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Update rec_nrtr_head.py (#8564)

pull/8572/head
topduke 2022-12-07 15:07:50 +08:00 committed by GitHub
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1 changed files with 2 additions and 7 deletions
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ppocr/modeling/heads/rec_nrtr_head.py
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@ -17,7 +17,6 @@ import paddle
from paddle import nn from paddle import nn
import paddle.nn.functional as F import paddle.nn.functional as F
from paddle.nn import LayerList from paddle.nn import LayerList
# from paddle.nn.initializer import XavierNormal as xavier_uniform_
from paddle.nn import Dropout, Linear, LayerNorm from paddle.nn import Dropout, Linear, LayerNorm
import numpy as np import numpy as np
from ppocr.modeling.backbones.rec_svtrnet import Mlp, zeros_, ones_ from ppocr.modeling.backbones.rec_svtrnet import Mlp, zeros_, ones_
@ -30,7 +29,6 @@ class Transformer(nn.Layer):
Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and 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 Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information
Processing Systems, pages 6000-6010. Processing Systems, pages 6000-6010.
Args: Args:
d_model: the number of expected features in the encoder/decoder inputs (default=512). d_model: the number of expected features in the encoder/decoder inputs (default=512).
nhead: the number of heads in the multiheadattention models (default=8). nhead: the number of heads in the multiheadattention models (default=8).
@ -162,7 +160,7 @@ class Transformer(nn.Layer):
memory = src memory = src
dec_seq = paddle.full((bs, 1), 2, dtype=paddle.int64) dec_seq = paddle.full((bs, 1), 2, dtype=paddle.int64)
dec_prob = paddle.full((bs, 1), 1., dtype=paddle.float32) dec_prob = paddle.full((bs, 1), 1., dtype=paddle.float32)
for len_dec_seq in range(1, self.max_len): for len_dec_seq in range(1, paddle.to_tensor(self.max_len)):
dec_seq_embed = self.embedding(dec_seq) dec_seq_embed = self.embedding(dec_seq)
dec_seq_embed = self.positional_encoding(dec_seq_embed) dec_seq_embed = self.positional_encoding(dec_seq_embed)
tgt_mask = self.generate_square_subsequent_mask( tgt_mask = self.generate_square_subsequent_mask(
@ -304,7 +302,7 @@ class Transformer(nn.Layer):
inst_idx_to_position_map = get_inst_idx_to_tensor_position_map( inst_idx_to_position_map = get_inst_idx_to_tensor_position_map(
active_inst_idx_list) active_inst_idx_list)
# Decode # Decode
for len_dec_seq in range(1, self.max_len): for len_dec_seq in range(1, paddle.to_tensor(self.max_len)):
src_enc_copy = src_enc.clone() src_enc_copy = src_enc.clone()
active_inst_idx_list = beam_decode_step( active_inst_idx_list = beam_decode_step(
inst_dec_beams, len_dec_seq, src_enc_copy, inst_dec_beams, len_dec_seq, src_enc_copy,
@ -348,15 +346,12 @@ class MultiheadAttention(nn.Layer):
"""Allows the model to jointly attend to information """Allows the model to jointly attend to information
from different representation subspaces. from different representation subspaces.
See reference: Attention Is All You Need See reference: Attention Is All You Need
.. math:: .. math::
\text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
\text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V) \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
Args: Args:
embed_dim: total dimension of the model embed_dim: total dimension of the model
num_heads: parallel attention layers, or heads num_heads: parallel attention layers, or heads
""" """
def __init__(self, embed_dim, num_heads, dropout=0., self_attn=False): def __init__(self, embed_dim, num_heads, dropout=0., self_attn=False):