MagNet, a multi-scale framework that resolves local ambiguity by looking at the image at multiple magnification levels, has multiple processing stages, where each stage corresponds to a magnification level, and the output of one stage is fed into the next stage for coarse-to-fine information propagation. Experiments on three high-resolution datasets of urban views, aerial scenes, and medical images show that MagNet consistently outperforms the state-of-the-art methods by a significant margin.
Details of the MagNet model architecture and experimental results can be found in our following paper:
@inproceedings{m_Huynh-etal-CVPR21,
author = {Chuong Huynh and Anh Tran and Khoa Luu and Minh Hoai},
title = {Progressive Semantic Segmentation},
year = {2021},
booktitle = {Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)},
}
Please CITE our paper when MagNet is used to help produce published results or incorporated into other software.
This current code provides configurations to train, evaluate on two datasets: Cityscapes and DeepGlobe. To prepare the datasets, in the ./data directory, please do following steps:
- Register an account on this page and log in.
- Download
leftImg8bit_trainvaltest.zipandgtFine_trainvaltest.zip. - Run the script below to extract zip files to correct locations:
sh ./prepare_cityscapes.sh- Register an account on this page and log in.
- Go to this page and download Starting Kit of the
#1 DevelopmentPhase. - Run the script below to extract zip files to correct locations:
sh ./prepare_deepglobe.shIf you want to train/evaluate with your dataset, follow the steps in this document
The framework is tested on machines with the following environment:
- Python >= 3.6
- CUDA >= 10.0
To install dependencies, please run the following command:
pip install -r requirements.txtPerformance of pre-trained models on datasets:
| Dataset | Backbone | Baseline IoU (%) | MagNet IoU (%) | MagNet-Fast IoU (%) | Download |
|---|---|---|---|---|---|
| Cityscapes | HRNetW18+OCR | 63.24 | 68.20 | 67.37 | backbone refine_512x256 refine_1024x512 refine_2048x1024 |
| DeepGlobe | Resnet50-FPN | 67.22 | 72.10 | 68.22 | backbone refine |
Please manually download pre-trained models to ./checkpoints or run the script below:
cd checkpoints
sh ./download_cityscapes.sh # for Cityscapes
# or
sh ./download_deepglobe.sh # for DeepGlobeYou can run this Google Colab Notebook to test our pre-trained models with street-view images. Please follow the instructions in the notebook to experience the performance of our network.
If you want to test our framework on your local machine:
- To test with a Cityscapes image, e.g
data/frankfurt_000001_003056_leftImg8bit.png:
- With MagNet refinement:
python demo.py --dataset cityscapes \
--image data/frankfurt_000001_003056_leftImg8bit.png \
--scales 256-128,512-256,1024-512,2048-1024 \
--crop_size 256 128 \
--input_size 256 128 \
--model hrnet18+ocr \
--pretrained checkpoints/cityscapes_hrnet.pth \
--pretrained_refinement checkpoints/cityscapes_refinement_512.pth checkpoints/cityscapes_refinement_1024.pth checkpoints/cityscapes_refinement_2048.pth \
--num_classes 19 \
--n_points 32768 \
--n_patches -1 \
--smooth_kernel 5 \
--save_pred \
--save_dir test_results/demo
# or in short, you can run
sh scripts/cityscapes/demo_magnet.sh data/frankfurt_000001_003056_leftImg8bit.png- With MagNet-Fast refinement
python demo.py --dataset cityscapes \
--image frankfurt_000001_003056_leftImg8bit.png \
--scales 256-128,512-256,1024-512,2048-1024 \
--crop_size 256 128 \
--input_size 256 128 \
