86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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sys.path.insert(0, ".")
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import time
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from paddlehub.utils.log import logger
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from paddlehub.module.module import moduleinfo, serving
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import cv2
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import numpy as np
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import paddle.nn as nn
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from tools.infer.predict import Predictor
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from tools.infer.utils import b64_to_np, postprocess
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from deploy.hubserving.clas.params import read_params
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@moduleinfo(
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name="clas_system",
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version="1.0.0",
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summary="class system service",
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author="paddle-dev",
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author_email="paddle-dev@baidu.com",
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type="cv/class")
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class ClasSystem(nn.Layer):
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def __init__(self, use_gpu=None, enable_mkldnn=None):
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"""
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initialize with the necessary elements
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"""
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cfg = read_params()
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if use_gpu is not None:
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cfg.use_gpu = use_gpu
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if enable_mkldnn is not None:
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cfg.enable_mkldnn = enable_mkldnn
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cfg.hubserving = True
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cfg.enable_benchmark = False
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self.args = cfg
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if cfg.use_gpu:
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try:
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_places = os.environ["CUDA_VISIBLE_DEVICES"]
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int(_places[0])
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print("Use GPU, GPU Memery:{}".format(cfg.gpu_mem))
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print("CUDA_VISIBLE_DEVICES: ", _places)
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except:
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raise RuntimeError(
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"Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
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)
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else:
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print("Use CPU")
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print("Enable MKL-DNN") if enable_mkldnn else None
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self.predictor = Predictor(self.args)
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def predict(self, batch_input_data, top_k=1):
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assert isinstance(
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batch_input_data,
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np.ndarray), "The input data is inconsistent with expectations."
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starttime = time.time()
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batch_outputs = self.predictor.predict(batch_input_data)
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elapse = time.time() - starttime
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batch_result_list = postprocess(batch_outputs, top_k)
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return {"prediction": batch_result_list, "elapse": elapse}
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@serving
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def serving_method(self, images, revert_params, **kwargs):
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"""
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Run as a service.
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"""
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input_data = b64_to_np(images, revert_params)
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results = self.predict(batch_input_data=input_data, **kwargs)
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return results
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