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[Enhance] Use Inferencer to implement Demo #2763

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merged 11 commits into from
Oct 18, 2023
Merged

[Enhance] Use Inferencer to implement Demo #2763

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40bd7b1
fix lidar demo
sunjiahao1999 Oct 8, 2023
5c51f1a
fix all demo
sunjiahao1999 Oct 9, 2023
510f50e
fix bevfusion demo
sunjiahao1999 Oct 11, 2023
b72da9b
rename
sunjiahao1999 Oct 16, 2023
53515b9
fix det version
sunjiahao1999 Oct 16, 2023
114059e
fix ut and fix input_to_list
sunjiahao1999 Oct 16, 2023
0b42058
resolve comments
sunjiahao1999 Oct 16, 2023
b3fb733
fix os.isfile when python=3.7
sunjiahao1999 Oct 16, 2023
0709e04
add note in get_started
sunjiahao1999 Oct 17, 2023
4506766
fix docstring
sunjiahao1999 Oct 18, 2023
93449e3
fix mmcv version
sunjiahao1999 Oct 18, 2023
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133 changes: 48 additions & 85 deletions demo/inference_demo.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,117 +2,83 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"source": [
"from mmdet3d.apis import inference_detector, init_model\n",
"from mmdet3d.registry import VISUALIZERS\n",
"from mmdet3d.utils import register_all_modules"
],
"outputs": [],
"execution_count": 25,
"metadata": {
"pycharm": {
"is_executing": false
}
}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# register all modules in mmdet3d into the registries\n",
"register_all_modules()"
],
},
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 8,
"source": [
"config_file = '../configs/second/hv_second_secfpn_6x8_80e_kitti-3d-car.py'\n",
"# download the checkpoint from model zoo and put it in `checkpoints/`\n",
"checkpoint_file = '../work_dirs/second/epoch_40.pth'"
],
"outputs": [],
"metadata": {
"pycharm": {
"is_executing": false
}
}
"from mmdet3d.apis import LidarDet3DInferencer"
]
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# build the model from a config file and a checkpoint file\n",
"model = init_model(config_file, checkpoint_file, device='cuda:0')"
],
"metadata": {},
"outputs": [],
"metadata": {}
"source": [
"# initialize inferencer\n",
"inferencer = LidarDet3DInferencer('pointpillars_kitti-3class')"
]
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# init visualizer\n",
"visualizer = VISUALIZERS.build(model.cfg.visualizer)\n",
"visualizer.dataset_meta = {\n",
" 'CLASSES': model.CLASSES,\n",
" 'PALETTE': model.PALETTE\n",
"}"
],
"outputs": [],
"metadata": {
"pycharm": {
"is_executing": false
}
}
},
"outputs": [],
"source": [
"# inference\n",
"inputs = dict(points='./data/kitti/000008.bin')\n",
"inferencer(inputs)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"source": [
"# test a single sample\n",
"pcd = './data/kitti/000008.bin'\n",
"result, data = inference_detector(model, pcd)\n",
"points = data['inputs']['points']\n",
"data_input = dict(points=points)"
],
"execution_count": null,
"metadata": {},
"outputs": [],
"metadata": {
"pycharm": {
"is_executing": false
}
}
"source": [
"# inference and visualize\n",
"# NOTE: use the `Esc` key to exit Open3D window in Jupyter Notebook Environment\n",
"inferencer(inputs, show=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# show the results\n",
"out_dir = './'\n",
"visualizer.add_datasample(\n",
" 'result',\n",
" data_input,\n",
" data_sample=result,\n",
" draw_gt=False,\n",
" show=True,\n",
" wait_time=0,\n",
" out_file=out_dir,\n",
" vis_task='det')"
],
"metadata": {},
"outputs": [],
"metadata": {
"pycharm": {
"is_executing": false
}
}
"source": [
"# If your operating environment does not have a display device,\n",
"# (e.g. a remote server), you can save the predictions and visualize\n",
"# them in local devices.\n",
"inferencer(inputs, show=False, out_dir='./remote_outputs')\n",
"\n",
"# Simulate the migration process\n",
"%mv ./remote_outputs ./local_outputs\n",
"\n",
"# Visualize the predictions from the saved files\n",
"# NOTE: use the `Esc` key to exit Open3D window in Jupyter Notebook Environment\n",
"local_inferencer = LidarDet3DInferencer('pointpillars_kitti-3class')\n",
"inputs = local_inferencer._inputs_to_list(inputs)\n",
"local_inferencer.visualize_preds_fromfile(inputs, ['local_outputs/preds/000008.json'], show=True)"
]
}
],
"metadata": {
"interpreter": {
"hash": "a0c343fece975dd89087e8c2194dd4d3db28d7000f1b32ed9ed9d584dd54dbbe"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3.7.6 64-bit ('torch1.7-cu10.1': conda)"
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -124,19 +90,16 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
"version": "3.9.16"
},
"pycharm": {
"stem_cell": {
"cell_type": "raw",
"source": [],
"metadata": {
"collapsed": false
}
},
"source": []
}
},
"interpreter": {
"hash": "a0c343fece975dd89087e8c2194dd4d3db28d7000f1b32ed9ed9d584dd54dbbe"
}
},
"nbformat": 4,
Expand Down
106 changes: 67 additions & 39 deletions demo/mono_det_demo.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,18 +1,19 @@
# Copyright (c) OpenMMLab. All rights reserved.
import logging
import os
from argparse import ArgumentParser

