New parameters have been added to define the input of the models:
* ResizeOperation: by default center-crop was being performed, now it is
configurable.
* InputOrder: by default RGB was being used as the order for the array
values of the input tensor, now it can be configured.
* InputInterval has been changed to InputIntervals (an slice). This
means that every channel can have its own interval conversion.
* InputInterval can define now stddev and mean, because sometimes
instead of adjusting the interval, the stddev and mean of the training
data should be use.
Now when loading labels internal/ai/tensorflow package will try to look
for all the files that match the glob label*.txt and will return the
labels that match the expected number. Some models add a first label
called background, which is a bias.
Also, a new parameter has been added to models to allow a second path to
look for the label files. This path is set to nasnet asset on
internal/ai/vision.
By inspecting existing models we saw that many times logits are returned
instead of probabilities. Photoprism uses probabilites to rank the
quality of the results, so we need to transform those logits. Our
approach is to add a new layer at runtime to the graph that performs the
softmax operation.
