Poplar Triton Backend blog post - client script content

client.py

import os

import numpy
from torchvision import transforms
from PIL import Image

import tritonclient.grpc

# Prepare tritonclient object needed to send a request to the server.
infer_client = tritonclient.grpc.InferenceServerClient("localhost:8001")
input = [tritonclient.grpc.InferInput("digit_image", [1, 1, 28, 28], "FP32")]

img = None

# Loop over all files in the "img" directory.
# Let's assume the directory contains images
# that can be used to send a request to the server.
for entry in os.scandir("./img"):
    try:
        img = Image.open(entry.path)
    except IOError:
        continue

    # Preprocess images data.
    img_tensor = transforms.ToTensor()(img)
    img_tensor = transforms.Normalize((0.1307,), (0.3081,)).forward(img_tensor)
    img_tensor = img_tensor[None, :]

    input[0].set_data_from_numpy(img_tensor.numpy())

    # Send the request.
    response = infer_client.infer(model_name="mnist", inputs=input)

    # Postprocess response data.
    prediction_result = numpy.argmax(response.as_numpy("output_labels"))

    # Print prediction result.
    print(f'Predicted digit for file \"{entry.name}\": {prediction_result}')