delete blank lines and modify forward_train

ppocr/modeling/backbones/__init__.py

@@ -27,8 +27,9 @@ def build_backbone(config, model_type):
         from .rec_resnet_fpn import ResNetFPN
         from .rec_mv1_enhance import MobileNetV1Enhance
         from .rec_nrtr_mtb import MTB
-        from .rec_swin import SwinTransformer
-        support_dict = ['MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB', 'SwinTransformer']
+        support_dict = [
+            'MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB'
+        ]
     elif model_type == "e2e":
         from .e2e_resnet_vd_pg import ResNet
         support_dict = ["ResNet"]

ppocr/modeling/backbones/rec_nrtr_mtb.py

+# copyright (c) 2020 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 nn
 
+
 class MTB(nn.Layer):
     def __init__(self, cnn_num, in_channels):
         super(MTB, self).__init__()
@@ -8,17 +23,20 @@ class MTB(nn.Layer):
         self.cnn_num = cnn_num
         if self.cnn_num == 2:
             for i in range(self.cnn_num):
-                self.block.add_sublayer('conv_{}'.format(i), nn.Conv2D(
-                    in_channels = in_channels if i == 0 else 32*(2**(i-1)), 
-                    out_channels = 32*(2**i), 
-                    kernel_size = 3, 
-                    stride = 2, 
+                self.block.add_sublayer(
+                    'conv_{}'.format(i),
+                    nn.Conv2D(
+                        in_channels=in_channels
+                        if i == 0 else 32 * (2**(i - 1)),
+                        out_channels=32 * (2**i),
+                        kernel_size=3,
+                        stride=2,
                         padding=1))
                 self.block.add_sublayer('relu_{}'.format(i), nn.ReLU())
-                self.block.add_sublayer('bn_{}'.format(i), nn.BatchNorm2D(32*(2**i)))
+                self.block.add_sublayer('bn_{}'.format(i),
+                                        nn.BatchNorm2D(32 * (2**i)))
 
     def forward(self, images):
-        
         x = self.block(images)
         if self.cnn_num == 2:
             # (b, w, h, c)

ppocr/modeling/heads/__init__.py

@@ -32,9 +32,8 @@ def build_head(config):
     from .cls_head import ClsHead
     support_dict = [
         'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
-
-        'SRNHead', 'PGHead', 'TransformerOptim', 'TableAttentionHead']
-
+        'SRNHead', 'PGHead', 'TransformerOptim', 'TableAttentionHead'
+    ]
 
     #table head
     from .table_att_head import TableAttentionHead

ppocr/modeling/heads/multiheadAttention.py

+# 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.
+
 import paddle
 from paddle import nn
 import paddle.nn.functional as F
@@ -11,7 +25,7 @@ ones_ = constant_(value=1.)
 
 
 class MultiheadAttentionOptim(nn.Layer):
-    r"""Allows the model to jointly attend to information
+    """Allows the model to jointly attend to information
     from different representation subspaces.
     See reference: Attention Is All You Need
 
@@ -23,37 +37,43 @@ class MultiheadAttentionOptim(nn.Layer):
         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):
+    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.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))
+        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):
+    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]
@@ -68,8 +88,6 @@ class MultiheadAttentionOptim(nn.Layer):
             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]
@@ -80,11 +98,12 @@ class MultiheadAttentionOptim(nn.Layer):
         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])
-
+        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]
 
