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# copyright (c) 2021 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.
"""
This code is refer from:
https://github.com/FangShancheng/ABINet/tree/main/modules
"""
import math
import paddle
from paddle import nn
import paddle.nn.functional as F
from paddle.nn import LayerList
from ppocr.modeling.heads.rec_nrtr_head import TransformerBlock, PositionalEncoding
class BCNLanguage(nn.Layer):
def __init__(self,
d_model=512,
nhead=8,
num_layers=4,
dim_feedforward=2048,
dropout=0.,
max_length=25,
detach=True,
num_classes=37):
super().__init__()
self.d_model = d_model
self.detach = detach
self.max_length = max_length + 1 # additional stop token
self.proj = nn.Linear(num_classes, d_model, bias_attr=False)
self.token_encoder = PositionalEncoding(
dropout=0.1, dim=d_model, max_len=self.max_length)
self.pos_encoder = PositionalEncoding(
dropout=0, dim=d_model, max_len=self.max_length)
self.decoder = nn.LayerList([
TransformerBlock(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
attention_dropout_rate=dropout,
residual_dropout_rate=dropout,
with_self_attn=False,
with_cross_attn=True) for i in range(num_layers)
])
self.cls = nn.Linear(d_model, num_classes)
def forward(self, tokens, lengths):
"""
Args:
tokens: (B, N, C) where N is length, B is batch size and C is classes number
lengths: (B,)
"""
if self.detach: tokens = tokens.detach()
embed = self.proj(tokens) # (B, N, C)
embed = self.token_encoder(embed) # (B, N, C)
padding_mask = _get_mask(lengths, self.max_length)
zeros = paddle.zeros_like(embed) # (B, N, C)
qeury = self.pos_encoder(zeros)
for decoder_layer in self.decoder:
qeury = decoder_layer(qeury, embed, cross_mask=padding_mask)
output = qeury # (B, N, C)
logits = self.cls(output) # (B, N, C)
return output, logits
def encoder_layer(in_c, out_c, k=3, s=2, p=1):
return nn.Sequential(
nn.Conv2D(in_c, out_c, k, s, p), nn.BatchNorm2D(out_c), nn.ReLU())
def decoder_layer(in_c,
out_c,
k=3,
s=1,
p=1,
mode='nearest',
scale_factor=None,
size=None):
align_corners = False if mode == 'nearest' else True
return nn.Sequential(
nn.Upsample(
size=size,
scale_factor=scale_factor,
mode=mode,
align_corners=align_corners),
nn.Conv2D(in_c, out_c, k, s, p),
nn.BatchNorm2D(out_c),
nn.ReLU())
class PositionAttention(nn.Layer):
def __init__(self,
max_length,
in_channels=512,
num_channels=64,
h=8,
w=32,
mode='nearest',
**kwargs):
super().__init__()
self.max_length = max_length
self.k_encoder = nn.Sequential(
encoder_layer(
in_channels, num_channels, s=(1, 2)),
encoder_layer(
num_channels, num_channels, s=(2, 2)),
encoder_layer(
num_channels, num_channels, s=(2, 2)),
encoder_layer(
num_channels, num_channels, s=(2, 2)))
self.k_decoder = nn.Sequential(
decoder_layer(
num_channels, num_channels, scale_factor=2, mode=mode),
decoder_layer(
num_channels, num_channels, scale_factor=2, mode=mode),
decoder_layer(
num_channels, num_channels, scale_factor=2, mode=mode),
decoder_layer(
num_channels, in_channels, size=(h, w), mode=mode))
self.pos_encoder = PositionalEncoding(
dropout=0, dim=in_channels, max_len=max_length)
self.project = nn.Linear(in_channels, in_channels)
def forward(self, x):
B, C, H, W = x.shape
k, v = x, x
# calculate key vector
features = []
for i in range(0, len(self.k_encoder)):
k = self.k_encoder[i](k)
features.append(k)
for i in range(0, len(self.k_decoder) - 1):
k = self.k_decoder[i](k)
# print(k.shape, features[len(self.k_decoder) - 2 - i].shape)
k = k + features[len(self.k_decoder) - 2 - i]
k = self.k_decoder[-1](k)
# calculate query vector
# TODO q=f(q,k)
zeros = paddle.zeros(
(B, self.max_length, C), dtype=x.dtype) # (T, N, C)
q = self.pos_encoder(zeros) # (B, N, C)
q = self.project(q) # (B, N, C)
