2-refinement.json

{
  "seed": 1,
  "run_name": "2-refinement",
  "description": "Train the refinement network on top of pretrained RecNet",

  "train_dataset": "ScarSeg",
  "validation_dataset": "ScarSeg",
  "split_ratio": [4, 1, 1],
  "input_mode": "2d",

  "undersampling": {
    "sampling_scheme": "varden",
    "acceleration_factor": 8,
    "variable_acceleration": false
  },
  
  "runner_type": "adversarial",
  "application": "reconstruction",

  "generator_model": {
    "name": "RefinementWrapper",
    "mode": "real-penalty-add",
    "input_mode": "output",
    "pretrained_model": {
      "name": "RecNet",
      "num_blocks": 3,
      "num_convs": 3,
      "num_filters": 32,
      "pretrained_weights": ["../resources/models/INSERT_CHECKPOINT_PATH_HERE", "model"]
    },
    "learnable_model": {
      "name": "UNET",
      "num_inputs": 2,
      "num_outputs": 1,
      "num_layers_per_scale": 2,
      "encode_filters": [32, 64, 128],
      "decode_filters": [64, 32],
      "kernel_size": 4,
      "upsampling_mode": "nn-resize-conv",
      "output_activation": "none",
      "padding": "reflection",
      "decoder_act_upsampling_only": true,
      "weight_init": {
        "conv_weight": ["orthogonal", "relu"],
        "batchnorm_weight": ["constant", 1.0]
      }
    }
  },

  "discriminator_model": {
    "spatial_shape": [512, 512],
    "num_inputs": 1,
    "input_method": "simple-magnitude",

    "num_filters_per_layer": [64, 128, 256, 512, 1024, 1024],
    "strides": [2, 2, 2, 2, 2, 1],
	"kernel_sizes": 4,
	"final_conv_kernel_size": 4,
	"padding": "reflection",

	"act_fn": "lrelu",
	"relu_leakiness": 0.2,
	
	"dropout_after": [3, 4, 5],
    "dropout_prob": 0.5,

	"use_norm_layers": "not-first",
	"norm_layer": "batch",

    "compute_features": true,
    "use_image_pool": true,
    "image_pool_size": 80
  },

  "generator_adversarial_losses": ["gan", "FeatureMatching"],
  "generator_losses": ["VGG19", "FeaturePenalty"],
  "generator_loss_weights": {
	  "gan": 0.5,
      "VGG19": 10,
      "FeaturePenalty": 2 
  },
  "discriminator_losses": ["gan"],
  "discriminator_label_smoothing": 0.1,

  "feature_penalty": {
    "criterion": "L1",
    "input_key": "prescaled_refinement"
  },
  
  "generator_optimizer": {
    "name": "Adam",
    "learning_rate": 0.0002,
    "beta1": 0.5
  },

  "discriminator_optimizer": {
    "name": "Adam",
    "learning_rate": 0.0002,
    "beta1": 0.5
  },

  "num_epochs": 200,
  "batch_size": 5,
  "validation_batch_size": 5,
  "epochs_per_validation": 1,
  "epochs_per_checkpoint": 1,
  "steps_per_train_summary": 20,
  "num_periodic_checkpoints": 20,
  "num_data_workers": 4,

  "train_generator_metrics": ["psnr"],
  "train_discriminator_metrics": ["binary_accuracy"],
  "validation_metrics": ["psnr", "ssim"],
  "best_checkpoint_metrics": ["gen_psnr"],

  "use_tensorboard": true,
  "num_image_summaries": 5
}