update vis
This commit is contained in:
D-X-Y
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@ -367,6 +367,160 @@ def write_video(save_dir):
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def plot_results_nas(api, dataset, xset, root, file_name, y_lims):
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print ('root-path={:}, dataset={:}, xset={:}'.format(root, dataset, xset))
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checkpoints = ['./output/search-cell-nas-bench-102/R-EA-cifar10/results.pth',
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'./output/search-cell-nas-bench-102/REINFORCE-cifar10/results.pth',
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'./output/search-cell-nas-bench-102/RAND-cifar10/results.pth',
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'./output/search-cell-nas-bench-102/BOHB-cifar10/results.pth'
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]
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legends, indexes = ['REA', 'REINFORCE', 'RANDOM', 'BOHB'], None
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All_Accs = OrderedDict()
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for legend, checkpoint in zip(legends, checkpoints):
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all_indexes = torch.load(checkpoint, map_location='cpu')
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accuracies = []
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for x in all_indexes:
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info = api.arch2infos_full[ x ]
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metrics = info.get_metrics(dataset, xset, None, False)
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accuracies.append( metrics['accuracy'] )
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if indexes is None: indexes = list(range(len(all_indexes)))
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All_Accs[legend] = sorted(accuracies)
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color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
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dpi, width, height = 300, 3400, 2600
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LabelSize, LegendFontsize = 28, 28
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figsize = width / float(dpi), height / float(dpi)
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fig = plt.figure(figsize=figsize)
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x_axis = np.arange(0, 600)
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plt.xlim(0, max(indexes))
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plt.ylim(y_lims[0], y_lims[1])
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interval_x, interval_y = 100, y_lims[2]
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plt.xticks(np.arange(0, max(indexes), interval_x), fontsize=LegendFontsize)
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plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
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plt.grid()
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plt.xlabel('The index of runs', fontsize=LabelSize)
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plt.ylabel('The accuracy (%)', fontsize=LabelSize)
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for idx, legend in enumerate(legends):
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plt.plot(indexes, All_Accs[legend], color=color_set[idx], linestyle='-', label='{:}'.format(legend), lw=2)
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print ('{:} : mean = {:}, std = {:} :: {:.2f}$\\pm${:.2f}'.format(legend, np.mean(All_Accs[legend]), np.std(All_Accs[legend]), np.mean(All_Accs[legend]), np.std(All_Accs[legend])))
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plt.legend(loc=4, fontsize=LegendFontsize)
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save_path = root / '{:}-{:}-{:}'.format(dataset, xset, file_name)
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print('save figure into {:}\n'.format(save_path))
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fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
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def just_show(api):
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xtimes = {'RSPS': [8082.5, 7794.2, 8144.7],
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'DARTS-V1': [11582.1, 11347.0, 11948.2],
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'DARTS-V2': [35694.7, 36132.7, 35518.0],
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'GDAS' : [31334.1, 31478.6, 32016.7],
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'SETN' : [33528.8, 33831.5, 35058.3],
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'ENAS' : [14340.2, 13817.3, 14018.9]}
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for xkey, xlist in xtimes.items():
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xlist = np.array(xlist)
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print ('{:4s} : mean-time={:.1f} s'.format(xkey, xlist.mean()))
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xpaths = {'RSPS' : 'output/search-cell-nas-bench-102/RANDOM-NAS-cifar10/checkpoint/',
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'DARTS-V1': 'output/search-cell-nas-bench-102/DARTS-V1-cifar10/checkpoint/',
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'DARTS-V2': 'output/search-cell-nas-bench-102/DARTS-V2-cifar10/checkpoint/',
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'GDAS' : 'output/search-cell-nas-bench-102/GDAS-cifar10/checkpoint/',
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'SETN' : 'output/search-cell-nas-bench-102/SETN-cifar10/checkpoint/',
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'ENAS' : 'output/search-cell-nas-bench-102/ENAS-cifar10/checkpoint/',
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}
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xseeds = {'RSPS' : [5349, 59613, 5983],
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'DARTS-V1': [11416, 72873, 81184],
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'DARTS-V2': [43330, 79405, 79423],
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'GDAS' : [19677, 884, 95950],
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'SETN' : [20518, 61817, 89144],
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'ENAS' : [30801, 75610, 97745],
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}
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def get_accs(xdata, index=-1):
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if index == -1:
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epochs = xdata['epoch']
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genotype = xdata['genotypes'][epochs-1]
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index = api.query_index_by_arch(genotype)
