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Source code for mmocr.models.textrecog.decoders.robust_scanner_decoder
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmocr.models.builder import DECODERS, build_decoder
from mmocr.models.textrecog.layers import RobustScannerFusionLayer
from .base_decoder import BaseDecoder
[docs]@DECODERS.register_module()
class RobustScannerDecoder(BaseDecoder):
"""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`.
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`.
start_idx (int): The index of `<SOS>`.
mask (bool): Whether to mask input features according to
``img_meta['valid_ratio']``.
padding_idx (int): The index of `<PAD>`.
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.
hybrid_decoder (dict): Configuration dict for hybrid decoder.
position_decoder (dict): Configuration dict for position decoder.
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,
dim_input=512,
dim_model=128,
max_seq_len=40,
start_idx=0,
mask=True,
padding_idx=None,
encode_value=False,
hybrid_decoder=None,
position_decoder=None,
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.encode_value = encode_value
self.start_idx = start_idx
self.padding_idx = padding_idx
self.mask = mask
# init hybrid decoder
hybrid_decoder.update(num_classes=self.num_classes)
hybrid_decoder.update(dim_input=self.dim_input)
hybrid_decoder.update(dim_model=self.dim_model)
hybrid_decoder.update(start_idx=self.start_idx)
hybrid_decoder.update(padding_idx=self.padding_idx)
hybrid_decoder.update(max_seq_len=self.max_seq_len)
hybrid_decoder.update(mask=self.mask)
hybrid_decoder.update(encode_value=self.encode_value)
hybrid_decoder.update(return_feature=True)
self.hybrid_decoder = build_decoder(hybrid_decoder)
# init position decoder
position_decoder.update(num_classes=self.num_classes)
position_decoder.update(dim_input=self.dim_input)
position_decoder.update(dim_model=self.dim_model)
position_decoder.update(max_seq_len=self.max_seq_len)
position_decoder.update(mask=self.mask)
position_decoder.update(encode_value=self.encode_value)
position_decoder.update(return_feature=True)
self.position_decoder = build_decoder(position_decoder)
self.fusion_module = RobustScannerFusionLayer(
self.dim_model if encode_value else dim_input)
pred_num_classes = num_classes - 1
self.prediction = nn.Linear(dim_model if encode_value else dim_input,
pred_num_classes)
[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)`.
"""
hybrid_glimpse = self.hybrid_decoder.forward_train(
feat, out_enc, targets_dict, img_metas)
position_glimpse = self.position_decoder.forward_train(
feat, out_enc, targets_dict, img_metas)
fusion_out = self.fusion_module(hybrid_glimpse, position_glimpse)
out = self.prediction(fusion_out)
return 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: The output logit sequence tensor of shape
:math:`(N, T, C-1)`.
"""
seq_len = self.max_seq_len
batch_size = feat.size(0)
decode_sequence = (feat.new_ones(
(batch_size, seq_len)) * self.start_idx).long()
position_glimpse = self.position_decoder.forward_test(
feat, out_enc, img_metas)
outputs = []
for i in range(seq_len):
hybrid_glimpse_step = self.hybrid_decoder.forward_test_step(
feat, out_enc, decode_sequence, i, img_metas)
fusion_out = self.fusion_module(hybrid_glimpse_step,
position_glimpse[:, i, :])
char_out = self.prediction(fusion_out)
char_out = F.softmax(char_out, -1)
outputs.append(char_out)
_, max_idx = torch.max(char_out, dim=1, keepdim=False)
if i < seq_len - 1:
decode_sequence[:, i + 1] = max_idx
outputs = torch.stack(outputs, 1)
return outputs