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lib/models/YOLOP.py

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+import torch
+from torch import tensor
+import torch.nn as nn
+import sys,os
+import math
+import sys
+sys.path.append(os.getcwd())
+#sys.path.append("lib/models")
+#sys.path.append("lib/utils")
+#sys.path.append("/workspace/wh/projects/DaChuang")
+from lib.utils import initialize_weights
+# from lib.models.common2 import DepthSeperabelConv2d as Conv
+# from lib.models.common2 import SPP, Bottleneck, BottleneckCSP, Focus, Concat, Detect
+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.core.evaluate import SegmentationMetric
+from lib.utils.utils import time_synchronized
+
+"""
+MCnet_SPP = [
+[ -1, Focus, [3, 32, 3]],
+[ -1, Conv, [32, 64, 3, 2]],
+[ -1, BottleneckCSP, [64, 64, 1]],
+[ -1, Conv, [64, 128, 3, 2]],
+[ -1, BottleneckCSP, [128, 128, 3]],
+[ -1, Conv, [128, 256, 3, 2]],
+[ -1, BottleneckCSP, [256, 256, 3]],
+[ -1, Conv, [256, 512, 3, 2]],
+[ -1, SPP, [512, 512, [5, 9, 13]]],
+[ -1, BottleneckCSP, [512, 512, 1, False]],
+[ -1, Conv,[512, 256, 1, 1]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ [-1, 6], Concat, [1]],
+[ -1, BottleneckCSP, [512, 256, 1, False]],
+[ -1, Conv, [256, 128, 1, 1]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ [-1,4], Concat, [1]],
+[ -1, BottleneckCSP, [256, 128, 1, False]],
+[ -1, Conv, [128, 128, 3, 2]],
+[ [-1, 14], Concat, [1]],
+[ -1, BottleneckCSP, [256, 256, 1, False]],
+[ -1, Conv, [256, 256, 3, 2]],
+[ [-1, 10], Concat, [1]],
+[ -1, BottleneckCSP, [512, 512, 1, False]],
+# [ [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]]],
+[ [17, 20, 23], Detect,  [13, [[3,9,5,11,4,20], [7,18,6,39,12,31], [19,50,38,81,68,157]], [128, 256, 512]]],
+[ 17, Conv, [128, 64, 3, 1]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ [-1,2], Concat, [1]],
+[ -1, BottleneckCSP, [128, 64, 1, False]],
+[ -1, Conv, [64, 32, 3, 1]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ -1, Conv, [32, 16, 3, 1]],
+[ -1, BottleneckCSP, [16, 8, 1, False]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ -1, SPP, [8, 2, [5, 9, 13]]] #segmentation output
+]
+# [2,6,3,9,5,13], [7,19,11,26,17,39], [28,64,44,103,61,183]
+MCnet_0 = [
+[ -1, Focus, [3, 32, 3]],
+[ -1, Conv, [32, 64, 3, 2]],
+[ -1, BottleneckCSP, [64, 64, 1]],
+[ -1, Conv, [64, 128, 3, 2]],
+[ -1, BottleneckCSP, [128, 128, 3]],
+[ -1, Conv, [128, 256, 3, 2]],
+[ -1, BottleneckCSP, [256, 256, 3]],
+[ -1, Conv, [256, 512, 3, 2]],
+[ -1, SPP, [512, 512, [5, 9, 13]]],
+[ -1, BottleneckCSP, [512, 512, 1, False]],
+[ -1, Conv,[512, 256, 1, 1]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ [-1, 6], Concat, [1]],
+[ -1, BottleneckCSP, [512, 256, 1, False]],
+[ -1, Conv, [256, 128, 1, 1]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ [-1,4], Concat, [1]],
+[ -1, BottleneckCSP, [256, 128, 1, False]],
+[ -1, Conv, [128, 128, 3, 2]],
+[ [-1, 14], Concat, [1]],
+[ -1, BottleneckCSP, [256, 256, 1, False]],
+[ -1, Conv, [256, 256, 3, 2]],
+[ [-1, 10], Concat, [1]],
+[ -1, BottleneckCSP, [512, 512, 1, False]],
+[ [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]]], #Detect output 24
+[ 16, Conv, [128, 64, 3, 1]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ [-1,2], Concat, [1]],
