Closed ejlee95 closed 2 years ago
yes padding token is also the ending token. with sequence augmentation, we won't use ending token, rather predict to maximum length, and use likelihood to score each instance/prediction.
Hi Ting,
Thanks for the great research work and the code. In the paper, you mentioned the prediction of a maximum length at inference. I didn't find it in the code. Could you please point me to where it is implemented?
On Tue, Dec 6, 2022 at 1:44 PM Yujie Qian @.***> wrote:
Hi Ting,
Thanks for the great research work and the code. In the paper, you mentioned the prediction of a maximum length at inference. I didn't find it in the code. Could you please point me to where it is implemented?
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Thanks for the prompt reply! I misunderstood your paper: I thought you were predicting an actual length of the generated sequence, but actually it is "predict to a maximum length".
Did you do any filtering based on the likelihood scores for the evaluation? Otherwise, the model is predicting the same number of objects for all images?
We rank the prediction using the likelihood score during evaluation (AP takes a ranked list to compute)
On Wed, Dec 7, 2022 at 2:07 PM Yujie Qian @.***> wrote:
Thanks for the prompt reply! I misunderstood your paper: I thought you were predicting an actual length of the generated sequence, but actually it is "predict to a maximum length".
Did you do any filtering based on the likelihood scores for the evaluation? Otherwise, the model is predicting the same number of objects for all images?
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I have a question about inference code.
In tasks/task_utils.py, function 'decode_object_seq_to_bbox', there is no function that acts as 'end-of-sequence'.
I think PADDING_TOKEN (which denoted as 0 in vocab.py) is the token that indicates end-of-seq, but in the inference time, the code does not use the token.
Am I right?