Question: why randomly transform image during test evalution?

[jmidwint]

Hi,
More of a question, then an issue at the moment, to try to understand ..

I run the evaluation.py with the test set and the best trained model, and am able to reproduce the results as per table 1 in the paper.

However, when running evaluation.py on the test set, this would eventually lead to invoking your next function, which then invokes the _get_batches_of_transformed_samples, which gets test images to build a batch. But it also does a random transformation of each image (see *** below in code snip).

I would think that you would want to do evaluation of performance on the actual images and not on images that are getting randomly transformed. Can you explain why you apply random transform on test set images and why not just do random transform during training?

Thanks.

def _get_batches_of_transformed_samples(self, index_array) :
"""
Public function to fetch next batch.
# Returns
The next batch of images and labels.
"""
.. snip

Build batch of image data

    for i, j in enumerate(index_array):
        fname = self.filenames[j]
        x = img_utils.load_img(os.path.join(self.directory, fname),
                grayscale=grayscale,
                crop_size=self.crop_size,
                target_size=self.target_size)

        x = self.image_data_generator.random_transform(x)    ***** why do this during test evaluation?
        x = self.image_data_generator.standardize(x)
        batch_x[i] = x

..snip

[jmidwint]

Now answering my own question ... the call to random_transform doesn't do any transformation as the base class ImageDataGenerator was initialized with all the transformation parameters turned off. Doh!