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import torch
from torch import nn
import torch.nn.functional as F
class Head(nn.Module):
def __init__(self, in_channels, kernel_list=[3, 2, 2], **kwargs):
super(Head, self).__init__()
self.conv1 = nn.Conv2d(
in_channels=in_channels,
out_channels=in_channels // 4,
kernel_size=kernel_list[0],
padding=int(kernel_list[0] // 2),
bias=False,
)
self.conv_bn1 = nn.BatchNorm2d(num_features=in_channels // 4, )
self.conv2 = nn.ConvTranspose2d(
in_channels=in_channels // 4,
out_channels=in_channels // 4,
kernel_size=kernel_list[1],
stride=2,
)
self.conv_bn2 = nn.BatchNorm2d(num_features=in_channels // 4, )
self.conv3 = nn.ConvTranspose2d(
in_channels=in_channels // 4,
out_channels=1,
kernel_size=kernel_list[2],
stride=2,
)
def forward(self, x, return_f=False):
x = self.conv1(x)
x = F.relu(self.conv_bn1(x))
x = self.conv2(x)
x = F.relu(self.conv_bn2(x))
if return_f is True:
f = x
x = self.conv3(x)
x = torch.sigmoid(x)
if return_f is True:
return x, f
return x
class DBHead(nn.Module):
"""
Differentiable Binarization (DB) for text detection:
see https://arxiv.org/abs/1911.08947
args:
params(dict): super parameters for build DB network
"""
def __init__(self, in_channels, k=50, **kwargs):
super(DBHead, self).__init__()
self.k = k
self.binarize = Head(in_channels, **kwargs)
self.thresh = Head(in_channels, **kwargs)
def step_function(self, x, y):
return torch.reciprocal(1 + torch.exp(-self.k * (x - y)))
def forward(self, x, data=None):
shrink_maps = self.binarize(x)
if not self.training:
return {'maps': shrink_maps}
threshold_maps = self.thresh(x)
binary_maps = self.step_function(shrink_maps, threshold_maps)
y = torch.concat([shrink_maps, threshold_maps, binary_maps], dim=1)
return {'maps': y}
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