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import pandas as pd
import numpy as np
from PIL import Image
import onnxruntime as ort
import os
from tqdm import tqdm
def is_gpu_available():
"""Check if the python package `onnxruntime-gpu` is installed."""
return ort.get_device() == "GPU"
class ONNXWorker:
"""Run inference using ONNX runtime."""
def __init__(self, onnx_path: str):
print("Setting up ONNX runtime session.")
self.use_gpu = is_gpu_available()
if self.use_gpu:
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
else:
providers = ["CPUExecutionProvider"]
print(f"Using {providers}")
self.ort_session = ort.InferenceSession(onnx_path, providers=providers)
def _resize_image(self, image: np.ndarray) -> np.ndarray:
"""
:param image:
:return:
"""
newsize = (300, 300)
im1 = im1.resize(newsize)
def predict_image(self, image: np.ndarray) -> list():
"""Run inference using ONNX runtime.
:param image: Input image as numpy array.
:return: A list with logits and confidences.
"""
logits, _ = self.ort_session.run(None, {"input": image.astype(dtype=np.uint8)})
return logits.tolist()
def make_submission(test_metadata, model_path, output_csv_path="./submission.csv", images_root_path="/tmp/data/private_testset"):
"""Make submission with given """
model = ONNXWorker(model_path)
predictions = []
for _, row in tqdm(test_metadata.iterrows(), total=len(test_metadata)):
image_path = os.path.join(images_root_path, row.filename)
test_image = Image.open(image_path).convert("RGB")
test_image_resized = np.asarray(test_image.resize((256, 256)))
logits = model.predict_image(test_image_resized)
predictions.append(np.argmax(logits))
test_metadata["class_id"] = predictions
user_pred_df = test_metadata.drop_duplicates("observation_id", keep="first")
user_pred_df[["observation_id", "class_id"]].to_csv(output_csv_path, index=None)
if __name__ == "__main__":
import zipfile
with zipfile.ZipFile("/tmp/data/private_testset.zip", 'r') as zip_ref:
zip_ref.extractall("/tmp/data")
ONNX_MODEL_PATH = "./swinv2_tiny_window16_256.onnx"
metadata_file_path = "./SnakeCLEF2024-TestMetadata.csv"
test_metadata = pd.read_csv(metadata_file_path)
make_submission(
test_metadata=test_metadata,
model_path=ONNX_MODEL_PATH,
)
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