Init
This commit is contained in:
FlintyLemming
210
AnyText/AnyText_scripts/ldm/models/diffusion/ocr_recog/RNN.py
Normal file
210
AnyText/AnyText_scripts/ldm/models/diffusion/ocr_recog/RNN.py
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from torch import nn
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import torch
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from .RecSVTR import Block
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class Swish(nn.Module):
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def __int__(self):
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super(Swish, self).__int__()
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def forward(self,x):
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return x*torch.sigmoid(x)
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class Im2Im(nn.Module):
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def __init__(self, in_channels, **kwargs):
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super().__init__()
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self.out_channels = in_channels
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def forward(self, x):
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return x
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class Im2Seq(nn.Module):
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def __init__(self, in_channels, **kwargs):
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super().__init__()
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self.out_channels = in_channels
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def forward(self, x):
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B, C, H, W = x.shape
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# assert H == 1
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x = x.reshape(B, C, H * W)
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x = x.permute((0, 2, 1))
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return x
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class EncoderWithRNN(nn.Module):
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def __init__(self, in_channels,**kwargs):
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super(EncoderWithRNN, self).__init__()
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hidden_size = kwargs.get('hidden_size', 256)
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self.out_channels = hidden_size * 2
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self.lstm = nn.LSTM(in_channels, hidden_size, bidirectional=True, num_layers=2,batch_first=True)
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def forward(self, x):
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self.lstm.flatten_parameters()
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x, _ = self.lstm(x)
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return x
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class SequenceEncoder(nn.Module):
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def __init__(self, in_channels, encoder_type='rnn', **kwargs):
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super(SequenceEncoder, self).__init__()
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self.encoder_reshape = Im2Seq(in_channels)
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self.out_channels = self.encoder_reshape.out_channels
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self.encoder_type = encoder_type
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if encoder_type == 'reshape':
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self.only_reshape = True
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else:
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support_encoder_dict = {
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'reshape': Im2Seq,
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'rnn': EncoderWithRNN,
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'svtr': EncoderWithSVTR
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}
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assert encoder_type in support_encoder_dict, '{} must in {}'.format(
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encoder_type, support_encoder_dict.keys())
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self.encoder = support_encoder_dict[encoder_type](
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self.encoder_reshape.out_channels,**kwargs)
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self.out_channels = self.encoder.out_channels
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self.only_reshape = False
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def forward(self, x):
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if self.encoder_type != 'svtr':
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x = self.encoder_reshape(x)
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if not self.only_reshape:
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x = self.encoder(x)
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return x
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else:
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x = self.encoder(x)
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x = self.encoder_reshape(x)
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return x
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class ConvBNLayer(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=0,
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bias_attr=False,
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groups=1,
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act=nn.GELU):
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super().__init__()
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self.conv = nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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groups=groups,
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# weight_attr=paddle.ParamAttr(initializer=nn.initializer.KaimingUniform()),
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bias=bias_attr)
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self.norm = nn.BatchNorm2d(out_channels)
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self.act = Swish()
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def forward(self, inputs):
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out = self.conv(inputs)
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out = self.norm(out)
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out = self.act(out)
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return out
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class EncoderWithSVTR(nn.Module):
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def __init__(
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self,
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in_channels,
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dims=64, # XS
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depth=2,
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hidden_dims=120,
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use_guide=False,
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num_heads=8,
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qkv_bias=True,
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mlp_ratio=2.0,
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drop_rate=0.1,
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attn_drop_rate=0.1,
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drop_path=0.,
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qk_scale=None):
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super(EncoderWithSVTR, self).__init__()
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self.depth = depth
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self.use_guide = use_guide
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self.conv1 = ConvBNLayer(
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in_channels, in_channels // 8, padding=1, act='swish')
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self.conv2 = ConvBNLayer(
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in_channels // 8, hidden_dims, kernel_size=1, act='swish')
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self.svtr_block = nn.ModuleList([
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Block(
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dim=hidden_dims,
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num_heads=num_heads,
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mixer='Global',
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HW=None,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate,
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act_layer='swish',
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attn_drop=attn_drop_rate,
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drop_path=drop_path,
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norm_layer='nn.LayerNorm',
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epsilon=1e-05,
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prenorm=False) for i in range(depth)
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])
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self.norm = nn.LayerNorm(hidden_dims, eps=1e-6)
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self.conv3 = ConvBNLayer(
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hidden_dims, in_channels, kernel_size=1, act='swish')
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# last conv-nxn, the input is concat of input tensor and conv3 output tensor
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self.conv4 = ConvBNLayer(
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2 * in_channels, in_channels // 8, padding=1, act='swish')
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self.conv1x1 = ConvBNLayer(
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in_channels // 8, dims, kernel_size=1, act='swish')
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self.out_channels = dims
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self.apply(self._init_weights)
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def _init_weights(self, m):
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# weight initialization
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out')
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if m.bias is not None:
