add rec_nrtr

configs/rec/rec_mtb_nrtr.yml

+Global:
+  use_gpu: True
+  epoch_num: 21
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/nrtr_final/
+  save_epoch_step: 1
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path: 
+  character_type: EN_symbol
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+  save_res_path: ./output/rec/predicts_nrtr.txt
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.99
+  clip_norm: 5.0
+  lr:
+    name: Cosine
+    learning_rate: 0.0005
+    warmup_epoch: 2
+  regularizer:
+    name: 'L2'
+    factor: 0.
+
+Architecture:
+  model_type: rec
+  algorithm: NRTR
+  in_channels: 1
+  Transform:
+  Backbone:
+    name: MTB
+    cnn_num: 2
+  Head:
+    name: TransformerOptim
+    d_model: 512
+    num_encoder_layers: 6
+    beam_size: -1    # When Beam size is greater than 0, it means to use beam search when evaluation.
+    
+
+Loss:
+  name: NRTRLoss
+  smoothing: True
+
+PostProcess:
+  name: NRTRLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: LMDBDataSet
+    data_dir: /paddle/data/ocr_data/training/
+    transforms:
+      - NRTRDecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - NRTRLabelEncode: # Class handling label
+      - PILResize:
+          image_shape: [100, 32]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 512
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: LMDBDataSet
+    data_dir: /paddle/data/ocr_data/evaluation/
+    transforms:
+      - NRTRDecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - NRTRLabelEncode: # Class handling label
+      - PILResize:
+          image_shape: [100, 32]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 1
+    use_shared_memory: False

ppocr/data/imaug/label_ops.py

@@ -159,6 +159,34 @@ class BaseRecLabelEncode(object):
         return text_list
 
 
+class NRTRLabelEncode(BaseRecLabelEncode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self,
+                 max_text_length,
+                 character_dict_path=None,
+                 character_type='EN_symbol',
+                 use_space_char=False,
+                 **kwargs):
+
+        super(NRTRLabelEncode,
+              self).__init__(max_text_length, character_dict_path,
+                             character_type, use_space_char)
+    def __call__(self, data):
+        text = data['label']
+        text = self.encode(text)
+        if text is None:
+            return None
+        data['length'] = np.array(len(text))
+        text.insert(0, 2)
+        text.append(3)
+        text = text + [0] * (self.max_text_len - len(text))
+        data['label'] = np.array(text)
+        return data
+    def add_special_char(self, dict_character):
+        dict_character = ['blank','<unk>','<s>','</s>'] + dict_character
+        return dict_character
+
 class CTCLabelEncode(BaseRecLabelEncode):
     """ Convert between text-label and text-index """
 

ppocr/modeling/heads/__init__.py

@@ -26,12 +26,13 @@ def build_head(config):
     from .rec_ctc_head import CTCHead
     from .rec_att_head import AttentionHead
     from .rec_srn_head import SRNHead
+    from .rec_nrtr_optim_head import TransformerOptim
    
     # cls head
     from .cls_head import ClsHead
     support_dict = [
         'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
-        'SRNHead', 'PGHead']
+        'SRNHead', 'PGHead', 'TransformerOptim']
 
 
     module_name = config.pop('name')

