colin1842 commited on
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22b2c75
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1 Parent(s): 1b4e2d7

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Files changed (1) hide show
  1. script.py +23 -23
script.py CHANGED
@@ -192,32 +192,32 @@ if __name__ == "__main__":
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  print('------------ Now you can do your solution ---------------')
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  solution = []
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- from concurrent.futures import ProcessPoolExecutor
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- with ProcessPoolExecutor(max_workers=1) as pool:
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- results = []
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- for i, sample in enumerate(tqdm(dataset)):
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- results.append(pool.submit(predict, sample, visualize=False))
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- for i, result in enumerate(tqdm(results)):
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- key, pred_vertices, pred_edges = result.result()
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- solution.append({
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- '__key__': key,
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- 'wf_vertices': pred_vertices.tolist(),
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- 'wf_edges': pred_edges
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- })
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  ####### added for removing multiprocessing ########
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- # for i, sample in enumerate(tqdm(dataset)):
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- # key, pred_vertices, pred_edges = predict(sample, visualize=False)
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- # solution.append({
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- # '__key__': key,
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- # 'wf_vertices': pred_vertices.tolist(),
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- # 'wf_edges': pred_edges
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- # })
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  ####### added for removing multiprocessing ########
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- if i % 10 == 0:
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- # incrementally save the results in case we run out of time
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- print(f"Processed {i} samples")
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- save_submission(solution, Path(params['output_path']) / "submission.parquet")
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  print('------------ Saving results ---------------')
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  save_submission(solution, Path(params['output_path']) / "submission.parquet")
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  print("------------ Done ------------ ")
 
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  print('------------ Now you can do your solution ---------------')
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  solution = []
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+ # from concurrent.futures import ProcessPoolExecutor
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+ # with ProcessPoolExecutor(max_workers=1) as pool:
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+ # results = []
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+ # for i, sample in enumerate(tqdm(dataset)):
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+ # results.append(pool.submit(predict, sample, visualize=False))
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+ # for i, result in enumerate(tqdm(results)):
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+ # key, pred_vertices, pred_edges = result.result()
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+ # solution.append({
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+ # '__key__': key,
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+ # 'wf_vertices': pred_vertices.tolist(),
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+ # 'wf_edges': pred_edges
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+ # })
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  ####### added for removing multiprocessing ########
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+ for i, sample in enumerate(tqdm(dataset)):
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+ key, pred_vertices, pred_edges = predict(sample, visualize=False)
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+ solution.append({
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+ '__key__': key,
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+ 'wf_vertices': pred_vertices.tolist(),
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+ 'wf_edges': pred_edges
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+ })
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  ####### added for removing multiprocessing ########
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+ if i % 2 == 0:
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+ # incrementally save the results in case we run out of time
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+ print(f"Processed {i} samples")
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+ save_submission(solution, Path(params['output_path']) / "submission.parquet")
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  print('------------ Saving results ---------------')
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  save_submission(solution, Path(params['output_path']) / "submission.parquet")
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  print("------------ Done ------------ ")