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.ones_(m.weight)
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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if m.bias is not None:
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.ConvTranspose2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out')
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if m.bias is not None:
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.LayerNorm):
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nn.init.ones_(m.weight)
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nn.init.zeros_(m.bias)
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def forward(self, x):
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# for use guide
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if self.use_guide:
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z = x.clone()
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z.stop_gradient = True
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else:
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z = x
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# for short cut
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h = z
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# reduce dim
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z = self.conv1(z)
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z = self.conv2(z)
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# SVTR global block
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B, C, H, W = z.shape
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z = z.flatten(2).permute(0, 2, 1)
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for blk in self.svtr_block:
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z = blk(z)
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z = self.norm(z)
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# last stage
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z = z.reshape([-1, H, W, C]).permute(0, 3, 1, 2)
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z = self.conv3(z)
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z = torch.cat((h, z), dim=1)
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z = self.conv1x1(self.conv4(z))
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return z
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if __name__=="__main__":
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svtrRNN = EncoderWithSVTR(56)
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print(svtrRNN)
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@@ -0,0 +1,48 @@
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from torch import nn
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class CTCHead(nn.Module):
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def __init__(self,
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in_channels,
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out_channels=6625,
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fc_decay=0.0004,
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mid_channels=None,
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return_feats=False,
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**kwargs):
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super(CTCHead, self).__init__()
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if mid_channels is None:
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self.fc = nn.Linear(
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in_channels,
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out_channels,
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bias=True,)
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else:
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self.fc1 = nn.Linear(
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in_channels,
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mid_channels,
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bias=True,
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)
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self.fc2 = nn.Linear(
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mid_channels,
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out_channels,
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bias=True,
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)
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self.out_channels = out_channels
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self.mid_channels = mid_channels
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self.return_feats = return_feats
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def forward(self, x, labels=None):
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if self.mid_channels is None:
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predicts = self.fc(x)
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else:
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x = self.fc1(x)
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predicts = self.fc2(x)
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if self.return_feats:
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result = dict()
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result['ctc'] = predicts
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result['ctc_neck'] = x
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else:
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result = predicts
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return result
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@@ -0,0 +1,45 @@
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from torch import nn
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from .RNN import SequenceEncoder, Im2Seq, Im2Im
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from .RecMv1_enhance import MobileNetV1Enhance
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from .RecCTCHead import CTCHead
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backbone_dict = {"MobileNetV1Enhance":MobileNetV1Enhance}
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neck_dict = {'SequenceEncoder': SequenceEncoder, 'Im2Seq': Im2Seq,'None':Im2Im}
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head_dict = {'CTCHead':CTCHead}
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class RecModel(nn.Module):
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def __init__(self, config):
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super().__init__()
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assert 'in_channels' in config, 'in_channels must in model config'
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backbone_type = config.backbone.pop('type')
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assert backbone_type in backbone_dict, f'backbone.type must in {backbone_dict}'
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self.backbone = backbone_dict[backbone_type](config.in_channels, **config.backbone)
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neck_type = config.neck.pop('type')
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assert neck_type in neck_dict, f'neck.type must in {neck_dict}'
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self.neck = neck_dict[neck_type](self.backbone.out_channels, **config.neck)
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head_type = config.head.pop('type')
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assert head_type in head_dict, f'head.type must in {head_dict}'
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self.head = head_dict[head_type](self.neck.out_channels, **config.head)
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self.name = f'RecModel_{backbone_type}_{neck_type}_{head_type}'
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def load_3rd_state_dict(self, _3rd_name, _state):
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self.backbone.load_3rd_state_dict(_3rd_name, _state)
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self.neck.load_3rd_state_dict(_3rd_name, _state)
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self.head.load_3rd_state_dict(_3rd_name, _state)
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def forward(self, x):
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x = self.backbone(x)
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x = self.neck(x)
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x = self.head(x)
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return x
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def encode(self, x):
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x = self.backbone(x)
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x = self.neck(x)
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x = self.head.ctc_encoder(x)
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return x
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@@ -0,0 +1,233 @@
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import os, sys
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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 .common import Activation
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class ConvBNLayer(nn.Module):
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def __init__(self,
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num_channels,
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filter_size,
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num_filters,
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stride,
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padding,
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channels=None,
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num_groups=1,
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act='hard_swish'):
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super(ConvBNLayer, self).__init__()
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self.act = act
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self._conv = nn.Conv2d(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=padding,
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groups=num_groups,
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bias=False)
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self._batch_norm = nn.BatchNorm2d(
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num_filters,
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)
