluation metrics.'
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return args
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def main():
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args = parse_args()
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cfg = mmcv.Config.fromfile(args.config)
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# register all modules in mmcls into the registries
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# do not init the default scope here because it will be init in the runner
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register_all_modules(init_default_scope=False)
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# load config
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cfg = Config.fromfile(args.config)
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cfg.launcher = args.launcher
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# set multi-process settings
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setup_multi_processes(cfg)
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# work_dir is determined in this priority: CLI > segment in file > filename
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if args.work_dir is not None:
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# update configs according to CLI args if args.work_dir is not None
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cfg.work_dir = args.work_dir
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elif cfg.get('work_dir', None) is None:
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# use config filename as default work_dir if cfg.work_dir is None
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cfg.work_dir = osp.join('./work_dirs',
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osp.splitext(osp.basename(args.config))[0])
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# set cudnn_benchmark
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if cfg.get('cudnn_benchmark', False):
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torch.backends.cudnn.benchmark = True
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cfg.model.pretrained = None
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cfg.load_from = args.checkpoint
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if args.gpu_ids is not None:
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cfg.gpu_ids = args.gpu_ids[0:1]
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warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
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'Because we only support single GPU mode in '
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'non-distributed testing. Use the first GPU '
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'in `gpu_ids` now.')
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else:
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cfg.gpu_ids = [args.gpu_id]
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# build the runner from config
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runner = Runner.from_cfg(cfg)
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# init distributed env first, since logger depends on the dist info.
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if args.launcher == 'none':
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distributed = False
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else:
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distributed = True
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init_dist(args.launcher, **cfg.dist_params)
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dataset = build_dataset(cfg.data.test, default_args=dict(test_mode=True))
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# build the dataloader
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# The default loader config
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loader_cfg = dict(
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# cfg.gpus will be ignored if distributed
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num_gpus=1 if args.device == 'ipu' else len(cfg.gpu_ids),
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dist=distributed,
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round_up=True,
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)
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# The overall dataloader settings
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loader_cfg.update({
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k: v
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for k, v in cfg.data.items() if k not in [
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'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
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'test_dataloader'
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]
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})
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test_loader_cfg = {
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**loader_cfg,
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'shuffle': False, # Not shuffle by default
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'sampler_cfg': None, # Not use sampler by default
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**cfg.data.get('test_dataloader', {}),
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}
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# the extra round_up data will be removed during gpu/cpu collect
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data_loader = build_dataloader(dataset, **test_loader_cfg)
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# build the model and load checkpoint
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model = build_classifier(cfg.model)
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fp16_cfg = cfg.get('fp16', None)
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if fp16_cfg is not None:
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wrap_fp16_model(model)
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checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
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if 'CLASSES' in checkpoint.get('meta', {}):
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CLASSES = checkpoint['meta']['CLASSES']
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else:
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from mmcls.datasets import ImageNet
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warnings.simplefilter('once')
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warnings.warn('Class names are not saved in the checkpoint\'s '
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'meta data, use imagenet by default.')
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CLASSES = ImageNet.CLASSES
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if not distributed:
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if args.device == 'cpu':
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model = model.cpu()
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elif args.device == 'ipu':
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from mmcv.device.ipu import cfg2options, ipu_model_wrapper
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opts = cfg2options(cfg.runner.get('options_cfg', {}))
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if fp16_cfg is not None:
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model.half()
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model = ipu_model_wrapper(model, opts, fp16_cfg=fp16_cfg)
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data_loader.init(opts['inference'])
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else:
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model = MMDataParallel(model, device_ids=cfg.gpu_ids)
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if not model.device_ids:
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assert mmcv.digit_version(mmcv.__version__) >= (1, 4, 4), \
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'To test with CPU, please confirm your mmcv version ' \
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'is not lower than v1.4.4'
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model.CLASSES = CLASSES
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show_kwargs = {} if args.show_options is None else args.show_options
