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import torch
import torch.nn as nn
import torch.nn.functional as F
from .nrtr_decoder import Embeddings, TransformerBlock
class PVAM(nn.Module):
def __init__(self,
in_channels,
char_num,
max_text_length,
num_heads,
hidden_dims,
dropout_rate=0):
super(PVAM, self).__init__()
self.char_num = char_num
self.max_length = max_text_length
self.num_heads = num_heads
self.hidden_dims = hidden_dims
self.dropout_rate = dropout_rate
#TODO
self.emb = nn.Embedding(num_embeddings=256,
embedding_dim=hidden_dims,
sparse=False)
self.drop_out = nn.Dropout(dropout_rate)
self.feat_emb = nn.Linear(in_channels, in_channels)
self.token_emb = nn.Embedding(max_text_length, in_channels)
self.score = nn.Linear(in_channels, 1, bias=False)
def feat_pos_mix(self, conv_features, encoder_word_pos, dropout_rate):
#b h*w c
pos_emb = self.emb(encoder_word_pos)
# pos_emb = pos_emb.detach()
enc_input = conv_features + pos_emb
if dropout_rate:
enc_input = self.drop_out(enc_input)
return enc_input
def forward(self, inputs):
b, c, h, w = inputs.shape
conv_features = inputs.view(-1, c, h * w)
conv_features = conv_features.permute(0, 2, 1).contiguous()
# b h*w c
# transformer encoder
b, t, c = conv_features.shape
encoder_feat_pos = torch.arange(t, dtype=torch.long).to(inputs.device)
enc_inputs = self.feat_pos_mix(conv_features, encoder_feat_pos,
self.dropout_rate)
inputs = self.feat_emb(enc_inputs) # feat emb
inputs = inputs.unsqueeze(1).expand(-1, self.max_length, -1, -1)
# b maxlen h*w c
tokens_pos = torch.arange(self.max_length,
dtype=torch.long).to(inputs.device)
tokens_pos = tokens_pos.unsqueeze(0).expand(b, -1)
tokens_pos_emd = self.token_emb(tokens_pos)
tokens_pos_emd = tokens_pos_emd.unsqueeze(2).expand(-1, -1, t, -1)
# b maxlen h*w c
attention_weight = torch.tanh(tokens_pos_emd + inputs)
attention_weight = torch.squeeze(self.score(attention_weight),
-1) #b,25,256
attention_weight = F.softmax(attention_weight, dim=-1) #b,25,256
pvam_features = torch.matmul(attention_weight, enc_inputs)
return pvam_features
class GSRM(nn.Module):
def __init__(self,
in_channel,
char_num,
max_len,
num_heads,
hidden_dims,
num_layers,
dropout_rate=0,
attention_dropout=0.1):
super(GSRM, self).__init__()
self.char_num = char_num
self.max_len = max_len
self.num_heads = num_heads
self.cls_op = nn.Linear(in_channel, self.char_num)
self.cls_final = nn.Linear(in_channel, self.char_num)
self.word_emb = Embeddings(d_model=hidden_dims, vocab=char_num)
self.pos_emb = nn.Embedding(char_num, hidden_dims)
self.dropout_rate = dropout_rate
self.emb_drop_out = nn.Dropout(dropout_rate)
self.forward_self_attn = nn.ModuleList([
TransformerBlock(
d_model=hidden_dims,
nhead=num_heads,
attention_dropout_rate=attention_dropout,
residual_dropout_rate=0.1,
dim_feedforward=hidden_dims,
with_self_attn=True,
with_cross_attn=False,
) for i in range(num_layers)
])
self.backward_self_attn = nn.ModuleList([
TransformerBlock(
d_model=hidden_dims,
nhead=num_heads,
attention_dropout_rate=attention_dropout,
residual_dropout_rate=0.1,
dim_feedforward=hidden_dims,
with_self_attn=True,
with_cross_attn=False,
) for i in range(num_layers)
])
def _pos_emb(self, word_seq, pos, dropoutrate):
"""
word_Seq: bsz len
pos: bsz len
"""
word_emb_seq = self.word_emb(word_seq)
pos_emb_seq = self.pos_emb(pos)
# pos_emb_seq = pos_emb_seq.detach()
input_mix = word_emb_seq + pos_emb_seq
if dropoutrate > 0:
input_mix = self.emb_drop_out(input_mix)
return input_mix
def forward(self, inputs):
bos_idx = self.char_num - 2
eos_idx = self.char_num - 1
b, t, c = inputs.size() #b,25,512