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if self.act is not None:
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self._act = Activation(act_type=act, inplace=True)
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def forward(self, inputs):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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if self.act is not None:
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y = self._act(y)
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return y
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class DepthwiseSeparable(nn.Module):
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def __init__(self,
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num_channels,
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num_filters1,
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num_filters2,
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num_groups,
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stride,
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scale,
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dw_size=3,
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padding=1,
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use_se=False):
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super(DepthwiseSeparable, self).__init__()
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self.use_se = use_se
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self._depthwise_conv = ConvBNLayer(
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num_channels=num_channels,
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num_filters=int(num_filters1 * scale),
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filter_size=dw_size,
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stride=stride,
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padding=padding,
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num_groups=int(num_groups * scale))
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if use_se:
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self._se = SEModule(int(num_filters1 * scale))
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self._pointwise_conv = ConvBNLayer(
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num_channels=int(num_filters1 * scale),
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filter_size=1,
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num_filters=int(num_filters2 * scale),
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stride=1,
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padding=0)
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def forward(self, inputs):
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y = self._depthwise_conv(inputs)
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if self.use_se:
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y = self._se(y)
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y = self._pointwise_conv(y)
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return y
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class MobileNetV1Enhance(nn.Module):
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def __init__(self,
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in_channels=3,
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scale=0.5,
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last_conv_stride=1,
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last_pool_type='max',
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**kwargs):
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super().__init__()
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self.scale = scale
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self.block_list = []
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self.conv1 = ConvBNLayer(
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num_channels=in_channels,
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filter_size=3,
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channels=3,
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num_filters=int(32 * scale),
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stride=2,
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padding=1)
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conv2_1 = DepthwiseSeparable(
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num_channels=int(32 * scale),
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num_filters1=32,
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num_filters2=64,
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num_groups=32,
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stride=1,
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scale=scale)
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self.block_list.append(conv2_1)
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conv2_2 = DepthwiseSeparable(
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num_channels=int(64 * scale),
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num_filters1=64,
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num_filters2=128,
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num_groups=64,
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stride=1,
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scale=scale)
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self.block_list.append(conv2_2)
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conv3_1 = DepthwiseSeparable(
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num_channels=int(128 * scale),
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num_filters1=128,
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num_filters2=128,
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num_groups=128,
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stride=1,
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scale=scale)
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self.block_list.append(conv3_1)
|
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conv3_2 = DepthwiseSeparable(
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num_channels=int(128 * scale),
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num_filters1=128,
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num_filters2=256,
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num_groups=128,
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stride=(2, 1),
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scale=scale)
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self.block_list.append(conv3_2)
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conv4_1 = DepthwiseSeparable(
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num_channels=int(256 * scale),
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num_filters1=256,
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num_filters2=256,
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num_groups=256,
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stride=1,
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scale=scale)
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self.block_list.append(conv4_1)
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conv4_2 = DepthwiseSeparable(
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num_channels=int(256 * scale),
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num_filters1=256,
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num_filters2=512,
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num_groups=256,
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stride=(2, 1),
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scale=scale)
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self.block_list.append(conv4_2)
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for _ in range(5):
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conv5 = DepthwiseSeparable(
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num_channels=int(512 * scale),
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num_filters1=512,
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num_filters2=512,
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num_groups=512,
|
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stride=1,
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dw_size=5,
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padding=2,
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scale=scale,
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use_se=False)
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self.block_list.append(conv5)
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conv5_6 = DepthwiseSeparable(
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num_channels=int(512 * scale),
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num_filters1=512,
|
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num_filters2=1024,
|
||||
num_groups=512,
|
||||
stride=(2, 1),
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||||
dw_size=5,
|
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padding=2,
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||||
scale=scale,
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use_se=True)
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self.block_list.append(conv5_6)
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conv6 = DepthwiseSeparable(
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num_channels=int(1024 * scale),
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num_filters1=1024,
|
||||
num_filters2=1024,
|
||||
num_groups=1024,
|
||||
stride=last_conv_stride,
|
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dw_size=5,
|
||||
padding=2,
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||||
use_se=True,
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||||
scale=scale)
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self.block_list.append(conv6)
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|
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self.block_list = nn.Sequential(*self.block_list)
|
||||
if last_pool_type == 'avg':
|
||||
self.pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
|
||||
else:
|
||||
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
|
||||
self.out_channels = int(1024 * scale)
|
||||
|
||||
def forward(self, inputs):
|
||||
y = self.conv1(inputs)
|
||||
y = self.block_list(y)
|
||||
y = self.pool(y)
|
||||
return y
|
||||
|
||||
def hardsigmoid(x):
|
||||
return F.relu6(x + 3., inplace=True) / 6.