@@ -92,44 +111,48 @@ class MultiheadAttentionOptim(nn.Layer):
             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]
+        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])
+            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)
-
+            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 = 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_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])
+        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
+            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)
@@ -139,7 +162,6 @@ class MultiheadAttentionOptim(nn.Layer):
         return res
 
     def _in_proj_k(self, key):
-        
         key = key.transpose([1, 2, 0])
         key = paddle.unsqueeze(key, axis=2)
         res = self.conv2(key)
@@ -148,8 +170,7 @@ class MultiheadAttentionOptim(nn.Layer):
         return res
 
     def _in_proj_v(self, value):
-        
-        value = value.transpose([1,2,0])#(1, 2, 0)
+        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)

ppocr/modeling/heads/rec_nrtr_optim_head.py

+# 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.
+
 import math
 import paddle
 import copy
@@ -14,8 +28,9 @@ 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
+    """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
@@ -31,39 +46,50 @@ class TransformerOptim(nn.Layer):
         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):
+    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
-        )
+            scale_embedding=scale_embedding)
         self.positional_encoding = PositionalEncoding(
             dropout=residual_dropout_rate,
-            dim=d_model,
-        )
+            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)
+            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)
+            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()
@@ -71,11 +97,11 @@ class TransformerOptim(nn.Layer):
         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)
+        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):
@@ -83,189 +109,193 @@ class TransformerOptim(nn.Layer):
             if m.bias is not None:
                 zeros_(m.bias)
 
-    def forward_train(self,src,tgt):
+    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 :
+        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,
+        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.
-
+    def forward(self, src, targets=None):
+        """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)
+            >>> output = transformer_model(src, tgt)
         """
-        if tgt is not None:
+
+        if self.training:
+            max_len = targets[1].max()
+            tgt = targets[0][:, :2 + max_len]
             return self.forward_train(src, tgt)
         else:
-            if self.beam_size > 0 :
+            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 :
+        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)
+        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_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
+            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')):
+            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)
+            dec_seq = paddle.concat(
+                [dec_seq, preds_idx.reshape([-1, 1])], axis=1)
 
         return dec_seq
 
-    def forward_beam(self,images):
-             
+    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)}
+            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):
+        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(
+                [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):
+        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 = [
+                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)
+            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):
+        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 = [
+                    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):
+            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)]
+                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)
+                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):
+            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])
+                tgt_mask = self.generate_square_subsequent_mask(dec_seq.shape[
+                    0])
                 dec_output = self.decoder(
-                    dec_seq, enc_output,
+                    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
+                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):
+            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])
+                    is_inst_complete = inst_beams[inst_idx].advance(word_prob[
+                        inst_position])
                     if not is_inst_complete:
                         active_inst_idx_list += [inst_idx]
 
@@ -274,7 +304,8 @@ class TransformerOptim(nn.Layer):
             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)
+            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)
@@ -285,14 +316,17 @@ class TransformerOptim(nn.Layer):
             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]]
+                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 :
+            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:
@@ -301,45 +335,53 @@ class TransformerOptim(nn.Layer):
             #-- 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)
+            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)
+            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)
+                    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)
+                    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]))
+            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)
+        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').
+        """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
+        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))
+        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."""
+        """Initiate parameters in the transformer model."""
 
         for p in self.parameters():
             if p.dim() > 1:
@@ -347,16 +389,11 @@ class TransformerOptim(nn.Layer):
 
 
 class TransformerEncoder(nn.Layer):
-    r"""TransformerEncoder is a stack of N encoder layers
-
+    """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):
@@ -364,38 +401,31 @@ class TransformerEncoder(nn.Layer):
         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.
-
+        """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,
+            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
+    """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):
@@ -403,11 +433,14 @@ class TransformerDecoder(nn.Layer):
         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,
+    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.
+        """Pass the inputs (and mask) through the decoder layer in turn.
 
