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export_onnx.py
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export_onnx.py
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import torch
import torch.nn as nn
from lib.models.common import Conv, SPP, Bottleneck, BottleneckCSP, Focus, Concat, Detect, SharpenConv
from torch.nn import Upsample
from lib.utils import check_anchor_order
from lib.utils import initialize_weights
import argparse
import onnx
import onnxruntime as ort
import onnxsim
import math
import cv2
# The lane line and the driving area segment branches without share information with each other and without link
YOLOP = [
[24, 33, 42], # Det_out_idx, Da_Segout_idx, LL_Segout_idx
[-1, Focus, [3, 32, 3]], # 0
[-1, Conv, [32, 64, 3, 2]], # 1
[-1, BottleneckCSP, [64, 64, 1]], # 2
[-1, Conv, [64, 128, 3, 2]], # 3
[-1, BottleneckCSP, [128, 128, 3]], # 4
[-1, Conv, [128, 256, 3, 2]], # 5
[-1, BottleneckCSP, [256, 256, 3]], # 6
[-1, Conv, [256, 512, 3, 2]], # 7
[-1, SPP, [512, 512, [5, 9, 13]]], # 8 SPP
[-1, BottleneckCSP, [512, 512, 1, False]], # 9
[-1, Conv, [512, 256, 1, 1]], # 10
[-1, Upsample, [None, 2, 'nearest']], # 11
[[-1, 6], Concat, [1]], # 12
[-1, BottleneckCSP, [512, 256, 1, False]], # 13
[-1, Conv, [256, 128, 1, 1]], # 14
[-1, Upsample, [None, 2, 'nearest']], # 15
[[-1, 4], Concat, [1]], # 16 #Encoder
[-1, BottleneckCSP, [256, 128, 1, False]], # 17
[-1, Conv, [128, 128, 3, 2]], # 18
[[-1, 14], Concat, [1]], # 19
[-1, BottleneckCSP, [256, 256, 1, False]], # 20
[-1, Conv, [256, 256, 3, 2]], # 21
[[-1, 10], Concat, [1]], # 22
[-1, BottleneckCSP, [512, 512, 1, False]], # 23
[[17, 20, 23], Detect,
[1, [[3, 9, 5, 11, 4, 20], [7, 18, 6, 39, 12, 31], [19, 50, 38, 81, 68, 157]], [128, 256, 512]]],
# Detection head 24: from_(features from specific layers), block, nc(num_classes) anchors ch(channels)
[16, Conv, [256, 128, 3, 1]], # 25
[-1, Upsample, [None, 2, 'nearest']], # 26
[-1, BottleneckCSP, [128, 64, 1, False]], # 27
[-1, Conv, [64, 32, 3, 1]], # 28
[-1, Upsample, [None, 2, 'nearest']], # 29
[-1, Conv, [32, 16, 3, 1]], # 30
[-1, BottleneckCSP, [16, 8, 1, False]], # 31
[-1, Upsample, [None, 2, 'nearest']], # 32
[-1, Conv, [8, 2, 3, 1]], # 33 Driving area segmentation head
[16, Conv, [256, 128, 3, 1]], # 34
[-1, Upsample, [None, 2, 'nearest']], # 35
[-1, BottleneckCSP, [128, 64, 1, False]], # 36
[-1, Conv, [64, 32, 3, 1]], # 37
[-1, Upsample, [None, 2, 'nearest']], # 38
[-1, Conv, [32, 16, 3, 1]], # 39
[-1, BottleneckCSP, [16, 8, 1, False]], # 40
[-1, Upsample, [None, 2, 'nearest']], # 41
[-1, Conv, [8, 2, 3, 1]] # 42 Lane line segmentation head
]
class MCnet(nn.Module):
def __init__(self, block_cfg):
super(MCnet, self).__init__()
layers, save = [], []
self.nc = 1 # traffic or not
self.detector_index = -1
self.det_out_idx = block_cfg[0][0]
self.seg_out_idx = block_cfg[0][1:]
self.num_anchors = 3
self.num_outchannel = 5 + self.nc # dx,dy,dw,dh,obj_conf+cls_conf
# Build model
for i, (from_, block, args) in enumerate(block_cfg[1:]):
block = eval(block) if isinstance(block, str) else block # eval strings
if block is Detect:
self.detector_index = i
block_ = block(*args)
block_.index, block_.from_ = i, from_
layers.append(block_)
save.extend(x % i for x in ([from_] if isinstance(from_, int) else from_) if x != -1) # append to savelist
assert self.detector_index == block_cfg[0][0]
self.model, self.save = nn.Sequential(*layers), sorted(save)
