🌱 Add MMSeg custom train.py, test.py

mmsegmentation/tools/test_jwseo.py

@@ -0,0 +1,320 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+import shutil
+import time
+import warnings
+
+import mmcv
+import torch
+from mmcv.cnn.utils import revert_sync_batchnorm
+from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
+                         wrap_fp16_model)
+from mmcv.utils import DictAction
+
+from mmseg import digit_version
+from mmseg.apis import multi_gpu_test, single_gpu_test
+from mmseg.datasets import build_dataloader, build_dataset
+from mmseg.models import build_segmentor
+from mmseg.utils import build_ddp, build_dp, get_device, setup_multi_processes
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='mmseg test (and eval) a model')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument(
+        '--work-dir',
+        help=('if specified, the evaluation metric results will be dumped'
+              'into the directory as json'))
+    parser.add_argument(
+        '--aug-test', action='store_true', help='Use Flip and Multi scale aug')
+    parser.add_argument('--out', help='output result file in pickle format')
+    parser.add_argument(
+        '--format-only',
+        action='store_true',
+        help='Format the output results without perform evaluation. It is'
+        'useful when you want to format the result to a specific format and '
+        'submit it to the test server')
+    parser.add_argument(
+        '--eval',
+        type=str,
+        nargs='+',
+        help='evaluation metrics, which depends on the dataset, e.g., "mIoU"'
+        ' for generic datasets, and "cityscapes" for Cityscapes')
+    parser.add_argument('--show', action='store_true', help='show results')
+    parser.add_argument(
+        '--show-dir', help='directory where painted images will be saved')
+    parser.add_argument(
+        '--gpu-collect',
+        action='store_true',
+        help='whether to use gpu to collect results.')
+    parser.add_argument(
+        '--gpu-id',
+        type=int,
+        default=0,
+        help='id of gpu to use '
+        '(only applicable to non-distributed testing)')
+    parser.add_argument(
+        '--tmpdir',
+        help='tmp directory used for collecting results from multiple '
+        'workers, available when gpu_collect is not specified')
+    parser.add_argument(
+        '--options',
+        nargs='+',
+        action=DictAction,
+        help="--options is deprecated in favor of --cfg_options' and it will "
+        'not be supported in version v0.22.0. Override some settings in the '
+        'used config, the key-value pair in xxx=yyy format will be merged '
+        'into config file. If the value to be overwritten is a list, it '
+        'should be like key="[a,b]" or key=a,b It also allows nested '
+        'list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation '
+        'marks are necessary and that no white space is allowed.')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--eval-options',
+        nargs='+',
+        action=DictAction,
+        help='custom options for evaluation')
+    parser.add_argument(
+        '--launcher',
+        choices=['none', 'pytorch', 'slurm', 'mpi'],
+        default='none',
+        help='job launcher')
+    parser.add_argument(
+        '--opacity',
+        type=float,
+        default=0.5,
+        help='Opacity of painted segmentation map. In (0, 1] range.')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+
+    if args.options and args.cfg_options:
+        raise ValueError(
+            '--options and --cfg-options cannot be both '
+            'specified, --options is deprecated in favor of --cfg-options. '
+            '--options will not be supported in version v0.22.0.')
+    if args.options:
+        warnings.warn('--options is deprecated in favor of --cfg-options. '
+                      '--options will not be supported in version v0.22.0.')
+        args.cfg_options = args.options
+
+    return args
+
+
+def main():
+    args = parse_args()
+    assert args.out or args.eval or args.format_only or args.show \
+        or args.show_dir, \
+        ('Please specify at least one operation (save/eval/format/show the '
+         'results / save the results) with the argument "--out", "--eval"'
+         ', "--format-only", "--show" or "--show-dir"')
+
+    if args.eval and args.format_only:
+        raise ValueError('--eval and --format_only cannot be both specified')
+
+    if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
+        raise ValueError('The output file must be a pkl file.')
+
+    cfg = mmcv.Config.fromfile(args.config)
+    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
+    if args.aug_test:
+        # hard code index
+        cfg.data.test.pipeline[1].img_ratios = [
+            0.5, 0.75, 1.0, 1.25, 1.5, 1.75
+        ]
+        cfg.data.test.pipeline[1].flip = True
+    cfg.model.pretrained = None
+    cfg.data.test.test_mode = True
+
+    if args.gpu_id is not None:
+        cfg.gpu_ids = [args.gpu_id]
+
+    # init distributed env first, since logger depends on the dist info.
+    if args.launcher == 'none':
+        cfg.gpu_ids = [args.gpu_id]
+        distributed = False
+        if len(cfg.gpu_ids) > 1:
+            warnings.warn(f'The gpu-ids is reset from {cfg.gpu_ids} to '
+                          f'{cfg.gpu_ids[0:1]} to avoid potential error in '
+                          'non-distribute testing time.')