|
||||
|
||||
class SEModule(nn.Module):
|
||||
def __init__(self, channel, reduction=4):
|
||||
super(SEModule, self).__init__()
|
||||
self.avg_pool = nn.AdaptiveAvgPool2d(1)
|
||||
self.conv1 = nn.Conv2d(
|
||||
in_channels=channel,
|
||||
out_channels=channel // reduction,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
bias=True)
|
||||
self.conv2 = nn.Conv2d(
|
||||
in_channels=channel // reduction,
|
||||
out_channels=channel,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
bias=True)
|
||||
|
||||
def forward(self, inputs):
|
||||
outputs = self.avg_pool(inputs)
|
||||
outputs = self.conv1(outputs)
|
||||
outputs = F.relu(outputs)
|
||||
outputs = self.conv2(outputs)
|
||||
outputs = hardsigmoid(outputs)
|
||||
x = torch.mul(inputs, outputs)
|
||||
|
||||
return x
|
||||
@@ -0,0 +1,591 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from torch.nn.init import trunc_normal_, zeros_, ones_
|
||||
from torch.nn import functional
|
||||
|
||||
|
||||
def drop_path(x, drop_prob=0., training=False):
|
||||
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
||||
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
|
||||
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
|
||||
"""
|
||||
if drop_prob == 0. or not training:
|
||||
return x
|
||||
keep_prob = torch.tensor(1 - drop_prob)
|
||||
shape = (x.size()[0], ) + (1, ) * (x.ndim - 1)
|
||||
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype)
|
||||
random_tensor = torch.floor(random_tensor) # binarize
|
||||
output = x.divide(keep_prob) * random_tensor
|
||||
return output
|
||||
|
||||
|
||||
class Swish(nn.Module):
|
||||
def __int__(self):
|
||||
super(Swish, self).__int__()
|
||||
|
||||
def forward(self,x):
|
||||
return x*torch.sigmoid(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 = act()
|
||||
|
||||
def forward(self, inputs):
|
||||
out = self.conv(inputs)
|
||||
out = self.norm(out)
|
||||
out = self.act(out)
|
||||
return out
|
||||
|
||||
|
||||
class DropPath(nn.Module):
|
||||
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
||||
"""
|
||||
|
||||
def __init__(self, drop_prob=None):
|
||||
super(DropPath, self).__init__()
|
||||
self.drop_prob = drop_prob
|
||||
|
||||
def forward(self, x):
|
||||
return drop_path(x, self.drop_prob, self.training)
|
||||
|
||||
|
||||
class Identity(nn.Module):
|
||||
def __init__(self):
|
||||
super(Identity, self).__init__()
|
||||
|
||||
def forward(self, input):
|
||||
return input
|
||||
|
||||
|
||||
class Mlp(nn.Module):
|
||||
def __init__(self,
|
||||
in_features,
|
||||
hidden_features=None,
|
||||
out_features=None,
|
||||
act_layer=nn.GELU,
|
||||
drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
if isinstance(act_layer, str):
|
||||
self.act = Swish()
|
||||
else:
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class ConvMixer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
num_heads=8,
|
||||
HW=(8, 25),
|
||||
local_k=(3, 3), ):
|
||||
super().__init__()
|
||||
self.HW = HW
|
||||
self.dim = dim
|
||||
self.local_mixer = nn.Conv2d(
|
||||
dim,
|
||||
dim,
|
||||
local_k,
|
||||
1, (local_k[0] // 2, local_k[1] // 2),
|
||||
groups=num_heads,
|
||||
# weight_attr=ParamAttr(initializer=KaimingNormal())
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
h = self.HW[0]
|
||||
w = self.HW[1]
|
||||
x = x.transpose([0, 2, 1]).reshape([0, self.dim, h, w])
|
||||
x = self.local_mixer(x)
|
||||
x = x.flatten(2).transpose([0, 2, 1])
|
||||
return x
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self,
|
||||
dim,
|
||||
num_heads=8,
|
||||
mixer='Global',
|
||||
HW=(8, 25),
|
||||
local_k=(7, 11),
|
||||
qkv_bias=False,
|
||||
qk_scale=None,
|
||||
attn_drop=0.,
|
||||
proj_drop=0.):
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim**-0.5
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
self.HW = HW
|
||||
if HW is not None:
|
||||
H = HW[0]
|
||||
W = HW[1]
|
||||
self.N = H * W
|
||||
self.C = dim
|
||||
if mixer == 'Local' and HW is not None:
|
||||
hk = local_k[0]
|
||||
wk = local_k[1]
|
||||
mask = torch.ones([H * W, H + hk - 1, W + wk - 1])
|
||||
for h in range(0, H):
|
||||
for w in range(0, W):
|
||||
mask[h * W + w, h:h + hk, w:w + wk] = 0.
