plugin.json

{
  "name": "WIPP UNet CNN Training Plugin",
  "version": "1.0.0",
  "title": "UNet CNN Training",
  "author": "Michael Majurski",
  "institution": "National Institute of Standards and Technology",
  "repository": "https://github.com/usnistgov/WIPP-unet-train-plugin",
  "citation": "Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. \"U-net: Convolutional networks for biomedical image segmentation.\" International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.",
  "description": "Train a UNet CNN model",
  "containerId": "wipp/wipp-unet-cnn-train-plugin:1.0.0",
  "inputs": [

    {
      "name": "imageDir",
      "type": "collection",
      "options": {
        "format": "tiledtiff"
      },
      "description": "Input images for the plugin.",
      "required": true
    },
    {
      "name": "maskDir",
      "type": "collection",
      "options": {
        "format": "tiledtiff"
      },
      "description": "Input masks for the plugin.",
      "required": true
    },
    {
      "name": "useTiling",
      "type": "enum",
			"options": {
				"values": [
					"NO","YES"
				]
			},
      "description": "Controls whether or not to tile the input images for training in order to fit them into GPU memory.",
      "required": true,
      "default": "NO"
    },
    {
      "name": "tileSize",
      "type": "number",
      "description": "Tile size for tiling input images in order to fit them into GPU memory.",
      "required": false,
      "default": 256
    },
    {
      "name": "trainFraction",
      "type": "number",
      "description": "What fraction of the annotated images to use for training (default 0.8).",
      "required": true,
      "default": 0.8
    },
    {
      "name": "batchSize",
      "type": "number",
      "description": "How many images/tiles to use per mini-batch. The number of images used to estimate the gradient per training step.",
      "required": true,
      "default": 8
    },
    {
      "name": "numberClasses",
      "type": "number",
      "description": "How many classes exists in the training data? Each pixel will be classified into one of N classes in the segmentation mask, what is N?",
      "required": true,
      "default": 2
    },
    {
      "name": "learningRate",
      "type": "number",
      "description": "What is the learning rate for the network?",
      "required": true,
      "default": 1e-4
    },
    {
      "name": "testEveryNSteps",
      "type": "number",
      "description": "How many gradient update (training) steps to take between test split evaluations. This controls how big the effective epoch is. It enables controlling the test frequency between experiments with different true epoch sizes.",
      "required": true,
      "default": 1000
    },
    {
      "name": "balanceClasses",
      "type": "enum",
			"options": {
				"values": [
					"YES",
                    "NO"
				]
			},
      "description": "Whether or not to sample data during training in a class balanced manner. If turned off, the data the network sees will be randomly sampled from the full training dataset.",
      "required": true,
      "default": "YES"
    },
    {
      "name": "earlyStoppingEpochCount",
      "type": "number",
      "description": "The number of epochs past the optimal model to continue to train, hoping to find an improvement.",
      "required": true,
      "default": 20
    },
    {
      "name": "convergenceTolerance",
      "type": "number",
      "description": "The epsilon buffer used to determine equivalence with early stopping metrics values. If two values differ by less than convergenceTolerance, they are considered equal. This value prevents machine precision improvements in loss from allowing the model to continue training.",
      "required": true,
      "default": 1e-4
    },
      {
      "name": "useIntensityScaling",
      "type": "enum",
			"options": {
				"values": [
					"YES","NO"
				]
			},
      "description": "Controls whether or not to intensity-scale the input images for training in order to accommodate unevenly distributed intensity-defining classes across multiple images.",
      "required": true,
      "default": "YES"
    },
    {
      "name": "useAugmentation",
      "type": "enum",
			"options": {
				"values": [
					"YES",
                    "NO"
				]
			},
      "description": "Whether or not to use data augmentation during training.",
      "required": true,
      "default": "YES"
    },
    {
      "name": "augmentationReflection",
      "type": "enum",
			"options": {
				"values": [
					"YES",
                    "NO"
				]
			},
      "description": "Whether or not to use the data augmentation technique image reflection during training.",
      "required": false,
      "default": "YES"
    },
    {
      "name": "augmentationRotation",
      "type": "enum",
			"options": {
				"values": [
					"YES",
                    "NO"
				]
			},
      "description": "Whether or not to use the data augmentation technique image rotation during training.",
      "required": false,
      "default": "YES"
    },
    {
      "name": "augmentationJitter",
      "type": "number",
      "description": "The severity of jitter data augmentation [0 = None, 1 = 100% of image size]. Jitter shifts the image left-right and up-down by a percentage of the image size.",
      "required": false,
      "default": 0.1
    },
    {
      "name": "augmentationScale",
      "type": "number",
      "description": "The severity of scale data augmentation [0 = None, 1 = 100% of image size]. Scale alters the image size by zooming in or out by a percentage of the image size.",
      "required": false,
      "default": 0.1
    },
    {
      "name": "augmentationNoise",
      "type": "number",
      "description": "The severity of noise data augmentation [0 = None, 1 = 100% of the image dynamic range]. Noise augmentation adds gaussian noise to the image as a percentage of the images dynamic range.",
      "required": false,
      "default": 0.02
    },
    {
      "name": "augmentationBlurMaxSigma",
      "type": "number",
      "description": "The max size of a gaussian blur kernel used to blur the images [0 = None]. Blur augmentation performs a gaussian smoothing of the image to blur out details. This controls how large the blur kernel is, and therefore the strength of the blurring effect.",
