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69bf713 07a087d 69bf713 07a087d 69bf713 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | import torch
import torch.nn as nn
from resnet import resnet50
import numpy as np
import cv2
def save_feats_mean(x, size=(256, 256)):
b, c, h, w = x.shape
with torch.no_grad():
x = x.detach().cpu().numpy()
x = np.transpose(x[0], (1, 2, 0))
x = np.mean(x, axis=-1)
x = x/np.max(x)
x = x * 255.0
x = x.astype(np.uint8)
if h != size[1]:
x = cv2.resize(x, size)
x = cv2.applyColorMap(x, cv2.COLORMAP_JET)
x = np.array(x, dtype=np.uint8)
return x
def get_mean_attention_map(x):
x = torch.mean(x, axis=1)
x = torch.unsqueeze(x, 1)
x = x / torch.max(x)
return x
class ResidualBlock(nn.Module):
def __init__(self, in_c, out_c):
super().__init__()
self.relu = nn.ReLU()
self.conv = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
nn.BatchNorm2d(out_c),
nn.ReLU(),
nn.Conv2d(out_c, out_c, kernel_size=3, padding=1),
nn.BatchNorm2d(out_c)
)
self.shortcut = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=1, padding=0),
nn.BatchNorm2d(out_c)
)
def forward(self, inputs):
x1 = self.conv(inputs)
x2 = self.shortcut(inputs)
x = self.relu(x1 + x2)
return x
class DilatedConv(nn.Module):
def __init__(self, in_c, out_c):
super().__init__()
self.c1 = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=3, padding=1, dilation=1),
nn.BatchNorm2d(out_c),
nn.ReLU()
)
self.c2 = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=3, padding=3, dilation=3),
nn.BatchNorm2d(out_c),
nn.ReLU()
)
self.c3 = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=3, padding=6, dilation=6),
nn.BatchNorm2d(out_c),
nn.ReLU()
)
self.c4 = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=3, padding=9, dilation=9),
nn.BatchNorm2d(out_c),
nn.ReLU()
)
self.c5 = nn.Sequential(
nn.Conv2d(out_c*4, out_c, kernel_size=1, padding=0),
nn.BatchNorm2d(out_c),
nn.ReLU()
)
def forward(self, inputs):
x1 = self.c1(inputs)
x2 = self.c2(inputs)
x3 = self.c3(inputs)
x4 = self.c4(inputs)
x = torch.cat([x1, x2, x3, x4], axis=1)
x = self.c5(x)
return x
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=16):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(in_planes, in_planes // 16, 1, bias=False)
self.relu1 = nn.ReLU()
self.fc2 = nn.Conv2d(in_planes // 16, in_planes, 1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x0 = x
avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
out = avg_out + max_out
return x0 * self.sigmoid(out)
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x0 = x
avg_out = torch.mean(x, dim=1, keepdim=True)
max_out, _ = torch.max(x, dim=1, keepdim=True)
x = torch.cat([avg_out, max_out], dim=1)
x = self.conv1(x)
return x0 * self.sigmoid(x)
class DecoderBlock(nn.Module):
def __init__(self, in_c, out_c):
super().__init__()
self.up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True)
self.r1 = ResidualBlock(in_c[0]+in_c[1], out_c)
self.r2 = ResidualBlock(out_c, out_c)
self.ca = ChannelAttention(out_c)
self.sa = SpatialAttention()
def forward(self, x, s):
x = self.up(x)
x = torch.cat([x, s], axis=1)
x = self.r1(x)
x = self.r2(x)
x = self.ca(x)
x = self.sa(x)
return x
class RUPNet(nn.Module):
def __init__(self):
super().__init__()
backbone = resnet50(pretrained=False)
self.layer0 = nn.Sequential(backbone.conv1, backbone.bn1, backbone.relu)
self.layer1 = nn.Sequential(backbone.maxpool, backbone.layer1)
self.layer2 = backbone.layer2
self.layer3 = backbone.layer3
self.r1 = nn.Sequential(DilatedConv(64, 64), nn.MaxPool2d((8, 8)))
self.r2 = nn.Sequential(DilatedConv(256, 64), nn.MaxPool2d((4, 4)))
self.r3 = nn.Sequential(DilatedConv(512, 64), nn.MaxPool2d((2, 2)))
self.r4 = DilatedConv(1024, 64)
self.d1 = DecoderBlock([256, 512], 256)
self.d2 = DecoderBlock([256, 256], 128)
self.d3 = DecoderBlock([128, 64], 64)
self.d4 = DecoderBlock([64, 3], 32)
self.y = nn.Conv2d(32, 1, kernel_size=1, padding=0)
def forward(self, x, heatmap=None):
s0 = x
s1 = self.layer0(s0)
s2 = self.layer1(s1)
s3 = self.layer2(s2)
s4 = self.layer3(s3)
r1 = self.r1(s1)
r2 = self.r2(s2)
r3 = self.r3(s3)
r4 = self.r4(s4)
rx = torch.cat([r1, r2, r3, r4], axis=1)
d1 = self.d1(rx, s3)
d2 = self.d2(d1, s2)
d3 = self.d3(d2, s1)
d4 = self.d4(d3, s0)
y = self.y(d4)
if heatmap is not None:
hmap = save_feats_mean(d4)
return hmap, y
else:
return y
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