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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Optional, Sequence
import torch
from mmocr.models.textrecog.encoders.base import BaseEncoder
from mmocr.models.textrecog.layers import BidirectionalLSTM
from mmocr.registry import MODELS
from mmocr.structures import TextRecogDataSample
@MODELS.register_module()
class ABCNetRecEncoder(BaseEncoder):
"""Encoder for ABCNet.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
init_cfg (dict or list[dict], optional): Initialization configs.
Defaults to dict(type='Xavier', layer='Conv2d').
"""
def __init__(self,
in_channels: int = 256,
hidden_channels: int = 256,
out_channels: int = 256,
init_cfg: Dict = None) -> None:
super().__init__(init_cfg=init_cfg)
self.layer = BidirectionalLSTM(in_channels, hidden_channels,
out_channels)
def forward(
self,
feat: torch.Tensor,
data_samples: Optional[Sequence[TextRecogDataSample]] = None
) -> torch.Tensor:
"""
Args:
feat (Tensor): Image features with the shape of
:math:`(N, C_{in}, H, W)`.
data_samples (list[TextRecogDataSample], optional): Batch of
TextRecogDataSample, containing valid_ratio information.
Defaults to None.
Returns:
Tensor: A tensor of shape :math:`(N, C_{out}, H, W)`.
"""
assert feat.size(2) == 1, 'feature height must be 1'
feat = feat.squeeze(2)
feat = feat.permute(2, 0, 1) # NxCxW -> WxNxC
feat = self.layer(feat)
# feat = feat.permute(1, 0, 2).contiguous()
return feat
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