+[ -1, BottleneckCSP, [128, 64, 1, False]],
+[ -1, Conv, [64, 32, 3, 1]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ -1, Conv, [32, 16, 3, 1]],
+[ -1, BottleneckCSP, [16, 8, 1, False]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ -1, Conv, [8, 2, 3, 1]], #Driving area segmentation output
+[ 16, Conv, [128, 64, 3, 1]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ [-1,2], Concat, [1]],
+[ -1, BottleneckCSP, [128, 64, 1, False]],
+[ -1, Conv, [64, 32, 3, 1]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ -1, Conv, [32, 16, 3, 1]],
+[ -1, BottleneckCSP, [16, 8, 1, False]],
+[ -1, Upsample, [None, 2, 'nearest']],
+[ -1, Conv, [8, 2, 3, 1]], #Lane line segmentation output
+]
+# The lane line and the driving area segment branches share information with each other
+MCnet_share = [
+[ -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
+[ -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]]], #Detect output 24
+[ 16, Conv, [256, 64, 3, 1]],   #25
+[ -1, Upsample, [None, 2, 'nearest']],  #26
+[ [-1,2], Concat, [1]],  #27
+[ -1, BottleneckCSP, [128, 64, 1, False]],  #28
+[ -1, Conv, [64, 32, 3, 1]],    #29
+[ -1, Upsample, [None, 2, 'nearest']],  #30
+[ -1, Conv, [32, 16, 3, 1]],    #31
+[ -1, BottleneckCSP, [16, 8, 1, False]],    #32 driving area segment neck
+[ 16, Conv, [256, 64, 3, 1]],   #33
+[ -1, Upsample, [None, 2, 'nearest']],  #34
+[ [-1,2], Concat, [1]], #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 lane line segment neck
+[ [31,39], Concat, [1]],    #41
+[ -1, Conv, [32, 8, 3, 1]],     #42    Share_Block
+[ [32,42], Concat, [1]],     #43
+[ -1, Upsample, [None, 2, 'nearest']],  #44
+[ -1, Conv, [16, 2, 3, 1]], #45 Driving area segmentation output
+[ [40,42], Concat, [1]],    #46
+[ -1, Upsample, [None, 2, 'nearest']],  #47
+[ -1, Conv, [16, 2, 3, 1]] #48Lane line segmentation output
+]
+# The lane line and the driving area segment branches without share information with each other
+MCnet_no_share = [
+[ -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
+[ -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]]], #Detect output 24
+[ 16, Conv, [256, 64, 3, 1]],   #25
+[ -1, Upsample, [None, 2, 'nearest']],  #26
+[ [-1,2], Concat, [1]],  #27
+[ -1, BottleneckCSP, [128, 64, 1, False]],  #28
+[ -1, Conv, [64, 32, 3, 1]],    #29
+[ -1, Upsample, [None, 2, 'nearest']],  #30
+[ -1, Conv, [32, 16, 3, 1]],    #31
+[ -1, BottleneckCSP, [16, 8, 1, False]],    #32 driving area segment neck
+[ -1, Upsample, [None, 2, 'nearest']],  #33
+[ -1, Conv, [8, 3, 3, 1]], #34 Driving area segmentation output
+[ 16, Conv, [256, 64, 3, 1]],   #35
+[ -1, Upsample, [None, 2, 'nearest']],  #36
+[ [-1,2], Concat, [1]], #37
+[ -1, BottleneckCSP, [128, 64, 1, False]],  #38
+[ -1, Conv, [64, 32, 3, 1]],    #39
+[ -1, Upsample, [None, 2, 'nearest']],  #40
+[ -1, Conv, [32, 16, 3, 1]],    #41
+[ -1, BottleneckCSP, [16, 8, 1, False]],    #42 lane line segment neck
+[ -1, Upsample, [None, 2, 'nearest']],  #43
+[ -1, Conv, [8, 2, 3, 1]] #44 Lane line segmentation output
+]
+MCnet_feedback = [
+[ -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
+[ -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]]], #Detect output 24
