pytorch-fpn
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PyTorch implementations of some FPN-based semantic segmentation architectures: vanilla FPN, Panoptic FPN, PANet FPN; with ResNet and EfficientNet backbones.
pytorch-fpn
PyTorch implementation of the following semantic segmentation architectures (see fpn.py
):
- FPN, Feature Pyramid Networks for Object Detection by Lin et al., https://arxiv.com/abs/1612.03144.
- Panoptic FPN, Panoptic Feature Pyramid Networks by Kirilov et al., https://arxiv.com/abs/1901.02446.
- PANet FPN, Path Aggregation Network for Instance Segmentation by Liu et al., https://arxiv.com/abs/1803.01534
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.
Backbones
The repo provides factory functions (make_fpn_resnet()
and make_fpn_efficientnet()
) for creating FPN's with 2 kinds of backbones:
- ResNet backbones (via Torch Vision)
- EfficientNet backbones (via https://github.com/lukemelas/EfficientNet-PyTorch)
Multiband images
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.
Usage
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))
Loading through torch.hub
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