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pairs = [('cifar10-valid', 'x-valid'), ('cifar10', 'ori-test'), ('cifar100', 'x-valid'), ('cifar100', 'x-test'), ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test')]
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xresults = []
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for dataset, xset in pairs:
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metrics = api.arch2infos_full[index].get_metrics(dataset, xset, None, False)
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xresults.append( metrics['accuracy'] )
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return xresults
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for xkey in xpaths.keys():
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all_paths = [ '{:}/seed-{:}-basic.pth'.format(xpaths[xkey], seed) for seed in xseeds[xkey] ]
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all_datas = [torch.load(xpath) for xpath in all_paths]
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accyss = [get_accs(xdatas) for xdatas in all_datas]
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accyss = np.array( accyss )
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print('\nxkey = {:}'.format(xkey))
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for i in range(accyss.shape[1]): print('---->>>> {:.2f}$\\pm${:.2f}'.format(accyss[:,i].mean(), accyss[:,i].std()))
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print('\n{:}'.format(get_accs(None, 11472))) # resnet
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pairs = [('cifar10-valid', 'x-valid'), ('cifar10', 'ori-test'), ('cifar100', 'x-valid'), ('cifar100', 'x-test'), ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test')]
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for dataset, metric_on_set in pairs:
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arch_index, highest_acc = api.find_best(dataset, metric_on_set)
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print ('[{:10s}-{:10s} ::: index={:5d}, accuracy={:.2f}'.format(dataset, metric_on_set, arch_index, highest_acc))
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def show_nas_sharing_w(api, dataset, subset, vis_save_dir, file_name, y_lims):
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color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
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dpi, width, height = 300, 3400, 2600
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LabelSize, LegendFontsize = 28, 28
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figsize = width / float(dpi), height / float(dpi)
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fig = plt.figure(figsize=figsize)
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x_maxs = 250
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x_axis = np.arange(0, x_maxs)
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plt.xlim(0, x_maxs)
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plt.ylim(y_lims[0], y_lims[1])
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interval_x, interval_y = x_maxs // 5, y_lims[2]
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plt.xticks(np.arange(0, x_maxs, interval_x), fontsize=LegendFontsize)
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plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
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plt.grid()
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plt.xlabel('The searching epoch', fontsize=LabelSize)
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plt.ylabel('The accuracy (%)', fontsize=LabelSize)
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xpaths = {'RSPS' : 'output/search-cell-nas-bench-102/RANDOM-NAS-cifar10/checkpoint/',
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'DARTS-V1': 'output/search-cell-nas-bench-102/DARTS-V1-cifar10/checkpoint/',
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'DARTS-V2': 'output/search-cell-nas-bench-102/DARTS-V2-cifar10/checkpoint/',
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'GDAS' : 'output/search-cell-nas-bench-102/GDAS-cifar10/checkpoint/',
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'SETN' : 'output/search-cell-nas-bench-102/SETN-cifar10/checkpoint/',
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'ENAS' : 'output/search-cell-nas-bench-102/ENAS-cifar10/checkpoint/',
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}
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xseeds = {'RSPS' : [5349, 59613, 5983],
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'DARTS-V1': [11416, 72873, 81184],
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'DARTS-V2': [43330, 79405, 79423],
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'GDAS' : [19677, 884, 95950],
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'SETN' : [20518, 61817, 89144],
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'ENAS' : [30801, 75610, 97745],
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}
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def get_accs(xdata):
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epochs, xresults = xdata['epoch'], []
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for iepoch in range(epochs):
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genotype = xdata['genotypes'][iepoch]
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index = api.query_index_by_arch(genotype)
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metrics = api.arch2infos_full[index].get_metrics(dataset, subset, None, False)
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xresults.append( metrics['accuracy'] )
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return xresults
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for idx, method in enumerate(['RSPS', 'GDAS', 'SETN', 'ENAS']):
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xkey = method
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all_paths = [ '{:}/seed-{:}-basic.pth'.format(xpaths[xkey], seed) for seed in xseeds[xkey] ]
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all_datas = [torch.load(xpath) for xpath in all_paths]
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accyss = [get_accs(xdatas) for xdatas in all_datas]
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accyss = np.array( accyss )
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epochs = list(range(accyss.shape[1]))
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plt.plot(epochs, [accyss[:,i].mean() for i in epochs], color=color_set[idx], linestyle='-', label='{:}'.format(method), lw=2)
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plt.fill_between(epochs, [accyss[:,i].mean()-accyss[:,i].std() for i in epochs], [accyss[:,i].mean()+accyss[:,i].std() for i in epochs], alpha=0.2, color=color_set[idx])
