seg-torch 0.1.1

Semantic Segmentation with Pytorch

Semantic-Segmentation-Pytorch

Pytorch implementation of FCN, UNet, PSPNet and various encoder models.

These are the reference implementation of the models.

• FCN (Fully Convolutional Networks for Sementic Segmentation) [Paper]
• UNet (Convolutional Networks for Biomedical Image Segmentation) [Paper]
• PSPNet (Pyramid Scene Parsing Network) [Paper]

Models

This project supports models as follow:

model name	encoder model	decoder model
fcn8_vgg11	VGG 11	FCN8
fcn8_vgg13	VGG 13	FCN8
fcn8_vgg16	VGG 16	FCN8
fcn8_vgg19	VGG 19	FCN8
fcn16_vgg11	VGG 11	FCN16
fcn16_vgg13	VGG 13	FCN16
fcn16_vgg16	VGG 16	FCN16
fcn16_vgg19	VGG 19	FCN16
fcn32_vgg11	VGG 11	FCN32
fcn32_vgg13	VGG 13	FCN32
fcn32_vgg16	VGG 16	FCN32
fcn32_vgg19	VGG 19	FCN32
fcn8_resnet18	Resnet-18	FCN8
fcn8_resnet34	Resnet-34	FCN8
fcn8_resnet50	Resnet-50	FCN8
fcn8_resnet101	Resnet-101	FCN8
fcn8_resnet152	Resnet-152	FCN8
fcn16_resnet18	Resnet-18	FCN16
fcn16_resnet34	Resnet-34	FCN16
fcn16_resnet50	Resnet-50	FCN16
fcn16_resnet101	Resnet-101	FCN16
fcn16_resnet152	Resnet-152	FCN16
fcn32_resnet18	Resnet-18	FCN32
fcn32_resnet34	Resnet-34	FCN32
fcn32_resnet50	Resnet-50	FCN32
fcn32_resnet101	Resnet-101	FCN32
fcn32_resnet152	Resnet-152	FCN32
fcn8_mobilenet_v2	MobileNet-v2	FCN8
fcn16_mobilenet_v2	MobileNet-v2	FCN16
fcn32_mobilenet_v2	MobileNet-v2	FCN32
unet	None	Unet
unet_vgg11	VGG11	Unet
unet_vgg13	VGG13	Unet
unet_vgg16	VGG16	Unet
unet_vgg19	VGG19	Unet
unet_resnet18	Resnet-18	Unet
unet_resnet34	Resnet-34	Unet
unet_resnet50	Resnet-50	Unet
unet_resnet101	Resnet-101	Unet
unet_resnet152	Resnet-152	Unet
unet_mobilenet_v2	MobileNet-v2	Unet
pspnet_vgg11	VGG11	PSPNet
pspnet_vgg13	VGG13	PSPNet
pspnet_vgg16	VGG16	PSPNet
pspnet_vgg19	VGG19	PSPNet
pspnet_resnet18	Resnet-18	PSPNet
pspnet_resnet34	Resnet-34	PSPNet
pspnet_resnet50	Resnet-50	PSPNet
pspnet_resnet101	Resnet-101	PSPNet
pspnet_resnet152	Resnet-152	PSPNet
pspnet_mobilenet_v2	MobileNet-v2	PSPNet

Example results of the pspnet_mobilenet_v2 model:

Input Image	Ground Truth Image	Result Image

Getting Started

Requirements

• pytorch >= 1.5.0
• torchvision >= 0.6.0
• opencv-python
• tqdm

Installation

pip install seg-torch

or

git clone https://github.com/IanTaehoonYoo/semantic-segmentation-pytorch/
cd semantic-segmentation-pytorch
python setup.py install

Preparing the data for training

In this project, the data for training is the [Cityspaces]. You can run this project using the sample dataset in the segmentation/test/dataset/cityspaces folder. If you want to run this project using another dataset, please refer to the dataset format as bellow.

1. There are two folders which are the training images folder and the groundtruth labeled images folder.
2. The training image and the labeled image must have the same file name and size.
3. The training image must be the RGB image, and the labeled image should have the class value, the range [0, n_classes].

