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"""
Copyright (c) 2019-present NAVER Corp.
MIT License
"""
# -*- coding: utf-8 -*-
from numpy import source
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import namedtuple
from taming.modules.losses.vgg16_bn import vgg16_bn, init_weights
class double_conv(nn.Module):
def __init__(self, in_ch, mid_ch, out_ch):
super(double_conv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch + mid_ch, mid_ch, kernel_size=1),
nn.BatchNorm2d(mid_ch),
nn.ReLU(inplace=True),
nn.Conv2d(mid_ch, out_ch, kernel_size=3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = self.conv(x)
return x
class CRAFT(nn.Module):
def __init__(self, pretrained=True, freeze=False, amp=False):
super(CRAFT, self).__init__()
self.amp = amp
""" Base network """
self.basenet = vgg16_bn(pretrained, freeze)
""" U network """
self.upconv1 = double_conv(1024, 512, 256)
self.upconv2 = double_conv(512, 256, 128)
self.upconv3 = double_conv(256, 128, 64)
self.upconv4 = double_conv(128, 64, 32)
num_class = 2
self.conv_cls = nn.Sequential(
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, 16, kernel_size=3, padding=1), nn.ReLU(inplace=True),
nn.Conv2d(16, 16, kernel_size=1), nn.ReLU(inplace=True),
nn.Conv2d(16, num_class, kernel_size=1),
)
init_weights(self.upconv1.modules())
init_weights(self.upconv2.modules())
init_weights(self.upconv3.modules())
init_weights(self.upconv4.modules())
init_weights(self.conv_cls.modules())
def forward(self, x):
""" Base network """
if self.amp:
with torch.cuda.amp.autocast():
sources = self.basenet(x)
""" U network """
y = torch.cat([sources[0], sources[1]], dim=1)
y = self.upconv1(y)
y = F.interpolate(y, size=sources[2].size()[2:], mode='bilinear', align_corners=False)
y = torch.cat([y, sources[2]], dim=1)
y = self.upconv2(y)
y = F.interpolate(y, size=sources[3].size()[2:], mode='bilinear', align_corners=False)
y = torch.cat([y, sources[3]], dim=1)
y = self.upconv3(y)
y = F.interpolate(y, size=sources[4].size()[2:], mode='bilinear', align_corners=False)
y = torch.cat([y, sources[4]], dim=1)
feature = self.upconv4(y)
y = self.conv_cls(feature)
return y.permute(0,2,3,1), feature
else:
sources = self.basenet(x)
""" U network """
y = torch.cat([sources[0], sources[1]], dim=1)
y = self.upconv1(y)
y1 = y
y = F.interpolate(y, size=sources[2].size()[2:], mode='bilinear', align_corners=False)
y = torch.cat([y, sources[2]], dim=1)
y = self.upconv2(y)
y2 = y
y = F.interpolate(y, size=sources[3].size()[2:], mode='bilinear', align_corners=False)
y = torch.cat([y, sources[3]], dim=1)
y = self.upconv3(y)
y3 = y
y = F.interpolate(y, size=sources[4].size()[2:], mode='bilinear', align_corners=False)
y = torch.cat([y, sources[4]], dim=1)
feature = self.upconv4(y)
y = self.conv_cls(feature)
OCRoutputs = namedtuple("OCROutputs", ['feature', 'y3', 'y2', 'y1'])
out = OCRoutputs(feature, y3, y2, y1)
return y.permute(0, 2, 3, 1), feature, sources
if __name__ == '__main__':
model = CRAFT(pretrained=True).cuda()
output, _ = model(torch.randn(1, 3, 768, 768).cuda())
print(output.shape) |