ppocr/modeling/heads/multiheadAttention.py

+import paddle
+from paddle import nn
+import paddle.nn.functional as F
+from paddle.nn import Linear
+from paddle.nn.initializer import XavierUniform as xavier_uniform_
+from paddle.nn.initializer import Constant as constant_
+from paddle.nn.initializer import XavierNormal as xavier_normal_
+
+zeros_ = constant_(value=0.)
+ones_ = constant_(value=1.)
+
+class MultiheadAttention(nn.Layer):
+    r"""Allows the model to jointly attend to information
+    from different representation subspaces.
+    See reference: Attention Is All You Need
+
+    .. math::
+        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
+        \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
+
+    Args:
+        embed_dim: total dimension of the model
+        num_heads: parallel attention layers, or heads
+
+    Examples::
+
+        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
+        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
+    """
+
+    def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False):
+        super(MultiheadAttention, self).__init__()
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.head_dim = embed_dim // num_heads
+        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+        self.scaling = self.head_dim ** -0.5
+
+        self.out_proj = Linear(embed_dim, embed_dim, bias_attr=bias)
+
+        if add_bias_kv:
+            self.bias_k = self.create_parameter(
+            shape=(1, 1, embed_dim), default_initializer=zeros_)
+            self.add_parameter("bias_k", self.bias_k)
+            self.bias_v = self.create_parameter(
+            shape=(1, 1, embed_dim), default_initializer=zeros_)
+            self.add_parameter("bias_v", self.bias_v)
+        else:
+            self.bias_k = self.bias_v = None
+
+        self.add_zero_attn = add_zero_attn
+
+        self._reset_parameters()
+
+        self.conv1 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
+        self.conv2 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim * 2, kernel_size=(1, 1))
+        self.conv3 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim * 3, kernel_size=(1, 1))
+
+    def _reset_parameters(self):
+
+
+        xavier_uniform_(self.out_proj.weight)
+        if self.bias_k is not None:
+            xavier_normal_(self.bias_k)
+        if self.bias_v is not None:
+            xavier_normal_(self.bias_v)
+
+    def forward(self, query, key, value, key_padding_mask=None, incremental_state=None,
+                need_weights=True, static_kv=False, attn_mask=None, qkv_ = [False,False,False]):
+        """
+        Inputs of forward function
+            query: [target length, batch size, embed dim]
+            key: [sequence length, batch size, embed dim]
+            value: [sequence length, batch size, embed dim]
+            key_padding_mask: if True, mask padding based on batch size
+            incremental_state: if provided, previous time steps are cashed
+            need_weights: output attn_output_weights
+            static_kv: key and value are static
+
+        Outputs of forward function
+            attn_output: [target length, batch size, embed dim]
+            attn_output_weights: [batch size, target length, sequence length]
+        """
+        qkv_same = qkv_[0]
+        kv_same = qkv_[1]
+
+        tgt_len, bsz, embed_dim = query.shape
+        assert embed_dim == self.embed_dim
+        assert list(query.shape) == [tgt_len, bsz, embed_dim]
+        assert key.shape == value.shape
+
+        if qkv_same:
+            # self-attention
+            q, k, v = self._in_proj_qkv(query)
+        elif kv_same:
+            # encoder-decoder attention
+            q = self._in_proj_q(query)
+            if key is None:
+                assert value is None
+                k = v = None
+            else:
+                k, v = self._in_proj_kv(key)
+        else:
+            q = self._in_proj_q(query)
+            k = self._in_proj_k(key)
+            v = self._in_proj_v(value)
+        q *= self.scaling
+
+        if self.bias_k is not None:
+            assert self.bias_v is not None
+            self.bias_k = paddle.concat([self.bias_k for i in range(bsz)],axis=1)
+            self.bias_v = paddle.concat([self.bias_v for i in range(bsz)],axis=1)
+            k = paddle.concat([k, self.bias_k])
+            v = paddle.concat([v, self.bias_v])
+            if attn_mask is not None:
+                attn_mask = paddle.concat([attn_mask, paddle.zeros([attn_mask.shape[0], 1],dtype=attn_mask.dtype)], axis=1)
+            if key_padding_mask is not None:
+                key_padding_mask = paddle.concat(
+                    [key_padding_mask,paddle.zeros([key_padding_mask.shape[0], 1],dtype=key_padding_mask.dtype)], axis=1)
+
+        q = q.reshape([tgt_len, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
+        if k is not None:
+            k = k.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
+        if v is not None:
+            v = v.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
+
+ 
+
+        src_len = k.shape[1]
+
+        if key_padding_mask is not None:
+            assert key_padding_mask.shape[0] == bsz
+            assert key_padding_mask.shape[1] == src_len
+
+        if self.add_zero_attn:
+            src_len += 1
+            k = paddle.concat([k, paddle.zeros((k.shape[0], 1) + k.shape[2:],dtype=k.dtype)], axis=1)
+            v = paddle.concat([v, paddle.zeros((v.shape[0], 1) + v.shape[2:],dtype=v.dtype)], axis=1)
+            if attn_mask is not None:
+                attn_mask = paddle.concat([attn_mask, paddle.zeros([attn_mask.shape[0], 1],dtype=attn_mask.dtype)], axis=1)
+            if key_padding_mask is not None:
+                key_padding_mask = paddle.concat(
+                    [key_padding_mask, paddle.zeros([key_padding_mask.shape[0], 1],dtype=key_padding_mask.dtype)], axis=1)
+        attn_output_weights = paddle.bmm(q, k.transpose([0,2,1]))
+        assert list(attn_output_weights.shape) == [bsz * self.num_heads, tgt_len, src_len]
+
+        if attn_mask is not None:
+            attn_mask = attn_mask.unsqueeze(0)
+            attn_output_weights += attn_mask
+        if key_padding_mask is not None:
+            attn_output_weights = attn_output_weights.reshape([bsz, self.num_heads, tgt_len, src_len])
+            key = key_padding_mask.unsqueeze(1).unsqueeze(2).astype('float32')
+            y = paddle.full(shape=key.shape, dtype='float32', fill_value='-inf')
+            y = paddle.where(key==0.,key, y)
+            attn_output_weights += y
+            attn_output_weights = attn_output_weights.reshape([bsz*self.num_heads, tgt_len, src_len])
+
+        attn_output_weights = F.softmax(
+            attn_output_weights.astype('float32'), axis=-1,
+            dtype=paddle.float32 if attn_output_weights.dtype == paddle.float16 else attn_output_weights.dtype)
+        attn_output_weights = F.dropout(attn_output_weights, p=self.dropout, training=self.training)
+
+        attn_output = paddle.bmm(attn_output_weights, v)
+        assert list(attn_output.shape) == [bsz * self.num_heads, tgt_len, self.head_dim]
+        attn_output = attn_output.transpose([1, 0,2]).reshape([tgt_len, bsz, embed_dim])
+        attn_output = self.out_proj(attn_output)