# calculate attention
attn_scores = q @k.flatten(2) # (B, N, (H*W))
attn_scores = attn_scores / (C**0.5)
attn_scores = F.softmax(attn_scores, axis=-1)
v = v.flatten(2).transpose([0, 2, 1]) # (B, (H*W), C)
attn_vecs = attn_scores @v # (B, N, C)
return attn_vecs, attn_scores.reshape([0, self.max_length, H, W])
class ABINetHead(nn.Layer):
def __init__(self,
in_channels,
out_channels,
d_model=512,
nhead=8,
num_layers=3,
dim_feedforward=2048,
dropout=0.1,
max_length=25,
use_lang=False,
iter_size=1):
super().__init__()
self.max_length = max_length + 1
self.pos_encoder = PositionalEncoding(
dropout=0.1, dim=d_model, max_len=8 * 32)
self.encoder = nn.LayerList([
TransformerBlock(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
attention_dropout_rate=dropout,
residual_dropout_rate=dropout,
with_self_attn=True,
with_cross_attn=False) for i in range(num_layers)
])
self.decoder = PositionAttention(
max_length=max_length + 1, # additional stop token
mode='nearest', )
self.out_channels = out_channels
self.cls = nn.Linear(d_model, self.out_channels)
self.use_lang = use_lang
if use_lang:
self.iter_size = iter_size
self.language = BCNLanguage(
d_model=d_model,
nhead=nhead,
num_layers=4,
dim_feedforward=dim_feedforward,
dropout=dropout,
max_length=max_length,
num_classes=self.out_channels)
# alignment
self.w_att_align = nn.Linear(2 * d_model, d_model)
self.cls_align = nn.Linear(d_model, self.out_channels)
def forward(self, x, targets=None):
x = x.transpose([0, 2, 3, 1])
_, H, W, C = x.shape
feature = x.flatten(1, 2)
feature = self.pos_encoder(feature)
for encoder_layer in self.encoder:
feature = encoder_layer(feature)
feature = feature.reshape([0, H, W, C]).transpose([0, 3, 1, 2])
v_feature, attn_scores = self.decoder(
feature) # (B, N, C), (B, C, H, W)
vis_logits = self.cls(v_feature) # (B, N, C)
logits = vis_logits
vis_lengths = _get_length(vis_logits)
if self.use_lang:
align_logits = vis_logits
align_lengths = vis_lengths
all_l_res, all_a_res = [], []
for i in range(self.iter_size):
tokens = F.softmax(align_logits, axis=-1)
lengths = align_lengths
lengths = paddle.clip(
lengths, 2, self.max_length) # TODO:move to langauge model
l_feature, l_logits = self.language(tokens, lengths)
# alignment
all_l_res.append(l_logits)
fuse = paddle.concat((l_feature, v_feature), -1)
f_att = F.sigmoid(self.w_att_align(fuse))
output = f_att * v_feature + (1 - f_att) * l_feature
align_logits = self.cls_align(output) # (B, N, C)
align_lengths = _get_length(align_logits)
all_a_res.append(align_logits)
if self.training:
return {
'align': all_a_res,
'lang': all_l_res,
'vision': vis_logits
}
else:
logits = align_logits
if self.training:
return logits
else:
return F.softmax(logits, -1)
def _get_length(logit):
""" Greed decoder to obtain length from logit"""
out = (logit.argmax(-1) == 0)
abn = out.any(-1)
out_int = out.cast('int32')
out = (out_int.cumsum(-1) == 1) & out
out = out.cast('int32')
out = out.argmax(-1)
out = out + 1
len_seq = paddle.zeros_like(out) + logit.shape[1]
out = paddle.where(abn, out, len_seq)
return out
def _get_mask(length, max_length):
"""Generate a square mask for the sequence. The masked positions are filled with float('-inf').
Unmasked positions are filled with float(0.0).
"""
length = length.unsqueeze(-1)
B = paddle.shape(length)[0]
grid = paddle.arange(0, max_length).unsqueeze(0).tile([B, 1])
zero_mask = paddle.zeros([B, max_length], dtype='float32')
inf_mask = paddle.full([B, max_length], '-inf', dtype='float32')
diag_mask = paddle.diag(
paddle.full(
[max_length], '-inf', dtype=paddle.float32),
offset=0,
name=None)
mask = paddle.where(grid >= length, inf_mask, zero_mask)
mask = mask.unsqueeze(1) + diag_mask
return mask.unsqueeze(1)
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