+[ 16, Conv, [256, 128, 3, 1]],   #25
+[ -1, Upsample, [None, 2, 'nearest']],  #26
+[ -1, BottleneckCSP, [128, 64, 1, False]],  #28
+[ -1, Conv, [64, 32, 3, 1]],    #29
+[ -1, Upsample, [None, 2, 'nearest']],  #30
+[ -1, Conv, [32, 16, 3, 1]],    #31
+[ -1, BottleneckCSP, [16, 8, 1, False]],    #32 driving area segment neck
+[ -1, Upsample, [None, 2, 'nearest']],  #33
+[ -1, Conv, [8, 2, 3, 1]], #34 Driving area segmentation output
+[ 16, Conv, [256, 128, 3, 1]],   #35
+[ -1, Upsample, [None, 2, 'nearest']],  #36
+[ -1, BottleneckCSP, [128, 64, 1, False]],  #38
+[ -1, Conv, [64, 32, 3, 1]],    #39
+[ -1, Upsample, [None, 2, 'nearest']],  #40
+[ -1, Conv, [32, 16, 3, 1]],    #41
+[ -1, BottleneckCSP, [16, 8, 1, False]],    #42 lane line segment neck
+[ -1, Upsample, [None, 2, 'nearest']],  #43
+[ -1, Conv, [8, 2, 3, 1]] #44 Lane line segmentation output
+]
+MCnet_Da_feedback1 = [
+[46, 26, 35],   #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     backbone+fpn
+[ -1,Conv,[256,256,1,1]],   #17
+[ 16, Conv, [256, 128, 3, 1]],   #18
+[ -1, Upsample, [None, 2, 'nearest']],  #19
+[ -1, BottleneckCSP, [128, 64, 1, False]],  #20
+[ -1, Conv, [64, 32, 3, 1]],    #21
+[ -1, Upsample, [None, 2, 'nearest']],  #22
+[ -1, Conv, [32, 16, 3, 1]],    #23
+[ -1, BottleneckCSP, [16, 8, 1, False]],    #24 driving area segment neck
+[ -1, Upsample, [None, 2, 'nearest']],  #25
+[ -1, Conv, [8, 2, 3, 1]], #26 Driving area segmentation output
+[ 16, Conv, [256, 128, 3, 1]],   #27
+[ -1, Upsample, [None, 2, 'nearest']],  #28
+[ -1, BottleneckCSP, [128, 64, 1, False]],  #29
+[ -1, Conv, [64, 32, 3, 1]],    #30
+[ -1, Upsample, [None, 2, 'nearest']],  #31
+[ -1, Conv, [32, 16, 3, 1]],    #32
+[ -1, BottleneckCSP, [16, 8, 1, False]],    #33 lane line segment neck
+[ -1, Upsample, [None, 2, 'nearest']],  #34
+[ -1, Conv, [8, 2, 3, 1]], #35Lane line segmentation output
+[ 23, Conv, [16, 16, 3, 2]],     #36
+[ -1, Conv, [16, 32, 3, 2]],    #2 times 2xdownsample    37
+[ [-1,17], Concat, [1]],       #38
+[ -1, BottleneckCSP, [288, 128, 1, False]],    #39
+[ -1, Conv, [128, 128, 3, 2]],      #40
+[ [-1, 14], Concat, [1]],       #41
+[ -1, BottleneckCSP, [256, 256, 1, False]],     #42
+[ -1, Conv, [256, 256, 3, 2]],      #43
+[ [-1, 10], Concat, [1]],   #44
+[ -1, BottleneckCSP, [512, 512, 1, False]],     #45
+[ [39, 42, 45], Detect,  [1, [[3,9,5,11,4,20], [7,18,6,39,12,31], [19,50,38,81,68,157]], [128, 256, 512]]] #Detect output 46
+]
+# The lane line and the driving area segment branches share information with each other and feedback to det_head
+MCnet_Da_feedback2 = [
+[47, 26, 35],   #Det_out_idx, Da_Segout_idx, LL_Segout_idx
+[25, 28, 31, 33],   #layer in Da_branch to do SAD
+[34, 37, 40, 42],   #layer in LL_branch to do SAD
+[ -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     backbone+fpn
+[ -1,Conv,[256,256,1,1]],   #17
+[ 16, Conv, [256, 128, 3, 1]],   #18
+[ -1, Upsample, [None, 2, 'nearest']],  #19
+[ -1, BottleneckCSP, [128, 64, 1, False]],  #20
+[ -1, Conv, [64, 32, 3, 1]],    #21
+[ -1, Upsample, [None, 2, 'nearest']],  #22
+[ -1, Conv, [32, 16, 3, 1]],    #23
+[ -1, BottleneckCSP, [16, 8, 1, False]],    #24 driving area segment neck
+[ -1, Upsample, [None, 2, 'nearest']],  #25
+[ -1, Conv, [8, 2, 3, 1]], #26 Driving area segmentation output
