mirror of
https://github.com/alibaba/EasyCV.git
synced 2025-06-03 14:49:00 +08:00
570 lines
18 KiB
Python
570 lines
18 KiB
Python
# Modified from https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppocr/modeling/backbones/rec_svtrnet.py
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from easycv.models.registry import BACKBONES
|
|
from easycv.models.utils.transformer import DropPath
|
|
from .det_mobilenet_v3 import Activation
|
|
|
|
|
|
class ConvBNLayer(nn.Module):
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=0,
|
|
bias_attr=False,
|
|
groups=1,
|
|
act='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,
|
|
bias=bias_attr)
|
|
self.norm = nn.BatchNorm2d(out_channels)
|
|
self.act = Activation(act_type=act, inplace=True)
|
|
|
|
def forward(self, inputs):
|
|
out = self.conv(inputs)
|
|
out = self.norm(out)
|
|
out = self.act(out)
|
|
return out
|
|
|
|
|
|
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='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)
|
|
self.act = Activation(act_type=act_layer, inplace=True)
|
|
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,
|
|
)
|
|
|
|
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).permute(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, dtype=torch.float32)
|
|
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'),
|
|
dtype=torch.float32)
|
|
mask = torch.where(mask_paddle < 1, mask_paddle, mask_inf)
|
|
self.mask = mask.unsqueeze(1).unsqueeze(0)
|
|
# self.mask = mask[None, None, :]
|
|
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 = nn.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='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='gelu',
|
|
bias_attr=True),
|
|
ConvBNLayer(
|
|
in_channels=embed_dim // 2,
|
|
out_channels=embed_dim,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
act='gelu',
|
|
bias_attr=True))
|
|
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='gelu',
|
|
bias_attr=True),
|
|
ConvBNLayer(
|
|
in_channels=embed_dim // 4,
|
|
out_channels=embed_dim // 2,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
act='gelu',
|
|
bias_attr=True),
|
|
ConvBNLayer(
|
|
in_channels=embed_dim // 2,
|
|
out_channels=embed_dim,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
act='gelu',
|
|
bias_attr=True))
|
|
|
|
def forward(self, x):
|
|
B, C, H, W = x.shape
|
|
assert H == self.img_size[0] and W == self.img_size[1], \
|
|
"Input image size ({}*{}) doesn't match model ({}*{}).".format(
|
|
H, W, 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,
|
|
)
|
|
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
|
|
|
|
|
|
@BACKBONES.register_module()
|
|
class SVTRNet(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
img_size=[32, 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=0.0,
|
|
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_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 = Activation('hard_swish', inplace=True)
|
|
# self.dropout = nn.Dropout(p=last_drop, mode="downscale_in_infer")
|
|
self.dropout = nn.Dropout(p=last_drop)
|
|
if not prenorm:
|
|
self.norm = eval(norm_layer)(embed_dim[-1], eps=epsilon)
|
|
self.use_lenhead = use_lenhead
|
|
if use_lenhead:
|
|
self.len_conv = nn.Linear(embed_dim[2], self.out_channels)
|
|
self.hardswish_len = Activation(
|
|
'hard_swish', inplace=True) # nn.Hardswish()
|
|
self.dropout_len = nn.Dropout(p=last_drop)
|
|
|
|
torch.nn.init.xavier_normal_(self.pos_embed)
|
|
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_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
|