inputs = inputs.view(-1, c)
cls_res = self.cls_op(inputs) #b,25,n_class
word_pred_PVAM = F.softmax(cls_res, dim=-1).argmax(-1)
word_pred_PVAM = word_pred_PVAM.view(-1, t, 1)
#b 25 1
word1 = F.pad(word_pred_PVAM, [0, 0, 1, 0], 'constant', value=bos_idx)
word_forward = word1[:, :-1, :].squeeze(-1)
word_backward = word_pred_PVAM.squeeze(-1)
#mask
attn_mask_forward = torch.triu(
torch.full((self.max_len, self.max_len),
dtype=torch.float32,
fill_value=-torch.inf),
diagonal=1,
).to(inputs.device)
attn_mask_forward = attn_mask_forward.unsqueeze(0).expand(
self.num_heads, -1, -1)
attn_mask_backward = torch.tril(
torch.full((self.max_len, self.max_len),
dtype=torch.float32,
fill_value=-torch.inf),
diagonal=-1,
).to(inputs.device)
attn_mask_backward = attn_mask_backward.unsqueeze(0).expand(
self.num_heads, -1, -1)
#B,25
pos = torch.arange(self.max_len, dtype=torch.long).to(inputs.device)
pos = pos.unsqueeze(0).expand(b, -1) #b,25
word_front_mix = self._pos_emb(word_forward, pos, self.dropout_rate)
word_backward_mix = self._pos_emb(word_backward, pos,
self.dropout_rate)
# b 25 emb_dim
for attn_layer in self.forward_self_attn:
word_front_mix = attn_layer(word_front_mix,
self_mask=attn_mask_forward)
for attn_layer in self.backward_self_attn:
word_backward_mix = attn_layer(word_backward_mix,
self_mask=attn_mask_backward)
#b,25,emb_dim
eos_emd = self.word_emb(torch.full(
(1, ), eos_idx).to(inputs.device)).expand(b, 1, -1)
word_backward_mix = torch.cat((word_backward_mix, eos_emd), dim=1)
word_backward_mix = word_backward_mix[:, 1:, ]
gsrm_features = word_front_mix + word_backward_mix
gsrm_out = self.cls_final(gsrm_features)
# torch.matmul(gsrm_features,
# self.word_emb.embedding.weight.permute(1, 0))
b, t, c = gsrm_out.size()
#b,25,n_class
gsrm_out = gsrm_out.view(-1, c).contiguous()
return gsrm_features, cls_res, gsrm_out
class VSFD(nn.Module):
def __init__(self, in_channels, out_channels):
super(VSFD, self).__init__()
self.char_num = out_channels
self.fc0 = nn.Linear(in_channels * 2, in_channels)
self.fc1 = nn.Linear(in_channels, self.char_num)
def forward(self, pvam_feature, gsrm_feature):
_, t, c1 = pvam_feature.size()
_, t, c2 = gsrm_feature.size()
combine_featurs = torch.cat([pvam_feature, gsrm_feature], dim=-1)
combine_featurs = combine_featurs.view(-1, c1 + c2).contiguous()
atten = self.fc0(combine_featurs)
atten = torch.sigmoid(atten)
atten = atten.view(-1, t, c1)
combine_featurs = atten * pvam_feature + (1 - atten) * gsrm_feature
combine_featurs = combine_featurs.view(-1, c1).contiguous()
out = self.fc1(combine_featurs)
return out
class SRNDecoder(nn.Module):
def __init__(self,
in_channels,
out_channels,
hidden_dims,
num_decoder_layers=4,
max_text_length=25,
num_heads=8,
**kwargs):
super(SRNDecoder, self).__init__()
self.max_text_length = max_text_length
self.num_heads = num_heads
self.pvam = PVAM(in_channels=in_channels,
char_num=out_channels,
max_text_length=max_text_length,
num_heads=num_heads,
hidden_dims=hidden_dims,
dropout_rate=0.1)
self.gsrm = GSRM(in_channel=in_channels,
char_num=out_channels,
max_len=max_text_length,
num_heads=num_heads,
num_layers=num_decoder_layers,
hidden_dims=hidden_dims)
self.vsfd = VSFD(in_channels=in_channels, out_channels=out_channels)
def forward(self, feat, data=None):
# feat [B,512,8,32]
pvam_feature = self.pvam(feat)
gsrm_features, pvam_preds, gsrm_preds = self.gsrm(pvam_feature)
vsfd_preds = self.vsfd(pvam_feature, gsrm_features)
if not self.training:
preds = F.softmax(vsfd_preds, dim=-1)
return preds
return [pvam_preds, gsrm_preds, vsfd_preds]
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