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delete blank lines and modify forward_train

This commit is contained in:
Topdu 2021-08-19 09:31:02 +00:00
parent a11e219970
commit 55b76dcaa5
7 changed files with 388 additions and 288 deletions
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2
configs/rec/rec_mtb_nrtr.yml
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@ -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:

5
ppocr/modeling/backbones/__init__.py
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@ -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"]

34
ppocr/modeling/backbones/rec_nrtr_mtb.py
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@ -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)

7
ppocr/modeling/heads/__init__.py
View File

@ -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

113
ppocr/modeling/heads/multiheadAttention.py
View File

@ -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)

504
ppocr/modeling/heads/rec_nrtr_optim_head.py
View File

@ -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]
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])
tgt_key_padding_mask = self.generate_padding_mask(tgt)
tgt = self.embedding(tgt).transpose([1, 0, 2])
tgt = self.positional_encoding(tgt)
tgt_mask = self.generate_square_subsequent_mask(tgt.shape[0])
if self.encoder is not None :
src = self.positional_encoding(src.transpose([1, 0, 2]))
memory = self.encoder(src)
else:
memory = src.squeeze(2).transpose([2, 0, 1])
output = self.decoder(tgt, memory, tgt_mask=tgt_mask, memory_mask=None,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=None)
output = output.transpose([1, 0, 2])
logit = self.tgt_word_prj(output)
return logit
def forward(self, src, tgt=None):
r"""Take in and process masked source/target sequences.
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 <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):
@ -364,50 +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.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.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.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.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]))

11
tools/program.py
View File

@ -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'