--model hrnet18+ocr \
--pretrained checkpoints/cityscapes_hrnet.pth \
--pretrained_refinement checkpoints/cityscapes_refinement_512.pth checkpoints/cityscapes_refinement_1024.pth checkpoints/cityscapes_refinement_2048.pth \
--num_classes 19 \
--n_points 0.9 \
--n_patches 4 \
--smooth_kernel 5 \
--save_pred \
--save_dir test_results/demo
# or in short, you can run
sh scripts/cityscapes/demo_magnet_fast.sh data/frankfurt_000001_003056_leftImg8bit.pngAll results will be stored at test_results/demo/frankfurt_000001_003056_leftImg8bit
- To test with a Deepglobe image, e.g
data/639004_sat.jpg:
- With MagNet refinement:
python demo.py --dataset deepglobe \
--image data/639004_sat.jpg \
--scales 612-612,1224-1224,2448-2448 \
--crop_size 612 612 \
--input_size 508 508 \
--model fpn \
--pretrained checkpoints/deepglobe_fpn.pth \
--pretrained_refinement checkpoints/deepglobe_refinement.pth \
--num_classes 7 \
--n_points 0.75 \
--n_patches -1 \
--smooth_kernel 11 \
--save_pred \
--save_dir test_results/demo
# or in short, you can run
sh scripts/deepglobe/demo_magnet.sh data/639004_sat.jpg- With MagNet-Fast refinement
python demo.py --dataset deepglobe \
--image data/639004_sat.jpg \
--scales 612-612,1224-1224,2448-2448 \
--crop_size 612 612 \
--input_size 508 508 \
--model fpn \
--pretrained checkpoints/deepglobe_fpn.pth \
--pretrained_refinement checkpoints/deepglobe_refinement.pth \
--num_classes 7 \
--n_points 0.9 \
--n_patches 3 \
--smooth_kernel 11 \
--save_pred \
--save_dir test_results/demo
# or in short, you can run
sh scripts/deepglobe/demo_magnet_fast.sh data/639004_sat.jpgAll results will be stored at test_results/demo/639004_sat
We customize the training script from HRNet repository to train our backbones. Please first go to this directory ./backbone and run the following scripts:
Download pre-trained weights on ImageNet and put into folder ./pretrained_weights.
Training the model:
# In ./backbone
python train.py --cfg experiments/cityscapes/hrnet_ocr_w18_train_256x128_sgd_lr1e-2_wd5e-4_bs_12_epoch484.yaml
The logs of training are stored at ./log/cityscapes/HRNetW18_OCR.
The checkpoint of backbone after training are stored at ./output/cityscapes/hrnet_ocr_w18_train_256x128_sgd_lr1e-2_wd5e-4_bs_12_epoch484/best.pth.
This checkpoint is used to train further refinement modules.
Training the model:
# In ./backbone
python train.py --cfg experiments/deepglobe/resnet_fpn_train_612x612_sgd_lr1e-2_wd5e-4_bs_12_epoch484.yaml
The logs of training are stored at ./log/deepglobe/ResnetFPN.
The checkpoint of backbone after training are stored at ./output/deepglobe/resnet_fpn_train_612x612_sgd_lr1e-2_wd5e-4_bs_12_epoch484/best.pth. This checkpoint is used to train further refinement modules.
Available arguments for training:
train.py [-h] --dataset DATASET [--root ROOT] [--datalist DATALIST]
--scales SCALES --crop_size N [N ...] --input_size N [N ...]
[--num_workers NUM_WORKERS] --model MODEL --num_classes
NUM_CLASSES --pretrained PRETRAINED
[--pretrained_refinement PRETRAINED_REFINEMENT [PRETRAINED_REFINEMENT ...]]
--batch_size BATCH_SIZE [--log_dir LOG_DIR] --task_name
TASK_NAME [--lr LR] [--momentum MOMENTUM] [--decay DECAY]
[--gamma GAMMA] [--milestones N [N ...]] [--epochs EPOCHS]
optional arguments:
-h, --help show this help message and exit
--dataset DATASET dataset name: cityscapes, deepglobe (default: None)
--root ROOT path to images for training and testing (default: )
--datalist DATALIST path to .txt containing image and label path (default:
)
--scales SCALES scales: w1-h1,w2-h2,... , e.g.