+        if need_weights:
+            # average attention weights over heads
+            attn_output_weights = attn_output_weights.reshape([bsz, self.num_heads, tgt_len, src_len])
+            attn_output_weights = attn_output_weights.sum(axis=1) / self.num_heads
+        else:
+            attn_output_weights = None
+
+        return attn_output, attn_output_weights
+
+    def _in_proj_qkv(self, query):
+        query = query.transpose([1, 2, 0])
+        query = paddle.unsqueeze(query, axis=2)
+        res = self.conv3(query)
+        res = paddle.squeeze(res, axis=2)
+        res = res.transpose([2, 0, 1])
+        return res.chunk(3, axis=-1)
+
+    def _in_proj_kv(self, key):
+        key = key.transpose([1, 2, 0])
+        key = paddle.unsqueeze(key, axis=2)
+        res = self.conv2(key)
+        res = paddle.squeeze(res, axis=2)
+        res = res.transpose([2, 0, 1])
+        return res.chunk(2, axis=-1)
+
+    def _in_proj_q(self, query):
+        query = query.transpose([1, 2, 0])
+        query = paddle.unsqueeze(query, axis=2)
+        res = self.conv1(query)
+        res = paddle.squeeze(res, axis=2)
+        res = res.transpose([2, 0, 1])
+        return res
+
+    def _in_proj_k(self, key):
+        
+        key = key.transpose([1, 2, 0])
+        key = paddle.unsqueeze(key, axis=2)
+        res = self.conv1(key)
+        res = paddle.squeeze(res, axis=2)
+        res = res.transpose([2, 0, 1])
+        return res
+
+    def _in_proj_v(self, value):
+        
+        value = value.transpose([1,2,0])#(1, 2, 0)
+        value = paddle.unsqueeze(value, axis=2)
+        res = self.conv1(value)
+        res = paddle.squeeze(res, axis=2)
+        res = res.transpose([2, 0, 1])
+        return res
+
+
+
+class MultiheadAttentionOptim(nn.Layer):
+    r"""Allows the model to jointly attend to information
+    from different representation subspaces.
+    See reference: Attention Is All You Need
+
+    .. math::
+        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
+        \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
+
+    Args:
+        embed_dim: total dimension of the model
+        num_heads: parallel attention layers, or heads
+
+    Examples::
+
+        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
+        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
+    """
+
+    def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False):
+        super(MultiheadAttentionOptim, self).__init__()
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.head_dim = embed_dim // num_heads
+        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+        self.scaling = self.head_dim ** -0.5
+
+        self.out_proj = Linear(embed_dim, embed_dim, bias_attr=bias)
+
+        self._reset_parameters()
+
+        self.conv1 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
+        self.conv2 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
+        self.conv3 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
+
+    def _reset_parameters(self):
+
+
+        xavier_uniform_(self.out_proj.weight)
+
+
+    def forward(self, query, key, value, key_padding_mask=None, incremental_state=None,
+                need_weights=True, static_kv=False, attn_mask=None):
+        """
+        Inputs of forward function
+            query: [target length, batch size, embed dim]
+            key: [sequence length, batch size, embed dim]
+            value: [sequence length, batch size, embed dim]
+            key_padding_mask: if True, mask padding based on batch size
+            incremental_state: if provided, previous time steps are cashed
+            need_weights: output attn_output_weights
+            static_kv: key and value are static
+
+        Outputs of forward function
+            attn_output: [target length, batch size, embed dim]
+            attn_output_weights: [batch size, target length, sequence length]
+        """
+
+
+        tgt_len, bsz, embed_dim = query.shape
+        assert embed_dim == self.embed_dim
+        assert list(query.shape) == [tgt_len, bsz, embed_dim]
+        assert key.shape == value.shape
+
+        q = self._in_proj_q(query)
+        k = self._in_proj_k(key)
+        v = self._in_proj_v(value)
+        q *= self.scaling
+
+
+        q = q.reshape([tgt_len, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
+        k = k.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
+        v = v.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
+
+
+        src_len = k.shape[1]
+
+        if key_padding_mask is not None:
+            assert key_padding_mask.shape[0] == bsz
+            assert key_padding_mask.shape[1] == src_len
+
+        
+        attn_output_weights = paddle.bmm(q, k.transpose([0,2,1]))
+        assert list(attn_output_weights.shape) == [bsz * self.num_heads, tgt_len, src_len]
+
+        if attn_mask is not None:
+            attn_mask = attn_mask.unsqueeze(0)
+            attn_output_weights += attn_mask
+        if key_padding_mask is not None:
+            attn_output_weights = attn_output_weights.reshape([bsz, self.num_heads, tgt_len, src_len])
+            key = key_padding_mask.unsqueeze(1).unsqueeze(2).astype('float32')
+            
+            y = paddle.full(shape=key.shape, dtype='float32', fill_value='-inf')
+           
+            y = paddle.where(key==0.,key, y)
+
+            attn_output_weights += y
+            attn_output_weights = attn_output_weights.reshape([bsz*self.num_heads, tgt_len, src_len])
+
+        attn_output_weights = F.softmax(
+            attn_output_weights.astype('float32'), axis=-1,
+            dtype=paddle.float32 if attn_output_weights.dtype == paddle.float16 else attn_output_weights.dtype)
+        attn_output_weights = F.dropout(attn_output_weights, p=self.dropout, training=self.training)
+
+        attn_output = paddle.bmm(attn_output_weights, v)
+        assert list(attn_output.shape) == [bsz * self.num_heads, tgt_len, self.head_dim]
+        attn_output = attn_output.transpose([1, 0,2]).reshape([tgt_len, bsz, embed_dim])
+        attn_output = self.out_proj(attn_output)
+
+        if need_weights:
+            # average attention weights over heads
+            attn_output_weights = attn_output_weights.reshape([bsz, self.num_heads, tgt_len, src_len])
+            attn_output_weights = attn_output_weights.sum(axis=1) / self.num_heads
+        else:
+            attn_output_weights = None
+
+        return attn_output, attn_output_weights
+
+
+    def _in_proj_q(self, query):
+        query = query.transpose([1, 2, 0])
+        query = paddle.unsqueeze(query, axis=2)
+        res = self.conv1(query)
+        res = paddle.squeeze(res, axis=2)
+        res = res.transpose([2, 0, 1])
+        return res
+
+    def _in_proj_k(self, key):
+        
+        key = key.transpose([1, 2, 0])
+        key = paddle.unsqueeze(key, axis=2)
+        res = self.conv2(key)
+        res = paddle.squeeze(res, axis=2)
+        res = res.transpose([2, 0, 1])
+        return res
+
+    def _in_proj_v(self, value):
+        
+        value = value.transpose([1,2,0])#(1, 2, 0)
+        value = paddle.unsqueeze(value, axis=2)
+        res = self.conv3(value)
+        res = paddle.squeeze(res, axis=2)
+        res = res.transpose([2, 0, 1])
+        return res
\ No newline at end of file