|
||||
mask_paddle = mask[:, hk // 2:H + hk // 2, wk // 2:W + wk //
|
||||
2].flatten(1)
|
||||
mask_inf = torch.full([H * W, H * W],fill_value=float('-inf'))
|
||||
mask = torch.where(mask_paddle < 1, mask_paddle, mask_inf)
|
||||
self.mask = mask[None,None,:]
|
||||
# self.mask = mask.unsqueeze([0, 1])
|
||||
self.mixer = mixer
|
||||
|
||||
def forward(self, x):
|
||||
if self.HW is not None:
|
||||
N = self.N
|
||||
C = self.C
|
||||
else:
|
||||
_, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape((-1, N, 3, self.num_heads, C //self.num_heads)).permute((2, 0, 3, 1, 4))
|
||||
q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
|
||||
|
||||
attn = (q.matmul(k.permute((0, 1, 3, 2))))
|
||||
if self.mixer == 'Local':
|
||||
attn += self.mask
|
||||
attn = functional.softmax(attn, dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn.matmul(v)).permute((0, 2, 1, 3)).reshape((-1, N, C))
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
def __init__(self,
|
||||
dim,
|
||||
num_heads,
|
||||
mixer='Global',
|
||||
local_mixer=(7, 11),
|
||||
HW=(8, 25),
|
||||
mlp_ratio=4.,
|
||||
qkv_bias=False,
|
||||
qk_scale=None,
|
||||
drop=0.,
|
||||
attn_drop=0.,
|
||||
drop_path=0.,
|
||||
act_layer=nn.GELU,
|
||||
norm_layer='nn.LayerNorm',
|
||||
epsilon=1e-6,
|
||||
prenorm=True):
|
||||
super().__init__()
|
||||
if isinstance(norm_layer, str):
|
||||
self.norm1 = eval(norm_layer)(dim, eps=epsilon)
|
||||
else:
|
||||
self.norm1 = norm_layer(dim)
|
||||
if mixer == 'Global' or mixer == 'Local':
|
||||
|
||||
self.mixer = Attention(
|
||||
dim,
|
||||
num_heads=num_heads,
|
||||
mixer=mixer,
|
||||
HW=HW,
|
||||
local_k=local_mixer,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
attn_drop=attn_drop,
|
||||
proj_drop=drop)
|
||||
elif mixer == 'Conv':
|
||||
self.mixer = ConvMixer(
|
||||
dim, num_heads=num_heads, HW=HW, local_k=local_mixer)
|
||||
else:
|
||||
raise TypeError("The mixer must be one of [Global, Local, Conv]")
|
||||
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
|
||||
if isinstance(norm_layer, str):
|
||||
self.norm2 = eval(norm_layer)(dim, eps=epsilon)
|
||||
else:
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp_ratio = mlp_ratio
|
||||
self.mlp = Mlp(in_features=dim,
|
||||
hidden_features=mlp_hidden_dim,
|
||||
act_layer=act_layer,
|
||||
drop=drop)
|
||||
self.prenorm = prenorm
|
||||
|
||||
def forward(self, x):
|
||||
if self.prenorm:
|
||||
x = self.norm1(x + self.drop_path(self.mixer(x)))
|
||||
x = self.norm2(x + self.drop_path(self.mlp(x)))
|
||||
else:
|
||||
x = x + self.drop_path(self.mixer(self.norm1(x)))
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||
return x
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
img_size=(32, 100),
|
||||
in_channels=3,
|
||||
embed_dim=768,
|
||||
sub_num=2):
|
||||
super().__init__()
|
||||
num_patches = (img_size[1] // (2 ** sub_num)) * \
|
||||
(img_size[0] // (2 ** sub_num))
|
||||
self.img_size = img_size
|
||||
self.num_patches = num_patches
|
||||
self.embed_dim = embed_dim
|
||||
self.norm = None
|
||||
if sub_num == 2:
|
||||
self.proj = nn.Sequential(
|
||||
ConvBNLayer(
|
||||
in_channels=in_channels,
|
||||
out_channels=embed_dim // 2,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
act=nn.GELU,
|
||||
bias_attr=False),
|
||||
ConvBNLayer(
|
||||
in_channels=embed_dim // 2,
|
||||
out_channels=embed_dim,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
act=nn.GELU,
|
||||
bias_attr=False))
|
||||
if sub_num == 3:
|
||||
self.proj = nn.Sequential(
|
||||
ConvBNLayer(
|
||||
in_channels=in_channels,
|
||||
out_channels=embed_dim // 4,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
act=nn.GELU,
|
||||
bias_attr=False),
|
||||
ConvBNLayer(
|
||||
in_channels=embed_dim // 4,
|
||||
out_channels=embed_dim // 2,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
act=nn.GELU,
|
||||
bias_attr=False),
|
||||
ConvBNLayer(
|
||||
in_channels=embed_dim // 2,
|
||||
out_channels=embed_dim,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
act=nn.GELU,
