rnn.py
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import torch
from torch import nn
from ..backbones.rec_svtrnet import Block, ConvBNLayer
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.squeeze(dim=2)
# x = x.transpose([0, 2, 1]) # paddle (NTC)(batch, width, channels)
x = x.permute(0, 2, 1)
return x
# def forward(self, x):
# B, C, H, W = x.shape
# # 处理四维张量，将空间维度展平为序列
# if H == 1:
# # 原来的处理逻辑，适用于H=1的情况
# x = x.squeeze(dim=2)
# x = x.permute(0, 2, 1) # (B, W, C)
# else:
# # 处理H不为1的情况
# x = x.permute(0, 2, 3, 1) # (B, H, W, C)
# x = x.reshape(B, H * W, C) # (B, H*W, C)
#
# return x
class EncoderWithRNN_(nn.Module):
def __init__(self, in_channels, hidden_size):
super(EncoderWithRNN_, self).__init__()
self.out_channels = hidden_size * 2
self.rnn1 = nn.LSTM(
in_channels,
hidden_size,
bidirectional=False,
batch_first=True,
num_layers=2,
)
self.rnn2 = nn.LSTM(
in_channels,
hidden_size,
bidirectional=False,
batch_first=True,
num_layers=2,
)
def forward(self, x):
self.rnn1.flatten_parameters()
self.rnn2.flatten_parameters()
out1, h1 = self.rnn1(x)
out2, h2 = self.rnn2(torch.flip(x, [1]))
return torch.cat([out1, torch.flip(out2, [1])], 2)
class EncoderWithRNN(nn.Module):
def __init__(self, in_channels, hidden_size):
super(EncoderWithRNN, self).__init__()
self.out_channels = hidden_size * 2
self.lstm = nn.LSTM(
in_channels, hidden_size, num_layers=2, batch_first=True, bidirectional=True
) # batch_first:=True
def forward(self, x):
x, _ = self.lstm(x)
return x
class EncoderWithFC(nn.Module):
def __init__(self, in_channels, hidden_size):
super(EncoderWithFC, self).__init__()
self.out_channels = hidden_size
self.fc = nn.Linear(
in_channels,
hidden_size,
bias=True,
)
def forward(self, x):
x = self.fc(x)
return x
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,
kernel_size=[3, 3],
attn_drop_rate=0.1,
drop_path=0.0,
qk_scale=None,
):
super(EncoderWithSVTR, self).__init__()
self.depth = depth
self.use_guide = use_guide
self.conv1 = ConvBNLayer(
in_channels,
in_channels // 8,
kernel_size=kernel_size,
padding=[kernel_size[0] // 2, kernel_size[1] // 2],
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
class SequenceEncoder(nn.Module):
def __init__(self, in_channels, encoder_type, hidden_size=48, **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,
"fc": EncoderWithFC,
"rnn": EncoderWithRNN,
"svtr": EncoderWithSVTR,
}
assert encoder_type in support_encoder_dict, "{} must in {}".format(
encoder_type, support_encoder_dict.keys()
)
if encoder_type == "svtr":
self.encoder = support_encoder_dict[encoder_type](
self.encoder_reshape.out_channels, **kwargs
)
else:
self.encoder = support_encoder_dict[encoder_type](
self.encoder_reshape.out_channels, hidden_size
)
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