deep_image_homography_estimation_backbone_increase.yml

experiment: 'deep_image_homography_estimation_backbone'
lightning_module: 'DeepImageHomographyEstimationModuleBackbone'
lightning_data_module: 'ImagePairHomographyDataModule'
model_checkpoint: 
    monitor: 'val/distance'
    mode: 'min'
trainer:
    accelerator: 'gpu'
    devices: 1
    max_epochs: 500
    log_every_n_steps: 100
    limit_train_batches: 1.0
    limit_val_batches: 1.0
    limit_test_batches: 1.0
    fast_dev_run: False
    profiler: False
    strategy: null
callback:
    rhos: [64, 128, 256]
    epochs: [0, 10, 20]  # 0 indexing
data:
    pkl_name: 'light_log_without_camera_motion.pkl'
    pkl_path: 'camera_motion_separated_npy/without_camera_motion'
    train_split: 0.8
    random_state: 42
    batch_size: 128
    num_workers: 16
    rho: 64  # initial rho
    crp_shape: [480, 640]
    p0: 0.05
    seq_len: 25
    unsupervised: False
    train_transforms: # augmentations from imgaug https://imgaug.readthedocs.io/en/latest/source/overview_of_augmenters.html
        # - {'chance': 1.0, 'module': 'imgaug.augmenters',                'type': 'Resize',             'kwargs': {'size': {'height': 612, 'width': 816}}}
        - {'chance': 1.0, 'module': 'imgaug.augmenters',                'type': 'ChangeColorspace',       'kwargs': {'to_colorspace': 'RGB', 'from_colorspace': 'BGR'}}
        - {'chance': 0.1, 'module': 'imgaug.augmenters',                'type': 'Grayscale',              'kwargs': {}}
        - {'chance': 0.5, 'module': 'imgaug.augmenters',                'type': 'HorizontalFlip',         'kwargs': {}}
        - {'chance': 0.5, 'module': 'imgaug.augmenters',                'type': 'VerticalFlip',           'kwargs': {}}
        - {'chance': 0.2, 'module': 'imgaug.augmenters',                'type': 'Crop',                   'kwargs': {'percent': !!python/tuple [0., 0.4]}}
        - {'chance': 0.3, 'module': 'imgaug.augmenters',                'type': 'MultiplyBrightness',     'kwargs': {'mul': [0.5, 1.5]}}
        - {'chance': 0.1, 'module': 'imgaug.augmenters',                'type': 'GaussianBlur',           'kwargs': {}}
        - {'chance': 0.2, 'module': 'imgaug.augmenters',                'type': 'ChangeColorTemperature', 'kwargs': {'kelvin': [3000, 15000]}}
        - {'chance': 0.2, 'module': 'imgaug.augmenters',                'type': 'AdditiveGaussianNoise',  'kwargs': {'scale': [0, 51]}}
        - {'chance': 0.1, 'module': 'imgaug.augmenters.imgcorruptlike', 'type': 'Fog',                    'kwargs': {'severity': 2}}
        - {'chance': 0.2, 'module': 'imgaug.augmenters.imgcorruptlike', 'type': 'Contrast',               'kwargs': {'severity': 1}}
    val_transforms:
        - {'chance': 1.0, 'module': 'imgaug.augmenters',                'type': 'ChangeColorspace',       'kwargs': {'to_colorspace': 'RGB', 'from_colorspace': 'BGR'}}
    test_transforms:
        - {'chance': 1.0, 'module': 'imgaug.augmenters',                'type': 'ChangeColorspace',       'kwargs': {'to_colorspace': 'RGB', 'from_colorspace': 'BGR'}}
model:
    shape: [3, 480, 640]
    pretrained: False
    lr: 1.e-4
    betas: [0.9, 0.999]
    milestones: [0]
    gamma: 1.0
    log_n_steps: 1000
    backbone: 'ResNet-34'