ppocr/modeling/heads/rec_nrtr_optim_head.py

+import math
+import paddle
+import copy
+from  paddle import nn
+import paddle.nn.functional as F
+from paddle.nn import LayerList
+from paddle.nn.initializer import XavierNormal as xavier_uniform_
+from paddle.nn import Dropout, Linear, LayerNorm, Conv2D
+import numpy as np
+from ppocr.modeling.heads.multiheadAttention import MultiheadAttentionOptim
+from paddle.nn.initializer import Constant as constant_
+from paddle.nn.initializer import XavierNormal as xavier_normal_
+
+zeros_ = constant_(value=0.)
+ones_ = constant_(value=1.)
+
+class TransformerOptim(nn.Layer):
+    r"""A transformer model. User is able to modify the attributes as needed. The architechture
+    is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer,
+    Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and
+    Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information
+    Processing Systems, pages 6000-6010.
+
+    Args:
+        d_model: the number of expected features in the encoder/decoder inputs (default=512).
+        nhead: the number of heads in the multiheadattention models (default=8).
+        num_encoder_layers: the number of sub-encoder-layers in the encoder (default=6).
+        num_decoder_layers: the number of sub-decoder-layers in the decoder (default=6).
+        dim_feedforward: the dimension of the feedforward network model (default=2048).
+        dropout: the dropout value (default=0.1).
+        custom_encoder: custom encoder (default=None).
+        custom_decoder: custom decoder (default=None).
+
+    Examples::
+        >>> transformer_model = nn.Transformer(src_vocab, tgt_vocab)
+        >>> transformer_model = nn.Transformer(src_vocab, tgt_vocab, nhead=16, num_encoder_layers=12)
+    """
+
+    def __init__(self, d_model=512, nhead=8, num_encoder_layers=6, beam_size=0,
+                 num_decoder_layers=6, dim_feedforward=1024, attention_dropout_rate=0.0, residual_dropout_rate=0.1,
+                 custom_encoder=None, custom_decoder=None,in_channels=0,out_channels=0,dst_vocab_size=99,scale_embedding=True):
+        super(TransformerOptim, self).__init__()
+        self.embedding = Embeddings(
+            d_model=d_model,
+            vocab=dst_vocab_size,
+            padding_idx=0,
+            scale_embedding=scale_embedding
+        )
+        self.positional_encoding = PositionalEncoding(
+            dropout=residual_dropout_rate,
+            dim=d_model,
+        )
+        if custom_encoder is not None:
+            self.encoder = custom_encoder
+        else:
+            if num_encoder_layers > 0 :
+                encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, attention_dropout_rate, residual_dropout_rate)
+            
+                self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers)
+            else:
+                self.encoder = None
+
+        if custom_decoder is not None:
+            self.decoder = custom_decoder
+        else:
+            decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, attention_dropout_rate, residual_dropout_rate)
+            self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers)
+
+        self._reset_parameters()
+        self.beam_size = beam_size
+        self.d_model = d_model
+        self.nhead = nhead
+        self.tgt_word_prj = nn.Linear(d_model, dst_vocab_size, bias_attr=False)
+        w0 = np.random.normal(0.0, d_model**-0.5,(d_model, dst_vocab_size)).astype(np.float32)
+        self.tgt_word_prj.weight.set_value(w0)
+        self.apply(self._init_weights)
+       
+
+    def _init_weights(self, m):
+        
+        if isinstance(m, nn.Conv2D):
+            xavier_normal_(m.weight)
+            if m.bias is not None:
+                zeros_(m.bias)
+
+    def forward_train(self,src,tgt):
+            tgt = tgt[:, :-1]
+
+            
+ 
+            tgt_key_padding_mask = self.generate_padding_mask(tgt)
+            tgt = self.embedding(tgt).transpose([1, 0, 2])
+            tgt = self.positional_encoding(tgt)
+            tgt_mask = self.generate_square_subsequent_mask(tgt.shape[0])
+
+            if self.encoder is not None :
+                src = self.positional_encoding(src.transpose([1, 0, 2]))
+                memory = self.encoder(src)
+            else:
+                memory = src.squeeze(2).transpose([2, 0, 1])
+            output = self.decoder(tgt, memory, tgt_mask=tgt_mask, memory_mask=None,
+                                tgt_key_padding_mask=tgt_key_padding_mask,
+                                memory_key_padding_mask=None)
+            output = output.transpose([1, 0, 2])
+            logit = self.tgt_word_prj(output)
+            return logit
+
+    def forward(self, src, tgt=None):
+        r"""Take in and process masked source/target sequences.
+
+        Args:
+            src: the sequence to the encoder (required).
+            tgt: the sequence to the decoder (required).
+            src_mask: the additive mask for the src sequence (optional).
+            tgt_mask: the additive mask for the tgt sequence (optional).
+            memory_mask: the additive mask for the encoder output (optional).
+            src_key_padding_mask: the ByteTensor mask for src keys per batch (optional).
+            tgt_key_padding_mask: the ByteTensor mask for tgt keys per batch (optional).
+            memory_key_padding_mask: the ByteTensor mask for memory keys per batch (optional).
+
+        Shape:
+            - src: :math:`(S, N, E)`.
+            - tgt: :math:`(T, N, E)`.
+            - src_mask: :math:`(S, S)`.
+            - tgt_mask: :math:`(T, T)`.
+            - memory_mask: :math:`(T, S)`.
+            - src_key_padding_mask: :math:`(N, S)`.
+            - tgt_key_padding_mask: :math:`(N, T)`.
+            - memory_key_padding_mask: :math:`(N, S)`.
+
+            Note: [src/tgt/memory]_mask should be filled with
+            float('-inf') for the masked positions and float(0.0) else. These masks
+            ensure that predictions for position i depend only on the unmasked positions
+            j and are applied identically for each sequence in a batch.
+            [src/tgt/memory]_key_padding_mask should be a ByteTensor where True values are positions
+            that should be masked with float('-inf') and False values will be unchanged.
+            This mask ensures that no information will be taken from position i if
+            it is masked, and has a separate mask for each sequence in a batch.
+
+            - output: :math:`(T, N, E)`.
+
+            Note: Due to the multi-head attention architecture in the transformer model,
+            the output sequence length of a transformer is same as the input sequence
+            (i.e. target) length of the decode.
+
+            where S is the source sequence length, T is the target sequence length, N is the
+            batch size, E is the feature number
+
+        Examples:
+            >>> output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask)
+        """
+        if tgt is not None:
+            return self.forward_train(src, tgt)
+        else:
+            if self.beam_size > 0 :
+                return self.forward_beam(src)
+            else:
+                return self.forward_test(src)
+
+    def forward_test(self, src):
+        bs = src.shape[0]
+        if self.encoder is not None :
+            src = self.positional_encoding(src.transpose([1, 0, 2]))
+            memory = self.encoder(src)
+        else:
+            memory = src.squeeze(2).transpose([2, 0, 1])
+        dec_seq = paddle.full((bs,1), 2, dtype=paddle.int64)
+        for len_dec_seq in range(1, 25):
+            src_enc = memory.clone()
+            tgt_key_padding_mask = self.generate_padding_mask(dec_seq)
+            dec_seq_embed = self.embedding(dec_seq).transpose([1, 0, 2])
+            dec_seq_embed = self.positional_encoding(dec_seq_embed)
+            tgt_mask = self.generate_square_subsequent_mask(dec_seq_embed.shape[0])
+            output = self.decoder(dec_seq_embed, src_enc, tgt_mask=tgt_mask, memory_mask=None,
+                                tgt_key_padding_mask=tgt_key_padding_mask,
+                                memory_key_padding_mask=None)
+            dec_output = output.transpose([1, 0, 2])
+            
+            dec_output = dec_output[:, -1, :]  # Pick the last step: (bh * bm) * d_h
+            word_prob = F.log_softmax(self.tgt_word_prj(dec_output), axis=1)
