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Last active October 5, 2022 12:21
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import argparse
import os
from glob import glob
import numpy as np
from PIL import Image
from tritony import InferenceClient
def preprocess(img, dtype=np.float32, h=224, w=224, scaling="INCEPTION"):
sample_img = img.convert("RGB")
resized_img = sample_img.resize((w, h), Image.Resampling.BILINEAR)
resized = np.array(resized_img)
if resized.ndim == 2:
resized = resized[:, :, np.newaxis]
scaled = (resized / 127.5) - 1
ordered = np.transpose(scaled, (2, 0, 1))
return ordered.astype(dtype)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--image_folder", type=str, help="Input folder.")
FLAGS = parser.parse_args()
client = InferenceClient.create_with("densenet_onnx", "0.0.0.0:8001", input_dims=3, protocol="grpc")
client.output_kwargs = {"class_count": 1}
image_data = []
for filename in glob(os.path.join(FLAGS.image_folder, "*")):
image_data.append(preprocess(Image.open(filename)))
result = client(np.asarray(image_data))
for output in result:
max_value, arg_max, class_name = output[0].decode("utf-8").split(":")
print(f"{max_value} ({arg_max}) = {class_name}")
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