512-512,1024-1024,2048-2048 (default: None)
--crop_size N [N ...]
crop size, e.g. 256 128 (default: None)
--input_size N [N ...]
input size, e.g. 256 128 (default: None)
--num_workers NUM_WORKERS
number of workers for dataloader (default: 1)
--model MODEL model name. One of: fpn, psp, hrnet18+ocr, hrnet48+ocr
(default: None)
--num_classes NUM_CLASSES
number of classes (default: None)
--pretrained PRETRAINED
pretrained weight (default: None)
--pretrained_refinement PRETRAINED_REFINEMENT [PRETRAINED_REFINEMENT ...]
pretrained weight (s) refinement module (default:
[''])
--batch_size BATCH_SIZE
batch size for training (default: None)
--log_dir LOG_DIR directory to store log file (default: runs)
--task_name TASK_NAME
task name, experiment name. The final path of your
logs is <log_dir>/<task_name>/<timestamp> (default:
None)
--lr LR learning rate (default: 0.001)
--momentum MOMENTUM momentum for optimizer (default: 0.9)
--decay DECAY weight decay for optimizer (default: 0.0005)
--gamma GAMMA gamma for lr scheduler (default: 0.1)
--milestones N [N ...]
milestones to reduce learning rate (default: [10, 20,
30, 40, 45])
--epochs EPOCHS number of epochs for training (default: 50)To train MagNet with Cityscapes dataset, please run this sample script:
python train.py --dataset cityscapes \
--root data/cityscapes \
--datalist data/list/cityscapes/train.txt \
--scales 256-128,512-256,1024-512,2048-1024 \
--crop_size 256 128 \
--input_size 256 128 \
--num_workers 8 \
--model hrnet18+ocr \
--pretrained checkpoints/cityscapes_hrnet.pth \
--num_classes 19 \
--batch_size 8 \
--task_name cityscapes_refinement \
--lr 0.001
# or in short, run the script below
sh scripts/cityscapes/train_magnet.shTo train MagNet with Deepglobe dataset, please run this sample script:
python train.py --dataset deepglobe \
--root data/deepglobe \
--datalist data/list/deepglobe/train.txt \
--scales 612-612,1224-1224,2448-2448 \
--crop_size 612 612 \
--input_size 508 508 \
--num_workers 8 \
--model fpn \
--pretrained checkpoints/deepglobe_fpn.pth \
--num_classes 7 \
--batch_size 8 \
--task_name deepglobe_refinement \
--lr 0.001
# or in short, run the script below
sh scripts/deepglobe/train_magnet.shAvailable arguments for testing:
test.py [-h] --dataset DATASET [--root ROOT] [--datalist DATALIST]
--scales SCALES --crop_size N [N ...] --input_size N [N ...]
[--num_workers NUM_WORKERS] --model MODEL --num_classes
NUM_CLASSES --pretrained PRETRAINED
[--pretrained_refinement PRETRAINED_REFINEMENT [PRETRAINED_REFINEMENT ...]]
[--image IMAGE] --sub_batch_size SUB_BATCH_SIZE
[--n_patches N_PATCHES] --n_points N_POINTS
[--smooth_kernel SMOOTH_KERNEL] [--save_pred]
[--save_dir SAVE_DIR]
optional arguments:
-h, --help show this help message and exit
--dataset DATASET dataset name: cityscapes, deepglobe (default: None)
--root ROOT path to images for training and testing (default: )
--datalist DATALIST path to .txt containing image and label path (default:
)
--scales SCALES scales: w1-h1,w2-h2,... , e.g.
512-512,1024-1024,2048-2048 (default: None)
--crop_size N [N ...]
crop size, e.g. 256 128 (default: None)
--input_size N [N ...]
input size, e.g. 256 128 (default: None)
--num_workers NUM_WORKERS
number of workers for dataloader (default: 1)
--model MODEL model name. One of: fpn, psp, hrnet18+ocr, hrnet48+ocr
(default: None)
--num_classes NUM_CLASSES
number of classes (default: None)
--pretrained PRETRAINED
pretrained weight (default: None)
--pretrained_refinement PRETRAINED_REFINEMENT [PRETRAINED_REFINEMENT ...]
