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# 用于 Object2Vec 推理的数据格式
<a name="object2vec-inference-formats"></a>

下一页描述了用于从 Amazon A SageMaker I Object2Vec 模型中获取评分推断的输入请求和输出响应格式。

## GPU 优化：分类或回归
<a name="object2vec-inference-gpu-optimize-classification"></a>

由于 GPU 内存稀缺，可以指定 `INFERENCE_PREFERRED_MODE` 环境变量来优化是将分类或回归还是将 [输出：编码器嵌入](object2vec-encoder-embeddings.md#object2vec-out-encoder-embeddings-data)推理网络加载到 GPU 中。如果您的大多数推理适用于分类或回归，请指定 `INFERENCE_PREFERRED_MODE=classification`。以下是使用 4 个 p3.2xlarge 实例来优化推理的 Batch Transform 示例： classification/regression 

```
transformer = o2v.transformer(instance_count=4,
                              instance_type="ml.p2.xlarge",
                              max_concurrent_transforms=2,
                              max_payload=1,  # 1MB
                              strategy='MultiRecord',
                              env={'INFERENCE_PREFERRED_MODE': 'classification'},  # only useful with GPU
                              output_path=output_s3_path)
```

## 输入：分类或回归请求格式
<a name="object2vec-in-inference-data"></a>

Content-type：application/json

```
{
  "instances" : [
    {"in0": [6, 17, 606, 19, 53, 67, 52, 12, 5, 10, 15, 10178, 7, 33, 652, 80, 15, 69, 821, 4], "in1": [16, 21, 13, 45, 14, 9, 80, 59, 164, 4]},
    {"in0": [22, 1016, 32, 13, 25, 11, 5, 64, 573, 45, 5, 80, 15, 67, 21, 7, 9, 107, 4], "in1": [22, 32, 13, 25, 1016, 573, 3252, 4]},
    {"in0": [774, 14, 21, 206], "in1": [21, 366, 125]}
  ]
}
```

Content-type：application/jsonlines

```
{"in0": [6, 17, 606, 19, 53, 67, 52, 12, 5, 10, 15, 10178, 7, 33, 652, 80, 15, 69, 821, 4], "in1": [16, 21, 13, 45, 14, 9, 80, 59, 164, 4]}
{"in0": [22, 1016, 32, 13, 25, 11, 5, 64, 573, 45, 5, 80, 15, 67, 21, 7, 9, 107, 4], "in1": [22, 32, 13, 25, 1016, 573, 3252, 4]}
{"in0": [774, 14, 21, 206], "in1": [21, 366, 125]}
```

对于分类问题，分数向量的长度对应于 `num_classes`。对于回归问题，长度为 1。

## 输出：分类或回归响应格式
<a name="object2vec-out-inference-data"></a>

Accept：application/json

```
{
    "predictions": [
        {
            "scores": [
                0.6533935070037842,
                0.07582679390907288,
                0.2707797586917877
            ]
        },
        {
            "scores": [
                0.026291321963071823,
                0.6577019095420837,
                0.31600672006607056
            ]
        }
    ]
}
```

Accept：application/jsonlines

```
{"scores":[0.195667684078216,0.395351558923721,0.408980727195739]}
{"scores":[0.251988261938095,0.258233487606048,0.489778339862823]}
{"scores":[0.280087798833847,0.368331134319305,0.351581096649169]}
```

在分类和回归格式中，分数应用于单个标签。