PyTorch implementations of some FPN-based semantic segmentation architectures: vanilla FPN, Panoptic FPN, PANet FPN; with ResNet and EfficientNet backbones.
MIT License
PyTorch implementation of the following semantic segmentation architectures (see fpn.py
):
The implementations are all based on nn.Sequential
with no fancy forward methods, meaning that they can be easily modified and combined together or with other modules.
The repo provides factory functions (make_fpn_resnet()
and make_fpn_efficientnet()
) for creating FPN's with 2 kinds of backbones:
The factory methods make_fpn_resnet()
and make_fpn_efficientnet()
support in_channels != 3
.
make_fpn_resnet()
, in particular, makes use of the fusion technique described in the paper, FuseNet, by Hazirbas et al. (https://vision.in.tum.de/_media/spezial/bib/hazirbasma2016fusenet.pdf) if in_channels > 3
that adds a parallel resnet backbone for the new channels. All the pretrained weights are retained.
from factory import make_fpn_resnet
model = make_fpn_resnet(
name='resnet18',
fpn_type='fpn',
pretrained=True,
num_classes=2,
fpn_channels=256,
in_channels=3,
out_size=(224, 224))
This repo supports importing modules through torch.hub
. The models can be easily imported into your code via the factory functions in factory.py
.
import torch
model = torch.hub.load(
'AdeelH/pytorch-fpn',
'make_fpn_resnet',
name='resnet18',
fpn_type='panoptic',
num_classes=2,
fpn_channels=256,
in_channels=3,
out_size=(224, 224)
)
Working example on Colab: https://colab.research.google.com/drive/1pjkw-QoqiXgKDrEYZ47EwfXcW7vgO4KX?usp=sharing