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YOLOP / lib /models /light.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())
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
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
CSPDarknet_s = [
[ -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]]
]
# MCnet = [
# [ -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, 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]] #segmentation output
# ]
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_fast = [
[ -1, Focus, [3, 32, 3]],#0
[ -1, Conv, [32, 64, 3, 2]],#1
[ -1, BottleneckCSP, [64, 128, 1, True, True]],#2
[ -1, BottleneckCSP, [128, 256, 1, True, True]],#4
[ -1, BottleneckCSP, [256, 512, 1, True, True]],#6
[ -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, True]],#17
[ [-1, 14], Concat, [1]],#19
[ -1, BottleneckCSP, [256, 256, 1, False, True]],#20
[ [-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, 32, 1, False]],#28
# [ -1, Conv, [64, 32, 1, 1]],#29
[ -1, Upsample, [None, 2, 'nearest']],#30
# [ -1, Conv, [32, 16, 1, 1]],#31
[ -1, BottleneckCSP, [32, 8, 1, False]],#32
[ -1, Upsample, [None, 2, 'nearest']],#33
[ -1, Conv, [8, 2, 1, 1]], #Driving area segmentation output#34
[ 16, Conv, [256, 64, 3, 1]],
[ -1, Upsample, [None, 2, 'nearest']],
[ [-1,2], Concat, [1]],
[ -1, BottleneckCSP, [128, 32, 1, False]],
# [ -1, Conv, [64, 32, 1, 1]],
[ -1, Upsample, [None, 2, 'nearest']],
# [ -1, Conv, [32, 16, 1, 1]],
[ 31, BottleneckCSP, [32, 8, 1, False]],#35
[ -1, Upsample, [None, 2, 'nearest']],#36
[ -1, Conv, [8, 2, 1, 1]], #Lane line segmentation output #37
]
MCnet_light = [
[ -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, [[4,12,6,18,10,27], [15,38,24,59,39,78], [51,125,73,168,97,292]], [128, 256, 512]]], #Detect output 24
[ 16, Conv, [256, 128, 3, 1]],#25
[ -1, Upsample, [None, 2, 'nearest']],#26
# [ [-1,2], Concat, [1]],#27
[ -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, 3, 3, 1]], #Driving area segmentation output#33
# [ 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]],
[ 30, BottleneckCSP, [16, 8, 1, False]],#34
[ -1, Upsample, [None, 2, 'nearest']],#35
[ -1, Conv, [8, 2, 3, 1]], #Lane line segmentation output #36
]
# 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, [13, [[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
]
class MCnet(nn.Module):
def __init__(self, block_cfg, **kwargs):
super(MCnet, self).__init__()
layers, save= [], []
self.nc = 13
self.detector_index = -1
self.Da_out_idx = 45 if len(block_cfg)==49 else 34
# self.Da_out_idx = 37
# Build model
# print(block_cfg)
for i, (from_, block, args) in enumerate(block_cfg):
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
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():
detects, _, _= self.forward(torch.zeros(1, 3, s, s))
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 = []
#times = []
for i, block in enumerate(self.model):
#t0 = time_synchronized()
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 isinstance(block, Detect): # save detector result
out.append(x)
if i == self.Da_out_idx: #save driving area segment result
m=nn.Sigmoid()
out.append(m(x))
cache.append(x if block.index in self.save else None)
"""t1 = time_synchronized()
print(str(i) + " : " + str(t1-t0))
times.append(t1-t0)
print(sum(times[:25]))
print(sum(times[25:33]))
print(sum(times[33:41]))
print(sum(times[41:43]))
print(sum(times[43:46]))
print(sum(times[46:]))"""
m=nn.Sigmoid()
out.append(m(x))
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[:, 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)
class CSPDarknet(nn.Module):
def __init__(self, block_cfg, **kwargs):
super(CSPDarknet, self).__init__()
layers, save= [], []
# self.nc = 13 #output category num
self.nc = 1
self.detector_index = -1
# Build model
for i, (from_, block, args) in enumerate(block_cfg):
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
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():
detects, _ = self.forward(torch.zeros(1, 3, s, s))
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 = []
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
start = time.time()
x = block(x)
end = time.time()
print(start-end)
"""y = None if isinstance(x, list) else x.shape"""
if isinstance(block, Detect): # save detector result
out.append(x)
cache.append(x if block.index in self.save else None)
m=nn.Sigmoid()
out.append(m(x))
# out.append(x)
# print(out[0][0].shape, out[0][1].shape, out[0][2].shape)
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[:, 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)
def get_net(cfg, **kwargs):
# m_block_cfg = MCnet_share if cfg.MODEL.STRU_WITHSHARE else MCnet_no_share
m_block_cfg = MCnet_no_share
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)
detects, dring_area_seg, lane_line_seg = model(input_)
for det in detects:
print(det.shape)
print(dring_area_seg.shape)
print(dring_area_seg.view(-1).shape)
_,predict=torch.max(dring_area_seg, 1)
print(predict.shape)
print(lane_line_seg.shape)
_,lane_line_pred=torch.max(lane_line_seg, 1)
_,lane_line_gt=torch.max(gt_, 1)
metric.reset()
metric.addBatch(lane_line_pred.cpu(), lane_line_gt.cpu())
acc = metric.pixelAccuracy()
meanAcc = metric.meanPixelAccuracy()
mIoU = metric.meanIntersectionOverUnion()
FWIoU = metric.Frequency_Weighted_Intersection_over_Union()
IoU = metric.IntersectionOverUnion()
print(IoU)
print(mIoU)