client.py

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import numpy as np
from torchvision import transforms
from PIL import Image
import tritonclient.http as httpclient
from tritonclient.utils import triton_to_np_dtype

# preprocessing function
def rn50_preprocess(img_path="img1.jpg"):
    img = Image.open(img_path)
    preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    return np.expand_dims(preprocess(img).numpy(),axis=0)

transformed_img = rn50_preprocess()

# Setup a connection with the Triton Inference Server
client = httpclient.InferenceServerClient(url="localhost:8000")

# Specify the names of the input and output layer(s) of our model 
# as well as describe the shape and datatype of the expected input
inputs = httpclient.InferInput("data_0", transformed_img.shape, datatype="FP32")
inputs.set_data_from_numpy(transformed_img, binary_data=True)
outputs = httpclient.InferRequestedOutput("fc6_1", binary_data=True, class_count=1000)

# Querying the server: Send an inference request to the Triton Inference Server.
results = client.infer(model_name="densenet_onnx", inputs=[inputs], outputs=[outputs])
inference_output = results.as_numpy('fc6_1').astype(str)

# Print the Inferenc result
print(np.squeeze(inference_output)[:5])