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export_onnx.py
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export_onnx.py
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# # Copyright (c) OpenMMLab. All rights reserved.
import os
import json
import warnings
import argparse
from io import BytesIO
import onnx
import torch
from mmdet.apis import init_detector
from mmengine.config import ConfigDict
from mmengine.logging import print_log
from mmengine.utils.path import mkdir_or_exist
from easydeploy.model import DeployModel, MMYOLOBackend # noqa E402
warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning)
warnings.filterwarnings(action='ignore', category=torch.jit.ScriptWarning)
warnings.filterwarnings(action='ignore', category=UserWarning)
warnings.filterwarnings(action='ignore', category=FutureWarning)
warnings.filterwarnings(action='ignore', category=ResourceWarning)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('config', help='Config file')
parser.add_argument('checkpoint', help='Checkpoint file')
parser.add_argument('--custom-text',
type=str,
help='custom text inputs (text json) for YOLO-World.')
parser.add_argument('--add-padding',
action="store_true",
help="add an empty padding to texts.")
parser.add_argument('--model-only',
action='store_true',
help='Export model only')
parser.add_argument('--without-nms',
action='store_true',
help='Export model without NMS')
parser.add_argument('--without-bbox-decoder',
action='store_true',
help='Export model without Bbox Decoder (for INT8 Quantization)')
parser.add_argument('--work-dir',
default='./work_dirs',
help='Path to save export model')
parser.add_argument('--img-size',
nargs='+',
type=int,
default=[640, 640],
help='Image size of height and width')
parser.add_argument('--batch-size', type=int, default=1, help='Batch size')
parser.add_argument('--device',
default='cuda:0',
help='Device used for inference')
parser.add_argument('--simplify',
action='store_true',
help='Simplify onnx model by onnx-sim')
parser.add_argument('--opset',
type=int,
default=11,
help='ONNX opset version')
parser.add_argument('--backend',
type=str,
default='onnxruntime',
help='Backend for export onnx')
parser.add_argument('--pre-topk',
type=int,
default=1000,
help='Postprocess pre topk bboxes feed into NMS')
parser.add_argument('--keep-topk',
type=int,
default=100,
help='Postprocess keep topk bboxes out of NMS')
parser.add_argument('--iou-threshold',
type=float,
default=0.65,
help='IoU threshold for NMS')
parser.add_argument('--score-threshold',
type=float,
default=0.25,
help='Score threshold for NMS')
args = parser.parse_args()
args.img_size *= 2 if len(args.img_size) == 1 else 1
return args
def build_model_from_cfg(config_path, checkpoint_path, device):
model = init_detector(config_path, checkpoint_path, device=device)
model.eval()
return model
def main():
args = parse_args()
mkdir_or_exist(args.work_dir)
backend = MMYOLOBackend(args.backend.lower())
if backend in (MMYOLOBackend.ONNXRUNTIME, MMYOLOBackend.OPENVINO,
MMYOLOBackend.TENSORRT8, MMYOLOBackend.TENSORRT7):
if not args.model_only:
print_log('Export ONNX with bbox decoder and NMS ...')
else:
args.model_only = True
print_log(f'Can not export postprocess for {args.backend.lower()}.\n'
f'Set "args.model_only=True" default.')
if args.model_only:
postprocess_cfg = None
output_names = None
else:
postprocess_cfg = ConfigDict(pre_top_k=args.pre_topk,
keep_top_k=args.keep_topk,
iou_threshold=args.iou_threshold,
score_threshold=args.score_threshold)
output_names = ['num_dets', 'boxes', 'scores', 'labels']
if args.without_bbox_decoder or args.without_nms:
output_names = ['scores', 'boxes']
if args.custom_text is not None and len(args.custom_text) > 0:
with open(args.custom_text) as f:
texts = json.load(f)
texts = [x[0] for x in texts]
else:
from mmdet.datasets import CocoDataset
texts = CocoDataset.METAINFO['classes']
if args.add_padding:
texts = texts + [' ']
baseModel = build_model_from_cfg(args.config, args.checkpoint, args.device)
if hasattr(baseModel, 'reparameterize'):
# reparameterize text into YOLO-World
baseModel.reparameterize([texts])
deploy_model = DeployModel(baseModel=baseModel,
backend=backend,
postprocess_cfg=postprocess_cfg,
with_nms=not args.without_nms,
without_bbox_decoder=args.without_bbox_decoder)
deploy_model.eval()
fake_input = torch.randn(args.batch_size, 3,
*args.img_size).to(args.device)
# dry run
deploy_model(fake_input)
save_onnx_path = os.path.join(
args.work_dir,
os.path.basename(args.checkpoint).replace('pth', 'onnx'))
# export onnx
with BytesIO() as f:
torch.onnx.export(deploy_model,
fake_input,
f,
input_names=['images'],
output_names=output_names,
opset_version=args.opset)
f.seek(0)
onnx_model = onnx.load(f)
onnx.checker.check_model(onnx_model)
# Fix tensorrt onnx output shape, just for view
if not args.model_only and not args.without_nms and backend in (
MMYOLOBackend.TENSORRT8, MMYOLOBackend.TENSORRT7):
shapes = [
args.batch_size, 1, args.batch_size, args.keep_topk, 4,
args.batch_size, args.keep_topk, args.batch_size,
args.keep_topk
]
for i in onnx_model.graph.output:
for j in i.type.tensor_type.shape.dim:
j.dim_param = str(shapes.pop(0))
if args.simplify:
try:
import onnxsim
onnx_model, check = onnxsim.simplify(onnx_model)
assert check, 'assert check failed'
except Exception as e:
print_log(f'Simplify failure: {e}')
onnx.save(onnx_model, save_onnx_path)
print_log(f'ONNX export success, save into {save_onnx_path}')
if __name__ == '__main__':
main()