+            word_prob = word_prob.reshape([1, bs, -1])
+            preds_idx = word_prob.argmax(axis=2)
+            
+            if paddle.equal_all(preds_idx[-1],paddle.full(preds_idx[-1].shape,3,dtype='int64')):
+                break
+
+            preds_prob = word_prob.max(axis=2)
+            dec_seq = paddle.concat([dec_seq,preds_idx.reshape([-1,1])],axis=1)
+
+        return dec_seq   
+
+    def forward_beam(self,images):
+             
+        ''' Translation work in one batch '''
+
+        def get_inst_idx_to_tensor_position_map(inst_idx_list):
+            ''' Indicate the position of an instance in a tensor. '''
+            return {inst_idx: tensor_position for tensor_position, inst_idx in enumerate(inst_idx_list)}
+
+        def collect_active_part(beamed_tensor, curr_active_inst_idx, n_prev_active_inst, n_bm):
+            ''' Collect tensor parts associated to active instances. '''
+
+            _, *d_hs = beamed_tensor.shape
+            n_curr_active_inst = len(curr_active_inst_idx)
+            new_shape = (n_curr_active_inst * n_bm, *d_hs)
+
+            beamed_tensor = beamed_tensor.reshape([n_prev_active_inst, -1])#contiguous()
+            beamed_tensor = beamed_tensor.index_select(paddle.to_tensor(curr_active_inst_idx),axis=0)
+            beamed_tensor = beamed_tensor.reshape([*new_shape])
+
+            return beamed_tensor
+
+
+        def collate_active_info(
+                src_enc, inst_idx_to_position_map, active_inst_idx_list):
+            # Sentences which are still active are collected,
+            # so the decoder will not run on completed sentences.
+           
+            n_prev_active_inst = len(inst_idx_to_position_map)
+            active_inst_idx = [inst_idx_to_position_map[k] for k in active_inst_idx_list]
+            active_inst_idx = paddle.to_tensor(active_inst_idx, dtype='int64')
+            active_src_enc = collect_active_part(src_enc.transpose([1, 0, 2]), active_inst_idx, n_prev_active_inst, n_bm).transpose([1, 0, 2])
+            active_inst_idx_to_position_map = get_inst_idx_to_tensor_position_map(active_inst_idx_list)
+            return active_src_enc, active_inst_idx_to_position_map
+
+        def beam_decode_step(
+                inst_dec_beams, len_dec_seq, enc_output, inst_idx_to_position_map, n_bm, memory_key_padding_mask):
+            ''' Decode and update beam status, and then return active beam idx '''
+
+            def prepare_beam_dec_seq(inst_dec_beams, len_dec_seq):
+                dec_partial_seq = [b.get_current_state() for b in inst_dec_beams if not b.done]
+                dec_partial_seq = paddle.stack(dec_partial_seq)
+                
+                dec_partial_seq = dec_partial_seq.reshape([-1, len_dec_seq])
+                return dec_partial_seq
+
+            def prepare_beam_memory_key_padding_mask(inst_dec_beams, memory_key_padding_mask, n_bm):
+                keep = []
+                for idx in (memory_key_padding_mask):
+                    if not inst_dec_beams[idx].done:
+                        keep.append(idx)
+                memory_key_padding_mask = memory_key_padding_mask[paddle.to_tensor(keep)]
+                len_s = memory_key_padding_mask.shape[-1]
+                n_inst = memory_key_padding_mask.shape[0]
+                memory_key_padding_mask = paddle.concat([memory_key_padding_mask for i in range(n_bm)],axis=1)
+                memory_key_padding_mask = memory_key_padding_mask.reshape([n_inst * n_bm, len_s])#repeat(1, n_bm)
+                return memory_key_padding_mask
+
+            def predict_word(dec_seq, enc_output, n_active_inst, n_bm, memory_key_padding_mask):
+                tgt_key_padding_mask = self.generate_padding_mask(dec_seq)
+                dec_seq = self.embedding(dec_seq).transpose([1, 0, 2])
+                dec_seq = self.positional_encoding(dec_seq)
+                tgt_mask = self.generate_square_subsequent_mask(dec_seq.shape[0])
+                dec_output = self.decoder(
+                    dec_seq, enc_output,
+                    tgt_mask=tgt_mask,
+                    tgt_key_padding_mask=tgt_key_padding_mask,
+                    memory_key_padding_mask=memory_key_padding_mask,
+                ).transpose([1, 0, 2])
+                dec_output = dec_output[:, -1, :]  # Pick the last step: (bh * bm) * d_h
+                word_prob = F.log_softmax(self.tgt_word_prj(dec_output), axis=1)
+                word_prob = word_prob.reshape([n_active_inst, n_bm, -1])
+                return word_prob
+
+            def collect_active_inst_idx_list(inst_beams, word_prob, inst_idx_to_position_map):
+                active_inst_idx_list = []
+                for inst_idx, inst_position in inst_idx_to_position_map.items():
+                    is_inst_complete = inst_beams[inst_idx].advance(word_prob[inst_position])
+                    if not is_inst_complete:
+                        active_inst_idx_list += [inst_idx]
+
+                return active_inst_idx_list
+
+            n_active_inst = len(inst_idx_to_position_map)
+            dec_seq = prepare_beam_dec_seq(inst_dec_beams, len_dec_seq)
+            memory_key_padding_mask = None
+            word_prob = predict_word(dec_seq, enc_output, n_active_inst, n_bm, memory_key_padding_mask)
+            # Update the beam with predicted word prob information and collect incomplete instances
+            active_inst_idx_list = collect_active_inst_idx_list(
+                inst_dec_beams, word_prob, inst_idx_to_position_map)
+            return active_inst_idx_list
+
+        def collect_hypothesis_and_scores(inst_dec_beams, n_best):
+            all_hyp, all_scores = [], []
+            for inst_idx in range(len(inst_dec_beams)):
+                scores, tail_idxs = inst_dec_beams[inst_idx].sort_scores()
+                all_scores += [scores[:n_best]]
+                hyps = [inst_dec_beams[inst_idx].get_hypothesis(i) for i in tail_idxs[:n_best]]
+                all_hyp += [hyps]
+            return all_hyp, all_scores
+
+        with paddle.no_grad():
+            #-- Encode
+ 
+            if self.encoder is not None :
+                src = self.positional_encoding(images.transpose([1, 0, 2]))
+                src_enc = self.encoder(src).transpose([1, 0, 2])
+            else:
+                src_enc = images.squeeze(2).transpose([0, 2, 1])
+
+            #-- Repeat data for beam search
+            n_bm = self.beam_size
+            n_inst, len_s, d_h = src_enc.shape
+            src_enc = paddle.concat([src_enc for i in range(n_bm)],axis=1)
+            src_enc = src_enc.reshape([n_inst * n_bm, len_s, d_h]).transpose([1, 0, 2])#repeat(1, n_bm, 1)
+            #-- Prepare beams
+            inst_dec_beams = [Beam(n_bm) for _ in range(n_inst)]
+
+            #-- Bookkeeping for active or not
+            active_inst_idx_list = list(range(n_inst))
+            inst_idx_to_position_map = get_inst_idx_to_tensor_position_map(active_inst_idx_list)
+            #-- Decode
+            for len_dec_seq in range(1, 25):
+                src_enc_copy = src_enc.clone()
+                active_inst_idx_list = beam_decode_step(
+                    inst_dec_beams, len_dec_seq, src_enc_copy, inst_idx_to_position_map, n_bm, None)
+                if not active_inst_idx_list:
+                    break  # all instances have finished their path to <EOS>
+                src_enc, inst_idx_to_position_map = collate_active_info(
+                    src_enc_copy, inst_idx_to_position_map, active_inst_idx_list)
+        batch_hyp, batch_scores = collect_hypothesis_and_scores(inst_dec_beams, 1)
+        result_hyp = []
+        for bs_hyp in batch_hyp:
+            bs_hyp_pad =bs_hyp[0]+[3]*(25-len(bs_hyp[0]))
+            result_hyp.append(bs_hyp_pad)
+        return paddle.to_tensor(np.array(result_hyp),dtype=paddle.int64)
+
+    def generate_square_subsequent_mask(self, sz):
+        r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf').
+            Unmasked positions are filled with float(0.0).
+        """
+        mask = paddle.zeros([sz, sz],dtype='float32')
+        mask_inf = paddle.triu(paddle.full(shape=[sz,sz], dtype='float32', fill_value='-inf'),diagonal=1)
+        mask = mask+mask_inf
+        return mask
+
+    def generate_padding_mask(self, x):
+        padding_mask = x.equal(paddle.to_tensor(0,dtype=x.dtype))
+        return padding_mask
+
+    def _reset_parameters(self):
+        r"""Initiate parameters in the transformer model."""