pretrained weight (s) refinement module (default:
[''])
--image IMAGE image path to test (demo only) (default: None)
--sub_batch_size SUB_BATCH_SIZE
batch size for patch processing (default: None)
--n_patches N_PATCHES
number of patches to be refined at each stage. if
n_patches=-1, all patches will be refined (default:
-1)
--n_points N_POINTS number of points to be refined at each stage. If
n_points < 1.0, it will be the proportion of total
points (default: None)
--smooth_kernel SMOOTH_KERNEL
kernel size of blur operation applied to error scores
(default: 16)
--save_pred save predictions or not, each image will contains:
image, ground-truth, coarse pred, fine pred (default:
False)
--save_dir SAVE_DIR saved directory (default: test_results)Otherwise, there are sample scripts below to test with our pre-trained models.
Full MagNet refinement:
python test.py --dataset cityscapes \
--root data/cityscapes \
--datalist data/list/cityscapes/val.txt \
--scales 256-128,512-256,1024-512,2048-1024 \
--crop_size 256 128 \
--input_size 256 128 \
--num_workers 8 \
--model hrnet18+ocr \
--pretrained checkpoints/cityscapes_hrnet.pth \
--pretrained_refinement checkpoints/cityscapes_refinement_512.pth checkpoints/cityscapes_refinement_1024.pth checkpoints/cityscapes_refinement_2048.pth \
--num_classes 19 \
--sub_batch_size 1 \
--n_points 32768 \
--n_patches -1 \
--smooth_kernel 5 \
--save_pred \
--save_dir test_results/cityscapes
# or in short, run the script below
sh scripts/cityscapes/test_magnet.shMagNet-Fast refinement:
python test.py --dataset cityscapes \
--root data/cityscapes \
--datalist data/list/cityscapes/val.txt \
--scales 256-128,512-256,1024-512,2048-1024 \
--crop_size 256 128 \
--input_size 256 128 \
--num_workers 8 \
--model hrnet18+ocr \
--pretrained checkpoints/cityscapes_hrnet.pth \
--pretrained_refinement checkpoints/cityscapes_refinement_512.pth checkpoints/cityscapes_refinement_1024.pth checkpoints/cityscapes_refinement_2048.pth \
--num_classes 19 \
--sub_batch_size 1 \
--n_points 0.9 \
--n_patches 4 \
--smooth_kernel 5 \
--save_pred \
--save_dir test_results/cityscapes_fast
# or in short, run the script below
sh scripts/cityscapes/test_magnet_fast.shFull MagNet refinement:
python test.py --dataset deepglobe \
--root data/deepglobe \
--datalist data/list/deepglobe/test.txt \
--scales 612-612,1224-1224,2448-2448 \
--crop_size 612 612 \
--input_size 508 508 \
--num_workers 8 \
--model fpn \
--pretrained checkpoints/deepglobe_fpn.pth \
--pretrained_refinement checkpoints/deepglobe_refinement.pth \
--num_classes 7 \
--sub_batch_size 1 \
--n_points 0.75 \
--n_patches -1 \
--smooth_kernel 11 \
--save_pred \
--save_dir test_results/deepglobe
# or in short, run the script below
sh scripts/deepglobe/test_magnet.shMagNet-Fast refinement:
python test.py --dataset deepglobe \
--root data/deepglobe \
--datalist data/list/deepglobe/test.txt \
--scales 612-612,1224-1224,2448-2448 \
--crop_size 612 612 \
--input_size 508 508 \
--num_workers 8 \
--model fpn \
--pretrained checkpoints/deepglobe_fpn.pth \
--pretrained_refinement checkpoints/deepglobe_refinement.pth \
--num_classes 7 \
--sub_batch_size 1 \
--n_points 0.9 \
--n_patches 3 \
--smooth_kernel 11 \
--save_pred \
--save_dir test_results/deepglobe_fast
# or in short, run the script below
sh scripts/deepglobe/test_magnet_fast.shThanks to High-resolution networks and Segmentation Transformer for Semantic Segmentation for the backbone training script.
If you have any question, please drop an email to minhchuong.itus@gmail.com or create an issue on this repository.