How do I add an image title to tensorboardX?

Question

I am currently using tensorboardX to visualize input images while training a ResNet image classifier. Is there a way to add the image title along with the added image? I would like to have the image name (as stored in the dataset) displayed below the image in the tensorboard display.

So far I have tried passing a comment parameter into my tensorboard writer, which does not seem to do the job. Currently, the relevant lines of my code are:

pretrain_train_writer = SummaryWriter('log/pretrain_train')
img_grid = vutils.make_grid(inputs[tp_idx_0], normalize=True, scale_each=True, nrow=8)
pretrain_val_writer.add_image('true_positive_class_0', img_grid, global_step=epoch, comment = img_path)

Answer 1

there is no way of doing it directly with tensorboard, instead you have to create images with titles using matplotlib and then supply them to tensorboard. Here is a sample code from the tensorboard documentation:

def plot_to_image(figure):
  """Converts the matplotlib plot specified by 'figure' to a PNG image and
  returns it. The supplied figure is closed and inaccessible after this call."""
  # Save the plot to a PNG in memory.
  buf = io.BytesIO()
  plt.savefig(buf, format='png')
  # Closing the figure prevents it from being displayed directly inside
  # the notebook.
  plt.close(figure)
  buf.seek(0)
  # Convert PNG buffer to TF image
  image = tf.image.decode_png(buf.getvalue(), channels=4)
  # Add the batch dimension
  image = tf.expand_dims(image, 0)
  return image

def image_grid():
  """Return a 5x5 grid of the MNIST images as a matplotlib figure."""
  # Create a figure to contain the plot.
  figure = plt.figure(figsize=(10,10))
  for i in range(25):
    # Start next subplot.
    plt.subplot(5, 5, i + 1, title=class_names[train_labels[i]])
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
  
  return figure

# Prepare the plot
figure = image_grid()
# Convert to image and log
with file_writer.as_default():
  tf.summary.image("Training data", plot_to_image(figure), step=0)

here is the link to the doc: https://www.tensorflow.org/tensorboard/image_summaries