File size: 7,225 Bytes
9dce458 |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torchvision.models import resnet34
import einops
import math
class ImageMultiheadSelfAttention(nn.Module):
def __init__(self, planes):
super(ImageMultiheadSelfAttention, self).__init__()
self.attn = nn.MultiheadAttention(planes, 4)
def forward(self, x):
res = x
n, c, h, w = x.shape
x = einops.rearrange(x, 'n c h w -> (h w) n c')
x = self.attn(x, x, x)[0]
x = einops.rearrange(x, '(h w) n c -> n c h w', n = n, c = c, h = h, w = w)
return res + x
class double_conv(nn.Module):
def __init__(self, in_ch, mid_ch, out_ch, stride = 1, planes = 256):
super(double_conv, self).__init__()
self.planes = planes
# down = None
# if stride > 1:
# down = nn.Sequential(
# nn.AvgPool2d(2, 2),
# nn.Conv2d(in_ch + mid_ch, self.planes * Bottleneck.expansion, kernel_size=1, stride=1, bias=False),nn.BatchNorm2d(self.planes * Bottleneck.expansion)
# )
self.down = None
if stride > 1:
self.down = nn.AvgPool2d(2,stride=2)
self.conv = nn.Sequential(
nn.Conv2d(in_ch + mid_ch, mid_ch, kernel_size=3, padding=1, stride = 1, bias=False),
nn.BatchNorm2d(mid_ch),
nn.ReLU(inplace=True),
#Bottleneck(mid_ch, self.planes, stride, down, 2, 1, avd = True, norm_layer = nn.BatchNorm2d),
nn.Conv2d(mid_ch, out_ch, kernel_size=3, stride = 1, padding=1, bias=False),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
)
def forward(self, x):
if self.down is not None:
x = self.down(x)
x = self.conv(x)
return x
class CRAFT_net(nn.Module):
def __init__(self):
super(CRAFT_net, self).__init__()
self.backbone = resnet34()
self.conv_rs = nn.Sequential(
nn.Conv2d(64, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True),
nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True),
nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True),
nn.Conv2d(32, 32, kernel_size=1), nn.ReLU(inplace=True),
nn.Conv2d(32, 1, kernel_size=1),
nn.Sigmoid()
)
self.conv_as = nn.Sequential(
nn.Conv2d(64, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True),
nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True),
nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True),
nn.Conv2d(32, 32, kernel_size=1), nn.ReLU(inplace=True),
nn.Conv2d(32, 1, kernel_size=1),
nn.Sigmoid()
)
self.conv_mask = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True),
nn.Conv2d(64, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True),
nn.Conv2d(32, 1, kernel_size=1),
nn.Sigmoid()
)
self.down_conv1 = double_conv(0, 512, 512, 2)
self.down_conv2 = double_conv(0, 512, 512, 2)
self.down_conv3 = double_conv(0, 512, 512, 2)
self.upconv1 = double_conv(0, 512, 256)
self.upconv2 = double_conv(256, 512, 256)
self.upconv3 = double_conv(256, 512, 256)
self.upconv4 = double_conv(256, 512, 256, planes = 128)
self.upconv5 = double_conv(256, 256, 128, planes = 64)
self.upconv6 = double_conv(128, 128, 64, planes = 32)
self.upconv7 = double_conv(64, 64, 64, planes = 16)
def forward_train(self, x):
x = self.backbone.conv1(x)
x = self.backbone.bn1(x)
x = self.backbone.relu(x)
x = self.backbone.maxpool(x) # 64@384
h4 = self.backbone.layer1(x) # 64@384
h8 = self.backbone.layer2(h4) # 128@192
h16 = self.backbone.layer3(h8) # 256@96
h32 = self.backbone.layer4(h16) # 512@48
h64 = self.down_conv1(h32) # 512@24
h128 = self.down_conv2(h64) # 512@12
h256 = self.down_conv3(h128) # 512@6
up256 = F.interpolate(self.upconv1(h256), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) # 512@12
up128 = F.interpolate(self.upconv2(torch.cat([up256, h128], dim = 1)), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) #51264@24
up64 = F.interpolate(self.upconv3(torch.cat([up128, h64], dim = 1)), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) # 256@48
up32 = F.interpolate(self.upconv4(torch.cat([up64, h32], dim = 1)), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) # 256@96
up16 = F.interpolate(self.upconv5(torch.cat([up32, h16], dim = 1)), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) # 128@192
up8 = F.interpolate(self.upconv6(torch.cat([up16, h8], dim = 1)), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) # 64@384
up4 = F.interpolate(self.upconv7(torch.cat([up8, h4], dim = 1)), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) # 64@768
ascore = self.conv_as(up4)
rscore = self.conv_rs(up4)
return torch.cat([rscore, ascore], dim = 1), self.conv_mask(up4)
def forward(self, x):
x = self.backbone.conv1(x)
x = self.backbone.bn1(x)
x = self.backbone.relu(x)
x = self.backbone.maxpool(x) # 64@384
h4 = self.backbone.layer1(x) # 64@384
h8 = self.backbone.layer2(h4) # 128@192
h16 = self.backbone.layer3(h8) # 256@96
h32 = self.backbone.layer4(h16) # 512@48
h64 = self.down_conv1(h32) # 512@24
h128 = self.down_conv2(h64) # 512@12
h256 = self.down_conv3(h128) # 512@6
up256 = F.interpolate(self.upconv1(h256), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) # 512@12
up128 = F.interpolate(self.upconv2(torch.cat([up256, h128], dim = 1)), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) #51264@24
up64 = F.interpolate(self.upconv3(torch.cat([up128, h64], dim = 1)), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) # 256@48
up32 = F.interpolate(self.upconv4(torch.cat([up64, h32], dim = 1)), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) # 256@96
up16 = F.interpolate(self.upconv5(torch.cat([up32, h16], dim = 1)), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) # 128@192
up8 = F.interpolate(self.upconv6(torch.cat([up16, h8], dim = 1)), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) # 64@384
up4 = F.interpolate(self.upconv7(torch.cat([up8, h4], dim = 1)), scale_factor = (2, 2), mode = 'bilinear', align_corners = False) # 64@768
ascore = self.conv_as(up4)
rscore = self.conv_rs(up4)
return torch.cat([rscore, ascore], dim = 1), self.conv_mask(up4)
if __name__ == '__main__':
net = CRAFT_net().cuda()
img = torch.randn(2, 3, 1536, 1536).cuda()
print(net.forward_train(img)[0].shape)
|