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| *__pycache__ | ||
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| /Result |
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| CUDA_VISIBLE_DEVICES=0 python main.py --methods GICD_retest --datasets CoCA --save_dir ./Result --root_dir ../SalMaps | ||
| CUDA_VISIBLE_DEVICES=0 python main.py --methods GICD_retest --datasets CoCA --save_dir ./Result/Detail --root_dir ../SalMaps |
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| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| import argparse | ||
| import os | ||
| import torch | ||
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| styles = ['.-r', '.--b', '.--g', '.--c', '.-m', '.-y', '.-k', '.-c'] | ||
| lines = ['-', '--', '--', '--', '-', '-', '-', '-'] | ||
| points = ['*', '.', '.', '.', '.', '.', '.', '.'] | ||
| colors = ['r', 'b', 'g', 'c', 'm', 'orange', 'k', 'navy'] | ||
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| def main(cfg): | ||
| method_names = cfg.methods.split('+') | ||
| dataset_names = cfg.datasets.split('+') | ||
| os.makedirs(cfg.out_dir, exist_ok=True) | ||
| # plt.style.use('seaborn-white') | ||
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| # Plot PR Cureve | ||
| for dataset in dataset_names: | ||
| plt.figure() | ||
| idx_style = 0 | ||
| for method in method_names: | ||
| iRes = torch.load( | ||
| os.path.join(cfg.res_dir, dataset + '_' + method + '.pth')) | ||
| imax = np.argmax(iRes['Fm']) | ||
| plt.plot( | ||
| iRes['Prec'], | ||
| iRes['Recall'], | ||
| # styles[idx_style], | ||
| color=colors[idx_style], | ||
| linestyle=lines[idx_style], | ||
| marker=points[idx_style], | ||
| markevery=[imax, imax], | ||
| label=method) | ||
| idx_style += 1 | ||
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| plt.grid(True, zorder=-1) | ||
| # plt.xlim(0, 1) | ||
| # plt.ylim(0, 1.02) | ||
| plt.ylabel('Precision', fontsize=25) | ||
| plt.xlabel('Recall', fontsize=25) | ||
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| plt.legend(loc='lower left', prop={'size': 15}) | ||
| plt.savefig(os.path.join(cfg.out_dir, 'PR_' + dataset + '.png'), | ||
| dpi=600, | ||
| bbox_inches='tight') | ||
| plt.close() | ||
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| # Plot Fm Cureve | ||
| for dataset in dataset_names: | ||
| plt.figure() | ||
| idx_style = 0 | ||
| for method in method_names: | ||
| iRes = torch.load( | ||
| os.path.join(cfg.res_dir, dataset + '_' + method + '.pth')) | ||
| imax = np.argmax(iRes['Fm']) | ||
| plt.plot( | ||
| np.arange(0, 255), | ||
| iRes['Fm'], | ||
| # styles[idx_style], | ||
| color=colors[idx_style], | ||
| linestyle=lines[idx_style], | ||
| marker=points[idx_style], | ||
| label=method, | ||
| markevery=[imax, imax]) | ||
| idx_style += 1 | ||
| plt.grid(True, zorder=-1) | ||
| # plt.ylim(0, 1) | ||
| plt.ylabel('F-measure', fontsize=25) | ||
| plt.xlabel('Threshold', fontsize=25) | ||
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| plt.legend(loc='lower left', prop={'size': 15}) | ||
| plt.savefig(os.path.join(cfg.out_dir, 'Fm_' + dataset + '.png'), | ||
| dpi=600, | ||
| bbox_inches='tight') | ||
| plt.close() | ||
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| # Plot Em Cureve | ||
| for dataset in dataset_names: | ||
| plt.figure() | ||
| idx_style = 0 | ||
| for method in method_names: | ||
| iRes = torch.load( | ||
