torchy.py 文件源码

def show_examples_pytorch(model, es, rlut1, rlut2, embed2, mxlen, sample, prob_clip, max_examples, reverse):
    si = np.random.randint(0, len(es))

    batch_dict = es[si]

    src_array = batch_dict['src']
    tgt_array = batch_dict['dst']
    src_len = batch_dict['src_len']
    #src_array, tgt_array, src_len, _ = es[si]

    if max_examples > 0:
        max_examples = min(max_examples, src_array.size(0))
        src_array = src_array[0:max_examples]
        tgt_array = tgt_array[0:max_examples]
        src_len = src_len[0:max_examples]

    GO = embed2.vocab['<GO>']
    EOS = embed2.vocab['<EOS>']

    # TODO: fix this, check for GPU first
    src_array = src_array.cuda()

    for src_len,src_i,tgt_i in zip(src_len, src_array, tgt_array):

        print('========================================================================')

        sent = lookup_sentence(rlut1, src_i.cpu().numpy(), reverse=reverse)
        print('[OP] %s' % sent)
        sent = lookup_sentence(rlut2, tgt_i)
        print('[Actual] %s' % sent)
        dst_i = torch.zeros(1, mxlen).long()
        #if use_gpu:
        dst_i = dst_i.cuda()

        next_value = GO
        src_i = src_i.view(1, -1)
        for j in range(mxlen):
            dst_i[0,j] = next_value
            probv = model((torch.autograd.Variable(src_i), torch.autograd.Variable(dst_i)))
            output = probv.squeeze()[j]
            if sample is False:
                _, next_value = torch.max(output, 0)
                next_value = int(next_value.data[0])
            else:
                probs = output.data.exp()
                # This is going to zero out low prob. events so they are not
                # sampled from
                best, ids = probs.topk(prob_clip, 0, largest=True, sorted=True)
                probs.zero_()
                probs.index_copy_(0, ids, best)
                probs.div_(torch.sum(probs))
                fv = torch.multinomial(probs, 1)[0]
                next_value = fv

            if next_value == EOS:
                break

        sent = lookup_sentence(rlut2, dst_i.squeeze())
        print('Guess: %s' % sent)
        print('------------------------------------------------------------------------')