FlintyLemming/ComfyUI-AnyText
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Init

Browse Source
This commit is contained in:
FlintyLemming
2024-09-25 15:18:31 +08:00
commit e1911954ed
99 changed files with 38062 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

210
AnyText/AnyText_scripts/cldm/ocr_recog/RNN.py Normal file
View File

@@ -0,0 +1,210 @@
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)

48
AnyText/AnyText_scripts/cldm/ocr_recog/RecCTCHead.py Normal file
View File

@@ -0,0 +1,48 @@
from torch import nn
class CTCHead(nn.Module):
def __init__(self,
in_channels,
out_channels=6625,
fc_decay=0.0004,
mid_channels=None,
return_feats=False,
**kwargs):
super(CTCHead, self).__init__()
if mid_channels is None:
self.fc = nn.Linear(
in_channels,
out_channels,
bias=True,)
else:
self.fc1 = nn.Linear(
in_channels,
mid_channels,
bias=True,
)
self.fc2 = nn.Linear(
mid_channels,
out_channels,
bias=True,
)
self.out_channels = out_channels
self.mid_channels = mid_channels
self.return_feats = return_feats
def forward(self, x, labels=None):
if self.mid_channels is None:
predicts = self.fc(x)
else:
x = self.fc1(x)
predicts = self.fc2(x)
if self.return_feats:
result = dict()
result['ctc'] = predicts
result['ctc_neck'] = x
else:
result = predicts
return result

45
AnyText/AnyText_scripts/cldm/ocr_recog/RecModel.py Normal file
View File

@@ -0,0 +1,45 @@
from torch import nn
from .RNN import SequenceEncoder, Im2Seq, Im2Im
from .RecMv1_enhance import MobileNetV1Enhance
from .RecCTCHead import CTCHead
backbone_dict = {"MobileNetV1Enhance":MobileNetV1Enhance}
neck_dict = {'SequenceEncoder': SequenceEncoder, 'Im2Seq': Im2Seq,'None':Im2Im}
head_dict = {'CTCHead':CTCHead}
class RecModel(nn.Module):
def __init__(self, config):
super().__init__()
assert 'in_channels' in config, 'in_channels must in model config'
backbone_type = config.backbone.pop('type')
assert backbone_type in backbone_dict, f'backbone.type must in {backbone_dict}'
self.backbone = backbone_dict[backbone_type](config.in_channels, **config.backbone)
neck_type = config.neck.pop('type')
assert neck_type in neck_dict, f'neck.type must in {neck_dict}'
self.neck = neck_dict[neck_type](self.backbone.out_channels, **config.neck)
head_type = config.head.pop('type')
assert head_type in head_dict, f'head.type must in {head_dict}'
self.head = head_dict[head_type](self.neck.out_channels, **config.head)
self.name = f'RecModel_{backbone_type}_{neck_type}_{head_type}'
def load_3rd_state_dict(self, _3rd_name, _state):
self.backbone.load_3rd_state_dict(_3rd_name, _state)
self.neck.load_3rd_state_dict(_3rd_name, _state)
self.head.load_3rd_state_dict(_3rd_name, _state)
def forward(self, x):
x = self.backbone(x)
x = self.neck(x)
x = self.head(x)
return x
def encode(self, x):
x = self.backbone(x)
x = self.neck(x)
x = self.head.ctc_encoder(x)
return x

