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# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmocr.models.builder import ENCODERS
from .base_encoder import BaseEncoder
@ENCODERS.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.
"""
def __init__(self,
in_channels,
out_channels,
init_cfg=dict(type='Xavier', layer='Conv2d')):
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, img_metas=None):
"""
Args:
feat (Tensor): Image features with the shape of
:math:`(N, C_{in}, H, W)`.
img_metas (None): Unused.
Returns:
Tensor: A tensor of shape :math:`(N, C_{out}, H, W)`.
"""
return self.layer(feat)
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