|
||||
bias_attr=False))
|
||||
|
||||
def forward(self, x):
|
||||
B, C, H, W = x.shape
|
||||
assert H == self.img_size[0] and W == self.img_size[1], \
|
||||
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
|
||||
x = self.proj(x).flatten(2).permute(0, 2, 1)
|
||||
return x
|
||||
|
||||
|
||||
class SubSample(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
types='Pool',
|
||||
stride=(2, 1),
|
||||
sub_norm='nn.LayerNorm',
|
||||
act=None):
|
||||
super().__init__()
|
||||
self.types = types
|
||||
if types == 'Pool':
|
||||
self.avgpool = nn.AvgPool2d(
|
||||
kernel_size=(3, 5), stride=stride, padding=(1, 2))
|
||||
self.maxpool = nn.MaxPool2d(
|
||||
kernel_size=(3, 5), stride=stride, padding=(1, 2))
|
||||
self.proj = nn.Linear(in_channels, out_channels)
|
||||
else:
|
||||
self.conv = nn.Conv2d(
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=stride,
|
||||
padding=1,
|
||||
# weight_attr=ParamAttr(initializer=KaimingNormal())
|
||||
)
|
||||
self.norm = eval(sub_norm)(out_channels)
|
||||
if act is not None:
|
||||
self.act = act()
|
||||
else:
|
||||
self.act = None
|
||||
|
||||
def forward(self, x):
|
||||
|
||||
if self.types == 'Pool':
|
||||
x1 = self.avgpool(x)
|
||||
x2 = self.maxpool(x)
|
||||
x = (x1 + x2) * 0.5
|
||||
out = self.proj(x.flatten(2).permute((0, 2, 1)))
|
||||
else:
|
||||
x = self.conv(x)
|
||||
out = x.flatten(2).permute((0, 2, 1))
|
||||
out = self.norm(out)
|
||||
if self.act is not None:
|
||||
out = self.act(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class SVTRNet(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
img_size=[48, 100],
|
||||
in_channels=3,
|
||||
embed_dim=[64, 128, 256],
|
||||
depth=[3, 6, 3],
|
||||
num_heads=[2, 4, 8],
|
||||
mixer=['Local'] * 6 + ['Global'] *
|
||||
6, # Local atten, Global atten, Conv
|
||||
local_mixer=[[7, 11], [7, 11], [7, 11]],
|
||||
patch_merging='Conv', # Conv, Pool, None
|
||||
mlp_ratio=4,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop_rate=0.,
|
||||
last_drop=0.1,
|
||||
attn_drop_rate=0.,
|
||||
drop_path_rate=0.1,
|
||||
norm_layer='nn.LayerNorm',
|
||||
sub_norm='nn.LayerNorm',
|
||||
epsilon=1e-6,
|
||||
out_channels=192,
|
||||
out_char_num=25,
|
||||
block_unit='Block',
|
||||
act='nn.GELU',
|
||||
last_stage=True,
|
||||
sub_num=2,
|
||||
prenorm=True,
|
||||
use_lenhead=False,
|
||||
**kwargs):
|
||||
super().__init__()
|
||||
self.img_size = img_size
|
||||
self.embed_dim = embed_dim
|
||||
self.out_channels = out_channels
|
||||
self.prenorm = prenorm
|
||||
patch_merging = None if patch_merging != 'Conv' and patch_merging != 'Pool' else patch_merging
|
||||
self.patch_embed = PatchEmbed(
|
||||
img_size=img_size,
|
||||
in_channels=in_channels,
|
||||
embed_dim=embed_dim[0],
|
||||
sub_num=sub_num)
|
||||
num_patches = self.patch_embed.num_patches
|
||||
self.HW = [img_size[0] // (2**sub_num), img_size[1] // (2**sub_num)]
|
||||
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim[0]))
|
||||
# self.pos_embed = self.create_parameter(
|
||||
# shape=[1, num_patches, embed_dim[0]], default_initializer=zeros_)
|
||||
|
||||
# self.add_parameter("pos_embed", self.pos_embed)
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
Block_unit = eval(block_unit)
|
||||
|
||||
dpr = np.linspace(0, drop_path_rate, sum(depth))
|
||||
self.blocks1 = nn.ModuleList(
|
||||
[
|
||||
Block_unit(
|
||||
dim=embed_dim[0],
|
||||
num_heads=num_heads[0],
|
||||
mixer=mixer[0:depth[0]][i],
|
||||
HW=self.HW,
|
||||
local_mixer=local_mixer[0],
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop_rate,
|
||||
act_layer=eval(act),
|
||||
attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[0:depth[0]][i],
|
||||
norm_layer=norm_layer,
|
||||
epsilon=epsilon,
|
||||
prenorm=prenorm) for i in range(depth[0])
|
||||
]
|
||||
)
|
||||
if patch_merging is not None:
|
||||
self.sub_sample1 = SubSample(
|
||||
embed_dim[0],
|
||||
embed_dim[1],
|
||||
sub_norm=sub_norm,
|
||||
stride=[2, 1],
|
||||
types=patch_merging)
|
||||
HW = [self.HW[0] // 2, self.HW[1]]