| os.path.join(cfg.res_dir, dataset + '_' + method + '.pth')) | ||
| imax = np.argmax(iRes['Em']) | ||
| plt.plot( | ||
| np.arange(0, 255), | ||
| iRes['Em'], | ||
| # styles[idx_style], | ||
| color=colors[idx_style], | ||
| linestyle=lines[idx_style], | ||
| marker=points[idx_style], | ||
| label=method, | ||
| markevery=[imax, imax]) | ||
| idx_style += 1 | ||
| plt.grid(True, zorder=-1) | ||
| plt.ylim(0, 1) | ||
| plt.ylabel('E-measure', fontsize=16) | ||
| plt.xlabel('Threshold', fontsize=16) | ||
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| plt.legend(loc='lower left', prop={'size': 15}) | ||
| plt.savefig(os.path.join(cfg.out_dir, 'Em_' + dataset + '.png'), | ||
| dpi=600, | ||
| bbox_inches='tight') | ||
| plt.close() | ||
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| # Plot ROC Cureve | ||
| for dataset in dataset_names: | ||
| plt.figure() | ||
| idx_style = 0 | ||
| for method in method_names: | ||
| iRes = torch.load( | ||
| os.path.join(cfg.res_dir, dataset + '_' + method + '.pth')) | ||
| imax = np.argmax(iRes['Fm']) | ||
| plt.plot( | ||
| iRes['FPR'], | ||
| iRes['TPR'], | ||
| # styles[idx_style][1:], | ||
| color=colors[idx_style], | ||
| linestyle=lines[idx_style], | ||
| label=method) | ||
| idx_style += 1 | ||
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| plt.grid(True, zorder=-1) | ||
| plt.xlim(0, 1) | ||
| plt.ylim(0, 1.02) | ||
| plt.ylabel('TPR', fontsize=16) | ||
| plt.xlabel('FPR', fontsize=16) | ||
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| plt.legend(loc='lower right') | ||
| plt.savefig(os.path.join(cfg.out_dir, 'ROC_' + dataset + '.png'), | ||
| dpi=600, | ||
| bbox_inches='tight') | ||
| plt.close() | ||
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| # Plot Sm-MAE | ||
| for dataset in dataset_names: | ||
| plt.figure() | ||
| plt.gca().invert_xaxis() | ||
| idx_style = 0 | ||
| for method in method_names: | ||
| iRes = torch.load( | ||
| os.path.join(cfg.res_dir, dataset + '_' + method + '.pth')) | ||
| plt.scatter(iRes['MAE'], | ||
| iRes['Sm'], | ||
| marker=points[idx_style], | ||
| c=colors[idx_style], | ||
| s=120) | ||
| plt.annotate(method, | ||
| xy=(iRes['MAE'], iRes['Sm']), | ||
| xytext=(iRes['MAE'] - 0.001, iRes['Sm'] - 0.001), | ||
| fontsize=14) | ||
| idx_style += 1 | ||
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| plt.grid(True, zorder=-1) | ||
| # plt.xlim(0, 1) | ||
| plt.ylim(0, 1) | ||
| plt.ylabel('S-measure', fontsize=16) | ||
| plt.xlabel('MAE', fontsize=16) | ||
| plt.savefig(os.path.join(cfg.out_dir, 'Sm-MAE_' + dataset + '.png'), | ||
| bbox_inches='tight') | ||
| plt.close() | ||
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument('--methods', type=str, default=None) | ||
| parser.add_argument('--datasets', type=str, default=None) | ||
| parser.add_argument('--res_dir', type=str, default='./') | ||
| parser.add_argument('--out_dir', type=str, default=None) | ||
| config = parser.parse_args() | ||
| main(config) |
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| Original file line number | Diff line number | Diff line change |
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| python plot_curve.py --methods GICD_retest --datasets CoCA --out_dir ./Result/Curves --res_dir ./Result | ||
| python plot_curve.py --methods GICD_retest --datasets CoCA --out_dir ./Result/Curves --res_dir ./Result/Detail |