+[ 16, Conv, [256, 128, 3, 1]],   #27
+[ -1, Upsample, [None, 2, 'nearest']],  #28
+[ -1, BottleneckCSP, [128, 64, 1, False]],  #29
+[ -1, Conv, [64, 32, 3, 1]],    #30
+[ -1, Upsample, [None, 2, 'nearest']],  #31
+[ -1, Conv, [32, 16, 3, 1]],    #32
+[ -1, BottleneckCSP, [16, 8, 1, False]],    #33 lane line segment neck
+[ -1, Upsample, [None, 2, 'nearest']],  #34
+[ -1, Conv, [8, 2, 3, 1]], #35Lane line segmentation output
+[ 23, Conv, [16, 64, 3, 2]],     #36
+[ -1, Conv, [64, 256, 3, 2]],    #2 times 2xdownsample    37
+[ [-1,17], Concat, [1]],       #38
+[-1, Conv, [512, 256, 3, 1]],     #39
+[ -1, BottleneckCSP, [256, 128, 1, False]],    #40
+[ -1, Conv, [128, 128, 3, 2]],      #41
+[ [-1, 14], Concat, [1]],       #42
+[ -1, BottleneckCSP, [256, 256, 1, False]],     #43
+[ -1, Conv, [256, 256, 3, 2]],      #44
+[ [-1, 10], Concat, [1]],   #45
+[ -1, BottleneckCSP, [512, 512, 1, False]],     #46
+[ [40, 42, 45], Detect,  [1, [[3,9,5,11,4,20], [7,18,6,39,12,31], [19,50,38,81,68,157]], [128, 256, 512]]] #Detect output 47
+]
+MCnet_share1 = [
+[24, 33, 45],   #Det_out_idx, Da_Segout_idx, LL_Segout_idx
+[25, 28, 31, 33],   #layer in Da_branch to do SAD
+[34, 37, 40, 42],   #layer in LL_branch to do SAD
+[ -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
+[ -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]]], #Detect output 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 driving area segment neck
+[ -1, Upsample, [None, 2, 'nearest']],  #32
+[ -1, Conv, [8, 2, 3, 1]], #33 Driving area segmentation output
+[ 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
+[ 30, SharpenConv, [16,16, 3, 1]], #40
+[ -1, Conv, [16, 16, 3, 1]], #41
+[ [-1, 39], Concat, [1]],   #42
+[ -1, BottleneckCSP, [32, 8, 1, False]],    #43 lane line segment neck
+[ -1, Upsample, [None, 2, 'nearest']],  #44
+[ -1, Conv, [8, 2, 3, 1]] #45 Lane line segmentation output
+]"""
+
+
+# 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, **kwargs):
+        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:]
+        
+
+        # 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
+        Da_fmap = []
+        LL_fmap = []
+        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))
+            if i == self.detector_index:
+                det_out = x
+            cache.append(x if block.index in self.save else None)
+        out.insert(0,det_out)
+        return out
+            
+    
+    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.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
+            b.data[:, 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)
+
+def get_net(cfg, **kwargs): 
+    m_block_cfg = YOLOP
+    model = MCnet(m_block_cfg, **kwargs)
+    return model
+
+
+if __name__ == "__main__":
+    from torch.utils.tensorboard import SummaryWriter
+    model = get_net(False)
+    input_ = torch.randn((1, 3, 256, 256))
+    gt_ = torch.rand((1, 2, 256, 256))
+    metric = SegmentationMetric(2)
+    model_out,SAD_out = model(input_)
+    detects, dring_area_seg, lane_line_seg = model_out
+    Da_fmap, LL_fmap = SAD_out
+    for det in detects:
+        print(det.shape)
+    print(dring_area_seg.shape)
+    print(lane_line_seg.shape)