|
||||
else:
|
||||
HW = self.HW
|
||||
self.patch_merging = patch_merging
|
||||
self.blocks2 = nn.ModuleList([
|
||||
Block_unit(
|
||||
dim=embed_dim[1],
|
||||
num_heads=num_heads[1],
|
||||
mixer=mixer[depth[0]:depth[0] + depth[1]][i],
|
||||
HW=HW,
|
||||
local_mixer=local_mixer[1],
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop_rate,
|
||||
act_layer=eval(act),
|
||||
attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[depth[0]:depth[0] + depth[1]][i],
|
||||
norm_layer=norm_layer,
|
||||
epsilon=epsilon,
|
||||
prenorm=prenorm) for i in range(depth[1])
|
||||
])
|
||||
if patch_merging is not None:
|
||||
self.sub_sample2 = SubSample(
|
||||
embed_dim[1],
|
||||
embed_dim[2],
|
||||
sub_norm=sub_norm,
|
||||
stride=[2, 1],
|
||||
types=patch_merging)
|
||||
HW = [self.HW[0] // 4, self.HW[1]]
|
||||
else:
|
||||
HW = self.HW
|
||||
self.blocks3 = nn.ModuleList([
|
||||
Block_unit(
|
||||
dim=embed_dim[2],
|
||||
num_heads=num_heads[2],
|
||||
mixer=mixer[depth[0] + depth[1]:][i],
|
||||
HW=HW,
|
||||
local_mixer=local_mixer[2],
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop_rate,
|
||||
act_layer=eval(act),
|
||||
attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[depth[0] + depth[1]:][i],
|
||||
norm_layer=norm_layer,
|
||||
epsilon=epsilon,
|
||||
prenorm=prenorm) for i in range(depth[2])
|
||||
])
|
||||
self.last_stage = last_stage
|
||||
if last_stage:
|
||||
self.avg_pool = nn.AdaptiveAvgPool2d((1, out_char_num))
|
||||
self.last_conv = nn.Conv2d(
|
||||
in_channels=embed_dim[2],
|
||||
out_channels=self.out_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
bias=False)
|
||||
self.hardswish = nn.Hardswish()
|
||||
self.dropout = nn.Dropout(p=last_drop)
|
||||
if not prenorm:
|
||||
self.norm = eval(norm_layer)(embed_dim[-1], epsilon=epsilon)
|
||||
self.use_lenhead = use_lenhead
|
||||
if use_lenhead:
|
||||
self.len_conv = nn.Linear(embed_dim[2], self.out_channels)
|
||||
self.hardswish_len = nn.Hardswish()
|
||||
self.dropout_len = nn.Dropout(
|
||||
p=last_drop)
|
||||
|
||||
trunc_normal_(self.pos_embed,std=.02)
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight,std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
zeros_(m.bias)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
zeros_(m.bias)
|
||||
ones_(m.weight)
|
||||
|
||||
def forward_features(self, x):
|
||||
x = self.patch_embed(x)
|
||||
x = x + self.pos_embed
|
||||
x = self.pos_drop(x)
|
||||
for blk in self.blocks1:
|
||||
x = blk(x)
|
||||
if self.patch_merging is not None:
|
||||
x = self.sub_sample1(
|
||||
x.permute([0, 2, 1]).reshape(
|
||||
[-1, self.embed_dim[0], self.HW[0], self.HW[1]]))
|
||||
for blk in self.blocks2:
|
||||
x = blk(x)
|
||||
if self.patch_merging is not None:
|
||||
x = self.sub_sample2(
|
||||
x.permute([0, 2, 1]).reshape(
|
||||
[-1, self.embed_dim[1], self.HW[0] // 2, self.HW[1]]))
|
||||
for blk in self.blocks3:
|
||||
x = blk(x)
|
||||
if not self.prenorm:
|
||||
x = self.norm(x)
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
if self.use_lenhead:
|
||||
len_x = self.len_conv(x.mean(1))
|
||||
len_x = self.dropout_len(self.hardswish_len(len_x))
|
||||
if self.last_stage:
|
||||
if self.patch_merging is not None:
|
||||
h = self.HW[0] // 4
|
||||
else:
|
||||
h = self.HW[0]
|
||||
x = self.avg_pool(
|
||||
x.permute([0, 2, 1]).reshape(
|
||||
[-1, self.embed_dim[2], h, self.HW[1]]))
|
||||
x = self.last_conv(x)
|
||||
x = self.hardswish(x)
|
||||
x = self.dropout(x)
|
||||
if self.use_lenhead:
|
||||
return x, len_x
|
||||
return x
|
||||
|
||||
|
||||
if __name__=="__main__":
|
||||
a = torch.rand(1,3,48,100)
|
||||
svtr = SVTRNet()
|
||||
|
||||
out = svtr(a)
|
||||
print(svtr)
|
||||
print(out.size())
|
||||
@@ -0,0 +1,74 @@
|
||||
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class Hswish(nn.Module):
|
||||
def __init__(self, inplace=True):
|
||||
super(Hswish, self).__init__()
|
||||
self.inplace = inplace
|
||||
|
||||
def forward(self, x):
|
||||
return x * F.relu6(x + 3., inplace=self.inplace) / 6.
