FPNC

class mmocr.models.textdet.FPNC(in_channels, lateral_channels=256, out_channels=64, bias_on_lateral=False, bn_re_on_lateral=False, bias_on_smooth=False, bn_re_on_smooth=False, asf_cfg=None, conv_after_concat=False, init_cfg=[{'type': 'Kaiming', 'layer': 'Conv'}, {'type': 'Constant', 'layer': 'BatchNorm', 'val': 1.0, 'bias': 0.0001}])

FPN-like fusion module in Real-time Scene Text Detection with Differentiable Binarization.

This was partially adapted from https://github.com/MhLiao/DB and https://github.com/WenmuZhou/DBNet.pytorch.

参数

• in_channels (list[int]) – A list of numbers of input channels.

• lateral_channels (int) – Number of channels for lateral layers.

• out_channels (int) – Number of output channels.

• bias_on_lateral (bool) – Whether to use bias on lateral convolutional layers.

• bn_re_on_lateral (bool) – Whether to use BatchNorm and ReLU on lateral convolutional layers.

• bias_on_smooth (bool) – Whether to use bias on smoothing layer.

• bn_re_on_smooth (bool) – Whether to use BatchNorm and ReLU on smoothing layer.

• asf_cfg (dict, optional) – Adaptive Scale Fusion module configs. The attention_type can be ‘ScaleChannelSpatial’.

• conv_after_concat (bool) – Whether to add a convolution layer after the concatenation of predictions.

• init_cfg (dict or list[dict], optional) – Initialization configs.

返回类型

None

forward(inputs)

参数

inputs (list[Tensor]) – Each tensor has the shape of \((N, C_i, H_i, W_i)\). It usually expects 4 tensors (C2-C5 features) from ResNet.

返回

A tensor of shape \((N, C_{out}, H_0, W_0)\) where \(C_{out}\) is out_channels.

返回类型

Tensor