Image segmentation models training of popular architectures.
Project description
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-segmentation-models-trainer --config-path /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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for pytorch_segmentation_models_trainer-0.1.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 383b7ffd9c1002bf5d9e6b5fd0c8b654fe7ad7db053a6d786b500519adfddb41 |
|
MD5 | 442b0fb1a27843e089b1152aad8a86a3 |
|
BLAKE2b-256 | d57b2c2a287c1f243500f4660ae2fa3ea46823f41559ab37fc6d9f85ed8ceb1d |
Hashes for pytorch_segmentation_models_trainer-0.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5cf5a0c93287b13cffaf3c013f96e8742502269c778638ab7a3f0a8e25d6b007 |
|
MD5 | 856d8108703cd34113abfb39dab57efb |
|
BLAKE2b-256 | 7647122d18c888fcf95b3fe81fba89a707ced256cf06f0327a85a2bda5153495 |