Release detection and segmentation

segmentation/slurm_test.sh

+#!/usr/bin/env bash
+
+set -x
+
+PARTITION=$1
+JOB_NAME=$2
+CONFIG=$3
+CHECKPOINT=$4
+GPUS=${GPUS:-8}
+GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+CPUS_PER_TASK=${CPUS_PER_TASK:-5}
+PY_ARGS=${@:5}
+SRUN_ARGS=${SRUN_ARGS:-""}
+
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+srun -p ${PARTITION} \
+    --job-name=${JOB_NAME} \
+    --gres=gpu:${GPUS_PER_NODE} \
+    --ntasks=${GPUS} \
+    --ntasks-per-node=${GPUS_PER_NODE} \
+    --cpus-per-task=${CPUS_PER_TASK} \
+    --kill-on-bad-exit=1 \
+    --quotatype=auto \
+    ${SRUN_ARGS} \
+    python -u test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS}

segmentation/slurm_train.sh

+#!/usr/bin/env bash
+
+set -x
+
+PARTITION=$1
+JOB_NAME=$2
+CONFIG=$3
+GPUS=${GPUS:-8}
+GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+CPUS_PER_TASK=${CPUS_PER_TASK:-5}
+SRUN_ARGS=${SRUN_ARGS:-""}
+PY_ARGS=${@:4}
+
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+srun -p ${PARTITION} \
+    --job-name=${JOB_NAME} \
+    --gres=gpu:${GPUS_PER_NODE} \
+    --ntasks=${GPUS} \
+    --ntasks-per-node=${GPUS_PER_NODE} \
+    --cpus-per-task=${CPUS_PER_TASK} \
+    --quotatype=spot \
+    --kill-on-bad-exit=1 \
+    ${SRUN_ARGS} \
+    python -u train.py ${CONFIG} --launcher="slurm" ${PY_ARGS}

segmentation/test.py

+# --------------------------------------------------------
+# InternImage
+# Copyright (c) 2022 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import argparse
+import os
+import os.path as osp
+import shutil
+import time
+import warnings
+
+import mmcv
+import torch
+from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
+from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
+                         wrap_fp16_model, load_state_dict)
+from mmcv.utils import DictAction
+from mmseg.apis import multi_gpu_test, single_gpu_test
+from mmseg.datasets import build_dataloader, build_dataset
+from mmseg.models import build_segmentor
+
+import mmcv_custom   # noqa: F401,F403
+import mmseg_custom   # noqa: F401,F403
+
+
+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('--dir-name', help='dir name')
+    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(
+        '--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 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
+
+    # 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)
+
+    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)
+    data_loader = build_dataloader(
+        dataset,
+        samples_per_gpu=1,
+        workers_per_gpu=cfg.data.workers_per_gpu,
+        dist=distributed,
+        shuffle=False)
+
+    # 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 hasattr(model, 'module'):
+        load_state_dict(model.module, checkpoint['state_dict'], strict=False)
+    else:
+        load_state_dict(model, checkpoint['state_dict'], strict=False)
+    
+    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
+
+    if not distributed:
+        model = MMDataParallel(model, device_ids=[0])
+        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 = MMDistributedDataParallel(
+            model.cuda(),
+            device_ids=[torch.cuda.current_device()],
+            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()

segmentation/train.py

+# --------------------------------------------------------
+# InternImage
+# Copyright (c) 2022 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import argparse
+import copy
+import os
+import os.path as osp
+import time
+import warnings
+
+import mmcv
+import torch
+import torch.distributed as dist
+from mmcv.cnn.utils import revert_sync_batchnorm
+from mmcv.runner import get_dist_info, init_dist
+from mmcv.utils import Config, DictAction, get_git_hash
+
+from mmseg import __version__
+from mmseg.apis import init_random_seed, set_random_seed, train_segmentor
+from mmseg.datasets import build_dataset
+from mmseg.models import build_segmentor
+from mmseg.utils import (collect_env, get_device, get_root_logger,
+                         setup_multi_processes)
+import mmcv_custom  # noqa: F401,F403
+import mmseg_custom  # noqa: F401,F403
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Train a segmentor')
+    parser.add_argument('config', help='train config file path')
+    parser.add_argument('--work-dir', help='the dir to save logs and models')
+    parser.add_argument('--load-from',
+                        help='the checkpoint file to load weights from')
+    parser.add_argument('--resume-from',
+                        help='the checkpoint file to resume from')
+    parser.add_argument(
+        '--no-validate',
+        action='store_true',
+        help='whether not to evaluate the checkpoint during training')
+    group_gpus = parser.add_mutually_exclusive_group()
+    group_gpus.add_argument(
+        '--gpus',
+        type=int,
+        help='(Deprecated, please use --gpu-id) number of gpus to use '
+        '(only applicable to non-distributed training)')
+    group_gpus.add_argument(
+        '--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('--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(
+        '--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('--launcher',
+                        choices=['none', 'pytorch', 'slurm', 'mpi'],
+                        default='none',
+                        help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    parser.add_argument(
+        '--auto-resume',
+        action='store_true',
+        help='resume from the latest checkpoint automatically.')
+    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()
+
+    cfg = Config.fromfile(args.config)
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+    # 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
+        cfg.work_dir = args.work_dir
+    elif cfg.get('work_dir', None) is None:
+        # 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.load_from is not None:
+        cfg.load_from = args.load_from
+    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]
+
+    cfg.auto_resume = args.auto_resume
+
+    # 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)
+        # gpu_ids is used to calculate iter when resuming checkpoint
+        _, 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)
+
+    # set multi-process settings
+    setup_multi_processes(cfg)
+
+    # 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
+    cfg.device = get_device()
+    seed = init_random_seed(args.seed, device=cfg.device)
+    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
+    meta['exp_name'] = osp.basename(args.config)
+
+    model = build_segmentor(cfg.model,
+                            train_cfg=cfg.get('train_cfg'),
+                            test_cfg=cfg.get('test_cfg'))
+    model.init_weights()
+
+    # SyncBN is not support for DP
+    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.')
+        model = revert_sync_batchnorm(model)
+
+    # logger.info(model)
+
+    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))
+    if cfg.checkpoint_config is not None:
+        # save mmseg version, config file content and class names in
+        # checkpoints as meta data
+        cfg.checkpoint_config.meta = dict(
+            mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
+            config=cfg.pretty_text,
+            CLASSES=datasets[0].CLASSES,
+            PALETTE=datasets[0].PALETTE)
+    # add an attribute for visualization convenience
+    model.CLASSES = datasets[0].CLASSES
+    # passing checkpoint meta for saving best checkpoint
+    meta.update(cfg.checkpoint_config.meta)
+    
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    
+    train_segmentor(model,
+                    datasets,
+                    cfg,
+                    distributed=distributed,
+                    validate=(not args.no_validate),
+                    timestamp=timestamp,
+                    meta=meta)
+
+
+if __name__ == '__main__':
+    main()