File size: 4,947 Bytes
039daa1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import segmentation_models_pytorch as smp
class SegformerBranch(nn.Module):
def __init__(self, in_channels=4, classes=4):
super(SegformerBranch, self).__init__()
self.segformer = smp.Segformer(
encoder_name="mobilenet_v2",
encoder_weights=None,
in_channels=in_channels,
classes=classes,
)
def forward(self, x):
return self.segformer(x)
class UNetBranch(nn.Module):
def __init__(self, in_channels=4, classes=4, benchmark=False):
super(UNetBranch, self).__init__()
self.unet = smp.Unet(
encoder_name="mobilenet_v2",
encoder_weights=None,
in_channels=in_channels,
classes=classes,
)
self.benchmark = benchmark
def forward(self, x):
results = self.unet(x)
if self.benchmark:
results = torch.sigmoid(results)
return results
class UNetPlusPlusBranch(nn.Module):
def __init__(self, in_channels=4, classes=4, benchmark=False):
super(UNetPlusPlusBranch, self).__init__()
self.unet_pp = smp.UnetPlusPlus(
encoder_name="mobilenet_v2",
encoder_weights=None,
in_channels=in_channels,
classes=classes
)
self.benchmark = benchmark
def forward(self, x):
results = self.unet_pp(x)
if self.benchmark:
results = torch.sigmoid(results)
return results
class DeepLabV3Branch(nn.Module):
def __init__(self, in_channels=4, classes=4):
super(DeepLabV3Branch, self).__init__()
self.deeplabv3 = smp.DeepLabV3(
encoder_name="mobilenet_v2",
encoder_weights=None,
in_channels=in_channels,
classes=classes,
)
def forward(self, x):
return self.deeplabv3(x)
class PixelWiseNet(nn.Module):
def __init__(self, in_channels=4, out_channels=4, base_channels=32):
super(PixelWiseNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels, base_channels, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(base_channels)
self.conv2 = nn.Conv2d(base_channels, base_channels, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(base_channels)
self.conv3 = nn.Conv2d(base_channels, out_channels, kernel_size=1, bias=False)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = self.conv3(x)
return x
class CombinedNet(nn.Module):
def __init__(self, in_channels=4, classes=4, base_channels=32, benchmark=False):
super(CombinedNet, self).__init__()
self.seg_branch = SegformerBranch(in_channels=in_channels, classes=classes)
self.pixel_branch = PixelWiseNet(in_channels=in_channels, out_channels=classes, base_channels=base_channels)
self.fusion_conv = nn.Conv2d(classes, classes, kernel_size=1, bias=False)
self.benchmark = benchmark
def forward(self, x):
seg_out = self.seg_branch(x)
pixel_out = self.pixel_branch(x)
fused = seg_out + pixel_out
out = self.fusion_conv(fused)
if self.benchmark:
out = torch.sigmoid(out)
return out
class CombinedNet3(nn.Module):
def __init__(self, in_channels=4, classes=4, base_channels=32, benchmark=False):
super(CombinedNet3, self).__init__()
self.seg_branch = UNetPlusPlusBranch(in_channels=in_channels, classes=classes)
self.pixel_branch = PixelWiseNet(
in_channels=in_channels,
out_channels=classes,
base_channels=base_channels,
)
self.fusion_conv = nn.Conv2d(classes, classes, kernel_size=1, bias=False)
self.benchmark = benchmark
def forward(self, x):
seg_out = self.seg_branch(x)
pixel_out = self.pixel_branch(x)
fused = seg_out + pixel_out
out = self.fusion_conv(fused)
if self.benchmark:
out = torch.sigmoid(out)
return out
class CombinedNet4(nn.Module):
def __init__(self, in_channels=4, classes=4, base_channels=32, benchmark=False):
super(CombinedNet4, self).__init__()
self.seg_branch = DeepLabV3Branch(in_channels=in_channels, classes=classes)
self.pixel_branch = PixelWiseNet(
in_channels=in_channels,
out_channels=classes,
base_channels=base_channels,
)
self.fusion_conv = nn.Conv2d(classes, classes, kernel_size=1, bias=False)
self.benchmark = benchmark
def forward(self, x):
seg_out = self.seg_branch(x)
pixel_out = self.pixel_branch(x)
fused = seg_out + pixel_out
out = self.fusion_conv(fused)
if self.benchmark:
out= torch.sigmoid(out)
return out
|