MAERec-Gradio / mmocr /models /textrecog /encoders /channel_reduction_encoder.py
Mountchicken's picture
Upload 704 files
9bf4bd7
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Optional, Sequence
import torch
import torch.nn as nn
from mmocr.registry import MODELS
from mmocr.structures import TextRecogDataSample
from .base import BaseEncoder
@MODELS.register_module()
class ChannelReductionEncoder(BaseEncoder):
"""Change the channel number with a one by one convoluational layer.
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,
out_channels: int,
init_cfg: Dict = dict(type='Xavier', layer='Conv2d')
) -> None:
super().__init__(init_cfg=init_cfg)
self.layer = nn.Conv2d(
in_channels, out_channels, kernel_size=1, stride=1, padding=0)
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)`.
"""
return self.layer(feat)