default_config ============== .. code:: yaml name: '' # Run name debug: False # Debugging flag arch: seed: 42 # Random seed for Pytorch/Numpy initialization min_epochs: 1 # Minimum number of epochs max_epochs: 50 # Maximum number of epochs checkpoint: filepath: '' # Checkpoint filepath to save data save_top_k: 5 # Number of best models to save monitor: 'loss' # Metric to monitor for logging monitor_index: 0 # Dataset index for the metric to monitor mode: 'auto' # Automatically determine direction of improvement (increase or decrease) s3_path: '' # s3 path for AWS model syncing s3_frequency: 1 # How often to s3 sync save: folder: '' # Folder where data will be saved viz: True # Flag for saving inverse depth map visualization npz: True # Flag for saving numpy depth maps wandb: dry_run: True # Wandb dry-run (not logging) name: '' # Wandb run name project: os.environ.get("WANDB_PROJECT", "") # Wandb project entity: os.environ.get("WANDB_ENTITY", "") # Wandb entity tags: [] # Wandb tags dir: '' # Wandb save folder model: name: '' # Training model checkpoint_path: '' # Checkpoint path for model saving optimizer: name: 'Adam' # Optimizer name depth: lr: 0.0002 # Depth learning rate weight_decay: 0.0 # Dept weight decay pose: lr: 0.0002 # Pose learning rate weight_decay: 0.0 # Pose weight decay scheduler: name: 'StepLR' # Scheduler name step_size: 10 # Scheduler step size gamma: 0.5 # Scheduler gamma value T_max: 20 # Scheduler maximum number of iterations params: crop: '' # Which crop should be used during evaluation min_depth: 0.0 # Minimum depth value to evaluate max_depth: 80.0 # Maximum depth value to evaluate loss: num_scales: 4 # Number of inverse depth scales to use progressive_scaling: 0.0 # Training percentage to decay number of scales flip_lr_prob: 0.5 # Probablity of horizontal flippping rotation_mode: 'euler' # Rotation mode upsample_depth_maps: True # Resize depth maps to highest resolution ssim_loss_weight: 0.85 # SSIM loss weight occ_reg_weight: 0.1 # Occlusion regularizer loss weight smooth_loss_weight: 0.001 # Smoothness loss weight C1: 1e-4 # SSIM parameter C2: 9e-4 # SSIM parameter photometric_reduce_op: 'min' # Method for photometric loss reducing disp_norm: True # Inverse depth normalization clip_loss: 0.0 # Clip loss threshold variance padding_mode: 'zeros' # Photometric loss padding mode automask_loss: True # Automasking to remove static pixels supervised_method: 'sparse-l1' # Method for depth supervision supervised_num_scales: 4 # Number of scales for supervised learning supervised_loss_weight: 0.9 # Supervised loss weight depth_net: name: '' # Depth network name checkpoint_path: '' # Depth checkpoint filepath version: '' # Depth network version dropout: 0.0 # Depth network dropout pose_net: name: '' # Pose network name checkpoint_path: '' # Pose checkpoint filepath version: '' # Pose network version dropout: 0.0 # Pose network dropout datasets: augmentation: image_shape: (192, 640) # Image shape jittering: (0.2, 0.2, 0.2, 0.05) # Color jittering values train: batch_size: 8 # Training batch size num_workers: 16 # Training number of workers back_context: 1 # Training backward context forward_context: 1 # Training forward context dataset: [] # Training dataset path: [] # Training data path split: [] # Training split depth_type: [''] # Training depth type cameras: [] # Training cameras repeat: [1] # Number of times training dataset is repeated per epoch num_logs: 5 # Number of training images to log validation: batch_size: 1 # Validation batch size num_workers: 8 # Validation number of workers back_context: 0 # Validation backward context forward_context: 0 # Validation forward contxt dataset: [] # Validation dataset path: [] # Validation data path split: [] # Validation split depth_type: [''] # Validation depth type cameras: [] # Validation cameras num_logs: 5 # Number of validation images to log test: batch_size: 1 # Test batch size num_workers: 8 # Test number of workers back_context: 0 # Test backward context forward_context: 0 # Test forward context dataset: [] # Test dataset path: [] # Test data path split: [] # Test split depth_type: [''] # Test depth type cameras: [] # Test cameras num_logs: 5 # Number of test images to log