model_fcn_resnet: Fully Convolutional Network for Semantic Segmentation

Description

Constructs an FCN (Fully Convolutional Network) model for semantic image segmentation, based on a ResNet backbone as described in Fully Convolutional Networks for Semantic Segmentation.

Usage

model_fcn_resnet50(
  pretrained = FALSE,
  progress = TRUE,
  num_classes = 21,
  aux_loss = NULL,
  pretrained_backbone = TRUE,
  ...
)
model_fcn_resnet101(
  pretrained = FALSE,
  progress = TRUE,
  num_classes = 21,
  aux_loss = NULL,
  pretrained_backbone = TRUE,
  ...
)

Value

An nn_module representing the FCN model.

Arguments

pretrained: (bool): If TRUE, returns a model pre-trained on ImageNet.
progress: (bool): If TRUE, displays a progress bar of the download to stderr.
num_classes: Number of output classes. Default: 21.
aux_loss: If TRUE, includes the auxiliary classifier. If NULL, defaults to TRUE when pretrained = TRUE.
pretrained_backbone: If TRUE, uses a backbone pre-trained on ImageNet.
...: Additional arguments passed to the backbone implementation.

Details

The 21 output classes follow the PASCAL VOC convention: background, aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, tv/monitor.

Pretrained weights require num_classes = 21.

Examples

Run this code

if (FALSE) {
library(magrittr)
norm_mean <- c(0.485, 0.456, 0.406) # ImageNet normalization constants, see
# https://pytorch.org/vision/stable/models.html
norm_std  <- c(0.229, 0.224, 0.225)
img_url <- "https://en.wikipedia.org/wiki/Special:FilePath/Felis_catus-cat_on_snow.jpg"
img <- base_loader(img_url)

input <- img %>%
 transform_to_tensor() %>%
 transform_resize(c(520, 520)) %>%
 transform_normalize(norm_mean, norm_std)
batch <- input$unsqueeze(1)

model <- model_fcn_resnet50(pretrained = TRUE)
model$eval()
output <- model(batch)

# visualize the result
# `draw_segmentation_masks()` turns the torch_float output into a boolean mask internaly:
segmented <- draw_segmentation_masks(input, output$out$squeeze(1))
tensor_image_display(segmented)


model <- model_fcn_resnet101(pretrained = TRUE)
model$eval()
output <- model(batch)

# visualize the result
segmented <- draw_segmentation_masks(input, output$out$squeeze(1))
tensor_image_display(segmented)
}