+
+        for p in self.parameters():
+            if p.dim() > 1:
+                xavier_uniform_(p)
+
+
+class TransformerEncoder(nn.Layer):
+    r"""TransformerEncoder is a stack of N encoder layers
+
+    Args:
+        encoder_layer: an instance of the TransformerEncoderLayer() class (required).
+        num_layers: the number of sub-encoder-layers in the encoder (required).
+        norm: the layer normalization component (optional).
+
+    Examples::
+        >>> encoder_layer = nn.TransformerEncoderLayer(d_model, nhead)
+        >>> transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers)
+    """
+
+    def __init__(self, encoder_layer, num_layers):
+        super(TransformerEncoder, self).__init__()
+        self.layers = _get_clones(encoder_layer, num_layers)
+        self.num_layers = num_layers
+
+
+    def forward(self, src):
+        r"""Pass the input through the endocder layers in turn.
+
+        Args:
+            src: the sequnce to the encoder (required).
+            mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        output = src
+
+        for i in range(self.num_layers):
+            output = self.layers[i](output, src_mask=None,
+                                    src_key_padding_mask=None)
+
+        return output
+
+
+class TransformerDecoder(nn.Layer):
+    r"""TransformerDecoder is a stack of N decoder layers
+
+    Args:
+        decoder_layer: an instance of the TransformerDecoderLayer() class (required).
+        num_layers: the number of sub-decoder-layers in the decoder (required).
+        norm: the layer normalization component (optional).
+
+    Examples::
+        >>> decoder_layer = nn.TransformerDecoderLayer(d_model, nhead)
+        >>> transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers)
+    """
+
+    def __init__(self, decoder_layer, num_layers):
+        super(TransformerDecoder, self).__init__()
+        self.layers = _get_clones(decoder_layer, num_layers)
+        self.num_layers = num_layers
+        
+
+    def forward(self, tgt, memory, tgt_mask=None,
+                memory_mask=None, tgt_key_padding_mask=None,
+                memory_key_padding_mask=None):
+        r"""Pass the inputs (and mask) through the decoder layer in turn.
+
+        Args:
+            tgt: the sequence to the decoder (required).
+            memory: the sequnce from the last layer of the encoder (required).
+            tgt_mask: the mask for the tgt sequence (optional).
+            memory_mask: the mask for the memory sequence (optional).
+            tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
+            memory_key_padding_mask: the mask for the memory keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        output = tgt
+        for i in range(self.num_layers):
+            output = self.layers[i](output, memory, tgt_mask=tgt_mask,
+                                    memory_mask=memory_mask,
+                                    tgt_key_padding_mask=tgt_key_padding_mask,
+                                    memory_key_padding_mask=memory_key_padding_mask)
+
+        return output
+
+class TransformerEncoderLayer(nn.Layer):
+    r"""TransformerEncoderLayer is made up of self-attn and feedforward network.
+    This standard encoder layer is based on the paper "Attention Is All You Need".
+    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
+    Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
+    Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
+    in a different way during application.
+
+    Args:
+        d_model: the number of expected features in the input (required).
+        nhead: the number of heads in the multiheadattention models (required).
+        dim_feedforward: the dimension of the feedforward network model (default=2048).
+        dropout: the dropout value (default=0.1).
+
+    Examples::
+        >>> encoder_layer = nn.TransformerEncoderLayer(d_model, nhead)
+    """
+
+    def __init__(self, d_model, nhead, dim_feedforward=2048, attention_dropout_rate=0.0, residual_dropout_rate=0.1):
+        super(TransformerEncoderLayer, self).__init__()
+        self.self_attn = MultiheadAttentionOptim(d_model, nhead, dropout=attention_dropout_rate)
+
+        self.conv1 = Conv2D(in_channels=d_model, out_channels=dim_feedforward, kernel_size=(1, 1))
+        self.conv2 = Conv2D(in_channels=dim_feedforward, out_channels=d_model, kernel_size=(1, 1))
+
+        self.norm1 = LayerNorm(d_model)
+        self.norm2 = LayerNorm(d_model)
+        self.dropout1 = Dropout(residual_dropout_rate)
+        self.dropout2 = Dropout(residual_dropout_rate)
+
+    def forward(self, src, src_mask=None, src_key_padding_mask=None):
+        r"""Pass the input through the endocder layer.
+
+        Args:
+            src: the sequnce to the encoder layer (required).
+            src_mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        src2 = self.self_attn(src, src, src, attn_mask=src_mask,
+                              key_padding_mask=src_key_padding_mask)[0]
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+
+        src = src.transpose([1, 2, 0])
+        src = paddle.unsqueeze(src, 2)
+        src2 = self.conv2(F.relu(self.conv1(src)))
+        src2 = paddle.squeeze(src2, 2)
+        src2 = src2.transpose([2, 0, 1])
+        src = paddle.squeeze(src, 2)
+        src = src.transpose([2, 0, 1])
+
+        src = src + self.dropout2(src2)
+        src = self.norm2(src)
+        return src
+
+class TransformerDecoderLayer(nn.Layer):
+    r"""TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.
+    This standard decoder layer is based on the paper "Attention Is All You Need".
+    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
+    Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
+    Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
+    in a different way during application.
+
+    Args:
+        d_model: the number of expected features in the input (required).
+        nhead: the number of heads in the multiheadattention models (required).
+        dim_feedforward: the dimension of the feedforward network model (default=2048).
+        dropout: the dropout value (default=0.1).
+
+    Examples::
+        >>> decoder_layer = nn.TransformerDecoderLayer(d_model, nhead)
+    """
+
+    def __init__(self, d_model, nhead, dim_feedforward=2048, attention_dropout_rate=0.0, residual_dropout_rate=0.1):
+        super(TransformerDecoderLayer, self).__init__()
+        self.self_attn = MultiheadAttentionOptim(d_model, nhead, dropout=attention_dropout_rate)
+        self.multihead_attn = MultiheadAttentionOptim(d_model, nhead, dropout=attention_dropout_rate)
+
+        self.conv1 = Conv2D(in_channels=d_model, out_channels=dim_feedforward, kernel_size=(1, 1))
+        self.conv2 = Conv2D(in_channels=dim_feedforward, out_channels=d_model, kernel_size=(1, 1))
+
+        self.norm1 = LayerNorm(d_model)
+        self.norm2 = LayerNorm(d_model)
+        self.norm3 = LayerNorm(d_model)
+        self.dropout1 = Dropout(residual_dropout_rate)
+        self.dropout2 = Dropout(residual_dropout_rate)
+        self.dropout3 = Dropout(residual_dropout_rate)
+
+    def forward(self, tgt, memory, tgt_mask=None, memory_mask=None,
+                tgt_key_padding_mask=None, memory_key_padding_mask=None):
+        r"""Pass the inputs (and mask) through the decoder layer.
+
+        Args:
+            tgt: the sequence to the decoder layer (required).
+            memory: the sequnce from the last layer of the encoder (required).
+            tgt_mask: the mask for the tgt sequence (optional).
+            memory_mask: the mask for the memory sequence (optional).
+            tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
+            memory_key_padding_mask: the mask for the memory keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        tgt2 = self.self_attn(tgt, tgt, tgt, attn_mask=tgt_mask,
+                              key_padding_mask=tgt_key_padding_mask)[0]
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+        tgt2 = self.multihead_attn(tgt, memory, memory, attn_mask=memory_mask,
+                                   key_padding_mask=memory_key_padding_mask)[0]
+        tgt = tgt + self.dropout2(tgt2)
+        tgt = self.norm2(tgt)
+
+        # default
+        tgt = tgt.transpose([1, 2, 0])
+        tgt = paddle.unsqueeze(tgt, 2)
+        tgt2 = self.conv2(F.relu(self.conv1(tgt)))
+        tgt2 = paddle.squeeze(tgt2, 2)
