sennnnn 4e9c26bbbc [Refactor] Support progressive test with fewer memory cost (#709)
* Support progressive test with fewer memory cost.

* Temp code

* Using processor to refactor evaluation workflow.

* refactor eval hook.

* Fix process bar.

* Fix middle save argument.

* Modify some variable name of dataset evaluate api.

* Modify some viriable name of eval hook.

* Fix some priority bugs of eval hook.

* Depreciated efficient_test.

* Fix training progress blocked by eval hook.

* Depreciated old test api.

* Fix test api error.

* Modify outer api.

* Build a sampler test api.

* TODO: Refactor format_results.

* Modify variable names.

* Fix num_classes bug.

* Fix sampler index bug.

* Fix grammaly bug.

* Support batch sampler.

* More readable test api.

* Remove some command arg and fix eval hook bug.

* Support format-only arg.

* Modify format_results of datasets.

* Modify tool which use test apis.

* support cityscapes eval

* fixed cityscapes

* 1. Add comments for batch_sampler;

2. Keep eval hook api same and add deprecated warning;

3. Add doc string for dataset.pre_eval;

* Add efficient_test doc string.

* Modify test tool to compat old version.

* Modify eval hook to compat with old version.

* Modify test api to compat old version api.

* Sampler explanation.

* update warning

* Modify deploy_test.py

* compatible with old output, add efficient test back

* clear logic of exclusive

* Warning about efficient_test.

* Modify format_results save folder.

* Fix bugs of format_results.

* Modify deploy_test.py.

* Update doc

* Fix deploy test bugs.

* Fix custom dataset unit tests.

* Fix dataset unit tests.

* Fix eval hook unit tests.

* Fix some imcompatible.

* Add pre_eval argument for eval hooks.

* Update eval hook doc string.

* Make pre_eval false in default.

* Add unit tests for dataset format_results.

* Fix some comments and bc-breaking bug.

* Fix pre_eval set cfg field.

* Remove redundant codes.

Co-authored-by: Jiarui XU <xvjiarui0826@gmail.com>
2021-08-19 20:44:58 -07:00

213 lines
7.7 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import shutil
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)
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
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(
'--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(
'--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='custom options')
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)
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.options is not None:
cfg.merge_from_dict(args.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)
# 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 '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:
dataset.evaluate(results, args.eval, **eval_kwargs)
if tmpdir is not None and eval_on_format_results:
# remove tmp dir when cityscapes evaluation
shutil.rmtree(tmpdir)
if __name__ == '__main__':
main()