self.names = [str(i) for i in range(self.nc)]
# set stride、anchor for detector
Detector = self.model[self.detector_index] # detector
if isinstance(Detector, Detect):
s = 128 # 2x min stride
# for x in self.forward(torch.zeros(1, 3, s, s)):
# print (x.shape)
with torch.no_grad():
model_out = self.forward(torch.zeros(1, 3, s, s))
detects, _, _ = model_out
Detector.stride = torch.tensor([s / x.shape[-2] for x in detects]) # forward
# print("stride"+str(Detector.stride ))
Detector.anchors /= Detector.stride.view(-1, 1, 1) # Set the anchors for the corresponding scale
check_anchor_order(Detector)
self.stride = Detector.stride
# self._initialize_biases()
initialize_weights(self)
def forward(self, x):
cache = []
out = []
det_out = None
for i, block in enumerate(self.model):
if block.from_ != -1:
x = cache[block.from_] if isinstance(block.from_, int) \
else [x if j == -1 else cache[j] for j in
block.from_] # calculate concat detect
x = block(x)
if i in self.seg_out_idx: # save driving area segment result
# m = nn.Sigmoid()
# out.append(m(x))
out.append(torch.sigmoid(x))
if i == self.detector_index:
# det_out = x
if self.training:
det_out = x
else:
det_out = x[0] # (torch.cat(z, 1), input_feat) if test
cache.append(x if block.index in self.save else None)
return det_out, out[0], out[1] # det, da, ll
# (1,na*ny*nx*nl,no=2+2+1+nc=xy+wh+obj_conf+cls_prob), (1,2,h,w) (1,2,h,w)
def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
# https://arxiv.org/abs/1708.02002 section 3.3
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
# m = self.model[-1] # Detect() module
m = self.model[self.detector_index] # Detect() module
for mi, s in zip(m.m, m.stride): # from
b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls
mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--height', type=int, default=640) # height
parser.add_argument('--width', type=int, default=640) # width
args = parser.parse_args()
do_simplify = True
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = MCnet(YOLOP)
checkpoint = torch.load('./weights/End-to-end.pth', map_location=device)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
height = args.height
width = args.width
print("Load ./weights/End-to-end.pth done!")
onnx_path = f'./weights/yolop-{height}-{width}.onnx'
inputs = torch.randn(1, 3, height, width)
print(f"Converting to {onnx_path}")
torch.onnx.export(model, inputs, onnx_path,
verbose=False, opset_version=12, input_names=['images'],
output_names=['det_out', 'drive_area_seg', 'lane_line_seg'])
print('convert', onnx_path, 'to onnx finish!!!')
# Checks
model_onnx = onnx.load(onnx_path) # load onnx model
onnx.checker.check_model(model_onnx) # check onnx model
print(onnx.helper.printable_graph(model_onnx.graph)) # print
if do_simplify:
print(f'simplifying with onnx-simplifier {onnxsim.__version__}...')
model_onnx, check = onnxsim.simplify(model_onnx, check_n=3)
assert check, 'assert check failed'
onnx.save(model_onnx, onnx_path)
x = inputs.cpu().numpy()
try:
sess = ort.InferenceSession(onnx_path)
for ii in sess.get_inputs():
print("Input: ", ii)
for oo in sess.get_outputs():
print("Output: ", oo)
print('read onnx using onnxruntime sucess')
except Exception as e:
print('read failed')
raise e
"""
PYTHONPATH=. python3 ./export_onnx.py --height 640 --width 640
PYTHONPATH=. python3 ./export_onnx.py --height 1280 --width 1280
PYTHONPATH=. python3 ./export_onnx.py --height 320 --width 320
"""