+        tgt2 = tgt2.transpose([2, 0, 1])
+        tgt = paddle.squeeze(tgt, 2)
+        tgt = tgt.transpose([2, 0, 1])
+
+        tgt = tgt + self.dropout3(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+
+def _get_clones(module, N):
+    return LayerList([copy.deepcopy(module) for i in range(N)])
+
+
+
+class PositionalEncoding(nn.Layer):
+    r"""Inject some information about the relative or absolute position of the tokens
+        in the sequence. The positional encodings have the same dimension as
+        the embeddings, so that the two can be summed. Here, we use sine and cosine
+        functions of different frequencies.
+    .. math::
+        \text{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model))
+        \text{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model))
+        \text{where pos is the word position and i is the embed idx)
+    Args:
+        d_model: the embed dim (required).
+        dropout: the dropout value (default=0.1).
+        max_len: the max. length of the incoming sequence (default=5000).
+    Examples:
+        >>> pos_encoder = PositionalEncoding(d_model)
+    """
+
+    def __init__(self, dropout, dim, max_len=5000):
+        super(PositionalEncoding, self).__init__()
+        self.dropout = nn.Dropout(p=dropout)
+
+        pe = paddle.zeros([max_len, dim])
+        position = paddle.arange(0, max_len, dtype=paddle.float32).unsqueeze(1)
+        div_term = paddle.exp(paddle.arange(0, dim, 2).astype('float32') * (-math.log(10000.0) / dim))
+        pe[:, 0::2] = paddle.sin(position * div_term)
+        pe[:, 1::2] = paddle.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        pe = pe.transpose([1, 0, 2])
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        r"""Inputs of forward function
+        Args:
+            x: the sequence fed to the positional encoder model (required).
+        Shape:
+            x: [sequence length, batch size, embed dim]
+            output: [sequence length, batch size, embed dim]
+        Examples:
+            >>> output = pos_encoder(x)
+        """
+        x = x + self.pe[:x.shape[0], :]
+        return self.dropout(x)
+
+
+class PositionalEncoding_2d(nn.Layer):
+    r"""Inject some information about the relative or absolute position of the tokens
+        in the sequence. The positional encodings have the same dimension as
+        the embeddings, so that the two can be summed. Here, we use sine and cosine
+        functions of different frequencies.
+    .. math::
+        \text{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model))
+        \text{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model))
+        \text{where pos is the word position and i is the embed idx)
+    Args:
+        d_model: the embed dim (required).
+        dropout: the dropout value (default=0.1).
+        max_len: the max. length of the incoming sequence (default=5000).
+    Examples:
+        >>> pos_encoder = PositionalEncoding(d_model)
+    """
+
+    def __init__(self, dropout, dim, max_len=5000):
+        super(PositionalEncoding_2d, self).__init__()
+        self.dropout = nn.Dropout(p=dropout)
+
+        pe = paddle.zeros([max_len, dim])
+        position = paddle.arange(0, max_len, dtype=paddle.float32).unsqueeze(1)
+        div_term = paddle.exp(paddle.arange(0, dim, 2).astype('float32') * (-math.log(10000.0) / dim))
+        pe[:, 0::2] = paddle.sin(position * div_term)
+        pe[:, 1::2] = paddle.cos(position * div_term)
+        pe = pe.unsqueeze(0).transpose([1, 0, 2])
+        self.register_buffer('pe', pe)
+
+        self.avg_pool_1 = nn.AdaptiveAvgPool2D((1, 1))
+        self.linear1 = nn.Linear(dim, dim)
+        self.linear1.weight.data.fill_(1.)
+        self.avg_pool_2 = nn.AdaptiveAvgPool2D((1, 1))
+        self.linear2 = nn.Linear(dim, dim)
+        self.linear2.weight.data.fill_(1.)
+
+    def forward(self, x):
+        r"""Inputs of forward function
+        Args:
+            x: the sequence fed to the positional encoder model (required).
+        Shape:
+            x: [sequence length, batch size, embed dim]
+            output: [sequence length, batch size, embed dim]
+        Examples:
+            >>> output = pos_encoder(x)
+        """
+        w_pe = self.pe[:x.shape[-1], :]
+        w1 = self.linear1(self.avg_pool_1(x).squeeze()).unsqueeze(0)
+        w_pe = w_pe * w1
+        w_pe = w_pe.transpose([1, 2, 0])
+        w_pe = w_pe.unsqueeze(2)
+
+        h_pe = self.pe[:x.shape[-2], :]
+        w2 = self.linear2(self.avg_pool_2(x).squeeze()).unsqueeze(0)
+        h_pe = h_pe * w2
+        h_pe = h_pe.transpose([1, 2, 0])
+        h_pe = h_pe.unsqueeze(3)
+
+        x = x + w_pe + h_pe
+        x = x.reshape([x.shape[0], x.shape[1], x.shape[2] * x.shape[3]]).transpose([2,0,1])
+
+        return self.dropout(x)
+
+
+class Embeddings(nn.Layer):
+    def __init__(self, d_model, vocab, padding_idx, scale_embedding):
+        super(Embeddings, self).__init__()
+        self.embedding = nn.Embedding(vocab, d_model, padding_idx=padding_idx)
+        w0 = np.random.normal(0.0, d_model**-0.5,(vocab, d_model)).astype(np.float32)
+        self.embedding.weight.set_value(w0) 
+        self.d_model = d_model
+        self.scale_embedding = scale_embedding
+
+    def forward(self, x):
+        if self.scale_embedding:
+            x = self.embedding(x)
+            return x * math.sqrt(self.d_model)
+        return self.embedding(x)
+
+
+
+
+
+class Beam():
+    ''' Beam search '''
+
+    def __init__(self, size, device=False):
+
+        self.size = size
+        self._done = False
+        # The score for each translation on the beam.
+        self.scores = paddle.zeros((size,), dtype=paddle.float32)
+        self.all_scores = []
+        # The backpointers at each time-step.
+        self.prev_ks = []
+        # The outputs at each time-step.
+        self.next_ys =  [paddle.full((size,), 0, dtype=paddle.int64)]
+        self.next_ys[0][0] = 2
+
+    def get_current_state(self):
+        "Get the outputs for the current timestep."
+        return self.get_tentative_hypothesis()
+
+    def get_current_origin(self):
+        "Get the backpointers for the current timestep."
+        return self.prev_ks[-1]
+
+    @property
+    def done(self):
+        return self._done
+
+    def advance(self, word_prob):
+        "Update beam status and check if finished or not."
+        num_words = word_prob.shape[1]
+
+        # Sum the previous scores.
+        if len(self.prev_ks) > 0:
+            beam_lk = word_prob + self.scores.unsqueeze(1).expand_as(word_prob)
+        else:
+            beam_lk = word_prob[0]
+
+        flat_beam_lk = beam_lk.reshape([-1])
+        best_scores, best_scores_id = flat_beam_lk.topk(self.size, 0, True, True) # 1st sort
+        self.all_scores.append(self.scores)
+        self.scores = best_scores
+
+        # bestScoresId is flattened as a (beam x word) array,
+        # so we need to calculate which word and beam each score came from
+        prev_k = best_scores_id // num_words
+        self.prev_ks.append(prev_k)
+        
+        self.next_ys.append(best_scores_id - prev_k * num_words) 
+
+        # End condition is when top-of-beam is EOS.
+        if self.next_ys[-1][0] == 3 :
+            self._done = True
+            self.all_scores.append(self.scores)
+      
+
+        return self._done
+
+    def sort_scores(self):
+        "Sort the scores."
+        return self.scores, paddle.to_tensor([i for i in range(self.scores.shape[0])],dtype='int32')
+
+    def get_the_best_score_and_idx(self):
+        "Get the score of the best in the beam."
+        scores, ids = self.sort_scores()
+        return scores[1], ids[1]
+
+    def get_tentative_hypothesis(self):
+        "Get the decoded sequence for the current timestep."
+
+        if len(self.next_ys) == 1:
+            dec_seq = self.next_ys[0].unsqueeze(1)
+        else:
+            _, keys = self.sort_scores()
+            hyps = [self.get_hypothesis(k) for k in keys]
+            hyps = [[2] + h for h in hyps]
+            dec_seq = paddle.to_tensor(hyps, dtype='int64')
+
+        return dec_seq
+
+    def get_hypothesis(self, k):
+        """ Walk back to construct the full hypothesis. """
+        hyp = []
+        for j in range(len(self.prev_ks) - 1, -1, -1):
+            hyp.append(self.next_ys[j+1][k])
+            k = self.prev_ks[j][k]
+        return list(map(lambda x: x.item(), hyp[::-1]))