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plt.legend(loc=4, fontsize=LegendFontsize)
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save_path = vis_save_dir / '{:}-{:}-{:}'.format(dataset, subset, file_name)
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print('save figure into {:}\n'.format(save_path))
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fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='NAS-Bench-102', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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@ -384,3 +538,15 @@ if __name__ == '__main__':
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#visualize_info(str(meta_file), 'cifar100', vis_save_dir)
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#visualize_info(str(meta_file), 'ImageNet16-120', vis_save_dir)
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#visualize_relative_ranking(vis_save_dir)
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api = API(args.api_path)
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show_nas_sharing_w(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-plot.pdf', (5,95,10))
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"""
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just_show(api)
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plot_results_nas(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-com.pdf', (85,95, 1))
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plot_results_nas(api, 'cifar10' , 'ori-test', vis_save_dir, 'nas-com.pdf', (85,95, 1))
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plot_results_nas(api, 'cifar100' , 'x-valid' , vis_save_dir, 'nas-com.pdf', (55,75, 3))
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plot_results_nas(api, 'cifar100' , 'x-test' , vis_save_dir, 'nas-com.pdf', (55,75, 3))
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plot_results_nas(api, 'ImageNet16-120', 'x-valid' , vis_save_dir, 'nas-com.pdf', (35,50, 3))
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plot_results_nas(api, 'ImageNet16-120', 'x-test' , vis_save_dir, 'nas-com.pdf', (35,50, 3))
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"""
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@ -1,135 +0,0 @@
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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# python ./exps/vis/show-results.py --api_path ${HOME}/.torch/NAS-Bench-102-v1_0-e61699.pth
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##################################################
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import os, sys, argparse
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from pathlib import Path
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import torch
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import numpy as np
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from collections import OrderedDict
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import matplotlib
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matplotlib.use('agg')
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import matplotlib.pyplot as plt
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from nas_102_api import NASBench102API as API
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def plot_results_nas(api, dataset, xset, root, file_name, y_lims):
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print ('root-path={:}, dataset={:}, xset={:}'.format(root, dataset, xset))
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checkpoints = ['./output/search-cell-nas-bench-102/R-EA-cifar10/results.pth',
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'./output/search-cell-nas-bench-102/REINFORCE-cifar10/results.pth',
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'./output/search-cell-nas-bench-102/RAND-cifar10/results.pth',
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'./output/search-cell-nas-bench-102/BOHB-cifar10/results.pth'
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]
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legends, indexes = ['REA', 'REINFORCE', 'RANDOM', 'BOHB'], None
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All_Accs = OrderedDict()
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for legend, checkpoint in zip(legends, checkpoints):
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all_indexes = torch.load(checkpoint, map_location='cpu')
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accuracies = []
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for x in all_indexes:
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info = api.arch2infos_full[ x ]
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metrics = info.get_metrics(dataset, xset, None, False)
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accuracies.append( metrics['accuracy'] )
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if indexes is None: indexes = list(range(len(all_indexes)))
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All_Accs[legend] = sorted(accuracies)
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color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
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dpi, width, height = 300, 3400, 2600
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LabelSize, LegendFontsize = 28, 28
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figsize = width / float(dpi), height / float(dpi)
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fig = plt.figure(figsize=figsize)
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x_axis = np.arange(0, 600)
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plt.xlim(0, max(indexes))
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plt.ylim(y_lims[0], y_lims[1])
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interval_x, interval_y = 100, y_lims[2]
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plt.xticks(np.arange(0, max(indexes), interval_x), fontsize=LegendFontsize)
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plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
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plt.grid()
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plt.xlabel('The index of runs', fontsize=LabelSize)
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plt.ylabel('The accuracy (%)', fontsize=LabelSize)
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for idx, legend in enumerate(legends):
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plt.plot(indexes, All_Accs[legend], color=color_set[idx], linestyle='-', label='{:}'.format(legend), lw=2)
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print ('{:} : mean = {:}, std = {:} :: {:.2f}$\\pm${:.2f}'.format(legend, np.mean(All_Accs[legend]), np.std(All_Accs[legend]), np.mean(All_Accs[legend]), np.std(All_Accs[legend])))
