Att2inModel.py 文件源码

def sample(self, fc_feats, att_feats, opt={}):
        sample_max = opt.get('sample_max', 1)
        beam_size = opt.get('beam_size', 1)
        temperature = opt.get('temperature', 1.0)
        if beam_size > 1:
            return self.sample_beam(fc_feats, att_feats, opt)

        batch_size = fc_feats.size(0)
        state = self.init_hidden(batch_size)

        # Project the attention feats first to reduce memory and computation comsumptions.
        p_att_feats = self.ctx2att(att_feats.view(-1, self.att_feat_size))
        p_att_feats = p_att_feats.view(*(att_feats.size()[:-1] + (self.att_hid_size,)))

        seq = []
        seqLogprobs = []
        for t in range(self.seq_length + 1):
            if t == 0: # input <bos>
                it = fc_feats.data.new(batch_size).long().zero_()
            elif sample_max:
                sampleLogprobs, it = torch.max(logprobs.data, 1)
                it = it.view(-1).long()
            else:
                if temperature == 1.0:
                    prob_prev = torch.exp(logprobs.data).cpu() # fetch prev distribution: shape Nx(M+1)
                else:
                    # scale logprobs by temperature
                    prob_prev = torch.exp(torch.div(logprobs.data, temperature)).cpu()
                it = torch.multinomial(prob_prev, 1).cuda()
                sampleLogprobs = logprobs.gather(1, Variable(it, requires_grad=False)) # gather the logprobs at sampled positions
                it = it.view(-1).long() # and flatten indices for downstream processing

            xt = self.embed(Variable(it, requires_grad=False))

            if t >= 1:
                # stop when all finished
                if t == 1:
                    unfinished = it > 0
                else:
                    unfinished = unfinished * (it > 0)
                if unfinished.sum() == 0:
                    break
                it = it * unfinished.type_as(it)
                seq.append(it) #seq[t] the input of t+2 time step

                seqLogprobs.append(sampleLogprobs.view(-1))

            output, state = self.core(xt, fc_feats, att_feats, p_att_feats, state)
            logprobs = F.log_softmax(self.logit(output))

        return torch.cat([_.unsqueeze(1) for _ in seq], 1), torch.cat([_.unsqueeze(1) for _ in seqLogprobs], 1)