From 55b76dcaa55fb69bf54dcac1f8d124deb15a5423 Mon Sep 17 00:00:00 2001 From: Topdu <784990967@qq.com> Date: Thu, 19 Aug 2021 09:31:02 +0000 Subject: [PATCH] delete blank lines and modify forward_train --- configs/rec/rec_mtb_nrtr.yml | 2 +- ppocr/modeling/backbones/__init__.py | 5 +- ppocr/modeling/backbones/rec_nrtr_mtb.py | 34 +- ppocr/modeling/heads/__init__.py | 7 +- ppocr/modeling/heads/multiheadAttention.py | 113 +++-- ppocr/modeling/heads/rec_nrtr_optim_head.py | 510 +++++++++++--------- tools/program.py | 11 +- 7 files changed, 391 insertions(+), 291 deletions(-) diff --git a/configs/rec/rec_mtb_nrtr.yml b/configs/rec/rec_mtb_nrtr.yml index 171ac7e3..c89de02b 100644 --- a/configs/rec/rec_mtb_nrtr.yml +++ b/configs/rec/rec_mtb_nrtr.yml @@ -46,7 +46,7 @@ Architecture: name: TransformerOptim d_model: 512 num_encoder_layers: 6 - beam_size: 10 # When Beam size is greater than 0, it means to use beam search when evaluation. + beam_size: 10 # When Beam size is greater than 0, it means to use beam search when evaluation. Loss: diff --git a/ppocr/modeling/backbones/__init__.py b/ppocr/modeling/backbones/__init__.py index 618b827d..f8ca7e40 100755 --- a/ppocr/modeling/backbones/__init__.py +++ b/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"] diff --git a/ppocr/modeling/backbones/rec_nrtr_mtb.py b/ppocr/modeling/backbones/rec_nrtr_mtb.py index 26a0dc7f..04b5c9bb 100644 --- a/ppocr/modeling/backbones/rec_nrtr_mtb.py +++ b/ppocr/modeling/backbones/rec_nrtr_mtb.py @@ -1,5 +1,20 @@ +# 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, - padding=1)) + 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) diff --git a/ppocr/modeling/heads/__init__.py b/ppocr/modeling/heads/__init__.py index 63951cd5..11fd4b26 100755 --- a/ppocr/modeling/heads/__init__.py +++ b/ppocr/modeling/heads/__init__.py @@ -27,14 +27,13 @@ def build_head(config): 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', 'TransformerOptim', 'TableAttentionHead'] - + 'SRNHead', 'PGHead', 'TransformerOptim', 'TableAttentionHead' + ] #table head from .table_att_head import TableAttentionHead diff --git a/ppocr/modeling/heads/multiheadAttention.py b/ppocr/modeling/heads/multiheadAttention.py index 6aba81de..4be37025 100755 --- a/ppocr/modeling/heads/multiheadAttention.py +++ b/ppocr/modeling/heads/multiheadAttention.py @@ -1,3 +1,17 @@ +# 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) diff --git a/ppocr/modeling/heads/rec_nrtr_optim_head.py b/ppocr/modeling/heads/rec_nrtr_optim_head.py index b9a5100a..98f212d0 100644 --- a/ppocr/modeling/heads/rec_nrtr_optim_head.py +++ b/ppocr/modeling/heads/rec_nrtr_optim_head.py @@ -1,7 +1,21 @@ +# 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 -from paddle import nn +from paddle import nn import paddle.nn.functional as F from paddle.nn import LayerList from paddle.nn.initializer import XavierNormal as xavier_uniform_ @@ -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() @@ -71,201 +97,205 @@ 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): 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_train(self, src, tgt): + tgt = tgt[:, :-1] - def forward(self, src, tgt=None): - r"""Take in and process masked source/target sequences. + 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, 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_key_padding_mask=tgt_key_padding_mask, - memory_key_padding_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 + 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 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): @@ -364,50 +401,46 @@ 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): 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, + 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, - memory_mask=memory_mask, - tgt_key_padding_mask=tgt_key_padding_mask, - memory_key_padding_mask=memory_key_padding_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) @@ -460,18 +504,18 @@ 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, - key_padding_mask=src_key_padding_mask)[0] + 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). @@ -532,15 +593,21 @@ 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, - key_padding_mask=tgt_key_padding_mask)[0] + 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] + 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) @@ -675,8 +747,9 @@ 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) + 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) - + 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])) diff --git a/tools/program.py b/tools/program.py index aa5f9388..60a5e482 100755 --- a/tools/program.py +++ b/tools/program.py @@ -189,9 +189,9 @@ def train(config, use_nrtr = config['Architecture']['algorithm'] == "NRTR" - try: + try: model_type = config['Architecture']['model_type'] - except: + except: model_type = None if 'start_epoch' in best_model_dict: @@ -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' -- GitLab