Hi, I am trying to utilize NPU for my currency model..for that i converted my tensorflow model to quantized tflite model (using EIQ). but still it is using Cores.I tried with the nxp example and it run successfully, i am attaching the screenshot for your reference.
While running my inference code, the logs it showing external delegate is created for NXP example. but it is not showing the message 'external delegate is created ' for my custom model.
In the last line it is shown that tensorflow lite delegate xnnpack is created for cpu. but why it is creating for cpu?
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
import tflite_runtime.interpreter as tflite
import cv2
import numpy as np
import os
USE_HARDWARE_INFERENCE = True
if os.environ.get('USE_HARDWARE_INFERENCE') == '0':
USE_HARWARE_INFERENCE = False
if (USE_HARDWARE_INFERENCE):
delegates = [tflite.load_delegate('/usr/lib/libethosu_delegate.so')]
else:
delegates = []
app = FastAPI()
# Load class names and TensorFlow Lite Interpreter for currency classification
currency_model_path = "/home/root/currency/Xception_saved_converted.tflite" # Update with the actual path to your classification model
currency_class_names_path = "labels.txt" # Update with the actual path to your class names file
with open(currency_class_names_path, "r") as file:
currency_class_names = [line.strip() for line in file.readlines()]
currency_interpreter = tflite.Interpreter(model_path=currency_model_path,experimental_delegates = delegates)
currency_interpreter.allocate_tensors()
# Serve static files
app.mount("/static", StaticFiles(directory="static"), name="static")
@app.get("/", response_class=HTMLResponse) async def get_index():
with open("static/index.html", "r") as file:
html_content = file.read()
return HTMLResponse(content=html_content)
def classify_currency(image
input_details = currency_interpreter.get_input_details()
output_details = currency_interpreter.get_output_details()
input_shape = input_details[0]['shape']
img = cv2.resize(image, (input_shape[1], input_shape[2]))
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
input_data = np.expand_dims(img_rgb, axis=0).astype(np.float32)
currency_interpreter.set_tensor(input_details[0]['index'], input_data)
currency_interpreter.invoke()
output_data = currency_interpreter.get_tensor(output_details[0]['index'])
class_index = np.argmax(output_data)
class_name = currency_class_names[class_index]
return class_name
@app.post("/capture-and-detect/") async def capture_and_detect(image_file: UploadFile = File(...)):
contents = await image_file.read()
nparr = np.frombuffer(contents, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
if img is None:
raise HTTPException(status_code=400, detail="Invalid image file")
# Classify the image
classification_result = classify_currency(img)
result = {
"classification": classification_result
}
return JSONResponse(content=result)
if __name__ == "__main__":
import uvicorn
import os
# Ensure the static directory exists
if not os.path.exists("static"
os.makedirs("static")
uvicorn.run(app, host="0.0.0.0", port=81)
