pytorch-segmentation-models-trainer 0.17.0

Description:

pytorchsegmentationmodelstrainer 0.17.0

pytorch_segmentation_models_trainer













Framework based on Pytorch, Pytorch Lightning, segmentation_models.pytorch and hydra to train semantic segmentation models using yaml config files as follows:
model:
_target_: segmentation_models_pytorch.Unet
encoder_name: resnet34
encoder_weights: imagenet
in_channels: 3
classes: 1

loss:
_target_: segmentation_models_pytorch.utils.losses.DiceLoss

optimizer:
_target_: torch.optim.AdamW
lr: 0.001
weight_decay: 1e-4

hyperparameters:
batch_size: 1
epochs: 2
max_lr: 0.1

pl_trainer:
max_epochs: ${hyperparameters.batch_size}
gpus: 0

train_dataset:
_target_: pytorch_segmentation_models_trainer.dataset_loader.dataset.SegmentationDataset
input_csv_path: /path/to/input.csv
data_loader:
shuffle: True
num_workers: 1
pin_memory: True
drop_last: True
prefetch_factor: 1
augmentation_list:
- _target_: albumentations.HueSaturationValue
always_apply: false
hue_shift_limit: 0.2
p: 0.5
- _target_: albumentations.RandomBrightnessContrast
brightness_limit: 0.2
contrast_limit: 0.2
p: 0.5
- _target_: albumentations.RandomCrop
always_apply: true
height: 256
width: 256
p: 1.0
- _target_: albumentations.Flip
always_apply: true
- _target_: albumentations.Normalize
p: 1.0
- _target_: albumentations.pytorch.transforms.ToTensorV2
always_apply: true

val_dataset:
_target_: pytorch_segmentation_models_trainer.dataset_loader.dataset.SegmentationDataset
input_csv_path: /path/to/input.csv
data_loader:
shuffle: True
num_workers: 1
pin_memory: True
drop_last: True
prefetch_factor: 1
augmentation_list:
- _target_: albumentations.Resize
always_apply: true
height: 256
width: 256
p: 1.0
- _target_: albumentations.Normalize
p: 1.0
- _target_: albumentations.pytorch.transforms.ToTensorV2
always_apply: true

To train a model with configuration path /path/to/config/folder and name test.yaml:
pytorch-smt --config-dir /path/to/config/folder --config-name test +mode=train

The mode can be stored in configuration yaml as well. In this case, do not pass the +mode= argument. If the mode is stored in the yaml and you want to overwrite the value, do not use the + clause, just mode= .
This module suports hydra features such as configuration composition. For further information, please visit https://hydra.cc/docs/intro
Install
If you are not using docker and if you want to enable gpu acceleration, before installing this package, you should install pytorch_scatter as instructed in https://github.com/rusty1s/pytorch_scatter
After installing pytorch_scatter, just do
pip install pytorch_segmentation_models_trainer

We have a docker container in which all dependencies are installed and ready for gpu usage. You can pull the image from dockerhub:
docker pull phborba/pytorch_segmentation_models_trainer:latest

Citing:

@software{philipe_borba_2021_5115127,
author = {Philipe Borba},
title = {{phborba/pytorch\_segmentation\_models\_trainer:
Version 0.8.0}},
month = jul,
year = 2021,
publisher = {Zenodo},
version = {v0.8.0},
doi = {10.5281/zenodo.5115127},
url = {https://doi.org/10.5281/zenodo.5115127}
}