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outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
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**show_kwargs)
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else:
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model = MMDistributedDataParallel(
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model.cuda(),
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device_ids=[torch.cuda.current_device()],
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broadcast_buffers=False)
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outputs = multi_gpu_test(model, data_loader, args.tmpdir,
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args.gpu_collect)
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rank, _ = get_dist_info()
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if rank == 0:
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results = {}
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logger = get_root_logger()
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if args.metrics:
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eval_results = dataset.evaluate(
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results=outputs,
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metric=args.metrics,
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metric_options=args.metric_options,
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logger=logger)
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results.update(eval_results)
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for k, v in eval_results.items():
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if isinstance(v, np.ndarray):
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v = [round(out, 2) for out in v.tolist()]
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elif isinstance(v, Number):
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v = round(v, 2)
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else:
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raise ValueError(f'Unsupport metric type: {type(v)}')
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print(f'\n{k} : {v}')
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if args.out:
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if 'none' not in args.out_items:
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scores = np.vstack(outputs)
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pred_score = np.max(scores, axis=1)
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pred_label = np.argmax(scores, axis=1)
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pred_class = [CLASSES[lb] for lb in pred_label]
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res_items = {
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'class_scores': scores,
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'pred_score': pred_score,
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'pred_label': pred_label,
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'pred_class': pred_class
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}
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if 'all' in args.out_items:
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results.update(res_items)
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else:
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for key in args.out_items:
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results[key] = res_items[key]
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print(f'\ndumping results to {args.out}')
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mmcv.dump(results, args.out)
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# start testing
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runner.test()
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if __name__ == '__main__':
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171
tools/train.py
171
tools/train.py
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@ -1,26 +1,16 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import copy
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import os
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import os.path as osp
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import time
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import warnings
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import mmcv
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import torch
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import torch.distributed as dist
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from mmcv import Config, DictAction
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from mmcv.runner import get_dist_info, init_dist
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from mmengine.config import Config, DictAction
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from mmengine.runner import Runner
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from mmcls import __version__
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from mmcls.apis import init_random_seed, set_random_seed, train_model
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from mmcls.datasets import build_dataset
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from mmcls.models import build_classifier
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from mmcls.utils import collect_env, get_root_logger, setup_multi_processes
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from mmcls.utils import register_all_modules
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def parse_args():
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parser = argparse.ArgumentParser(description='Train a model')
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parser = argparse.ArgumentParser(description='Train a classifier')
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parser.add_argument('config', help='train config file path')
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parser.add_argument('--work-dir', help='the dir to save logs and models')
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parser.add_argument(
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@ -29,40 +19,6 @@ def parse_args():
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'--no-validate',
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action='store_true',
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help='whether not to evaluate the checkpoint during training')
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group_gpus = parser.add_mutually_exclusive_group()
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group_gpus.add_argument(
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'--device', help='device used for training. (Deprecated)')
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group_gpus.add_argument(
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'--gpus',
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type=int,
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help='(Deprecated, please use --gpu-id) number of gpus to use '
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'(only applicable to non-distributed training)')
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group_gpus.add_argument(
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||||
'--gpu-ids',
|
||||
type=int,
|
||||
nargs='+',
|
||||
help='(Deprecated, please use --gpu-id) ids of gpus to use '
|
||||
'(only applicable to non-distributed training)')
|
||||
group_gpus.add_argument(
|
||||
'--gpu-id',
|
||||
type=int,
|
||||
default=0,
|
||||
help='id of gpu to use '
|
||||
'(only applicable to non-distributed training)')
|
||||
parser.add_argument(
|
||||
'--ipu-replicas',
|
||||
type=int,
|
||||
default=None,
|
||||
help='num of ipu replicas to use')
|
||||
parser.add_argument('--seed', type=int, default=None, help='random seed')
|
||||
parser.add_argument(
|
||||
'--diff-seed',
|
||||
action='store_true',
|
||||
help='Whether or not set different seeds for different ranks')
|
||||
parser.add_argument(
|
||||
'--deterministic',
|
||||
action='store_true',
|
||||
help='whether to set deterministic options for CUDNN backend.')
|
||||
parser.add_argument(
|
||||
'--cfg-options',
|
||||
nargs='+',
|
||||
|
|
@ -86,20 +42,34 @@ def parse_args():
|
|||
return args
|
||||
|
||||
|
||||
def merge_args(cfg, args):
|
||||
"""Merge CLI arguments to config."""
