mirror of https://github.com/alibaba/EasyCV.git
83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
# Modified from https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppocr/modeling/heads/det_db_head.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from easycv.models.builder import HEADS
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class DBBaseHead(nn.Module):
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def __init__(self, in_channels, kernel_list=[3, 2, 2], **kwargs):
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super(DBBaseHead, self).__init__()
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self.conv1 = nn.Conv2d(
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in_channels=in_channels,
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out_channels=in_channels // 4,
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kernel_size=kernel_list[0],
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padding=int(kernel_list[0] // 2),
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bias=False)
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self.conv_bn1 = nn.BatchNorm2d(in_channels // 4)
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self.relu1 = nn.ReLU(inplace=True)
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self.conv2 = nn.ConvTranspose2d(
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in_channels=in_channels // 4,
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out_channels=in_channels // 4,
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kernel_size=kernel_list[1],
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stride=2)
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self.conv_bn2 = nn.BatchNorm2d(in_channels // 4)
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self.relu2 = nn.ReLU(inplace=True)
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self.conv3 = nn.ConvTranspose2d(
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in_channels=in_channels // 4,
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out_channels=1,
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kernel_size=kernel_list[2],
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stride=2)
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def forward(self, x):
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x = self.conv1(x)
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x = self.conv_bn1(x)
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x = self.relu1(x)
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x = self.conv2(x)
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x = self.conv_bn2(x)
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x = self.relu2(x)
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x = self.conv3(x)
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x = torch.sigmoid(x)
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return x
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@HEADS.register_module()
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class DBHead(nn.Module):
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"""
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Differentiable Binarization (DB) for text detection:
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see https://arxiv.org/abs/1911.08947
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args:
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params(dict): super parameters for build DB network
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"""
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def __init__(self, in_channels, k=50, **kwargs):
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super(DBHead, self).__init__()
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self.k = k
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binarize_name_list = [
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'conv2d_56', 'batch_norm_47', 'conv2d_transpose_0',
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'batch_norm_48', 'conv2d_transpose_1', 'binarize'
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]
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thresh_name_list = [
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'conv2d_57', 'batch_norm_49', 'conv2d_transpose_2',
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'batch_norm_50', 'conv2d_transpose_3', 'thresh'
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]
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self.binarize = DBBaseHead(in_channels, **kwargs)
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self.thresh = DBBaseHead(in_channels, **kwargs)
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def step_function(self, x, y):
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return torch.reciprocal(1 + torch.exp(-self.k * (x - y)))
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def forward(self, x):
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shrink_maps = self.binarize(x)
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if not self.training:
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return {'maps': shrink_maps}
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threshold_maps = self.thresh(x)
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binary_maps = self.step_function(shrink_maps, threshold_maps)
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y = torch.cat([shrink_maps, threshold_maps, binary_maps], dim=1)
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return {'maps': y}
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