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# -*- coding: utf-8 -*-
__author__ = "S.X.Zhang"
import torch
import torch.nn as nn
import torch.nn.functional as F
from IndicPhotoOCR.detection.textbpn.network.layers.vgg import VggNet
from IndicPhotoOCR.detection.textbpn.network.layers.resnet import ResNet
from IndicPhotoOCR.detection.textbpn.network.layers.resnet_dcn import ResNet_DCN
from IndicPhotoOCR.detection.textbpn.cfglib.config import config as cfg
class UpBlok(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1x1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
self.conv3x3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.deconv = nn.ConvTranspose2d(out_channels, out_channels, kernel_size=4, stride=2, padding=1)
def forward(self, upsampled, shortcut):
x = torch.cat([upsampled, shortcut], dim=1)
x = self.conv1x1(x)
x = F.relu(x)
x = self.conv3x3(x)
x = F.relu(x)
x = self.deconv(x)
return x
class MergeBlok(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1x1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
self.conv3x3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
def forward(self, upsampled, shortcut):
x = torch.cat([upsampled, shortcut], dim=1)
x = self.conv1x1(x)
x = F.relu(x)
x = self.conv3x3(x)
return x
class FPN(nn.Module):
def __init__(self, backbone='resnet50', is_training=True):
super().__init__()
self.is_training = is_training
self.backbone_name = backbone
if backbone in ['vgg_bn', 'vgg']:
self.backbone = VggNet(name=backbone, pretrain=is_training)
self.deconv5 = nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1)
self.merge4 = UpBlok(512 + 256, 128)
self.merge3 = UpBlok(256 + 128, 64)
if cfg.scale == 1:
self.merge2 = UpBlok(128 + 64, 32) # FPN 1/2
self.merge1 = UpBlok(64 + 32, 32) # FPN 1/1
elif cfg.scale == 2:
self.merge2 = UpBlok(128 + 64, 32) # FPN 1/2
self.merge1 = MergeBlok(64 + 32, 32) # FPN 1/2
elif cfg.scale == 4:
self.merge2 = MergeBlok(128 + 64, 32) # FPN 1/4
elif backbone in ['resnet50']:
self.backbone = ResNet(name=backbone, pretrain=is_training)
self.deconv5 = nn.ConvTranspose2d(2048, 256, kernel_size=4, stride=2, padding=1)
self.merge4 = UpBlok(1024 + 256, 128)
self.merge3 = UpBlok(512 + 128, 64)
if cfg.scale == 1:
self.merge2 = UpBlok(256 + 64, 32) # FPN 1/2
self.merge1 = UpBlok(64 + 32, 32) # FPN 1/1
elif cfg.scale == 2:
self.merge2 = UpBlok(256 + 64, 32) # FPN 1/2
self.merge1 = MergeBlok(64 + 32, 32) # FPN 1/2
elif cfg.scale == 4:
self.merge2 = MergeBlok(256 + 64, 32) # FPN 1/4
self.merge1 = MergeBlok(64 + 32, 32) # FPN 1/4
elif backbone in ['resnet18']:
self.backbone = ResNet(name=backbone, pretrain=is_training)
self.deconv5 = nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1)
self.merge4 = UpBlok(256 + 256, 128)
self.merge3 = UpBlok(128 + 128, 64)
if cfg.scale == 1:
self.merge2 = UpBlok(64 + 64, 32) # FPN 1/2
self.merge1 = UpBlok(64 + 32, 32) # FPN 1/1
elif cfg.scale == 2:
self.merge2 = UpBlok(64 + 64, 32) # FPN 1/2
self.merge1 = MergeBlok(64 + 32, 32) # FPN 1/2
elif cfg.scale == 4:
self.merge2 = MergeBlok(64 + 64, 32) # FPN 1/4
self.merge1 = MergeBlok(64 + 32, 32) # FPN 1/4
elif backbone in ["deformable_resnet18"]:
self.backbone = ResNet_DCN(name=backbone, pretrain=is_training)
self.deconv5 = nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1)
self.merge4 = UpBlok(256 + 256, 128)
self.merge3 = UpBlok(128 + 128, 64)
if cfg.scale == 1:
self.merge2 = UpBlok(64 + 64, 32) # FPN 1/2
self.merge1 = UpBlok(64 + 32, 32) # FPN 1/1
elif cfg.scale == 2:
self.merge2 = UpBlok(64 + 64, 32) # FPN 1/2
self.merge1 = MergeBlok(64 + 32, 32) # FPN 1/2
elif cfg.scale == 4:
self.merge2 = MergeBlok(64 + 64, 32) # FPN 1/4
self.merge1 = MergeBlok(64 + 32, 32) # FPN 1/4
elif backbone in ["deformable_resnet50"]:
self.backbone = ResNet_DCN(name=backbone, pretrain=is_training)
self.deconv5 = nn.ConvTranspose2d(2048, 256, kernel_size=4, stride=2, padding=1)
self.merge4 = UpBlok(1024 + 256, 128)
self.merge3 = UpBlok(512 + 128, 64)
if cfg.scale == 1:
self.merge2 = UpBlok(256 + 64, 32) # FPN 1/2
self.merge1 = UpBlok(64 + 32, 32) # FPN 1/1
elif cfg.scale == 2:
self.merge2 = UpBlok(256 + 64, 32) # FPN 1/2
self.merge1 = MergeBlok(64 + 32, 32) # FPN 1/2
elif cfg.scale == 4:
self.merge2 = MergeBlok(256 + 64, 32) # FPN 1/4
self.merge1 = MergeBlok(64 + 32, 32) # FPN 1/4
else:
print("backbone is not support !")
def forward(self, x):
C1, C2, C3, C4, C5 = self.backbone(x)
#print(C5.size())
#print(C4.size())
#print(C3.size())
#print(C2.size())
#print(C1.size())
up5 = self.deconv5(C5)
up5 = F.relu(up5)
up4 = self.merge4(C4, up5)
up4 = F.relu(up4)
up3 = self.merge3(C3, up4)
up3 = F.relu(up3)
up2 = self.merge2(C2, up3)
up2 = F.relu(up2)
up1 = self.merge1(C1, up2)
up1 = F.relu(up1)
return up1, up2, up3, up4, up5
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