explore the 201 space script
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								graph_dit/exp_201/main.py
									
									
									
									
									
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|  | ||||
| import matplotlib.pyplot as plt | ||||
| import pandas as pd | ||||
| from nas_201_api import NASBench201API as API | ||||
| from naswot.score_networks import get_nasbench201_idx_score | ||||
| from naswot import datasets as dt | ||||
| from naswot import nasspace | ||||
|  | ||||
| class Args(): | ||||
|     pass | ||||
| args = Args() | ||||
| args.trainval = True | ||||
| args.augtype = 'none' | ||||
| args.repeat = 1 | ||||
| args.score = 'hook_logdet' | ||||
| args.sigma = 0.05 | ||||
| args.nasspace = 'nasbench201' | ||||
| args.batch_size = 128 | ||||
| args.GPU = '0' | ||||
| args.dataset = 'cifar10' | ||||
| args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth' | ||||
| args.data_loc = '../cifardata/' | ||||
| args.seed = 777 | ||||
| args.init = '' | ||||
| args.save_loc = 'results' | ||||
| args.save_string = 'naswot' | ||||
| args.dropout = False | ||||
| args.maxofn = 1 | ||||
| args.n_samples = 100 | ||||
| args.n_runs = 500 | ||||
| args.stem_out_channels = 16 | ||||
| args.num_stacks = 3 | ||||
| args.num_modules_per_stack = 3 | ||||
| args.num_labels = 1 | ||||
| searchspace = nasspace.get_search_space(args) | ||||
| train_loader = dt.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args) | ||||
| device = torch.device('cuda:2') | ||||
|  | ||||
|  | ||||
|           | ||||
| # source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth' | ||||
| # api = API(source) | ||||
|  | ||||
|  | ||||
|  | ||||
| # 示例百分数列表,精确到小数点后两位 | ||||
| # percentages = [5.12, 15.78, 25.43, 35.22, 45.99, 55.34, 65.12, 75.68, 85.99, 95.25, 23.45, 12.34, 37.89, 58.67, 64.23, 72.15, 81.76, 99.99, 42.11, 61.58, 77.34, 14.56] | ||||
| percentages = [] | ||||
|  | ||||
| len_201 = 15625 | ||||
|  | ||||
| for i in range(len_201): | ||||
|     percentage = get_nasbench201_idx_score(i, train_loader, searchspace, args, device) | ||||
|     percentages.append(percentage) | ||||
|  | ||||
| # 定义10%区间 | ||||
| bins = [i for i in range(0, 101, 10)] | ||||
|  | ||||
| # 对数据进行分箱,计算每个区间的数据量 | ||||
| hist, bin_edges = pd.cut(percentages, bins=bins, right=False, retbins=True, include_lowest=True) | ||||
| bin_counts = hist.value_counts().sort_index() | ||||
|  | ||||
| total_counts = len(percentages) | ||||
| percentages_in_bins = (bin_counts / total_counts) * 100 | ||||
|  | ||||
| # 绘制条形图 | ||||
| plt.figure(figsize=(10, 6)) | ||||
| bars = plt.bar(bin_counts.index.astype(str), bin_counts.values, width=0.9, color='skyblue') | ||||
|  | ||||
| for bar, percentage in zip(bars, percentages_in_bins): | ||||
|     plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height(), | ||||
|             f'{percentage:.2f}%', ha='center', va='bottom') | ||||
|  | ||||
| # 添加标题和标签 | ||||
| plt.title('Distribution of Percentages in 10% Intervals') | ||||
| plt.xlabel('Percentage Interval') | ||||
| plt.ylabel('Count') | ||||
|  | ||||
| # 显示图表 | ||||
| plt.xticks(rotation=45) | ||||
| plt.savefig('barplog.png') | ||||
|  | ||||
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