210 lines
6.5 KiB
Python
210 lines
6.5 KiB
Python
|
from torch import nn
|
||
|
|
import torch
|
||
|
|
from .RecSVTR import Block
|
||
|
|
|
||
|
|
class Swish(nn.Module):
|
||
|
|
def __int__(self):
|
||
|
|
super(Swish, self).__int__()
|
||
|
|
|
||
|
|
def forward(self,x):
|
||
|
|
return x*torch.sigmoid(x)
|
||
|
|
|
||
|
|
class Im2Im(nn.Module):
|
||
|
|
def __init__(self, in_channels, **kwargs):
|
||
|
|
super().__init__()
|
||
|
|
self.out_channels = in_channels
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
return x
|
||
|
|
|
||
|
|
class Im2Seq(nn.Module):
|
||
|
|
def __init__(self, in_channels, **kwargs):
|
||
|
|
super().__init__()
|
||
|
|
self.out_channels = in_channels
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
B, C, H, W = x.shape
|
||
|
|
# assert H == 1
|
||
|
|
x = x.reshape(B, C, H * W)
|
||
|
|
x = x.permute((0, 2, 1))
|
||
|
|
return x
|
||
|
|
|
||
|
|
class EncoderWithRNN(nn.Module):
|
||
|
|
def __init__(self, in_channels,**kwargs):
|
||
|
|
super(EncoderWithRNN, self).__init__()
|
||
|
|
hidden_size = kwargs.get('hidden_size', 256)
|
||
|
|
self.out_channels = hidden_size * 2
|
||
|
|
self.lstm = nn.LSTM(in_channels, hidden_size, bidirectional=True, num_layers=2,batch_first=True)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
self.lstm.flatten_parameters()
|
||
|
|
x, _ = self.lstm(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
class SequenceEncoder(nn.Module):
|
||
|
|
def __init__(self, in_channels, encoder_type='rnn', **kwargs):
|
||
|
|
super(SequenceEncoder, self).__init__()
|
||
|
|
self.encoder_reshape = Im2Seq(in_channels)
|
||
|
|
self.out_channels = self.encoder_reshape.out_channels
|
||
|
|
self.encoder_type = encoder_type
|
||
|
|
if encoder_type == 'reshape':
|
||
|
|
self.only_reshape = True
|
||
|
|
else:
|
||
|
|
support_encoder_dict = {
|
||
|
|
'reshape': Im2Seq,
|
||
|
|
'rnn': EncoderWithRNN,
|
||
|
|
'svtr': EncoderWithSVTR
|
||
|
|
}
|
||
|
|
assert encoder_type in support_encoder_dict, '{} must in {}'.format(
|
||
|
|
encoder_type, support_encoder_dict.keys())
|
||
|
|
|
||
|
|
self.encoder = support_encoder_dict[encoder_type](
|
||
|
|
self.encoder_reshape.out_channels,**kwargs)
|
||
|
|
self.out_channels = self.encoder.out_channels
|
||
|
|
self.only_reshape = False
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
if self.encoder_type != 'svtr':
|
||
|
|
x = self.encoder_reshape(x)
|
||
|
|
if not self.only_reshape:
|
||
|
|
x = self.encoder(x)
|
||
|
|
return x
|
||
|
|
else:
|
||
|
|
x = self.encoder(x)
|
||
|
|
x = self.encoder_reshape(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
class ConvBNLayer(nn.Module):
|
||
|
|
def __init__(self,
|
||
|
|
in_channels,
|
||
|
|
out_channels,
|
||
|
|
kernel_size=3,
|
||
|
|
stride=1,
|
||
|
|
padding=0,
|
||
|
|
bias_attr=False,
|
||
|
|
groups=1,
|
||
|
|
act=nn.GELU):
|
||
|
|
super().__init__()
|
||
|
|
self.conv = nn.Conv2d(
|
||
|
|
in_channels=in_channels,
|
||
|
|
out_channels=out_channels,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
stride=stride,
|
||
|
|
padding=padding,
|
||
|
|
groups=groups,
|
||
|
|
# weight_attr=paddle.ParamAttr(initializer=nn.initializer.KaimingUniform()),
|
||
|
|
bias=bias_attr)
|
||
|
|
self.norm = nn.BatchNorm2d(out_channels)
|
||
|
|
self.act = Swish()
|
||
|
|
|
||
|
|
def forward(self, inputs):
|
||
|
|
out = self.conv(inputs)
|
||
|
|
