Inconsistent Number of Predictions

[agp-ka32]

Hi @ppwwyyxx ,

====Some background:====
I am able to run a pre-trained model on any input image to get mask predictions as well as the bounding boxes. I have set my model in eval mode, as required.

In doing so, I also display the output of my model as below:

output = model([inputs])
print('output instances are:', output[0]['instances'])

This gives me 7 class predictions for different objects in my image, i.e, num_instances = 7.
So far so good.

====My question:====
I am also getting the mask features from the same pre-trained model used above, using the following code:

features = model.backbone(images.tensor)
proposals, _ = model.proposal_generator(images, features)
instances, _ = model.roi_heads(images, features, proposals)

(I have omitted the required two lines to get mask features for brevity.)

The num_instances here is 5, which is different from when I run the model without any incisions, as described earlier (it was 7 there).

I am curious to know why this inconsistency.

Can you clarify? Thanks.