RTSDS/config.yaml at master · sina-behnam/RTSDS · GitHub

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# Data Configuration
data:
  cityscapes:
    images_train_dir: "data/Cityscapes/Cityspaces/images/train"
    images_val_dir: "data/Cityscapes/Cityspaces/images/val"
    segmentation_train_dir: "data/Cityscapes/Cityspaces/gtFine/train"
    segmentation_val_dir: "data/Cityscapes/Cityspaces/gtFine/val"
    image_size: 512, 1024
    num_classes: 19
    batch_size : 4
    num_workers: 4
  gta5_modified:
    images_dir : "data/GTA5_Modified/images"
    segmentation_dir: "data/GTA5_Modified/labels"
    image_size: 720, 1280
    num_classes: 19
    batch_size : 4
    num_workers: 4

# Meta data
meta:
  class_names : [
        "road", "sidewalk", "building",  "wall", "fence", "pole", "traffic light", "traffic sign",
        "vegetation", "terrain", "sky", "person", "rider", "car",
        "truck", "bus", "train", "motorcycle", "bicycle"
    ]


# Model Configuration
model:

  deeplab:
    backbone: "resnet18"
    output_stride: 16
    num_classes: 19
    pretrained: true
    pretrained_path: "pretrained_models/deeplabv3_resnet18_coco-586e9e4e.pth"
    optimizer :
      name : "Adam"
      lr: 0.0001
    criterion:
      name: "CrossEntropy"
      ignore_index: 19

  bisenet:
    backbone: "resnet18"
    num_classes: 19
    pretrained: true
    power_lr_factor: 0.9
    optimizer :
      name : "Adam"
      lr: 0.0001
    criterion:
      name: "CrossEntropy"
      ignore_index: 19


  adversarial_model:
    generator:
      name : "bisenet"
      power_lr_factor: 0.9
      optimizer :
        name : "Adam"
        lr: 0.0001
      criterion:
        name: "CrossEntropy"
        ignore_index: 19

    discriminator:
      name : "tiny"
      power_lr_factor: 0.05
      input_channels: 19
      optimizer :
        name : "Adam"
        lr: 0.0001
        weight_decay: 0.0001
      criterion:
        name: "BCEWithLogits"

# Training Configuration
training:
  segmentation:
    num_classes : 19
    lambda : 0.1
    lr_decay_iter : 1
    epochs: 50
    do_validation : 1
    when_print : -1

  domain_adaptation:
    num_classes : 19
    iterations: 100
    lambda : 0.1
    lr_decay_iter : 1
    epochs: 50
    do_validation : 1
    when_print : -1


# Augmentation Configuration
augmentation:
  p: 0.5

  GaussianBlur:
    kernel_size: 5, 9
    sigma : 0.1, 5

  RandomHorizontalFlip:
    p : 0.5

  # ColorJitter:
  #   brightness: 0.2
  #   contrast: 0.2
  #   saturation: 0.2
  #   hue: 0.1


  # ColorJitterWithRandomBrightness:
  #   brightness: 0.2
  #   contrast: 0.2
  #   saturation: 0.2
  #   hue: 0.1
  #   RandomHorizontalFlip_p : 0.5


# Callbacks Configuration
callbacks:
  model_checkpoint:
    save_dir: "checkpoints"
    save_name: "model"
    save_best: true
    monitor: "val_loss"
    mode: "min"
    save_freq: 1

  early_stopping:
    monitor: "val_loss"
    mode: "min"
    patience: 5

  logging:
    wandb:
      project_name : "domain_adaptation"
      run_name : "v1"
      note : "Domain Adaptation"

  images_plots:
    save_dir: "images"
    number_of_samples: 4


device: "cpu"  # "cuda" or "cpu"