Created by: Payne-Zhu

使用resnet_v2_50_imagenet迁移学习后新产生的模型按照下列方式转化为PaddleHubModule； 再使用hub install 安装到本机后新模型xxx_test使用hub serving start -m xxx_test部署可以成功； 调用这个服务参数要怎么写？使用示例中的方法会出错；返回需要使用json数据(arnning: "This usage is out of date, please use 'application/json' as content-type to post to )，但是使用json的话图片如何转换传递呢？ 如何实现原版的 resnet_v2_50_imagenet 通过 http://localhost:8866/predict/image/resnet_v2_50_imagenet 传递Post参数image: data:image/jpeg;base64,... 就能直接返回的方式呢？

`import os import numpy as np import paddlehub as hub from paddlehub.module.module import moduleinfo, serving

@moduleinfo( name="xxx_test", version="1.0.0", summary="ERNIE tiny which was fine-tuned on the chnsenticorp dataset.", author="payne", author_email="payne@qq.com", type="cv/xxx_model", ) class RestNetFinetuned(hub.Module): def _initialize(self, ckpt_dir="xxx_model", num_class=4, max_seq_len=128, use_gpu=False, batch_size=4): self.ckpt_dir = os.path.join(self.directory, ckpt_dir)

    self.module = hub.Module(name="resnet_v2_50_imagenet")
    inputs, outputs, program = self.module.context(trainable=True)

    self.vocab_path = self.module.get_vocab_path()

    reader = hub.reader.ImageClassificationReader(
        image_width=self.module.get_expected_image_width(),
        image_height=self.module.get_expected_image_height(),
        images_mean=self.module.get_pretrained_images_mean(),
        images_std=self.module.get_pretrained_images_std(),
    )

    pooled_output = outputs["feature_map"]

    feed_list = [inputs["image"].name]

    config = hub.RunConfig(
        use_data_parallel=False,
        use_cuda=use_gpu,
        batch_size=batch_size,
        checkpoint_dir=self.ckpt_dir,
        strategy=hub.finetune.strategy.DefaultFinetuneStrategy())

    self.cls_task = hub.ImageClassifierTask(
        data_reader=reader,
        feature=pooled_output,
        feed_list=feed_list,
        num_classes=4,
        config=config)

@serving
def predict(self, data, return_result=False, accelerate_mode=True):
    run_states = self.cls_task.predict(data=data)
    # return run_states
    results = [run_state.run_results for run_state in run_states]
    prediction = []
    for batch_result in results:
        # get predict index
        batch_result = np.argmax(batch_result, axis=2)[0]
        batch_result = batch_result.tolist()
        prediction += batch_result
    return prediction

if name == "main": dm_4_npc = RestNetFinetuned(ckpt_dir="dm_4_npc") data = ["data/1.png","data/4.png","data/2.png","data/3.png"] run_states = dm_4_npc.predict(data=data) print(run_states)`