Delete script.py

script.py

@@ -1,65 +0,0 @@
-import os
-import torch
-import pandas as pd
-from PIL import Image
-import numpy as np
-from rfdetr import RFDETRBase
-
-
-def run_inference(model, image_path, conf_threshold, save_path):
-
-    test_images = sorted(os.listdir(image_path))
-
-    bboxes = []
-    category_ids = []
-    test_images_names = []
-
-    for image_name in test_images:
-        test_images_names.append(image_name)
-
-        image_file = os.path.join(image_path, image_name)
-        image = Image.open(image_file).convert("RGB")
-
-        preds = model.predict(image)
-
-        image_bboxes = []
-        image_categories = []
-
-        for box, score, label in zip(
-            preds["boxes"], preds["scores"], preds["labels"]
-        ):
-            if score >= conf_threshold:
-                xmin, ymin, xmax, ymax = box.tolist()
-                width = xmax - xmin
-                height = ymax - ymin
-
-                image_bboxes.append([xmin, ymin, width, height])
-                image_categories.append(int(label))
-
-        bboxes.append(image_bboxes)
-        category_ids.append(image_categories)
-
-    df_predictions = pd.DataFrame(columns=["file_name", "bbox", "category_id"])
-
-    for i in range(len(test_images_names)):
-        df_predictions.loc[i] = [
-            test_images_names[i],
-            str(bboxes[i]),
-            str(category_ids[i]),
-        ]
-
-    df_predictions.to_csv(save_path, index=False)
-
-
-if __name__ == "__main__":
-
-    TEST_IMAGE_PATH = "/tmp/data/test_images"
-    SUBMISSION_SAVE_PATH = "submission.csv"
-    CONF_THRESHOLD = 0.30
-
-    model = RFDETRBase(
-        checkpoint_path="checkpoint_best_ema.pth",
-        device="cuda" if torch.cuda.is_available() else "cpu"
-    )
-
-    run_inference(model, TEST_IMAGE_PATH, CONF_THRESHOLD, SUBMISSION_SAVE_PATH)