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plt.legend(loc=4, fontsize=LegendFontsize)
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save_path = root / '{:}-{:}-{:}'.format(dataset, xset, file_name)
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print('save figure into {:}\n'.format(save_path))
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fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
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def just_show(api):
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xtimes = {'RSPS': [8082.5, 7794.2, 8144.7],
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'DARTS-V1': [11582.1, 11347.0, 11948.2],
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'DARTS-V2': [35694.7, 36132.7, 35518.0],
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'GDAS' : [31334.1, 31478.6, 32016.7],
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'SETN' : [33528.8, 33831.5, 35058.3],
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'ENAS' : [14340.2, 13817.3, 14018.9]}
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for xkey, xlist in xtimes.items():
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xlist = np.array(xlist)
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print ('{:4s} : mean-time={:.1f} s'.format(xkey, xlist.mean()))
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xpaths = {'RSPS' : 'output/search-cell-nas-bench-102/RANDOM-NAS-cifar10/checkpoint/',
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'DARTS-V1': 'output/search-cell-nas-bench-102/DARTS-V1-cifar10/checkpoint/',
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'DARTS-V2': 'output/search-cell-nas-bench-102/DARTS-V2-cifar10/checkpoint/',
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'GDAS' : 'output/search-cell-nas-bench-102/GDAS-cifar10/checkpoint/',
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'SETN' : 'output/search-cell-nas-bench-102/SETN-cifar10/checkpoint/',
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'ENAS' : 'output/search-cell-nas-bench-102/ENAS-cifar10/checkpoint/',
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}
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xseeds = {'RSPS' : [5349, 59613, 5983],
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'DARTS-V1': [11416, 72873, 81184],
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'DARTS-V2': [43330, 79405, 79423],
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'GDAS' : [19677, 884, 95950],
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'SETN' : [20518, 61817, 89144],
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'ENAS' : [30801, 75610, 97745],
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}
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def get_accs(xdata, index=-1):
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if index == -1:
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epochs = xdata['epoch']
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genotype = xdata['genotypes'][epochs-1]
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index = api.query_index_by_arch(genotype)
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pairs = [('cifar10-valid', 'x-valid'), ('cifar10', 'ori-test'), ('cifar100', 'x-valid'), ('cifar100', 'x-test'), ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test')]
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xresults = []
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for dataset, xset in pairs:
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metrics = api.arch2infos_full[index].get_metrics(dataset, xset, None, False)
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xresults.append( metrics['accuracy'] )
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return xresults
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for xkey in xpaths.keys():
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all_paths = [ '{:}/seed-{:}-basic.pth'.format(xpaths[xkey], seed) for seed in xseeds[xkey] ]
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all_datas = [torch.load(xpath) for xpath in all_paths]
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accyss = [get_accs(xdatas) for xdatas in all_datas]
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accyss = np.array( accyss )
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print('\nxkey = {:}'.format(xkey))
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for i in range(accyss.shape[1]): print('---->>>> {:.2f}$\\pm${:.2f}'.format(accyss[:,i].mean(), accyss[:,i].std()))
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print('\n{:}'.format(get_accs(None, 11472))) # resnet
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pairs = [('cifar10-valid', 'x-valid'), ('cifar10', 'ori-test'), ('cifar100', 'x-valid'), ('cifar100', 'x-test'), ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test')]
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for dataset, metric_on_set in pairs:
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arch_index, highest_acc = api.find_best(dataset, metric_on_set)
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print ('[{:10s}-{:10s} ::: index={:5d}, accuracy={:.2f}'.format(dataset, metric_on_set, arch_index, highest_acc))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='NAS-Bench-102', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--save_dir', type=str, default='./output/search-cell-nas-bench-102/visuals', help='The base-name of folder to save checkpoints and log.')
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parser.add_argument('--api_path', type=str, default=None, help='The path to the NAS-Bench-102 benchmark file.')
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args = parser.parse_args()
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api = API(args.api_path)
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root = Path(args.save_dir).resolve()
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root.mkdir(parents=True, exist_ok=True)
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just_show(api)
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"""
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plot_results_nas(api, 'cifar10-valid' , 'x-valid' , root, 'nas-com.pdf', (85,95, 1))
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plot_results_nas(api, 'cifar10' , 'ori-test', root, 'nas-com.pdf', (85,95, 1))
|
||||
plot_results_nas(api, 'cifar100' , 'x-valid' , root, 'nas-com.pdf', (55,75, 3))
|
||||
plot_results_nas(api, 'cifar100' , 'x-test' , root, 'nas-com.pdf', (55,75, 3))
|
||||
plot_results_nas(api, 'ImageNet16-120', 'x-valid' , root, 'nas-com.pdf', (35,50, 3))
|
||||
plot_results_nas(api, 'ImageNet16-120', 'x-test' , root, 'nas-com.pdf', (35,50, 3))
|
||||
"""
|
||||
Loading…
Reference in New Issue
Block a user