Uploaded create_sample_submission.py

create_sample_submission.py

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+import numpy as np
+import pandas as pd
+import h5py
+
+from trimesh.voxel.runlength import dense_to_brle
+from pathlib import Path
+from collections import defaultdict
+
+from typing import Any, Union, Dict, Literal
+from numpy.typing import NDArray
+
+# class RandomModel:
+#     def __init__(self, shape):
+#         self.shape = shape
+#         return
+
+#     def __call__(self, input):
+#         # input is ignored, just generate some random predictions
+#         return np.random.randint(0, 2, size=self.shape, dtype=bool)
+    
+class FixedModel:
+    def __init__(self, shape) -> None:
+        self.shape = shape
+        return
+    
+    def __call__(self, input) -> Any:
+        # input is ignored, just generate a mask filled with zeros, with fixed pixels set to 1
+        mask = np.zeros(self.shape, dtype=bool)
+        mask[100:250, 100:250] = True
+        return mask
+
+def retrieve_validation_fold(path: Union[str, Path]) -> Dict[str, NDArray]:
+    result = defaultdict(dict)
+    with h5py.File(path, 'r') as fp:
+        for uuid, values in fp.items():
+            if values.attrs['fold'] != 0:
+                continue
+            
+            result[uuid]['post'] = values['post_fire'][...]
+            # result[uuid]['pre'] = values['pre_fire'][...]
+
+    return dict(result)
+
+def compute_submission_mask(id: str, mask: NDArray):
+    brle = dense_to_brle(mask.astype(bool).flatten())
+    return {"id": id, "rle_mask": brle, "index": np.arange(len(brle))}
+
+if __name__ == '__main__':
+    validation_fold = retrieve_validation_fold('train_eval.hdf5')
+
+    # use a list to accumulate results
+    result = []
+    # instantiate the model
+    model = FixedModel(shape=(512, 512))
+    for uuid in validation_fold:
+        input_images = validation_fold[uuid]
+
+        # perform the prediction
+        predicted = model(input_images)
+        # convert the prediction in RLE format
+        encoded_prediction = compute_submission_mask(uuid, predicted)
+        result.append(pd.DataFrame(encoded_prediction))
+
+    # concatenate all dataframes
+    submission_df = pd.concat(result)
+    submission_df.to_csv('predictions.csv', index=False)