Spaces:
Sleeping
Sleeping
File size: 5,490 Bytes
186701e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
from copy import deepcopy
from mmengine import DictAction
from mmdeploy.apis import build_task_processor
from mmdeploy.utils.config_utils import load_config
from mmdeploy.utils.timer import TimeCounter
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', type=str, nargs='+', help='Input model files.')
parser.add_argument(
'--device', help='device used for conversion', default='cpu')
parser.add_argument(
'--work-dir',
default='./work_dir',
help='the directory to save the file containing evaluation metrics')
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('--show', action='store_true', help='show results')
parser.add_argument(
'--show-dir', help='directory where painted images will be saved')
parser.add_argument(
'--interval',
type=int,
default=1,
help='visualize per interval samples.')
parser.add_argument(
'--wait-time',
type=float,
default=2,
help='display time of every window. (second)')
parser.add_argument(
'--log2file',
type=str,
help='log evaluation results and speed to file',
default=None)
parser.add_argument(
'--speed-test', action='store_true', help='activate speed test')
parser.add_argument(
'--warmup',
type=int,
help='warmup before counting inference elapse, require setting '
'speed-test first',
default=10)
parser.add_argument(
'--log-interval',
type=int,
help='the interval between each log, require setting '
'speed-test first',
default=100)
parser.add_argument(
'--batch-size',
type=int,
default=1,
help='the batch size for test, would override `samples_per_gpu`'
'in data config.')
parser.add_argument(
'--uri',
action='store_true',
default='192.168.1.1:60000',
help='Remote ipv4:port or ipv6:port for inference on edge device.')
args = parser.parse_args()
return args
def main():
args = parse_args()
deploy_cfg_path = args.deploy_cfg
model_cfg_path = args.model_cfg
# load deploy_cfg
deploy_cfg, model_cfg = load_config(deploy_cfg_path, model_cfg_path)
# 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
work_dir = args.work_dir
elif model_cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
# merge options for model cfg
if args.cfg_options is not None:
model_cfg.merge_from_dict(args.cfg_options)
task_processor = build_task_processor(model_cfg, deploy_cfg, args.device)
# prepare the dataset loader
test_dataloader = deepcopy(model_cfg['test_dataloader'])
if isinstance(test_dataloader, list):
dataset = []
for loader in test_dataloader:
ds = task_processor.build_dataset(loader['dataset'])
dataset.append(ds)
loader['dataset'] = ds
loader['batch_size'] = args.batch_size
loader = task_processor.build_dataloader(loader)
dataloader = test_dataloader
else:
test_dataloader['batch_size'] = args.batch_size
dataset = task_processor.build_dataset(test_dataloader['dataset'])
test_dataloader['dataset'] = dataset
dataloader = task_processor.build_dataloader(test_dataloader)
# load the model of the backend
model = task_processor.build_backend_model(
args.model,
data_preprocessor_updater=task_processor.update_data_preprocessor)
destroy_model = model.destroy
is_device_cpu = (args.device == 'cpu')
runner = task_processor.build_test_runner(
model,
work_dir,
log_file=args.log2file,
show=args.show,
show_dir=args.show_dir,
wait_time=args.wait_time,
interval=args.interval,
dataloader=dataloader)
if args.speed_test:
with_sync = not is_device_cpu
with TimeCounter.activate(
warmup=args.warmup,
log_interval=args.log_interval,
with_sync=with_sync,
file=args.log2file,
batch_size=args.batch_size):
runner.test()
else:
runner.test()
# only effective when the backend requires explicit clean-up (e.g. Ascend)
destroy_model()
if __name__ == '__main__':
main()
|