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if name == main
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1 changed files with 26 additions and 20 deletions
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@ -14,27 +14,33 @@ def plot_metric(ax, train, val, name, dir_output):
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# plt.savefig(os.path.join(dir_output, f"{name}.png"))
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# plt.close()
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FILEPATH_INPUT = os.environ["INPUT"]
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DIRPATH_OUTPUT = os.environ["OUTPUT"] if "OUTPUT" in os.environ else os.getcwd()
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def plot_metrics(filepath_input, dirpath_output):
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df = pd.read_csv(filepath_input, sep="\t")
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fig = plt.figure(figsize=(10,13))
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for i, colname in enumerate(filter(lambda colname: colname != "epoch" and not colname.startswith("val_"), df.columns.values.tolist())):
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train = df[colname]
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val = df[f"val_{colname}"]
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colname_display = colname.replace("metric_dice_coefficient", "dice coefficient") \
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.replace("one_hot_mean_iou", "mean iou")
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ax = fig.add_subplot(3, 2, i+1)
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plot_metric(ax, train, val, name=colname_display, dir_output=dirpath_output)
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fig.tight_layout()
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target=os.path.join(dirpath_output, f"metrics.png")
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plt.savefig(target)
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print(f">>> Saved to {target}")
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df = pd.read_csv(FILEPATH_INPUT, sep="\t")
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if __name__ == "__main__":
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FILEPATH_INPUT = os.environ["INPUT"]
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DIRPATH_OUTPUT = os.environ["OUTPUT"] if "OUTPUT" in os.environ else os.getcwd()
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fig = plt.figure(figsize=(10,13))
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for i, colname in enumerate(filter(lambda colname: colname != "epoch" and not colname.startswith("val_"), df.columns.values.tolist())):
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train = df[colname]
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val = df[f"val_{colname}"]
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plot_metrics(FILEPATH_INPUT, DIRPATH_OUTPUT)
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colname_display = colname.replace("metric_dice_coefficient", "dice coefficient") \
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.replace("one_hot_mean_iou", "mean iou")
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ax = fig.add_subplot(3, 2, i+1)
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plot_metric(ax, train, val, name=colname_display, dir_output=DIRPATH_OUTPUT)
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fig.tight_layout()
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target=os.path.join(DIRPATH_OUTPUT, f"metrics.png")
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plt.savefig(target)
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print(f">>> Saved to {target}")
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