Configuration files should be created for training, testing and inference. Our configuration file has a yaml format. The following items and sub-items are provided in configuration file.
"model":
"type": # str (default: "DFNet"), model type, now only support "DFNet".
"params": # parameters for the model.
"in_channels": # int (default: 4), the input channels of the model (default: 4).
"hidden_channels": # int (default: 64), the hiden channels of the model.
"L": # int (default: 5), the number of layers in a dense block.
"k": # int (default: 12), the output channel of a single layer in a dense block.
"use_DUC": # bool (default: True), whether to use the DUC instead of the deconvolution in up-sampling process.
"optimizer":
"type": # str (default: "AdamW"), optimizer type, refer to objects in torch.optim.
"params": # parameters for the optimizer, see specific optimizer parameters in torch for details.
"lr_scheduler":
"type": # str (default: ""), learning rate scheduler type, refer to objects in torch.optim.lr_scheduler. Specially, "" for no learning rate scheduler.
"params": # parameters for the learning rate scheduler, see specific learning rate scheduler parameters in torch for details.
"dataset":
"train": # list or dict, one item for one dataset.
"type": # str (default: "transcg"), dataset type, support type: ["transcg", "cleargrasp-real", "cleargrasp-syn", "omniverse", "transparent-object"]
"data_dir": # str, directory to the data
"image_size": # tuple of (int, int) (default: (1280, 720)), the resolution of the data (samples will be scaled to this size and fed into the network).
"use_augmentation": # bool (default: True), whether to use data augmentation.
"rgb_augmentation_probability": # float (default: 0.8), RGB augmentation probability.
"depth_min": # float (default: 0.3), the minimum depth.
"depth_max": # float (default: 1.5), the maximum depth
"depth_norm": # float (default: 1.0), the depth normalization coefficient.
"test": # list or dict, one item for one dataset.
# The dataset description is the same as the training dataset description.
"dataloader":
# the following parameters are the same as parameters in torch.utils.data.DataLoader.
"num_workers": # the number of workers in data loader.
"shuffle": # whether shuffle the data.
"drop_last": # whether drop the last data (that is not in a batch).
"trainer":
"batch_size": # int (default: 32), batch size
"test_batch_size": # int (default: 1), batch size during testing.
"multigpu": # bool (default: False), whether to use the multigpu for training/testing.
"max_epoch": # int (default: 40), the number of epochs.
"resume_lr": # float (default: 0.001), the resume learning rate (only used when resume training from checkpoints).
"criterion":
"type": # main loss type, support type: ["mse_loss", "masked_mse_loss", "custom_masked_mse_loss", "l1_loss", "masked_l1_loss", "custom_masked_l1_loss", "l2_loss", "masked_l2_loss", "custom_masked_l2_loss","huber_loss", "masked_huber_loss", "custom_masked_huber_loss"]
"epsilon": # float (default: 1e-8), the epsilon to avoid NaN during calculation.
"huber_k": # float (default: 0.1) the k value in huber loss. The parameter is only used when using loss type concerning huber loss.
"combined_smooth": # bool (default: False), whether to combine the smooth loss calculated by the cosine similarity between surface normals of ground-truth depth maps and predicted depth maps.
"combined_beta": # float (default: 0.005), the balance coefficient between depth loss and the smooth loss.
"combined_beta_decay": # float (default: 0.1), the balance coefficient decay value.
"combined_beta_decay_milestones": # list (default: []), the milestones of balance coefficient decay.
"metrics":
"types": # list of str (default: []), metric types, supporting type: ["MSE", "MaskedMSE", "RMSE", "MaskedRMSE", "REL", "MaskedREL", "MAE", "MaskedMAE", "Threshold@k", "MaskedThreshold@k"] (where k is a float value denoting "delta" in the paper).
"epsilon": # float (default: 1e-8), the epsilon to avoid NaN during calculation.
"depth_scale": # float (default: 1.0), the depth scale, should be the same as "depth_norm" in the testing set.
"stats":
"stats_dir": # str (default: "stats"), the directory to the statistics.
"stats_exper": # str (default: "default"), the experiment directory to the statistics. Notice that if the path is not empty and contains a "checkpoint.tar" file, then the training script will resume training from the checkpoint, and the testing script will test the model using the checkpoint.
####### Only Used For Inference #######
"inference":
"checkpoint_path": # str (default: "checkpoint/checkpoint.tar"), the path to the checkpoint.
"image_size": # pair of (int, int) (default: (320, 240)), the image size to feed into the network.
"cuda_id": # int (default: 0), the cuda ID.
"depth_min": # float (default: 0.3), the minimum depth.
"depth_max": # float (default: 1.5), the maximum depth
"depth_norm": # float (default: 1.0), the depth normalization coefficient.