FPNF

class mmocr.models.textdet.FPNF(in_channels=[256, 512, 1024, 2048], out_channels=256, fusion_type='concat', init_cfg={'distribution': 'uniform', 'layer': 'Conv2d', 'type': 'Xavier'})

FPN-like fusion module in Shape Robust Text Detection with Progressive Scale Expansion Network.

Parameters

• in_channels (list[int]) – A list of number of input channels. Defaults to [256, 512, 1024, 2048].

• out_channels (int) – The number of output channels. Defaults to 256.

• fusion_type (str) – Type of the final feature fusion layer. Available options are “concat” and “add”. Defaults to “concat”.

• init_cfg (dict or list[dict], optional) – Initialization configs. Defaults to dict(type=’Xavier’, layer=’Conv2d’, distribution=’uniform’)

Return type

None

forward(inputs)

Parameters

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.

Returns

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

Return type

Tensor