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FCHarDNet

Aug-15-2021 Update

Added a new v2_transform() method to replace torch.cat + nn.Conv2d combinations with CatConv2d, an all-in-1 fused cuda kernel combining Concat and Conv2d.
Inference on TitanV speedup from 70 fps to 99 fps
CatConv2d Installation

cd CatConv2d/
python setup.py install

(Please note that backward path for CatConv2d hasn't been implemented)

Fully Convolutional HarDNet for Segmentation in Pytorch

Implementaion based on Harmonic DenseNet: A low memory traffic network (ICCV 2019)
Refer to Pytorch-HarDNet for more information about the backbone model
This repo was forked from meetshah1995/pytorch-semseg

Architecture

Simple U-shaped encoder-decoder structure
Conv3x3/Conv1x1 only (including the first layer)
No self-attention layer or Pyramid Pooling

Results

Method	#Param <br>(M)	GMACs /<br> GFLOPs	Cityscapes <br> mIoU	fps on Titan-V <br>@1024x2048	fps on 1080ti <br>@1024x2048
ICNet	7.7	30.7	69.5	63	48
SwiftNetRN-18	11.8	104	75.5	-	39.9
BiSeNet (1024x2048)	13.4	119	77.7	36	27
BiSeNet (768x1536)	13.4	66.8	74.7	72**	54**
FC-HarDNet-70	4.1	35.4	76.0	70	53
FC-HarDNet-70 V2 <br />(with CatConv2d)	4.1	35.4	76.0	99	63

** Speed tested in 1536x768 instead of full resolution.

DataLoaders implemented

Requirements

pytorch >=0.4.0
torchvision ==0.2.0
scipy
tqdm
tensorboardX

Usage

Setup config file

Please see the usage section in meetshah1995/pytorch-semseg

To train the model :

python train.py [-h] [--config [CONFIG]]

--config                Configuration file to use (default: hardnet.yml)

To validate the model :

usage: validate.py [-h] [--config [CONFIG]] [--model_path [MODEL_PATH]] [--save_image]
                       [--eval_flip] [--measure_time]

  --config              Config file to be used
  --model_path          Path to the saved model
  --eval_flip           Enable evaluation with flipped image | False by default
  --measure_time        Enable evaluation with time (fps) measurement | True by default
  --save_image          Enable writing result images to out_rgb (pred label blended images) and out_predID

Pretrained Weights

Cityscapes pretrained weights: Download (Val mIoU: 77.7, Test mIoU: 75.9)
Cityscapes pretrained with color jitter augmentation: Download (Val mIoU: 77.4, Test mIoU: 76.0)
HarDNet-Petite weights pretrained by ImageNet: included in weights/hardnet_petite_base.pth

Prediction Samples