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import os, sys

import numpy as np

import json

import tritongrpcclient

import argparse

def load_image(img_path: str):

"""

Loads an encoded image as an array of bytes.

"""

return np.fromfile(img_path, dtype='uint8')

if __name__ == "__main__":

parser = argparse.ArgumentParser()

parser.add_argument("--model_name",

type=str,

required=False,

default="ensemble_python_resnet50",

help="Model name")

parser.add_argument("--image",

type=str,

required=True,

help="Path to the image")

parser.add_argument("--url",

type=str,

required=False,

default="localhost:8001",

help="Inference server URL. Default is localhost:8001.")

parser.add_argument('-v',

"--verbose",

action="store_true",

required=False,

default=False,

help='Enable verbose output')

parser.add_argument(

"--label_file",

type=str,

default="./model_repository/resnet50_trt/labels.txt",

help="Path to the file with text representation of available labels")

args = parser.parse_args()

try:

triton_client = tritongrpcclient.InferenceServerClient(

url=args.url, verbose=args.verbose)

except Exception as e:

print("channel creation failed: " + str(e))

sys.exit(1)

with open(args.label_file) as f:

labels_dict = {idx: line.strip() for idx, line in enumerate(f)}

inputs = []

outputs = []

input_name = "INPUT"

output_name = "OUTPUT"

image_data = load_image(args.image)

image_data = np.expand_dims(image_data, axis=0)

inputs.append(

tritongrpcclient.InferInput(input_name, image_data.shape, "UINT8"))

outputs.append(tritongrpcclient.InferRequestedOutput(output_name))

inputs[0].set_data_from_numpy(image_data)

results = triton_client.infer(model_name=args.model_name,

inputs=inputs,

outputs=outputs)

output0_data = results.as_numpy(output_name)

print(output0_data)

maxs = np.argmax(output0_data, axis=1)

print(maxs)

print("Result is class: {}".format(labels_dict[maxs[0]]))