import mmcv
from mmengine.logging import print_log

from mmdet3d.apis import inference_mono_3d_detector, init_model
from mmdet3d.registry import VISUALIZERS
from mmdet3d.apis import MonoDet3DInferencer


def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='image file')
parser.add_argument('ann', help='ann file')
parser.add_argument('config', help='Config file')
parser.add_argument('checkpoint', help='Checkpoint file')
parser.add_argument('img', help='Image file')
parser.add_argument('infos', help='Infos file with annotations')
parser.add_argument('model', help='Config file')
parser.add_argument('weights', help='Checkpoint file')
parser.add_argument(
'--device', default='cuda:0', help='Device used for inference')
parser.add_argument(
Expand All @@ -21,50 +22,77 @@ def parse_args():
default='CAM_BACK',
help='choose camera type to inference')
parser.add_argument(
'--score-thr', type=float, default=0.30, help='bbox score threshold')
'--pred-score-thr',
type=float,
default=0.3,
help='bbox score threshold')
parser.add_argument(
'--out-dir', type=str, default='demo', help='dir to save results')
'--out-dir',
type=str,
default='outputs',
help='Output directory of prediction and visualization results.')
parser.add_argument(
'--show',
action='store_true',
help='show online visualization results')
help='Show online visualization results')
parser.add_argument(
'--wait-time',
type=float,
default=-1,
help='The interval of show (s). Demo will be blocked in showing'
'results, if wait_time is -1. Defaults to -1.')
parser.add_argument(
'--snapshot',
'--no-save-vis',
action='store_true',
help='whether to save online visualization results')
args = parser.parse_args()
return args
help='Do not save detection visualization results')
parser.add_argument(
'--no-save-pred',
action='store_true',
help='Do not save detection prediction results')
parser.add_argument(
'--print-result',
action='store_true',
help='Whether to print the results.')
call_args = vars(parser.parse_args())

call_args['inputs'] = dict(
img=call_args.pop('img'), infos=call_args.pop('infos'))
call_args.pop('cam_type')

if call_args['no_save_vis'] and call_args['no_save_pred']:
call_args['out_dir'] = ''

init_kws = ['model', 'weights', 'device']
init_args = {}
for init_kw in init_kws:
init_args[init_kw] = call_args.pop(init_kw)

# NOTE: If your operating environment does not have a display device,
# (e.g. a remote server), you can save the predictions and visualize
# them in local devices.
if os.environ.get('DISPLAY') is None and call_args['show']:
print_log(
'Display device not found. `--show` is forced to False',
logger='current',
level=logging.WARNING)
call_args['show'] = False

def main(args):
# build the model from a config file and a checkpoint file
model = init_model(args.config, args.checkpoint, device=args.device)
return init_args, call_args

# init visualizer
visualizer = VISUALIZERS.build(model.cfg.visualizer)
visualizer.dataset_meta = model.dataset_meta

# test a single image
result = inference_mono_3d_detector(model, args.img, args.ann,
args.cam_type)
def main():
# TODO: Support inference of point cloud numpy file.
init_args, call_args = parse_args()

img = mmcv.imread(args.img)
img = mmcv.imconvert(img, 'bgr', 'rgb')
inferencer = MonoDet3DInferencer(**init_args)
inferencer(**call_args)

data_input = dict(img=img)
# show the results
visualizer.add_datasample(
'result',
data_input,
data_sample=result,
draw_gt=False,
show=args.show,
wait_time=-1,
out_file=args.out_dir,
pred_score_thr=args.score_thr,
vis_task='mono_det')
if call_args['out_dir'] != '' and not (call_args['no_save_vis']
and call_args['no_save_pred']):
print_log(
f'results have been saved at {call_args["out_dir"]}',
logger='current')


if __name__ == '__main__':
args = parse_args()
main(args)
main()
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