60 lines
1.5 KiB
Python
60 lines
1.5 KiB
Python
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import matplotlib.pyplot as plt
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import pandas as pd
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import glob
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import argparse
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import os
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#create parser
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def parse_input_args():
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parser = argparse.ArgumentParser(description='Parse arguments for training a Reconstruction model')
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parser.add_argument('train_out_dir',
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type=str,
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help='Directory name in train_output dir of the desired experiment folder. There should be a .csv file in this directory with train statistics.')
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args = parser.parse_args()
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return args
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args = parse_input_args()
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print(f"Plotting {args}")
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# find csv file
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# csv = glob.glob(f"train_output/{args.train_out_dir}/*.csv")[0]
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folder_input = args.train_out_dir
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# load csv file
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df = pd.read_csv(f'{folder_input}')
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# read csv file
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for metric in df:
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# if not metric == 'epoch':
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if metric == 'loss' or metric == 'val_loss':
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plt.plot(df['epoch'], df[metric], label=metric)
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plt.ylim(ymin=0,ymax=0.01)
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folder, csvfile = os.path.split(args.train_out_dir)
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root, experiment = os.path.split(os.path.split(folder)[0])
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plt.title(experiment)
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plt.xlabel('Epoch')
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plt.ylabel('Loss')
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plt.grid()
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plt.legend()
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plt.savefig(f"{folder}/{experiment}.png")
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plt.clf()
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plt.close()
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print(folder+".png")
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print(f"\nsaved figure to {folder}")
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# van yaml inladen 'loss'
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# vanuit utils > dict to yaml
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# csv viewer extension
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# course .venv/bin/activate
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# loop over alles en if metric then 1-metric
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# kleur codering color=[]
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