config.json

{
  "debug": false,
  "tag": "tag",
  "upsamaple_steps": [2, 2, 2] ,
  //upsampling scale for each iteration of the network. When using same network for all steps - all steps need to be identical.
  "backprojection": true,
  //See paper. Network trains on low spatial scale and uses BP in coarse-to-fine fashion.
  "final_no_BP": true,
  //If true (and backprojection is true), the final temporal run through the network is w.o. BP. Enables going down spatially one less scale.
  "checkpoint": "",
  //load checkpoint. Needs to be same Network type as one specified below
  "ckpt_first_trained": false,
  //Requires checkpoint. If ckpt_first_trained is true - will not train the loaded net on the first iteration. Enables examining finetuning on/off for a given network.
  "fix_network": false,
  //when fix_network==true trains once and fixes the net, so only trains on single (smallest) spatial scale and iterates with/without BP. REQUIRES ALL upsamaple_steps ARE IDENTICAL
  "fine_tune": true,
  //When fine_tune=True, start next step's train with existing network. REQUIRES ALL upsamaple_steps ARE IDENTICAL
  "working_dir_base": "./results",
  //Where to save results. If results already exist - advances in numbering
  "network": "residual",
  //"base" or "residual". The paper's results are with "residual" (Network's ouput is added to naive upsampling), "base" uses ConvTranspose3d instead.
  "res3d_up_method": "resize",
  //method to upsample temporally to use as input add residual to. "resize"-linear/cubic resize, "duplicate"-duplicates frames, "zero_gap"-adds empty frame/s after each real one.
  "num_epochs": 5,
  "fine_tuning_epochs_factor": 2,
  //factor to divide "num_epochs" by when fine-tuning. Note: divides by this factor for EACH fine-tuning iteration.
  "num_iter_per_epoch": 10,
  "batch_size": 10,
  "save_every": 5000,
  //saves network's model every X epochs.
  "val_every": 500,
  //performs validation and saves results to track progress. Validation upsamples back to input size.
  "prefix": "",
  "dtype": "float32",
  "data": {
    "params": {
      "frames_folder": "./Example-Data/bus_swapped",
	  //Input frames folder
      "prefix": "",
	  //If frame names containg prefix, such as "frame00020.png", "prefix" needs to be "frame"
	  "hr_lr_relation": 0,
	  //0: full exposure: blur+sample 2 frames, 1: delta: sample 2 frames. In paper used "full exposure"
	  "gradient_percentile": 70,
	  //Used for crop selection. Should not change.
	  "augmentation_params": {
		"crop_sizes": {
		  "crop_size_spatial": 16,
		  "loss_mask_spatial": 0,
		  //loss_mask: how much to ignore patch AT EACH SIDE
		  "crop_size_temporal": 16,
		  "loss_mask_temporal": 2
		},
		"spatial_resize_options": [1.0],
        //0.9, 0.81, 0.73, 0.65, 0.59, 0.53, 0.47, 0.43, 0.38, 0.34, 0.31, 0.28, 0.25] ,
		"temporal_jump_options": [1],
        //2, 3, 4, 5],
		//Resizes for creating additional HR training examples.
        "within": {
		//Parameters for "within dimension" training examples. See paper.
		  "probability": 0.4,
          "flip_prob": 0.33,
		  "rotation_prob": 0.5,
		  "z_flip_prob": 0.33,
          "tps_prob": 1
        },
        "across": {
		//Parameters for "across dimension" training examples. See paper.
		  "probability": 0.4,
		  "prob_ver_new_z": 0.5,
		  "prob_hor_new_z": 0.5,
		  "new_z_sample_range": [1, 2],
		  "flip_prob": 0.33,
		  "rotation_prob": 0.5,
		  "new_z_flip_prob": 0.33,
		  //rotation and flip should be only in directions that are ok
          "tps_prob": 1
        },
        "shift": {
		//Parameters for shifted training examples. See paper.
          "probability": 0.2,
          "range_hor": [-1, 1],
		  "range_ver": [-1, 1],
		  "entire_pixels": true,
		  "prob_for_across": 0.3,
		  //probability the shifted crop will be permuted and used as across example. Else, as within.
		  "flip_prob": 0.3,
		  "rotation_prob": 0.5,
		  "z_flip_prob": 0.0,
		  "valid": true,
          "tps_prob": 1
        }
      },
	  "eval_params": {
		"size_frames": 16,
        "size_height": 36,
        "size_width": 36,
		"pad_frames": 4,
        "pad_height": 8,
        "pad_width": 8
	  }
    }
  },
  "loss": {
    "name": "MSE",
    "params": {
    }
  },
  "optimization": {
    "name": "Adam",
    "params": {
      "lr": 0.001,
	  "SGD_momentum": 0.9
    }
  },
  "lr_sched": {
    "name": "StepLR",
    // MultiStepLR - for manually set milestones , StepLR for % out of total epochs
    "params": {
      "gamma": 0.5,
      "step_size": 0.25,
      "milestones": [
        30000,
        40000,
        45000
      ]
    }
  },
  "trainer" : {
  }
}