         Args:
             tgt: the sequence to the decoder (required).
@@ -416,21 +449,22 @@ class TransformerDecoder(nn.Layer):
             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,
+            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.
+    """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
@@ -443,16 +477,26 @@ class TransformerEncoderLayer(nn.Layer):
         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):
+    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.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)
@@ -460,17 +504,17 @@ class TransformerEncoderLayer(nn.Layer):
         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.
-
+        """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,
+        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)
@@ -487,8 +531,9 @@ class TransformerEncoderLayer(nn.Layer):
         src = self.norm2(src)
         return src
 
+
 class TransformerDecoderLayer(nn.Layer):
-    r"""TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.
+    """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
@@ -501,17 +546,28 @@ class TransformerDecoderLayer(nn.Layer):
         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):
+    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.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)
@@ -520,9 +576,14 @@ class TransformerDecoderLayer(nn.Layer):
         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.
+    def forward(self,
+                tgt,
+                memory,
+                tgt_mask=None,
+                memory_mask=None,
+                tgt_key_padding_mask=None,
+                memory_key_padding_mask=None):
+        """Pass the inputs (and mask) through the decoder layer.
 
         Args:
             tgt: the sequence to the decoder layer (required).
@@ -532,14 +593,20 @@ class TransformerDecoderLayer(nn.Layer):
             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,
+        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,
+        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)
@@ -562,9 +629,8 @@ 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
+    """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.
@@ -586,7 +652,9 @@ class PositionalEncoding(nn.Layer):
 
         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))
+        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)
@@ -594,7 +662,7 @@ class PositionalEncoding(nn.Layer):
         self.register_buffer('pe', pe)
 
     def forward(self, x):
-        r"""Inputs of forward function
+        """Inputs of forward function
         Args:
             x: the sequence fed to the positional encoder model (required).
         Shape:
@@ -608,7 +676,7 @@ class PositionalEncoding(nn.Layer):
 
 
 class PositionalEncoding_2d(nn.Layer):
-    r"""Inject some information about the relative or absolute position of the tokens
+    """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.
@@ -630,7 +698,9 @@ class PositionalEncoding_2d(nn.Layer):
 
         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))
+        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])
@@ -644,7 +714,7 @@ class PositionalEncoding_2d(nn.Layer):
         self.linear2.weight.data.fill_(1.)
 
     def forward(self, x):
-        r"""Inputs of forward function
+        """Inputs of forward function
         Args:
             x: the sequence fed to the positional encoder model (required).
         Shape:
@@ -666,7 +736,9 @@ class PositionalEncoding_2d(nn.Layer):
         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])
+        x = x.reshape(
+            [x.shape[0], x.shape[1], x.shape[2] * x.shape[3]]).transpose(
+                [2, 0, 1])
 
         return self.dropout(x)
 
@@ -675,7 +747,8 @@ 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)
+        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
@@ -687,9 +760,6 @@ class Embeddings(nn.Layer):
         return self.embedding(x)
 
 
-
-
-
 class Beam():
     ''' Beam search '''
 
@@ -698,12 +768,12 @@ class Beam():
         self.size = size
         self._done = False
         # The score for each translation on the beam.
-        self.scores = paddle.zeros((size,), dtype=paddle.float32)
+        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 = [paddle.full((size, ), 0, dtype=paddle.int64)]
         self.next_ys[0][0] = 2
 
     def get_current_state(self):
@@ -729,28 +799,26 @@ class Beam():
             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
+        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 :
+        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')
+        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."
@@ -759,7 +827,6 @@ class Beam():
 
     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:
@@ -767,13 +834,12 @@ class Beam():
             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])
+            hyp.append(self.next_ys[j + 1][k])
             k = self.prev_ks[j][k]
         return list(map(lambda x: x.item(), hyp[::-1]))

tools/program.py

@@ -216,11 +216,8 @@ def train(config,
             images = batch[0]
             if use_srn:
                 model_average = True
-            if use_srn or model_type == 'table':
+            if use_srn or model_type == 'table' or use_nrtr:
                 preds = model(images, data=batch[1:])
-            elif use_nrtr:
-                max_len = batch[2].max()
-                preds = model(images, batch[1][:,:2+max_len])
             else:
                 preds = model(images)
             loss = loss_class(preds, batch)
@@ -405,9 +402,7 @@ def preprocess(is_train=False):
     alg = config['Architecture']['algorithm']
     assert alg in [
         'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
-
         'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn'
-
     ]
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'