|
||||
|
||||
# out = max(0, min(1, slop*x+offset))
|
||||
# paddle.fluid.layers.hard_sigmoid(x, slope=0.2, offset=0.5, name=None)
|
||||
class Hsigmoid(nn.Module):
|
||||
def __init__(self, inplace=True):
|
||||
super(Hsigmoid, self).__init__()
|
||||
self.inplace = inplace
|
||||
|
||||
def forward(self, x):
|
||||
# torch: F.relu6(x + 3., inplace=self.inplace) / 6.
|
||||
# paddle: F.relu6(1.2 * x + 3., inplace=self.inplace) / 6.
|
||||
return F.relu6(1.2 * x + 3., inplace=self.inplace) / 6.
|
||||
|
||||
class GELU(nn.Module):
|
||||
def __init__(self, inplace=True):
|
||||
super(GELU, self).__init__()
|
||||
self.inplace = inplace
|
||||
|
||||
def forward(self, x):
|
||||
return torch.nn.functional.gelu(x)
|
||||
|
||||
|
||||
class Swish(nn.Module):
|
||||
def __init__(self, inplace=True):
|
||||
super(Swish, self).__init__()
|
||||
self.inplace = inplace
|
||||
|
||||
def forward(self, x):
|
||||
if self.inplace:
|
||||
x.mul_(torch.sigmoid(x))
|
||||
return x
|
||||
else:
|
||||
return x*torch.sigmoid(x)
|
||||
|
||||
|
||||
class Activation(nn.Module):
|
||||
def __init__(self, act_type, inplace=True):
|
||||
super(Activation, self).__init__()
|
||||
act_type = act_type.lower()
|
||||
if act_type == 'relu':
|
||||
self.act = nn.ReLU(inplace=inplace)
|
||||
elif act_type == 'relu6':
|
||||
self.act = nn.ReLU6(inplace=inplace)
|
||||
elif act_type == 'sigmoid':
|
||||
raise NotImplementedError
|
||||
elif act_type == 'hard_sigmoid':
|
||||
self.act = Hsigmoid(inplace)
|
||||
elif act_type == 'hard_swish':
|
||||
self.act = Hswish(inplace=inplace)
|
||||
elif act_type == 'leakyrelu':
|
||||
self.act = nn.LeakyReLU(inplace=inplace)
|
||||
elif act_type == 'gelu':
|
||||
self.act = GELU(inplace=inplace)
|
||||
elif act_type == 'swish':
|
||||
self.act = Swish(inplace=inplace)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
def forward(self, inputs):
|
||||
return self.act(inputs)
|
||||
@@ -0,0 +1,95 @@
|
||||
0
|
||||
1
|
||||
2
|
||||
3
|
||||
4
|
||||
5
|
||||
6
|
||||
7
|
||||
8
|
||||
9
|
||||
:
|
||||
;
|
||||
<
|
||||
=
|
||||
>
|
||||
?
|
||||
@
|
||||
A
|
||||
B
|
||||
C
|
||||
D
|
||||
E
|
||||
F
|
||||
G
|
||||
H
|
||||
I
|
||||
J
|
||||
K
|
||||
L
|
||||
M
|
||||
N
|
||||
O
|
||||
P
|
||||
Q
|
||||
R
|
||||
S
|
||||
T
|
||||
U
|
||||
V
|
||||
W
|
||||
X
|
||||
Y
|
||||
Z
|
||||
[
|
||||
\
|
||||
]
|
||||
^
|
||||
_
|
||||
`
|
||||
a
|
||||
b
|
||||
c
|
||||
d
|
||||
e
|
||||
f
|
||||
g
|
||||
h
|
||||
i
|
||||
j
|
||||
k
|
||||
l
|
||||
m
|
||||
n
|
||||
o
|
||||
p
|
||||
q
|
||||
r
|
||||
s
|
||||
t
|
||||
u
|
||||
v
|
||||
w
|
||||
x
|
||||
y
|
||||
z
|
||||
{
|
||||
|
|
||||
}
|
||||
~
|
||||
!
|
||||
"
|
||||
#
|
||||
$
|
||||
%
|
||||
&
|
||||
'
|
||||
(
|
||||
)
|
||||
*
|
||||
+
|
||||
,
|
||||
-
|
||||
.
|
||||
/
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user