Image Segmentation using Tensorflow and Keras

[Maya_Samuels-Fair]

I am learning to perform image segmentation in RStudio instead of a python IDE. I am using a script adapted from this article: https://blogs.rstudio.com/ai/posts/2019-08-23-unet/
My code works until I actually try to fit the model, when I get this error:

Error in py_call_impl(callable, dots$args, dots$keywords) :
ValueError: in user code:

/Users/mayasamuels-fair/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:1478 predict_function  *
    return step_function(self, iterator)
/Users/mayasamuels-fair/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:1468 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
/Users/mayasamuels-fair/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/Users/mayasamuels-fair/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/Users/mayasamuels-fair/Library/r-miniconda/envs/r-reticula

Here is my code:

data <- tibble(
  img = list.files(here::here("/Volumes/Maya\'sDrive/CurrentProjects/OstracodSegmentation/train_png"), full.names = TRUE),
  mask = list.files(here::here("/Volumes/Maya\'sDrive/CurrentProjects/OstracodSegmentation/train_masks"), full.names = TRUE)
)

data <- initial_split(data, prop = 0.8)

training_dataset <- training(data) %>%  
  tensor_slices_dataset() %>% 
  dataset_map(~.x %>% list_modify(
    img = tf$image$decode_png(tf$io$read_file(.x$img), channels = 3),
    mask = tf$image$decode_png(tf$io$read_file(.x$mask), channels = 3)
  ))

training_dataset <- training_dataset %>% 
  dataset_map(~.x %>% list_modify(
    img = tf$image$convert_image_dtype(.x$img, dtype = tf$float32),
    mask = tf$image$convert_image_dtype(.x$mask, dtype = tf$float32)
  ))

training_dataset <- training_dataset %>% 
  dataset_map(~.x %>% list_modify(
    img = tf$image$resize(.x$img, size = shape(128, 128)),
    mask = tf$image$resize(.x$mask, size = shape(128, 128))
  ))

random_bsh <- function(img) {
  img %>% 
    tf$image$random_brightness(max_delta = 0.3) %>% 
    tf$image$random_contrast(lower = 0.5, upper = 0.7) %>% 
    tf$image$random_saturation(lower = 0.5, upper = 0.7) %>% 
    tf$clip_by_value(0, 1) # clip the values into [0,1] range.
}

training_dataset <- training_dataset %>% 
  dataset_map(~.x %>% list_modify(
    img = random_bsh(.x$img)
  ))

create_dataset <- function(data, train, batch_size = 30) {
  
  dataset <- data %>% 
    tensor_slices_dataset() %>% 
    dataset_map(~.x %>% list_modify(
      img = tf$image$decode_png(tf$io$read_file(.x$img), channels=3),
      mask = tf$image$decode_png(tf$io$read_file(.x$mask), channels=3)
    )) %>% 
    dataset_map(~.x %>% list_modify(
      img = tf$image$convert_image_dtype(.x$img, dtype = tf$float32),
      mask = tf$image$convert_image_dtype(.x$mask, dtype = tf$float32)
    )) %>% 
    dataset_map(~.x %>% list_modify(
      img = tf$image$resize(.x$img, size = shape(128, 128)),
      mask = tf$image$resize(.x$mask, size = shape(128, 128))
    ))
  
  if (train) {
    dataset <- dataset %>% 
      dataset_map(~.x %>% list_modify(
        img = random_bsh(.x$img)
      )) 
  }
  
  if (train) {
    dataset <- dataset %>% 
      dataset_shuffle(buffer_size = batch_size*128)
  }
  
  dataset <- dataset %>% 
    dataset_batch(batch_size)

dataset %>% 
    dataset_map(unname) # Keras needs an unnamed output.
}

training_dataset <- create_dataset(training(data), train = TRUE)
validation_dataset <- create_dataset(testing(data), train = FALSE)

model <- unet(input_shape = c(128, 128, 3))

dice <- custom_metric("dice", function(y_true, y_pred, smooth = 1.0) {
  y_true_f <- k_flatten(y_true)
  y_pred_f <- k_flatten(y_pred)
  intersection <- k_sum(y_true_f * y_pred_f)
  (2 * intersection + smooth) / (k_sum(y_true_f) + k_sum(y_pred_f) + smooth)
})

model %>% compile(optimizer = optimizer_rmsprop(lr = 1e-5), loss = "binary_crossentropy",
metrics = list(dice, metric_binary_accuracy))

model %>% fit(training_dataset, epochs = 50, validation_data = validation_dataset)

Does anyone know what the problem is? I can't make sense of the error message.