geodl (version 0.3.1)

viewBatchPreds: viewBatchPreds

Description

Generate image grid of mini-batch of image chips, masks, and predictions for all samples in a DataLoader mini-batch.

Usage

viewBatchPreds(
  dataLoader,
  model,
  mode = "multiclass",
  nCols = 4,
  padding = 10,
  r = 1,
  g = 2,
  b = 3,
  cCodes,
  cNames,
  cColors,
  useCUDA = TRUE,
  probs = FALSE,
  usedDS = FALSE
)

Value

Image grids of example chips, reference masks, and predictions loaded from a mini-batch provided by the DataLoader.

Arguments

dataLoader

Instantiated instance of a DataLoader created using torch::dataloader().

model

Fitted model used to predict mini-batch.

mode

"multiclass" or "binary". If the prediction returns the positive case logit for a binary classification problem, use "binary". If 2 or more class logits are returned, use "multiclass". This package treats all cases as multiclass.

nCols

Number of columns in the image grid. Default is 3.

padding

Number of cells of padding to add around each example in resulting image.

r

Index of channel to assign to red channel. Default is 1 or the first channel. For gray scale or single-band images, assign the same index to all three bands.

g

Index of channel to assign to green channel. Default is 2 or the second channel. For gray scale or single-band images, assign the same index to all three bands.

b

Index of channel to assign to blue channel. Default is 3 or the third channel. For gray scale or single-band images, assign the same index to all three bands.

cCodes

Integer codes assigned to each class. Should be in the same order as cNames.

cNames

Vector of class names. Must be the same length as number of classes.

cColors

Vector of color values to use to display the masks. Colors are applied based on the order of class indices. Length of vector must be the same as the number of classes.

useCUDA

TRUE or FALSE. Default is FALSE. If TRUE, GPU will be used to predict the data mini-batch. If FALSE, predictions will be made on the CPU. We recommend using a GPU.

probs

TRUE or FALSE. Default is FALSE. If TRUE, rescaled logits will be shown as opposed to the hard classification. If FALSE, hard classification will be shown. For a binary problem where only the positive case logit is returned, the logit is transformed using a sigmoid function. When 2 or more classes are predicted, softmax is used to rescale the logits.

usedDS

TRUE or FALSE. Must be set to TRUE when using deep supervision. Default is FALSE, or it is assumed that deep supervision is not used.

Details

The goal of this function is to provide a visual check of predictions for a mini-batch of data.

Examples

if (FALSE) {
viewBatchPreds(dataLoader=testDL,
               model=model,
               mode="multiclass",
               nCols =5,
               r = 1,
               g = 2,
               b = 3,
               cCodes=c(1,2),
               cNames=c("Not Mine", "Mine"),
               cColors=c("gray", "darksalmon"),
               useCUDA=TRUE,
               probs=FALSE,
               usedDS=FALSE)
}