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from train import train |
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from argparse import ArgumentParser |
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def save_log(file, str): |
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"""Write brief logs for every training of the pipeline""" |
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pipeline_log = open(file, "a") |
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pipeline_log.write(str) |
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pipeline_log.close() |
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if __name__ == "__main__": |
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"""Pipeline which directly call the train function of the train.py file, with the necessary arguments to reproduce the paper results """ |
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parser = ArgumentParser() |
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parser.add_argument("--model_name") |
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parser.add_argument("--size") |
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parser.add_argument("--cropped") |
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parser.add_argument("--device") |
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parser.add_argument("--labels") |
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args = parser.parse_args() |
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for i in range(5): |
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for label in ["pressure", "wind"]: |
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for model in ["resnet18", "resnet50"]: |
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args.model_name, args.size, args.cropped, args.device, args.labels = model, "512", False, 0, label |
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train_log = train(args) |
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save_log("pipeline_logs.txt", "training session " + str(i*3) + " : " + str(args) + " " + train_log + "\n") |
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args.model_name, args.size, args.cropped, args.device, args.labels = model, "224", "False", 0, label |
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train_log = train(args) |
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save_log("pipeline_logs.txt", "training session " + str(i*3 +1) + " : " + str(args) + " " + train_log + "\n") |
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args.model_name, args.size, args.cropped, args.device, args.labels = model, "224", "True", 0, label |
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train_log = train(args) |
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save_log("pipeline_logs.txt", "training session " + str(i*3 +2) + " : " + str(args) + " " + train_log + "\n") |
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