102 lines
3.9 KiB
Python
102 lines
3.9 KiB
Python
|
import argparse
|
||
|
|
|
||
|
|
import mmcv
|
||
|
|
from mmcv import DictAction
|
||
|
|
from mmcv.parallel import MMDataParallel
|
||
|
|
|
||
|
|
from mmdeploy.apis import (init_backend_model, post_process_outputs,
|
||
|
|
prepare_data_loader, single_gpu_test)
|
||
|
|
from mmdeploy.apis.utils import assert_cfg_valid, get_classes_from_config
|
||
|
|
|
||
|
|
|
||
|
|
def parse_args():
|
||
|
|
parser = argparse.ArgumentParser(
|
||
|
|
description='MMDeploy test (and eval) a backend.')
|
||
|
|
parser.add_argument('deploy_cfg', help='Deploy config path')
|
||
|
|
parser.add_argument('model_cfg', help='Model config path')
|
||
|
|
parser.add_argument('model', help='Input model file.')
|
||
|
|
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(
|
||
|
|
'--metrics',
|
||
|
|
type=str,
|
||
|
|
nargs='+',
|
||
|
|
help='evaluation metrics, which depends on the codebase and the '
|
||
|
|
'dataset, e.g., "bbox", "segm", "proposal" for COCO, and "mAP", '
|
||
|
|
'"recall" for PASCAL VOC in mmdet; "accuracy", "precision", "recall", '
|
||
|
|
'"f1_score", "support" for single label dataset, and "mAP", "CP", "CR"'
|
||
|
|
', "CF1", "OP", "OR", "OF1" for multi-label dataset in mmcls')
|
||
|
|
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(
|
||
|
|
'--device', help='device used for conversion', default='cpu')
|
||
|
|
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(
|
||
|
|
'--metric-options',
|
||
|
|
nargs='+',
|
||
|
|
action=DictAction,
|
||
|
|
help='custom options for evaluation, the key-value pair in xxx=yyy '
|
||
|
|
'format will be kwargs for dataset.evaluate() function')
|
||
|
|
args = parser.parse_args()
|
||
|
|
|
||
|
|
return args
|
||
|
|
|
||
|
|
|
||
|
|
def main():
|
||
|
|
args = parse_args()
|
||
|
|
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
|
||
|
|
raise ValueError('The output file must be a pkl file.')
|
||
|
|
|
||
|
|
deploy_cfg_path = args.deploy_cfg
|
||
|
|
model_cfg_path = args.model_cfg
|
||
|
|
|
||
|
|
# load deploy_cfg
|
||
|
|
deploy_cfg = mmcv.Config.fromfile(deploy_cfg_path)
|
||
|
|
model_cfg = mmcv.Config.fromfile(model_cfg_path)
|
||
|
|
assert_cfg_valid(deploy_cfg, model_cfg)
|
||
|
|
|
||
|
|
# prepare the dataset loader
|
||
|
|
codebase = deploy_cfg['codebase']
|
||
|
|
dataset, data_loader = prepare_data_loader(codebase, model_cfg)
|
||
|
|
|
||
|
|
# load the model of the backend
|
||
|
|
device_id = -1 if args.device == 'cpu' else 0
|
||
|
|
backend = deploy_cfg.get('backend', 'default')
|
||
|
|
model = init_backend_model([args.model],
|
||
|
|
codebase=codebase,
|
||
|
|
backend=backend,
|
||
|
|
class_names=get_classes_from_config(
|
||
|
|
codebase, model_cfg),
|
||
|
|
device_id=device_id)
|
||
|
|
|
||
|
|
model = MMDataParallel(model, device_ids=[0])
|
||
|
|
outputs = single_gpu_test(codebase, model, data_loader, args.show,
|
||
|
|
args.show_dir, args.show_score_thr)
|
||
|
|
|
||
|
|
post_process_outputs(outputs, dataset, model_cfg, codebase, args.metrics,
|
||
|
|
args.out, args.metric_options, args.format_only)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
main()
|