|
||||
if args.resume_from is not None:
|
||||
cfg.resume = True
|
||||
cfg.load_from = args.resume_from
|
||||
|
||||
if args.no_validate is not None:
|
||||
cfg.val_cfg = None
|
||||
cfg.val_dataloader = None
|
||||
cfg.val_evaluator = None
|
||||
|
||||
return cfg
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
|
||||
# register all modules in mmcls into the registries
|
||||
# do not init the default scope here because it will be init in the runner
|
||||
register_all_modules(init_default_scope=False)
|
||||
|
||||
# load config
|
||||
cfg = Config.fromfile(args.config)
|
||||
cfg = merge_args(cfg, args)
|
||||
cfg.launcher = args.launcher
|
||||
if args.cfg_options is not None:
|
||||
cfg.merge_from_dict(args.cfg_options)
|
||||
|
||||
# set multi-process settings
|
||||
setup_multi_processes(cfg)
|
||||
|
||||
# set cudnn_benchmark
|
||||
if cfg.get('cudnn_benchmark', False):
|
||||
torch.backends.cudnn.benchmark = True
|
||||
|
||||
# work_dir is determined in this priority: CLI > segment in file > filename
|
||||
if args.work_dir is not None:
|
||||
# update configs according to CLI args if args.work_dir is not None
|
||||
|
|
@ -108,95 +78,12 @@ def main():
|
|||
# use config filename as default work_dir if cfg.work_dir is None
|
||||
cfg.work_dir = osp.join('./work_dirs',
|
||||
osp.splitext(osp.basename(args.config))[0])
|
||||
if args.resume_from is not None:
|
||||
cfg.resume_from = args.resume_from
|
||||
if args.gpus is not None:
|
||||
cfg.gpu_ids = range(1)
|
||||
warnings.warn('`--gpus` is deprecated because we only support '
|
||||
'single GPU mode in non-distributed training. '
|
||||
'Use `gpus=1` now.')
|
||||
if args.gpu_ids is not None:
|
||||
cfg.gpu_ids = args.gpu_ids[0:1]
|
||||
warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
|
||||
'Because we only support single GPU mode in '
|
||||
'non-distributed training. Use the first GPU '
|
||||
'in `gpu_ids` now.')
|
||||
if args.gpus is None and args.gpu_ids is None:
|
||||
cfg.gpu_ids = [args.gpu_id]
|
||||
|
||||
if args.ipu_replicas is not None:
|
||||
cfg.ipu_replicas = args.ipu_replicas
|
||||
args.device = 'ipu'
|
||||
# build the runner from config
|
||||
runner = Runner.from_cfg(cfg)
|
||||
|
||||
# init distributed env first, since logger depends on the dist info.
|
||||
if args.launcher == 'none':
|
||||
distributed = False
|
||||
else:
|
||||
distributed = True
|
||||
init_dist(args.launcher, **cfg.dist_params)
|
||||
_, world_size = get_dist_info()
|
||||
cfg.gpu_ids = range(world_size)
|
||||
|
||||
# create work_dir
|
||||
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
|
||||
# dump config
|
||||
cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
|
||||
# init the logger before other steps
|
||||
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
|
||||
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
|
||||
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
|
||||
|
||||
# init the meta dict to record some important information such as
|
||||
# environment info and seed, which will be logged
|
||||
meta = dict()
|
||||
# log env info
|
||||
env_info_dict = collect_env()
|
||||
env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
|
||||
dash_line = '-' * 60 + '\n'
|
||||
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
|
||||
dash_line)
|
||||
meta['env_info'] = env_info
|
||||
|
||||
# log some basic info
|
||||
logger.info(f'Distributed training: {distributed}')
|
||||
logger.info(f'Config:\n{cfg.pretty_text}')
|
||||
|
||||
# set random seeds
|
||||
seed = init_random_seed(args.seed)
|
||||
seed = seed + dist.get_rank() if args.diff_seed else seed
|
||||
logger.info(f'Set random seed to {seed}, '
|
||||
f'deterministic: {args.deterministic}')
|
||||
set_random_seed(seed, deterministic=args.deterministic)
|
||||
cfg.seed = seed
|
||||
meta['seed'] = seed
|
||||
|
||||
model = build_classifier(cfg.model)
|
||||
model.init_weights()
|
||||
|
||||
datasets = [build_dataset(cfg.data.train)]
|
||||
if len(cfg.workflow) == 2:
|
||||
val_dataset = copy.deepcopy(cfg.data.val)
|
||||
val_dataset.pipeline = cfg.data.train.pipeline
|
||||
datasets.append(build_dataset(val_dataset))
|
||||
|
||||
# save mmcls version, config file content and class names in
|
||||
# runner as meta data
|
||||
meta.update(
|
||||
dict(
|
||||
mmcls_version=__version__,
|
||||
config=cfg.pretty_text,
|
||||
CLASSES=datasets[0].CLASSES))
|
||||
|
||||
# add an attribute for visualization convenience
|
||||
train_model(
|
||||
model,
|
||||
datasets,
|
||||
cfg,
|
||||
distributed=distributed,
|
||||
validate=(not args.no_validate),
|
||||
timestamp=timestamp,
|
||||
device=args.device,
|
||||
meta=meta)
|
||||
# start training
|
||||
runner.train()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
|||
Loading…
Reference in New Issue