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export_onnx.py
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export_onnx.py
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
import argparse
import time
import sys
import os
import torch
import torch.nn as nn
import onnx
ROOT = os.getcwd()
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT))
from yolov6.models.yolo import *
from yolov6.models.effidehead import Detect
from yolov6.layers.common import *
from yolov6.utils.events import LOGGER
from yolov6.utils.checkpoint import load_checkpoint
from io import BytesIO
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='./yolov6s.pt', help='weights path')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size, the order is: height width') # height, width
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
parser.add_argument('--inplace', action='store_true', help='set Detect() inplace=True')
parser.add_argument('--simplify', action='store_true', help='simplify onnx model')
parser.add_argument('--dynamic-batch', action='store_true', help='export dynamic batch onnx model')
parser.add_argument('--end2end', action='store_true', help='export end2end onnx')
parser.add_argument('--trt-version', type=int, default=8, help='tensorrt version')
parser.add_argument('--ort', action='store_true', help='export onnx for onnxruntime')
parser.add_argument('--with-preprocess', action='store_true', help='export bgr2rgb and normalize')
parser.add_argument('--topk-all', type=int, default=100, help='topk objects for every images')
parser.add_argument('--iou-thres', type=float, default=0.65, help='iou threshold for NMS')
parser.add_argument('--conf-thres', type=float, default=0.5, help='conf threshold for NMS')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
args = parser.parse_args()
args.img_size *= 2 if len(args.img_size) == 1 else 1 # expand
print(args)
t = time.time()
# Check device
cuda = args.device != 'cpu' and torch.cuda.is_available()
device = torch.device(f'cuda:{args.device}' if cuda else 'cpu')
assert not (device.type == 'cpu' and args.half), '--half only compatible with GPU export, i.e. use --device 0'
# Load PyTorch model
model = load_checkpoint(args.weights, map_location=device, inplace=True, fuse=True) # load FP32 model
for layer in model.modules():
if isinstance(layer, RepVGGBlock):
layer.switch_to_deploy()
elif isinstance(layer, nn.Upsample) and not hasattr(layer, 'recompute_scale_factor'):
layer.recompute_scale_factor = None # torch 1.11.0 compatibility
# Input
img = torch.zeros(args.batch_size, 3, *args.img_size).to(device) # image size(1,3,320,192) iDetection
# Update model
if args.half:
img, model = img.half(), model.half() # to FP16
model.eval()
for k, m in model.named_modules():
if isinstance(m, ConvModule): # assign export-friendly activations
if hasattr(m, 'act') and isinstance(m.act, nn.SiLU):
m.act = SiLU()
elif isinstance(m, Detect):
m.inplace = args.inplace
dynamic_axes = None
if args.dynamic_batch:
args.batch_size = 'batch'
dynamic_axes = {
'images' :{
0:'batch',
},}
if args.end2end:
output_axes = {
'num_dets': {0: 'batch'},
'det_boxes': {0: 'batch'},
'det_scores': {0: 'batch'},
'det_classes': {0: 'batch'},
}
else:
output_axes = {
'outputs': {0: 'batch'},
}
dynamic_axes.update(output_axes)
if args.end2end:
from yolov6.models.end2end import End2End
model = End2End(model, max_obj=args.topk_all, iou_thres=args.iou_thres,score_thres=args.conf_thres,
device=device, ort=args.ort, trt_version=args.trt_version, with_preprocess=args.with_preprocess)
print("===================")
print(model)
print("===================")
y = model(img) # dry run
# ONNX export
try:
LOGGER.info('\nStarting to export ONNX...')
export_file = args.weights.replace('.pt', '.onnx') # filename
with BytesIO() as f:
torch.onnx.export(model, img, f, verbose=False, opset_version=13,
training=torch.onnx.TrainingMode.EVAL,
do_constant_folding=True,
input_names=['images'],
output_names=['num_dets', 'det_boxes', 'det_scores', 'det_classes']
if args.end2end else ['outputs'],
dynamic_axes=dynamic_axes)
f.seek(0)
# Checks
onnx_model = onnx.load(f) # load onnx model
onnx.checker.check_model(onnx_model) # check onnx model
# Fix output shape
if args.end2end and not args.ort:
shapes = [args.batch_size, 1, args.batch_size, args.topk_all, 4,
args.batch_size, args.topk_all, args.batch_size, args.topk_all]
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
LOGGER.info('\nStarting to simplify ONNX...')
onnx_model, check = onnxsim.simplify(onnx_model)
assert check, 'assert check failed'
except Exception as e:
LOGGER.info(f'Simplifier failure: {e}')
onnx.save(onnx_model, export_file)
LOGGER.info(f'ONNX export success, saved as {export_file}')
except Exception as e:
LOGGER.info(f'ONNX export failure: {e}')
# Finish
LOGGER.info('\nExport complete (%.2fs)' % (time.time() - t))
if args.end2end:
if not args.ort:
info = f'trtexec --onnx={export_file} --saveEngine={export_file.replace(".onnx",".engine")}'
if args.dynamic_batch:
LOGGER.info('Dynamic batch export should define min/opt/max batchsize\n'+
'We set min/opt/max = 1/16/32 default!')
wandh = 'x'.join(list(map(str,args.img_size)))
info += (f' --minShapes=images:1x3x{wandh}'+
f' --optShapes=images:16x3x{wandh}'+
f' --maxShapes=images:32x3x{wandh}'+
f' --shapes=images:16x3x{wandh}')
LOGGER.info('\nYou can export tensorrt engine use trtexec tools.\nCommand is:')
LOGGER.info(info)