out = self.norm(out)
|
||
|
|
out = self.act(out)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
class EncoderWithSVTR(nn.Module):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
in_channels,
|
||
|
|
dims=64, # XS
|
||
|
|
depth=2,
|
||
|
|
hidden_dims=120,
|
||
|
|
use_guide=False,
|
||
|
|
num_heads=8,
|
||
|
|
qkv_bias=True,
|
||
|
|
mlp_ratio=2.0,
|
||
|
|
drop_rate=0.1,
|
||
|
|
attn_drop_rate=0.1,
|
||
|
|
drop_path=0.,
|
||
|
|
qk_scale=None):
|
||
|
|
super(EncoderWithSVTR, self).__init__()
|
||
|
|
self.depth = depth
|
||
|
|
self.use_guide = use_guide
|
||
|
|
self.conv1 = ConvBNLayer(
|
||
|
|
in_channels, in_channels // 8, padding=1, act='swish')
|
||
|
|
self.conv2 = ConvBNLayer(
|
||
|
|
in_channels // 8, hidden_dims, kernel_size=1, act='swish')
|
||
|
|
|
||
|
|
self.svtr_block = nn.ModuleList([
|
||
|
|
Block(
|
||
|
|
dim=hidden_dims,
|
||
|
|
num_heads=num_heads,
|
||
|
|
mixer='Global',
|
||
|
|
HW=None,
|
||
|
|
mlp_ratio=mlp_ratio,
|
||
|
|
qkv_bias=qkv_bias,
|
||
|
|
qk_scale=qk_scale,
|
||
|
|
drop=drop_rate,
|
||
|
|
act_layer='swish',
|
||
|
|
attn_drop=attn_drop_rate,
|
||
|
|
drop_path=drop_path,
|
||
|
|
norm_layer='nn.LayerNorm',
|
||
|
|
epsilon=1e-05,
|
||
|
|
prenorm=False) for i in range(depth)
|
||
|
|
])
|
||
|
|
self.norm = nn.LayerNorm(hidden_dims, eps=1e-6)
|
||
|
|
self.conv3 = ConvBNLayer(
|
||
|
|
hidden_dims, in_channels, kernel_size=1, act='swish')
|
||
|
|
# last conv-nxn, the input is concat of input tensor and conv3 output tensor
|
||
|
|
self.conv4 = ConvBNLayer(
|
||
|
|
2 * in_channels, in_channels // 8, padding=1, act='swish')
|
||
|
|
|
||
|
|
self.conv1x1 = ConvBNLayer(
|
||
|
|
in_channels // 8, dims, kernel_size=1, act='swish')
|
||
|
|
self.out_channels = dims
|
||
|
|
self.apply(self._init_weights)
|
||
|
|
|
||
|
|
def _init_weights(self, m):
|
||
|
|
# weight initialization
|
||
|
|
if isinstance(m, nn.Conv2d):
|
||
|
|
nn.init.kaiming_normal_(m.weight, mode='fan_out')
|
||
|
|
if m.bias is not None:
|
||
|
|
nn.init.zeros_(m.bias)
|
||
|
|
elif isinstance(m, nn.BatchNorm2d):
|
||
|
|
nn.init.ones_(m.weight)
|
||
|
|
nn.init.zeros_(m.bias)
|
||
|
|
elif isinstance(m, nn.Linear):
|
||
|
|
nn.init.normal_(m.weight, 0, 0.01)
|
||
|
|
if m.bias is not None:
|
||
|
|
nn.init.zeros_(m.bias)
|
||
|
|
elif isinstance(m, nn.ConvTranspose2d):
|
||
|
|
nn.init.kaiming_normal_(m.weight, mode='fan_out')
|
||
|
|
if m.bias is not None:
|
||
|
|
nn.init.zeros_(m.bias)
|
||
|
|
elif isinstance(m, nn.LayerNorm):
|
||
|
|
nn.init.ones_(m.weight)
|
||
|
|
nn.init.zeros_(m.bias)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
# for use guide
|
||
|
|
if self.use_guide:
|
||
|
|
z = x.clone()
|
||
|
|
z.stop_gradient = True
|
||
|
|
else:
|
||
|
|
z = x
|
||
|
|
# for short cut
|
||
|
|
h = z
|
||
|
|
# reduce dim
|
||
|
|
z = self.conv1(z)
|
||
|
|
z = self.conv2(z)
|
||
|
|
# SVTR global block
|
||
|
|
B, C, H, W = z.shape
|
||
|
|
z = z.flatten(2).permute(0, 2, 1)
|
||
|
|
|
||
|
|
for blk in self.svtr_block:
|
||
|
|
z = blk(z)
|
||
|
|
|
||
|
|
z = self.norm(z)
|
||
|
|
# last stage
|
||
|
|
z = z.reshape([-1, H, W, C]).permute(0, 3, 1, 2)
|
||
|
|
z = self.conv3(z)
|
||
|
|
z = torch.cat((h, z), dim=1)
|
||
|
|
z = self.conv1x1(self.conv4(z))
|
||
|
|
|
||
|
|
return z
|
||
|
|
|
||
|
|
if __name__=="__main__":
|
||
|
|
svtrRNN = EncoderWithSVTR(56)
|
||
|
|
print(svtrRNN)
|