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"""
Copyright (c) 2019-present NAVER Corp.
MIT License
"""
from __future__ import annotations
from collections import namedtuple
from typing import Iterable, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from packaging import version
from torchvision import models
VGGOutputs = namedtuple(
"VggOutputs", ["fc7", "relu5_3", "relu4_3", "relu3_2", "relu2_2"]
)
def init_weights(modules: Iterable[nn.Module]):
for m in modules:
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1.0)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.zeros_(m.bias)
class VGG16_BN(nn.Module):
def __init__(self, pretrained: bool=True, freeze: bool=True):
super().__init__()
if version.parse(torchvision.__version__) >= version.parse("0.13"):
vgg_pretrained_features = models.vgg16_bn(
weights=models.VGG16_BN_Weights.DEFAULT if pretrained else None
).features
else: # torchvision.__version__ < 0.13
models.vgg.model_urls["vgg16_bn"] = models.vgg.model_urls[
"vgg16_bn"
].replace("https://", "http://")
vgg_pretrained_features = models.vgg16_bn(pretrained=pretrained).features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
self.slice4 = torch.nn.Sequential()
self.slice5 = torch.nn.Sequential()
for x in range(12): # conv2_2
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(12, 19): # conv3_3
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(19, 29): # conv4_3
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(29, 39): # conv5_3
self.slice4.add_module(str(x), vgg_pretrained_features[x])
# fc6, fc7 without atrous conv
self.slice5 = torch.nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6),
nn.Conv2d(1024, 1024, kernel_size=1),
)
if not pretrained:
init_weights(self.slice1.modules())
init_weights(self.slice2.modules())
init_weights(self.slice3.modules())
init_weights(self.slice4.modules())
init_weights(self.slice5.modules()) # no pretrained model for fc6 and fc7
if freeze:
for param in self.slice1.parameters(): # only first conv
param.requires_grad = False
def forward(self, x: torch.Tensor) -> VGGOutputs:
h = self.slice1(x)
h_relu2_2 = h
h = self.slice2(h)
h_relu3_2 = h
h = self.slice3(h)
h_relu4_3 = h
h = self.slice4(h)
h_relu5_3 = h
h = self.slice5(h)
h_fc7 = h
out = VGGOutputs(h_fc7, h_relu5_3, h_relu4_3, h_relu3_2, h_relu2_2)
return out
class DoubleConv(nn.Module):
def __init__(self, in_ch: int, mid_ch: int, out_ch: int):
super().__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: torch.Tensor) -> torch.Tensor:
x = self.conv(x)
return x
class CRAFT(nn.Module):
def __init__(self, pretrained: bool=False, freeze: bool=False):
super(CRAFT, self).__init__()
""" Base network """
self.basenet = VGG16_BN(pretrained, freeze)
""" U network """
self.upconv1 = DoubleConv(1024, 512, 256)
self.upconv2 = DoubleConv(512, 256, 128)
self.upconv3 = DoubleConv(256, 128, 64)
self.upconv4 = DoubleConv(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: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""Base network"""
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
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