Skip to content

Latest commit

 

History

History
82 lines (67 loc) · 2.62 KB

README.md

File metadata and controls

82 lines (67 loc) · 2.62 KB
Raw

OASIS-Semantic-Image-Synthesis

(M2 DAC AMAL Project)

Implementation of the paper 'You Only Need Adversarial Supervision for Semantic Image Synthesis'

  • Authors of this implementation : All authors provided the same work in terms of time, effort and contributions
    • Claire Bizon Monroc ( Discriminator, Training/test loop)
    • Amine Djeghri (Datasets, Generator)
    • Idles Mamou (scores, configs)
  • config.yml: contains all architecture and training hyperparameters

Training steps for ADEDataChallenge2016:

  1. Clone repository
  2. Download ADEChallengeData2016 dataset: wget http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip
  3. Unzip ADEChallengeData2016.zip to replace ADEChallengeData2016 folder
  4. Adapt config.yml with your parameters.
  5. Launch training: python src/train.py config.yml

Generating Validation Data:

  1. generate.py: generates images from the validation segmentation maps Generated images can then be used to compute the FID with the pytorch-fid
  2. scores.py: returns the mIOU score from the validation dataset

Other datasets:

To use with other datasets, follow this organization:

data_samples:
    - name_of_dataset
        - annotations
            - training
            - validation
        - images
            - training
            - validation

To compute the class weights (which are specific to your dataset), use compute_class_weights from utils.py

cd /AMAL-Project/src
from torch.utils.data import DataLoader
from utils import compute_class_weights, get_weights
from dataset import ADEDataset # in this example we use ADE
train_dataset = ADEDataset(Path("../cityscapes_data/images/training"),Path("../cityscapes_data/annotations/training"),128)
train_loader = DataLoader(train_dataset, batch_size, True, drop_last=True)
class_weights = compute_class_weights(train_loader, C=number_of_classes, H=height_of_images, W=width_of_images) # C=51 classes, H,W of ADE20 dataset
class_weights = get_weights(class_weights, device,opt)

The class_weights can then be serialized locally to avoid being recomputed at each beginning of training.

torch.save(class_weights, open(local_path, "wb"))

In config.yml:

data:
  path: "path_to_new_dataset"
  ...
  class_weights: "path_to_serialized_weights"

# Tensorboard: path to your tensorboard directory
tb_folder: "XP"

# Checkpoints: path to your checkpoints directory
checkpoint_path: "/tempory/oasis"

More: