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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import torch.nn as nn | |
| from mmcv.runner import BaseModule | |
| from mmocr.models.common.modules import (MultiHeadAttention, | |
| PositionwiseFeedForward) | |
| class TFEncoderLayer(BaseModule): | |
| """Transformer Encoder Layer. | |
| Args: | |
| d_model (int): The number of expected features | |
| in the decoder inputs (default=512). | |
| d_inner (int): The dimension of the feedforward | |
| network model (default=256). | |
| n_head (int): The number of heads in the | |
| multiheadattention models (default=8). | |
| d_k (int): Total number of features in key. | |
| d_v (int): Total number of features in value. | |
| dropout (float): Dropout layer on attn_output_weights. | |
| qkv_bias (bool): Add bias in projection layer. Default: False. | |
| act_cfg (dict): Activation cfg for feedforward module. | |
| operation_order (tuple[str]): The execution order of operation | |
| in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm') | |
| or ('norm', 'self_attn', 'norm', 'ffn'). | |
| Default：None. | |
| """ | |
| def __init__(self, | |
| d_model=512, | |
| d_inner=256, | |
| n_head=8, | |
| d_k=64, | |
| d_v=64, | |
| dropout=0.1, | |
| qkv_bias=False, | |
| act_cfg=dict(type='mmcv.GELU'), | |
| operation_order=None): | |
| super().__init__() | |
| self.attn = MultiHeadAttention( | |
| n_head, d_model, d_k, d_v, qkv_bias=qkv_bias, dropout=dropout) | |
| self.norm1 = nn.LayerNorm(d_model) | |
| self.mlp = PositionwiseFeedForward( | |
| d_model, d_inner, dropout=dropout, act_cfg=act_cfg) | |
| self.norm2 = nn.LayerNorm(d_model) | |
| self.operation_order = operation_order | |
| if self.operation_order is None: | |
| self.operation_order = ('norm', 'self_attn', 'norm', 'ffn') | |
| assert self.operation_order in [('norm', 'self_attn', 'norm', 'ffn'), | |
| ('self_attn', 'norm', 'ffn', 'norm')] | |
| def forward(self, x, mask=None): | |
| if self.operation_order == ('self_attn', 'norm', 'ffn', 'norm'): | |
| residual = x | |
| x = residual + self.attn(x, x, x, mask) | |
| x = self.norm1(x) | |
| residual = x | |
| x = residual + self.mlp(x) | |
| x = self.norm2(x) | |
| elif self.operation_order == ('norm', 'self_attn', 'norm', 'ffn'): | |
| residual = x | |
| x = self.norm1(x) | |
| x = residual + self.attn(x, x, x, mask) | |
| residual = x | |
| x = self.norm2(x) | |
| x = residual + self.mlp(x) | |
| return x | |
| class TFDecoderLayer(nn.Module): | |
| """Transformer Decoder Layer. | |
| Args: | |
| d_model (int): The number of expected features | |
| in the decoder inputs (default=512). | |
| d_inner (int): The dimension of the feedforward | |
| network model (default=256). | |
| n_head (int): The number of heads in the | |
| multiheadattention models (default=8). | |
| d_k (int): Total number of features in key. | |
| d_v (int): Total number of features in value. | |
| dropout (float): Dropout layer on attn_output_weights. | |
| qkv_bias (bool): Add bias in projection layer. Default: False. | |
| act_cfg (dict): Activation cfg for feedforward module. | |
| operation_order (tuple[str]): The execution order of operation | |
| in transformer. Such as ('self_attn', 'norm', 'enc_dec_attn', | |
| 'norm', 'ffn', 'norm') or ('norm', 'self_attn', 'norm', | |
| 'enc_dec_attn', 'norm', 'ffn'). | |
| Default：None. | |
| """ | |
| def __init__(self, | |
| d_model=512, | |
| d_inner=256, | |
| n_head=8, | |
| d_k=64, | |
| d_v=64, | |
| dropout=0.1, | |
| qkv_bias=False, | |
| act_cfg=dict(type='mmcv.GELU'), | |
| operation_order=None): | |
| super().__init__() | |
| self.norm1 = nn.LayerNorm(d_model) | |
| self.norm2 = nn.LayerNorm(d_model) | |
| self.norm3 = nn.LayerNorm(d_model) | |
| self.self_attn = MultiHeadAttention( | |
| n_head, d_model, d_k, d_v, dropout=dropout, qkv_bias=qkv_bias) | |
| self.enc_attn = MultiHeadAttention( | |
| n_head, d_model, d_k, d_v, dropout=dropout, qkv_bias=qkv_bias) | |
| self.mlp = PositionwiseFeedForward( | |
| d_model, d_inner, dropout=dropout, act_cfg=act_cfg) | |
| self.operation_order = operation_order | |
| if self.operation_order is None: | |
| self.operation_order = ('norm', 'self_attn', 'norm', | |
| 'enc_dec_attn', 'norm', 'ffn') | |
| assert self.operation_order in [ | |
| ('norm', 'self_attn', 'norm', 'enc_dec_attn', 'norm', 'ffn'), | |
| ('self_attn', 'norm', 'enc_dec_attn', 'norm', 'ffn', 'norm') | |
| ] | |
| def forward(self, | |
| dec_input, | |
| enc_output, | |
| self_attn_mask=None, | |
| dec_enc_attn_mask=None): | |
| if self.operation_order == ('self_attn', 'norm', 'enc_dec_attn', | |
| 'norm', 'ffn', 'norm'): | |
| dec_attn_out = self.self_attn(dec_input, dec_input, dec_input, | |
| self_attn_mask) | |
| dec_attn_out += dec_input | |
| dec_attn_out = self.norm1(dec_attn_out) | |
| enc_dec_attn_out = self.enc_attn(dec_attn_out, enc_output, | |
| enc_output, dec_enc_attn_mask) | |
| enc_dec_attn_out += dec_attn_out | |
| enc_dec_attn_out = self.norm2(enc_dec_attn_out) | |
| mlp_out = self.mlp(enc_dec_attn_out) | |
| mlp_out += enc_dec_attn_out | |
| mlp_out = self.norm3(mlp_out) | |
| elif self.operation_order == ('norm', 'self_attn', 'norm', | |
| 'enc_dec_attn', 'norm', 'ffn'): | |
| dec_input_norm = self.norm1(dec_input) | |
| dec_attn_out = self.self_attn(dec_input_norm, dec_input_norm, | |
| dec_input_norm, self_attn_mask) | |
| dec_attn_out += dec_input | |
| enc_dec_attn_in = self.norm2(dec_attn_out) | |
| enc_dec_attn_out = self.enc_attn(enc_dec_attn_in, enc_output, | |
| enc_output, dec_enc_attn_mask) | |
| enc_dec_attn_out += dec_attn_out | |
| mlp_out = self.mlp(self.norm3(enc_dec_attn_out)) | |
| mlp_out += enc_dec_attn_out | |
| return mlp_out | |