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61d360d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | import torch
import torch.nn as nn
from pretrainedmodels import inceptionresnetv2
from torchsummary import summary
import torch.nn.functional as F
class FPNHead(nn.Module):
def __init__(self, num_in, num_mid, num_out):
super().__init__()
self.block0 = nn.Conv2d(num_in, num_mid, kernel_size=3, padding=1, bias=False)
self.block1 = nn.Conv2d(num_mid, num_out, kernel_size=3, padding=1, bias=False)
def forward(self, x):
x = nn.functional.relu(self.block0(x), inplace=True)
x = nn.functional.relu(self.block1(x), inplace=True)
return x
class ConvBlock(nn.Module):
def __init__(self, num_in, num_out, norm_layer):
super().__init__()
self.block = nn.Sequential(nn.Conv2d(num_in, num_out, kernel_size=3, padding=1),
norm_layer(num_out),
nn.ReLU(inplace=True))
def forward(self, x):
x = self.block(x)
return x
class FPNInception(nn.Module):
def __init__(self, norm_layer, output_ch=3, num_filters=128, num_filters_fpn=256):
super().__init__()
# Feature Pyramid Network (FPN) with four feature maps of resolutions
# 1/4, 1/8, 1/16, 1/32 and `num_filters` filters for all feature maps.
self.fpn = FPN(num_filters=num_filters_fpn, norm_layer=norm_layer)
# The segmentation heads on top of the FPN
self.head1 = FPNHead(num_filters_fpn, num_filters, num_filters)
self.head2 = FPNHead(num_filters_fpn, num_filters, num_filters)
self.head3 = FPNHead(num_filters_fpn, num_filters, num_filters)
self.head4 = FPNHead(num_filters_fpn, num_filters, num_filters)
self.smooth = nn.Sequential(
nn.Conv2d(4 * num_filters, num_filters, kernel_size=3, padding=1),
norm_layer(num_filters),
nn.ReLU(),
)
self.smooth2 = nn.Sequential(
nn.Conv2d(num_filters, num_filters // 2, kernel_size=3, padding=1),
norm_layer(num_filters // 2),
nn.ReLU(),
)
self.final = nn.Conv2d(num_filters // 2, output_ch, kernel_size=3, padding=1)
def unfreeze(self):
self.fpn.unfreeze()
def forward(self, x):
map0, map1, map2, map3, map4 = self.fpn(x)
map4 = nn.functional.upsample(self.head4(map4), scale_factor=8, mode="nearest")
map3 = nn.functional.upsample(self.head3(map3), scale_factor=4, mode="nearest")
map2 = nn.functional.upsample(self.head2(map2), scale_factor=2, mode="nearest")
map1 = nn.functional.upsample(self.head1(map1), scale_factor=1, mode="nearest")
smoothed = self.smooth(torch.cat([map4, map3, map2, map1], dim=1))
smoothed = nn.functional.upsample(smoothed, scale_factor=2, mode="nearest")
smoothed = self.smooth2(smoothed + map0)
smoothed = nn.functional.upsample(smoothed, scale_factor=2, mode="nearest")
final = self.final(smoothed)
res = torch.tanh(final) + x
return torch.clamp(res, min = -1,max = 1)
class FPN(nn.Module):
def __init__(self, norm_layer, num_filters=256):
"""Creates an `FPN` instance for feature extraction.
Args:
num_filters: the number of filters in each output pyramid level
pretrained: use ImageNet pre-trained backbone feature extractor
"""
super().__init__()
self.inception = inceptionresnetv2(num_classes=1000, pretrained='imagenet')
self.enc0 = self.inception.conv2d_1a
self.enc1 = nn.Sequential(
self.inception.conv2d_2a,
self.inception.conv2d_2b,
self.inception.maxpool_3a,
) # 64
self.enc2 = nn.Sequential(
self.inception.conv2d_3b,
self.inception.conv2d_4a,
self.inception.maxpool_5a,
) # 192
self.enc3 = nn.Sequential(
self.inception.mixed_5b,
self.inception.repeat,
self.inception.mixed_6a,
) # 1088
self.enc4 = nn.Sequential(
self.inception.repeat_1,
self.inception.mixed_7a,
) #2080
self.td1 = nn.Sequential(nn.Conv2d(num_filters, num_filters, kernel_size=3, padding=1),
norm_layer(num_filters),
nn.ReLU(inplace=True))
self.td2 = nn.Sequential(nn.Conv2d(num_filters, num_filters, kernel_size=3, padding=1),
norm_layer(num_filters),
nn.ReLU(inplace=True))
self.td3 = nn.Sequential(nn.Conv2d(num_filters, num_filters, kernel_size=3, padding=1),
norm_layer(num_filters),
nn.ReLU(inplace=True))
self.pad = nn.ReflectionPad2d(1)
self.lateral4 = nn.Conv2d(2080, num_filters, kernel_size=1, bias=False)
self.lateral3 = nn.Conv2d(1088, num_filters, kernel_size=1, bias=False)
self.lateral2 = nn.Conv2d(192, num_filters, kernel_size=1, bias=False)
self.lateral1 = nn.Conv2d(64, num_filters, kernel_size=1, bias=False)
self.lateral0 = nn.Conv2d(32, num_filters // 2, kernel_size=1, bias=False)
for param in self.inception.parameters():
param.requires_grad = False
def unfreeze(self):
for param in self.inception.parameters():
param.requires_grad = True
def forward(self, x):
# Bottom-up pathway, from ResNet
enc0 = self.enc0(x)
enc1 = self.enc1(enc0) # 256
enc2 = self.enc2(enc1) # 512
enc3 = self.enc3(enc2) # 1024
enc4 = self.enc4(enc3) # 2048
# Lateral connections
lateral4 = self.pad(self.lateral4(enc4))
lateral3 = self.pad(self.lateral3(enc3))
lateral2 = self.lateral2(enc2)
lateral1 = self.pad(self.lateral1(enc1))
lateral0 = self.lateral0(enc0)
# Top-down pathway
pad = (1, 2, 1, 2) # pad last dim by 1 on each side
pad1 = (0, 1, 0, 1)
map4 = lateral4
map3 = self.td1(lateral3 + nn.functional.upsample(map4, scale_factor=2, mode="nearest"))
map2 = self.td2(F.pad(lateral2, pad, "reflect") + nn.functional.upsample(map3, scale_factor=2, mode="nearest"))
map1 = self.td3(lateral1 + nn.functional.upsample(map2, scale_factor=2, mode="nearest"))
return F.pad(lateral0, pad1, "reflect"), map1, map2, map3, map4
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