defineMobileUNet: defineMobileUNet
In geodl: Geospatial Semantic Segmentation with Torch and Terra

defineMobileUNet

R Documentation

defineMobileUNet

Description

Define a UNet architecture for geospatial semantic segmentation with a MobileNet-v2 backbone.

Usage

defineMobileUNet(
  inChn = 3,
  nCls = 3,
  pretrainedEncoder = TRUE,
  freezeEncoder = TRUE,
  avgImNetWeights = FALSE,
  actFunc = "relu",
  useAttn = FALSE,
  useDS = FALSE,
  dcChn = c(256, 128, 64, 32, 16),
  negative_slope = 0.01
)

Arguments

`inChn`	Number of input channels or predictor variables. Default is 3.
`nCls`	Number of classes being differentiated. For a binary classification, this can be either 1 or 2. If 2, the problem is treated as a multiclass problem, and a multiclass loss metric should be used. Default is 3.
`pretrainedEncoder`	TRUE or FALSE. Whether or not to initialized using pre-trained ImageNet weights for the MobileNet-v2 encoder. Default is TRUE.
`freezeEncoder`	TRUE or FALSE. Whether or not to freeze the encoder during training. T he default is TRUE. If TRUE, only the decoder component is trained.
`avgImNetWeights`	TRUE or FALSE. If three predictor variables are provided and ImageNet weights are used, whether or not to use the original weights or average them. Default is FALSE.
`actFunc`	Defines activation function to use throughout the network (note that MobileNet-v2 layers are not impacted). "relu" = rectified linear unit (ReLU); "lrelu" = leaky ReLU; "swish" = swish. Default is "relu".
`useAttn`	TRUE or FALSE. Whether to add attention gates along the skip connections. Default is FALSE or no attention gates are added.
`useDS`	TRUE or FALSE. Whether or not to use deep supervision. If TRUE, four predictions are made, one at each of the four largest decoder block resolutions, and the predictions are returned as a list object containing the 4 predictions. If FALSE, only the final prediction at the original resolution is returned. Default is FALSE or deep supervision is not implemented.
`dcChn`	Vector of 4 integers defining the number of output feature maps for each of the 4 decoder blocks. Default is 128, 64, 32, and 16.
`negative_slope`	If actFunc = "lrelu", specifies the negative slope term to use. Default is 0.01.

Details

Define a UNet architecture with a MobileNet-v2 backbone or encoder. This UNet implementation was inspired by a blog post by Sigrid Keydana available here. This architecture has 6 blocks in the encoder (including the bottleneck) and 5 blocks in the decoder. The user is able to implement deep supervision (useDS = TRUE) and attention gates along the skip connections (useAttn = TRUE). If ImageNet weights are used and more then three predictor variables are provided, ImageNet weights in the layer of the encoder block are averaged. If three channels or predictor variables are provided, the user can specify to user the ImageNet weights or average them.