adae08c077d07f6da6c95d995d8211cf9e9590e4,eval_utils.py,,eval_split,#Any#Any#Any#Any#,94

Before Change

        with torch.no_grad():
            seq, seq_logprobs = model(fc_feats, att_feats, att_masks, opt=eval_kwargs, mode="sample")
            seq = seq.data
            perplexity = seq_logprobs.sum(1) / ((seq>0).float().sum(1)+1)
        
        // Print beam search
        if beam_size > 1 and verbose_beam:

After Change

            seq, seq_logprobs = model(fc_feats, att_feats, att_masks, opt=eval_kwargs, mode="sample")
            seq = seq.data
            entropy = - (F.softmax(seq_logprobs, dim=2) * seq_logprobs).sum(2).sum(1) / ((seq>0).float().sum(1)+1)
            perplexity = - seq_logprobs.gather(2, seq.unsqueeze(2)).squeeze(2).sum(1) / ((seq>0).float().sum(1)+1)
        
        // Print beam search
        if beam_size > 1 and verbose_beam:
            for i in range(loader.batch_size):

Instances