233
AnyText/AnyText_scripts/cldm/ocr_recog/RecMv1_enhance.py Normal file
View File

@@ -0,0 +1,233 @@
import os, sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from .common import Activation
class ConvBNLayer(nn.Module):
def __init__(self,
num_channels,
filter_size,
num_filters,
stride,
padding,
channels=None,
num_groups=1,
act='hard_swish'):
super(ConvBNLayer, self).__init__()
self.act = act
self._conv = nn.Conv2d(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
bias=False)
self._batch_norm = nn.BatchNorm2d(
num_filters,
)
if self.act is not None:
self._act = Activation(act_type=act, inplace=True)
def forward(self, inputs):
y = self._conv(inputs)
y = self._batch_norm(y)
if self.act is not None:
y = self._act(y)
return y
class DepthwiseSeparable(nn.Module):
def __init__(self,
num_channels,
num_filters1,
num_filters2,
num_groups,
stride,
scale,
dw_size=3,
padding=1,
use_se=False):
super(DepthwiseSeparable, self).__init__()
self.use_se = use_se
self._depthwise_conv = ConvBNLayer(
num_channels=num_channels,
num_filters=int(num_filters1 * scale),
filter_size=dw_size,
stride=stride,
padding=padding,
num_groups=int(num_groups * scale))
if use_se:
self._se = SEModule(int(num_filters1 * scale))
self._pointwise_conv = ConvBNLayer(
num_channels=int(num_filters1 * scale),
filter_size=1,
num_filters=int(num_filters2 * scale),
stride=1,
padding=0)
def forward(self, inputs):
y = self._depthwise_conv(inputs)
if self.use_se:
y = self._se(y)
y = self._pointwise_conv(y)
return y
class MobileNetV1Enhance(nn.Module):
def __init__(self,
in_channels=3,
scale=0.5,
last_conv_stride=1,
last_pool_type='max',
**kwargs):
super().__init__()
self.scale = scale
self.block_list = []
self.conv1 = ConvBNLayer(
num_channels=in_channels,
filter_size=3,
channels=3,
num_filters=int(32 * scale),
stride=2,
padding=1)
conv2_1 = DepthwiseSeparable(
num_channels=int(32 * scale),
num_filters1=32,
num_filters2=64,
num_groups=32,
stride=1,
scale=scale)
self.block_list.append(conv2_1)
conv2_2 = DepthwiseSeparable(
num_channels=int(64 * scale),
num_filters1=64,
num_filters2=128,
num_groups=64,
stride=1,
scale=scale)
self.block_list.append(conv2_2)
conv3_1 = DepthwiseSeparable(
num_channels=int(128 * scale),
num_filters1=128,
num_filters2=128,
num_groups=128,
stride=1,
scale=scale)
self.block_list.append(conv3_1)
conv3_2 = DepthwiseSeparable(
num_channels=int(128 * scale),
num_filters1=128,
num_filters2=256,
num_groups=128,
stride=(2, 1),
scale=scale)
self.block_list.append(conv3_2)
conv4_1 = DepthwiseSeparable(
num_channels=int(256 * scale),
num_filters1=256,
num_filters2=256,
num_groups=256,
stride=1,
scale=scale)
self.block_list.append(conv4_1)
conv4_2 = DepthwiseSeparable(
num_channels=int(256 * scale),
num_filters1=256,
num_filters2=512,
num_groups=256,
stride=(2, 1),
scale=scale)
self.block_list.append(conv4_2)
for _ in range(5):
conv5 = DepthwiseSeparable(
num_channels=int(512 * scale),
num_filters1=512,
num_filters2=512,
num_groups=512,
stride=1,
dw_size=5,
padding=2,
scale=scale,
use_se=False)
self.block_list.append(conv5)
conv5_6 = DepthwiseSeparable(
num_channels=int(512 * scale),
num_filters1=512,
num_filters2=1024,
num_groups=512,
stride=(2, 1),
dw_size=5,
padding=2,
scale=scale,
use_se=True)
self.block_list.append(conv5_6)
conv6 = DepthwiseSeparable(
num_channels=int(1024 * scale),
num_filters1=1024,
num_filters2=1024,
num_groups=1024,
stride=last_conv_stride,
dw_size=5,
padding=2,
use_se=True,
scale=scale)
self.block_list.append(conv6)
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

591
AnyText/AnyText_scripts/cldm/ocr_recog/RecSVTR.py Normal file
View File

@@ -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())

74
AnyText/AnyText_scripts/cldm/ocr_recog/common.py Normal file
View File

@@ -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)

95
AnyText/AnyText_scripts/cldm/ocr_recog/en_dict.txt Normal file
View File

@@ -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
{
|
}
~
!
"
#
$
%
&
'
(
)
*
+
,
-
.
/

6623
AnyText/AnyText_scripts/cldm/ocr_recog/ppocr_keys_v1.txt Normal file
View File

File diff suppressed because it is too large Load Diff