wehub-resource-sync
50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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#
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import numpy as np
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from torchvision import transforms
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from PIL import Image
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import tritonclient.http as httpclient
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from tritonclient.utils import triton_to_np_dtype
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def rn50_preprocess(img_path="img1.jpg"):
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img = Image.open(img_path)
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preprocess = transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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return preprocess(img).numpy()
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transformed_img = rn50_preprocess()
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# Setup a connection with the Triton Inference Server.
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triton_client = httpclient.InferenceServerClient(url="localhost:8000")
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# Specify the names of the input and output layer(s) of our model.
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test_input = httpclient.InferInput("input", transformed_img.shape, datatype="FP32")
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test_input.set_data_from_numpy(transformed_img, binary_data=True)
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test_output = httpclient.InferRequestedOutput("output", binary_data=True, class_count=1000)
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# Querying the server
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results = triton_client.infer(model_name="resnet50", inputs=[test_input], outputs=[test_output])
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test_output_fin = results.as_numpy('output')
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print(test_output_fin[:5])
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