训练的图片多标签识别模型，使用paddleserving部署模型，预测结果和在本地使用命令行预测结果不一致

[syge]

PaddleClas：2.5.2 PaddleServing：paddlepaddle/serving：0.7.0-devel
以下是classification_web_service.py脚本内容

class ImagenetOp(Op):
def init_op(self):
self.seq = Sequential([
Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225],
True)
])
self.label_dict = {}
label_idx = 0
with open("gas.label") as fin:
for line in fin:
self.label_dict[label_idx] = line.strip()
label_idx += 1

def preprocess(self, input_dicts, data_id, log_id):
(_, input_dict), = input_dicts.items()
batch_size = len(input_dict.keys())
imgs = []
for key in input_dict.keys():
data = base64.b64decode(input_dict[key].encode('utf8'))
data = np.fromstring(data, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
img = self.seq(im)
imgs.append(img[np.newaxis, :].copy())
input_imgs = np.concatenate(imgs, axis=0)
return {"x": input_imgs}, False, None, ""

def postprocess(self, input_dicts, fetch_dict, data_id, log_id):
score_list = fetch_dict["prediction"]
result = {"scores": str(score_list)}
return result, None, ""
class ImageService(WebService):
def get_pipeline_response(self, read_op):
image_op = ImagenetOp(name="imagenet", input_ops=[read_op])
return image_op

uci_service = ImageService(name="imagenet")
uci_service.prepare_pipeline_config("config.yml")
uci_service.run_service()

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