全景驾驶感知网络YOLOP部署与实现（交通目标检测可驾驶区域分割车道线检测）

Posted 2022-02-24 踟蹰横渡口,彳亍上滩舟。

全景驾驶感知网络YOLOP部署与实现（交通目标检测、可驾驶区域分割、车道线检测）

项目下载地址

包含C++和Python两种版本的程序实现：下载地址

YOLOP开源项目：

https://github.com/hustvl/YOLOP

ONNX导出：

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
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
    [-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

    [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
        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
        # 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
    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
            cache.append(x if block.index in self.save else None)
        out[0] = out[0].view(2, 640, 640)
        out[1] = out[1].view(2, 640, 640)
        return det_out, out[0], out[1]

if __name__ == "__main__":
    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()
    output_onnx = 'yolop.onnx'
    inputs = torch.randn(1, 3, 640, 640)
    # with torch.no_grad():
    #     output = model(inputs)
    # print(output)

    torch.onnx.export(model, inputs, output_onnx, verbose=False, opset_version=12, input_names=['images'], output_names=['det_out', 'drive_area_seg', 'lane_line_seg'])
    print('convert', output_onnx, 'to onnx finish!!!')

    try:
        dnnnet = cv2.dnn.readNet(output_onnx)
        print('read sucess')
    except:
        print('read failed')