      "required": false,
      "default": 2
    },
    {
      "name": "augmentationIntensity",
      "type": "number",
      "description": "The severity of intensity data augmentation [0 = None, 1 = 100% of dynamic range]. Intensity augmentation shifts the intensity values by a percentage of the dynamic range.",
      "required": false,
      "default": 0.0
    }
  ],
  "outputs": [
    {
      "name": "outputDir",
      "type": "tensorflowModel",
      "description": "Output directory data for the plugin"
    },
    {
      "name": "tensorboardDir",
      "type": "tensorboardLogs",
      "description": "tensorboard directory for Logs data from the plugin"
    }
  ],
  "ui": [
    {
      "key": "inputs.imageDir",
      "title": "Image Collection: ",
      "description": "Pick a collection..."
    },
    {
      "key": "inputs.maskDir",
      "title": "Mask Collection: ",
      "description": "Pick a collection..."
    },
    {
      "key": "inputs.numberClasses",
      "title": "Number of Classes: ",
      "description": "How many classes exists in the training data?"
    },
    {
      "key": "inputs.learningRate",
      "title": "Learning Rate: "
    },
    {
      "key": "inputs.batchSize",
      "title": "Batch Size: ",
      "description": "How many images/tiles to use per mini-batch?"
    },
    {
      "key": "inputs.useTiling",
      "title": "Tile Input Images: ",
      "description": "Whether to tile images to fit them in GPU memory"
    },
    {
      "key": "inputs.tileSize",
      "title": "Tile Size: ",
      "description": "What size to make the image tiles?",
      "condition": "model.inputs.useTiling=='YES'"
    },
    {
      "key": "inputs.trainFraction",
      "title": "Train Fraction: ",
      "description": "What fraction of the image collection to use for training?"
    },
    {
      "key": "inputs.testEveryNSteps",
      "title": "Test Every N Steps: ",
      "description": "How many gradient update steps to take between test evaluations."
    },
    {
      "key": "inputs.balanceClasses",
      "title": "Balance Classes: ",
      "description": "Whether or not to balance classes when sampling training data."
    },
    {
      "key": "inputs.earlyStoppingEpochCount",
      "title": "Early Stopping Epoch Count: ",
      "description": "The number of epochs past the optimal model to continue to train, hoping to find an improvement."
    },
    {
      "key": "inputs.convergenceTolerance",
      "title": "Counvergence Tolerance: ",
      "description": "The epsilon buffer used to determine equivalence with early stopping metrics values."
    },
    {
      "key": "inputs.useIntensityScaling",
      "title": "Scale intensities: ",
      "description": "Whether or not to intensity-scale classes when sampling training data."
    },
    {
      "key": "inputs.useAugmentation",
      "title": "Use Data Augmentation: ",
      "description": "Whether or not to use data augmentation during training."
    },
    {
      "key": "inputs.augmentationRotation",
      "title": "Use Rotation Augmentation: ",
      "condition": "model.inputs.useAugmentation=='YES'"
    },
    {
      "key": "inputs.augmentationReflection",
      "title": "Use Reflection Augmentation: ",
      "condition": "model.inputs.useAugmentation=='YES'"
    },
    {
      "key": "inputs.augmentationJitter",
      "title": "Jitter Augmentation Severity: ",
      "description": "The severity of jitter data augmentation [0 = None, 1 = 100% of image size].",
      "condition": "model.inputs.useAugmentation=='YES'"
    },
    {
      "key": "inputs.augmentationScale",
      "title": "Scale Augmentation Severity: ",
      "description": "The severity of scale data augmentation [0 = None, 1 = 100% of image size].",
      "condition": "model.inputs.useAugmentation=='YES'"
    },
    {
      "key": "inputs.augmentationNoise",
      "title": "Noise Augmentation Severity: ",
      "description": "The severity of noise data augmentation [0 = None, 1 = 100% of the image dynamic range].",
      "condition": "model.inputs.useAugmentation=='YES'"
    },
    {
      "key": "inputs.augmentationBlurMaxSigma",
      "title": "Blur Augmentation Max Sigma: ",
      "description": "The max size of a gaussian blur kernel used to blur the images [0 = None]. ",
      "condition": "model.inputs.useAugmentation=='YES'"
    },
	{
      "key": "inputs.augmentationIntensity",
      "title": "Intensity Augmentation Severity: ",
      "description": "The severity of intensity data augmentation [0 = None, 1 = 100% of the image dynamic range].",
      "condition": "model.inputs.useAugmentation=='YES'"
    },
    {
            "key" : "fieldsets",
            "fieldsets": [
                {
                  "title": "-- Input images --",
                  "fields": [
                    "imageDir",
                    "maskDir"
                  ]
                },
                {
                  "title": "-- Data Configuration --",
                  "fields": [
                    "useTiling",
                    "tileSize",
                    "numberClasses"
                  ]
                },
                {
                  "title": "-- Training Configuration --",
                  "fields": [
                    "trainFraction",
                    "batchSize",
                    "learningRate",
                    "testEveryNSteps",
                    "balanceClasses",
                    "earlyStoppingEpochCount",
                    "convergenceTolerance",
                    "useIntensityScaling"
                  ]
                },
                {
                  "title": "-- Augmentation Configuration --",
                  "fields": [
                    "useAugmentation",
                    "augmentationRotation",
                    "augmentationReflection",
                    "augmentationJitter",
                    "augmentationScale",
                    "augmentationNoise",
                    "augmentationBlurMaxSigma",
                    "augmentationIntensity"
                  ]
                }
            ]
        }

  ]
}