258 lines
9.6 KiB
Python
258 lines
9.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
try:
|
|
import mmdet
|
|
except (ImportError, ModuleNotFoundError):
|
|
mmdet = None
|
|
|
|
if mmdet is None:
|
|
raise RuntimeError('mmdet is not installed')
|
|
import argparse
|
|
import os
|
|
import os.path as osp
|
|
import time
|
|
import warnings
|
|
|
|
import mmcv
|
|
import torch
|
|
from mmcv import Config, DictAction
|
|
from mmcv.cnn import fuse_conv_bn
|
|
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
|
|
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
|
|
wrap_fp16_model)
|
|
from mmdet.apis import multi_gpu_test, single_gpu_test
|
|
from mmdet.datasets import (build_dataloader, build_dataset,
|
|
replace_ImageToTensor)
|
|
|
|
from mmrazor.models.builder import build_algorithm
|
|
from mmrazor.utils import setup_multi_processes
|
|
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser(
|
|
description='MMDet 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='the directory to save the file containing evaluation metrics')
|
|
parser.add_argument('--out', help='output result file in pickle format')
|
|
parser.add_argument(
|
|
'--fuse-conv-bn',
|
|
action='store_true',
|
|
help='Whether to fuse conv and bn, this will slightly increase'
|
|
'the inference speed')
|
|
parser.add_argument(
|
|
'--gpu-ids',
|
|
type=int,
|
|
nargs='+',
|
|
help='(Deprecated, please use --gpu-id) ids of gpus to use '
|
|
'(only applicable to non-distributed training)')
|
|
parser.add_argument(
|
|
'--gpu-id',
|
|
type=int,
|
|
default=0,
|
|
help='id of gpu to use '
|
|
'(only applicable to non-distributed testing)')
|
|
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., "bbox",'
|
|
' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
|
|
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(
|
|
'--show-score-thr',
|
|
type=float,
|
|
default=0.3,
|
|
help='score threshold (default: 0.3)')
|
|
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(
|
|
'--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, the key-value pair in xxx=yyy '
|
|
'format will be kwargs for dataset.evaluate() function')
|
|
parser.add_argument(
|
|
'--launcher',
|
|
choices=['none', 'pytorch', 'slurm', 'mpi'],
|
|
default='none',
|
|
help='job launcher')
|
|
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 = 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
|
|
|
|
cfg.algorithm.architecture.model.pretrained = None
|
|
if cfg.algorithm.architecture.model.get('neck'):
|
|
if isinstance(cfg.algorithm.architecture.model.neck, list):
|
|
for neck_cfg in cfg.algorithm.architecture.neck:
|
|
if neck_cfg.get('rfp_backbone'):
|
|
if neck_cfg.rfp_backbone.get('pretrained'):
|
|
neck_cfg.rfp_backbone.pretrained = None
|
|
elif cfg.algorithm.architecture.model.neck.get('rfp_backbone'):
|
|
if cfg.algorithm.architecture.model.neck.rfp_backbone.get(
|
|
'pretrained'):
|
|
cfg.algorithm.architecture.model.neck.rfp_backbone.pretrained = None # noqa E501
|
|
|
|
# in case the test dataset is concatenated
|
|
samples_per_gpu = 1
|
|
if isinstance(cfg.data.test, dict):
|
|
cfg.data.test.test_mode = True
|
|
samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
|
|
if samples_per_gpu > 1:
|
|
# Replace 'ImageToTensor' to 'DefaultFormatBundle'
|
|
cfg.data.test.pipeline = replace_ImageToTensor(
|
|
cfg.data.test.pipeline)
|
|
elif isinstance(cfg.data.test, list):
|
|
for ds_cfg in cfg.data.test:
|
|
ds_cfg.test_mode = True
|
|
samples_per_gpu = max(
|
|
[ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test])
|
|
if samples_per_gpu > 1:
|
|
for ds_cfg in cfg.data.test:
|
|
ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)
|
|
|
|
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 testing. Use the first GPU '
|
|
'in `gpu_ids` now.')
|
|
else:
|
|
cfg.gpu_ids = [args.gpu_id]
|
|
|
|
# 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())
|
|
json_file = osp.join(args.work_dir, f'eval_{timestamp}.json')
|
|
|
|
# build the dataloader
|
|
dataset = build_dataset(cfg.data.test)
|
|
data_loader = build_dataloader(
|
|
dataset,
|
|
samples_per_gpu=samples_per_gpu,
|
|
workers_per_gpu=cfg.data.workers_per_gpu,
|
|
dist=distributed,
|
|
shuffle=False)
|
|
|
|
# build the algorithm and load checkpoint
|
|
cfg.algorithm.architecture.model.train_cfg = None
|
|
algorithm = build_algorithm(cfg.algorithm)
|
|
model = algorithm.architecture.model
|
|
|
|
fp16_cfg = cfg.get('fp16', None)
|
|
if fp16_cfg is not None:
|
|
wrap_fp16_model(model)
|
|
checkpoint = load_checkpoint(
|
|
algorithm, args.checkpoint, map_location='cpu')
|
|
if args.fuse_conv_bn:
|
|
model = fuse_conv_bn(model)
|
|
# old versions did not save class info in checkpoints, this walkaround is
|
|
# for backward compatibility
|
|
if 'CLASSES' in checkpoint.get('meta', {}):
|
|
model.CLASSES = checkpoint['meta']['CLASSES']
|
|
else:
|
|
model.CLASSES = dataset.CLASSES
|
|
|
|
if not distributed:
|
|
algorithm = MMDataParallel(algorithm, device_ids=cfg.gpu_ids)
|
|
outputs = single_gpu_test(algorithm, data_loader, args.show,
|
|
args.show_dir, args.show_score_thr)
|
|
else:
|
|
algorithm = MMDistributedDataParallel(
|
|
algorithm.cuda(),
|
|
device_ids=[torch.cuda.current_device()],
|
|
broadcast_buffers=False)
|
|
outputs = multi_gpu_test(algorithm, data_loader, args.tmpdir,
|
|
args.gpu_collect)
|
|
|
|
rank, _ = get_dist_info()
|
|
if rank == 0:
|
|
if args.out:
|
|
print(f'\nwriting results to {args.out}')
|
|
mmcv.dump(outputs, args.out)
|
|
kwargs = {} if args.eval_options is None else args.eval_options
|
|
if args.format_only:
|
|
dataset.format_results(outputs, **kwargs)
|
|
if args.eval:
|
|
eval_kwargs = cfg.get('evaluation', {}).copy()
|
|
# hard-code way to remove EvalHook args
|
|
for key in [
|
|
'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
|
|
'rule', 'dynamic_intervals'
|
|
]:
|
|
eval_kwargs.pop(key, None)
|
|
eval_kwargs.update(dict(metric=args.eval, **kwargs))
|
|
metric = dataset.evaluate(outputs, **eval_kwargs)
|
|
print(metric)
|
|
metric_dict = dict(config=args.config, metric=metric)
|
|
if args.work_dir is not None and rank == 0:
|
|
mmcv.dump(metric_dict, json_file)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|