Example code to use this project with python

import torch
from torchvision import transforms

from segmentation.data_loader.segmentation_dataset import SegmentationDataset
from segmentation.data_loader.transform import Rescale, ToTensor
from segmentation.trainer import Trainer
from segmentation.models import all_models
from util.logger import Logger

train_images = r'dataset/cityspaces/images/train'
test_images = r'dataset/cityspaces/images/test'
train_labled = r'dataset/cityspaces/labeled/train'
test_labeled = r'dataset/cityspaces/labeled/test'

if __name__ == '__main__':
    model_name = "fcn8_vgg16"
    device = 'cuda'
    batch_size = 4
    n_classes = 34
    num_epochs = 300
    image_axis_minimum_size = 200
    pretrained = True
    fixed_feature = False

    logger = Logger(model_name=model_name, data_name='example')

    # Loader
    compose = transforms.Compose([
        Rescale(image_axis_minimum_size),
        ToTensor()
         ])

    train_datasets = SegmentationDataset(train_images, train_labled, n_classes, compose)
    train_loader = torch.utils.data.DataLoader(train_datasets, batch_size=batch_size, shuffle=True, drop_last=True)

    test_datasets = SegmentationDataset(test_images, test_labeled, n_classes, compose)
    test_loader = torch.utils.data.DataLoader(test_datasets, batch_size=batch_size, shuffle=True, drop_last=True)

    # Model
    batch_norm = False if batch_size == 1 else True
    model = all_models.model_from_name[model_name](n_classes,
                                                   batch_norm=batch_norm,
                                                   pretrained=pretrained,
                                                   fixed_feature=fixed_feature)
    model.to(device)

    # Optimizers
    if pretrained and fixed_feature: #fine-tunning
        params_to_update = model.parameters()
        print("Params to learn:")
        params_to_update = []
        for name, param in model.named_parameters():
            if param.requires_grad == True:
                params_to_update.append(param)
                print("\t", name)
        optimizer = torch.optim.Adadelta(params_to_update)
    else:
        optimizer = torch.optim.Adadelta(model.parameters())

    # Train
    #scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
    trainer = Trainer(model, optimizer, logger, num_epochs, train_loader, test_loader)
    trainer.train()

Pre-trained models (Encoder models)

This project uses pre-trained models such as VGG, ResNet, and MobileNet from the torchvision library. If you want the fine-tunning model, you can change the input parameters which are 'pretrained' and 'fixed_feature' when calling a model. And then, you should set the optimizer to freeze the model like as follow.

    model = all_models.model_from_name[model_name](n_classes,
                                                   batch_norm=batch_norm,
                                                   pretrained=pretrained,
                                                   fixed_feature=fixed_feature)

    # Optimizers
    if pretrained and fixed_feature: #fine-tunning
        params_to_update = model.parameters()
        print("Params to learn:")
        params_to_update = []
        for name, param in model.named_parameters():
            if param.requires_grad == True:
                params_to_update.append(param)
                print("\t", name)
        optimizer = torch.optim.Adadelta(params_to_update)
    else:
        optimizer = torch.optim.Adadelta(model.parameters())

Getting the learning results on Tensorboard

The Logger class is to write the result such as mean IoU, accuracy, loss, and predict labeled images. The logger class gets the model name and the data name. So, it can generate the tensorboard files automatically in the runs folder, .\segmentation\runs\

Here is example command to see the result

tensorboard --logdir=%project_path\segmentation\runs --host localhost

If you don't know about Tensorboard, please refer to [Tensorboard]

Cite This Project

If you find this code useful, please consider the following BibTeX entry.

@misc{seg-pytorch,
  author =       {Ian Yoo},
  title =        {{sementic-segmentation-pytorch: Pytorch implementation of FCN, UNet, PSPNet and various encoder models}},
  howpublished = {\url{https://github.com/IanTaehoonYoo/semantic-segmentation-pytorch}},
  year =         {2020}
}