ppocr/postprocess/rec_postprocess.py

@@ -156,6 +156,69 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
         return output
 
 
+class NRTRLabelDecode(BaseRecLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self,
+                 character_dict_path=None,
+                 character_type='EN_symbol',
+                 use_space_char=True,
+                 **kwargs):
+        super(NRTRLabelDecode, self).__init__(character_dict_path,
+                                             character_type, use_space_char)
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        if preds.dtype == paddle.int64:
+            if isinstance(preds, paddle.Tensor):
+                preds = preds.numpy()
+            if preds[0][0]==2:
+                preds_idx = preds[:,1:]
+            else:
+                preds_idx = preds
+
+            text = self.decode(preds_idx)
+            if label is None:
+                return text
+            label = self.decode(label[:,1:])
+        else:
+            if isinstance(preds, paddle.Tensor):
+                preds = preds.numpy()
+            preds_idx = preds.argmax(axis=2)
+            preds_prob = preds.max(axis=2)
+            text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
+            if label is None:
+                return text
+            label = self.decode(label[:,1:])
+        return text, label
+
+    def add_special_char(self, dict_character):
+        dict_character = ['blank','<unk>','<s>','</s>'] + dict_character
+        return dict_character
+    
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        result_list = []
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            char_list = []
+            conf_list = []
+            for idx in range(len(text_index[batch_idx])):
+                if text_index[batch_idx][idx] == 3: # end
+                    break
+                try:
+                    char_list.append(self.character[int(text_index[batch_idx][idx])])
+                except:
+                    continue
+                if text_prob is not None:
+                    conf_list.append(text_prob[batch_idx][idx])
+                else:
+                    conf_list.append(1)
+            text = ''.join(char_list)
+            result_list.append((text.lower(), np.mean(conf_list)))
+        return result_list
+
+
+
 class AttnLabelDecode(BaseRecLabelDecode):
     """ Convert between text-label and text-index """
 

tools/eval.py

@@ -22,7 +22,6 @@ import sys
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
 sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
-
 from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
@@ -31,7 +30,6 @@ from ppocr.utils.save_load import init_model
 from ppocr.utils.utility import print_dict
 import tools.program as program
 
-
 def main():
     global_config = config['Global']
     # build dataloader