+            cfg.gpu_ids = cfg.gpu_ids[0:1]
+    else:
+        distributed = True
+        init_dist(args.launcher, **cfg.dist_params)
+
+    rank, _ = get_dist_info()
+    # allows not to create
+    if args.work_dir is not None and rank == 0:
+        mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
+        timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+        if args.aug_test:
+            json_file = osp.join(args.work_dir,
+                                 f'eval_multi_scale_{timestamp}.json')
+        else:
+            json_file = osp.join(args.work_dir,
+                                 f'eval_single_scale_{timestamp}.json')
+    elif rank == 0:
+        work_dir = osp.join('./work_dirs',
+                            osp.splitext(osp.basename(args.config))[0])
+        mmcv.mkdir_or_exist(osp.abspath(work_dir))
+        timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+        if args.aug_test:
+            json_file = osp.join(work_dir,
+                                 f'eval_multi_scale_{timestamp}.json')
+        else:
+            json_file = osp.join(work_dir,
+                                 f'eval_single_scale_{timestamp}.json')
+
+    # build the dataloader
+    # TODO: support multiple images per gpu (only minor changes are needed)
+    dataset = build_dataset(cfg.data.test)
+    # The default loader config
+    loader_cfg = dict(
+        # cfg.gpus will be ignored if distributed
+        num_gpus=len(cfg.gpu_ids),
+        dist=distributed,
+        shuffle=False)
+    # The overall dataloader settings
+    loader_cfg.update({
+        k: v
+        for k, v in cfg.data.items() if k not in [
+            'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
+            'test_dataloader'
+        ]
+    })
+    test_loader_cfg = {
+        **loader_cfg,
+        'samples_per_gpu': 1,
+        'shuffle': False,  # Not shuffle by default
+        **cfg.data.get('test_dataloader', {})
+    }
+    # build the dataloader
+    data_loader = build_dataloader(dataset, **test_loader_cfg)
+
+    # build the model and load checkpoint
+    cfg.model.train_cfg = None
+    model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
+    fp16_cfg = cfg.get('fp16', None)
+    if fp16_cfg is not None:
+        wrap_fp16_model(model)
+    checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
+    if 'CLASSES' in checkpoint.get('meta', {}):
+        model.CLASSES = checkpoint['meta']['CLASSES']
+    else:
+        print('"CLASSES" not found in meta, use dataset.CLASSES instead')
+        model.CLASSES = dataset.CLASSES
+    if 'PALETTE' in checkpoint.get('meta', {}):
+        model.PALETTE = checkpoint['meta']['PALETTE']
+    else:
+        print('"PALETTE" not found in meta, use dataset.PALETTE instead')
+        model.PALETTE = dataset.PALETTE
+
+    # clean gpu memory when starting a new evaluation.
+    torch.cuda.empty_cache()
+    eval_kwargs = {} if args.eval_options is None else args.eval_options
+
+    # Deprecated
+    efficient_test = eval_kwargs.get('efficient_test', False)
+    if efficient_test:
+        warnings.warn(
+            '``efficient_test=True`` does not have effect in tools/test.py, '
+            'the evaluation and format results are CPU memory efficient by '
+            'default')
+
+    eval_on_format_results = (
+        args.eval is not None and 'cityscapes' in args.eval)
+    if eval_on_format_results:
+        assert len(args.eval) == 1, 'eval on format results is not ' \
+                                    'applicable for metrics other than ' \
+                                    'cityscapes'
+    if args.format_only or eval_on_format_results:
+        if 'imgfile_prefix' in eval_kwargs:
+            tmpdir = eval_kwargs['imgfile_prefix']
+        else:
+            tmpdir = '.format_cityscapes'
+            eval_kwargs.setdefault('imgfile_prefix', tmpdir)
+        mmcv.mkdir_or_exist(tmpdir)
+    else:
+        tmpdir = None
+
+    cfg.device = get_device()
+    if not distributed:
+        warnings.warn(
+            'SyncBN is only supported with DDP. To be compatible with DP, '
+            'we convert SyncBN to BN. Please use dist_train.sh which can '
+            'avoid this error.')
+        if not torch.cuda.is_available():
+            assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
+                'Please use MMCV >= 1.4.4 for CPU training!'
+        model = revert_sync_batchnorm(model)
+        model = build_dp(model, cfg.device, device_ids=cfg.gpu_ids)
+        results = single_gpu_test(
+            model,
+            data_loader,
+            args.show,
+            args.show_dir,
+            False,
+            args.opacity,
+            pre_eval=args.eval is not None and not eval_on_format_results,
+            format_only=args.format_only or eval_on_format_results,
+            format_args=eval_kwargs)
+    else:
+        model = build_ddp(
+            model,
+            cfg.device,
+            device_ids=[int(os.environ['LOCAL_RANK'])],
+            broadcast_buffers=False)
+        results = multi_gpu_test(
+            model,
+            data_loader,
+            args.tmpdir,
+            args.gpu_collect,
+            False,
+            pre_eval=args.eval is not None and not eval_on_format_results,
+            format_only=args.format_only or eval_on_format_results,
+            format_args=eval_kwargs)
+
+    rank, _ = get_dist_info()
+    if rank == 0:
+        if args.out:
+            warnings.warn(
+                'The behavior of ``args.out`` has been changed since MMSeg '
+                'v0.16, the pickled outputs could be seg map as type of '
+                'np.array, pre-eval results or file paths for '
+                '``dataset.format_results()``.')
+            print(f'\nwriting results to {args.out}')
+            mmcv.dump(results, args.out)
+        if args.eval:
+            eval_kwargs.update(metric=args.eval)
+            metric = dataset.evaluate(results, **eval_kwargs)
+            metric_dict = dict(config=args.config, metric=metric)
+            mmcv.dump(metric_dict, json_file, indent=4)
+            if tmpdir is not None and eval_on_format_results:
+                # remove tmp dir when cityscapes evaluation
+                shutil.rmtree(tmpdir)
+
+
+if __name__ == '__main__':
+    main()