53 lines
1.8 KiB
Python
53 lines
1.8 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 sys
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from paddle_serving_client import Client
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#app
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from paddle_serving_app.reader import Sequential, URL2Image, Resize
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from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize
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import time
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client = Client()
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client.load_client_config("./ResNet50_vd_serving/serving_server_conf.prototxt")
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client.connect(["127.0.0.1:9292"])
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label_dict = {}
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label_idx = 0
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with open("imagenet.label") as fin:
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for line in fin:
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label_dict[label_idx] = line.strip()
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label_idx += 1
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#preprocess
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seq = Sequential([
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URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
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Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
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])
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start = time.time()
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image_file = "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"
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for i in range(1):
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img = seq(image_file)
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fetch_map = client.predict(
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feed={"inputs": img}, fetch=["prediction"], batch=False)
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prob = max(fetch_map["prediction"][0])
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label = label_dict[fetch_map["prediction"][0].tolist().index(prob)].strip(
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).replace(",", "")
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print("prediction: {}, probability: {}".format(label, prob))
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end = time.time()
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print(end - start)
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