mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-10-31 09:49:30 +00:00
585 lines
19 KiB
Python
585 lines
19 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from paddle import ParamAttr
|
|
from paddle.nn.initializer import KaimingNormal
|
|
import numpy as np
|
|
import paddle
|
|
import paddle.nn as nn
|
|
from paddle.nn.initializer import TruncatedNormal, Constant, Normal
|
|
|
|
trunc_normal_ = TruncatedNormal(std=.02)
|
|
normal_ = Normal
|
|
zeros_ = Constant(value=0.)
|
|
ones_ = Constant(value=1.)
|
|
|
|
|
|
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 = paddle.to_tensor(1 - drop_prob)
|
|
shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
|
|
random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
|
|
random_tensor = paddle.floor(random_tensor) # binarize
|
|
output = x.divide(keep_prob) * random_tensor
|
|
return output
|
|
|
|
|
|
class ConvBNLayer(nn.Layer):
|
|
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_attr=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.Layer):
|
|
"""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.Layer):
|
|
def __init__(self):
|
|
super(Identity, self).__init__()
|
|
|
|
def forward(self, input):
|
|
return input
|
|
|
|
|
|
class Mlp(nn.Layer):
|
|
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)
|
|
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.Layer):
|
|
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.Layer):
|
|
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_attr=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 = paddle.ones([H * W, H + hk - 1, W + wk - 1], dtype='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 = paddle.full([H * W, H * W], '-inf', dtype='float32')
|
|
mask = paddle.where(mask_paddle < 1, mask_paddle, mask_inf)
|
|
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((0, N, 3, self.num_heads, C //
|
|
self.num_heads)).transpose((2, 0, 3, 1, 4))
|
|
q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
|
|
|
|
attn = (q.matmul(k.transpose((0, 1, 3, 2))))
|
|
if self.mixer == 'Local':
|
|
attn += self.mask
|
|
attn = nn.functional.softmax(attn, axis=-1)
|
|
attn = self.attn_drop(attn)
|
|
|
|
x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape((0, N, C))
|
|
x = self.proj(x)
|
|
x = self.proj_drop(x)
|
|
return x
|
|
|
|
|
|
class Block(nn.Layer):
|
|
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, epsilon=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, epsilon=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.Layer):
|
|
""" 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=None),
|
|
ConvBNLayer(
|
|
in_channels=embed_dim // 2,
|
|
out_channels=embed_dim,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
act=nn.GELU,
|
|
bias_attr=None))
|
|
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=None),
|
|
ConvBNLayer(
|
|
in_channels=embed_dim // 4,
|
|
out_channels=embed_dim // 2,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
act=nn.GELU,
|
|
bias_attr=None),
|
|
ConvBNLayer(
|
|
in_channels=embed_dim // 2,
|
|
out_channels=embed_dim,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
act=nn.GELU,
|
|
bias_attr=None))
|
|
|
|
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).transpose((0, 2, 1))
|
|
return x
|
|
|
|
|
|
class SubSample(nn.Layer):
|
|
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).transpose((0, 2, 1)))
|
|
else:
|
|
x = self.conv(x)
|
|
out = x.flatten(2).transpose((0, 2, 1))
|
|
out = self.norm(out)
|
|
if self.act is not None:
|
|
out = self.act(out)
|
|
|
|
return out
|
|
|
|
|
|
class SVTRNet(nn.Layer):
|
|
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=0.1,
|
|
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 = 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.LayerList([
|
|
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.LayerList([
|
|
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.LayerList([
|
|
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_attr=False)
|
|
self.hardswish = nn.Hardswish()
|
|
self.dropout = nn.Dropout(p=last_drop, mode="downscale_in_infer")
|
|
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, mode="downscale_in_infer")
|
|
|
|
trunc_normal_(self.pos_embed)
|
|
self.apply(self._init_weights)
|
|
|
|
def _init_weights(self, m):
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight)
|
|
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.transpose([0, 2, 1]).reshape(
|
|
[0, 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.transpose([0, 2, 1]).reshape(
|
|
[0, 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.transpose([0, 2, 1]).reshape(
|
|
[0, 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
|