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Source code for mmocr.models.textrecog.decoders.position_attention_decoder
# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
from mmocr.models.builder import DECODERS
from mmocr.models.textrecog.layers import (DotProductAttentionLayer,
PositionAwareLayer)
from .base_decoder import BaseDecoder
[docs]@DECODERS.register_module()
class PositionAttentionDecoder(BaseDecoder):
"""Position attention decoder for RobustScanner.
RobustScanner: `RobustScanner: Dynamically Enhancing Positional Clues for
Robust Text Recognition <https://arxiv.org/abs/2007.07542>`_
Args:
num_classes (int): Number of output classes :math:`C`.
rnn_layers (int): Number of RNN layers.
dim_input (int): Dimension :math:`D_i` of input vector ``feat``.
dim_model (int): Dimension :math:`D_m` of the model. Should also be the
same as encoder output vector ``out_enc``.
max_seq_len (int): Maximum output sequence length :math:`T`.
mask (bool): Whether to mask input features according to
``img_meta['valid_ratio']``.
return_feature (bool): Return feature or logits as the result.
encode_value (bool): Whether to use the output of encoder ``out_enc``
as `value` of attention layer. If False, the original feature
``feat`` will be used.
init_cfg (dict or list[dict], optional): Initialization configs.
Warning:
This decoder will not predict the final class which is assumed to be
`<PAD>`. Therefore, its output size is always :math:`C - 1`. `<PAD>`
is also ignored by loss as specified in
:obj:`mmocr.models.textrecog.recognizer.EncodeDecodeRecognizer`.
"""
def __init__(self,
num_classes=None,
rnn_layers=2,
dim_input=512,
dim_model=128,
max_seq_len=40,
mask=True,
return_feature=False,
encode_value=False,
init_cfg=None):
super().__init__(init_cfg=init_cfg)
self.num_classes = num_classes
self.dim_input = dim_input
self.dim_model = dim_model
self.max_seq_len = max_seq_len
self.return_feature = return_feature
self.encode_value = encode_value
self.mask = mask
self.embedding = nn.Embedding(self.max_seq_len + 1, self.dim_model)
self.position_aware_module = PositionAwareLayer(
self.dim_model, rnn_layers)
self.attention_layer = DotProductAttentionLayer()
self.prediction = None
if not self.return_feature:
pred_num_classes = num_classes - 1
self.prediction = nn.Linear(
dim_model if encode_value else dim_input, pred_num_classes)
def _get_position_index(self, length, batch_size, device=None):
position_index = torch.arange(0, length, device=device)
position_index = position_index.repeat([batch_size, 1])
position_index = position_index.long()
return position_index
[docs] def forward_train(self, feat, out_enc, targets_dict, img_metas):
"""
Args:
feat (Tensor): Tensor of shape :math:`(N, D_i, H, W)`.
out_enc (Tensor): Encoder output of shape
:math:`(N, D_m, H, W)`.
targets_dict (dict): A dict with the key ``padded_targets``, a
tensor of shape :math:`(N, T)`. Each element is the index of a
character.
img_metas (dict): A dict that contains meta information of input
images. Preferably with the key ``valid_ratio``.
Returns:
Tensor: A raw logit tensor of shape :math:`(N, T, C-1)` if
``return_feature=False``. Otherwise it will be the hidden feature
before the prediction projection layer, whose shape is
:math:`(N, T, D_m)`.
"""
valid_ratios = [
img_meta.get('valid_ratio', 1.0) for img_meta in img_metas
] if self.mask else None
targets = targets_dict['padded_targets'].to(feat.device)
#
n, c_enc, h, w = out_enc.size()
assert c_enc == self.dim_model
_, c_feat, _, _ = feat.size()
assert c_feat == self.dim_input
_, len_q = targets.size()
assert len_q <= self.max_seq_len
position_index = self._get_position_index(len_q, n, feat.device)
position_out_enc = self.position_aware_module(out_enc)
query = self.embedding(position_index)
query = query.permute(0, 2, 1).contiguous()
key = position_out_enc.view(n, c_enc, h * w)
if self.encode_value:
value = out_enc.view(n, c_enc, h * w)
else:
value = feat.view(n, c_feat, h * w)
mask = None
if valid_ratios is not None:
mask = query.new_zeros((n, h, w))
for i, valid_ratio in enumerate(valid_ratios):
valid_width = min(w, math.ceil(w * valid_ratio))
mask[i, :, valid_width:] = 1
mask = mask.bool()
mask = mask.view(n, h * w)
attn_out = self.attention_layer(query, key, value, mask)
attn_out = attn_out.permute(0, 2, 1).contiguous() # [n, len_q, dim_v]
if self.return_feature:
return attn_out
return self.prediction(attn_out)
[docs] def forward_test(self, feat, out_enc, img_metas):
"""
Args:
feat (Tensor): Tensor of shape :math:`(N, D_i, H, W)`.
out_enc (Tensor): Encoder output of shape
:math:`(N, D_m, H, W)`.
img_metas (dict): A dict that contains meta information of input
images. Preferably with the key ``valid_ratio``.
Returns:
Tensor: A raw logit tensor of shape :math:`(N, T, C-1)` if
``return_feature=False``. Otherwise it would be the hidden feature
before the prediction projection layer, whose shape is
:math:`(N, T, D_m)`.
"""
valid_ratios = [
img_meta.get('valid_ratio', 1.0) for img_meta in img_metas
] if self.mask else None
seq_len = self.max_seq_len
n, c_enc, h, w = out_enc.size()
assert c_enc == self.dim_model
_, c_feat, _, _ = feat.size()
assert c_feat == self.dim_input
position_index = self._get_position_index(seq_len, n, feat.device)
position_out_enc = self.position_aware_module(out_enc)
query = self.embedding(position_index)
query = query.permute(0, 2, 1).contiguous()
key = position_out_enc.view(n, c_enc, h * w)
if self.encode_value:
value = out_enc.view(n, c_enc, h * w)
else:
value = feat.view(n, c_feat, h * w)
mask = None
if valid_ratios is not None:
mask = query.new_zeros((n, h, w))
for i, valid_ratio in enumerate(valid_ratios):
valid_width = min(w, math.ceil(w * valid_ratio))
mask[i, :, valid_width:] = 1
mask = mask.bool()
mask = mask.view(n, h * w)
attn_out = self.attention_layer(query, key, value, mask)
attn_out = attn_out.permute(0, 2, 1).contiguous()
if self.return_feature:
return attn_out
return self.prediction(attn_out)