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update the script to use nasbench-201 api

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mhz 2024-08-21 10:40:00 +02:00
parent 6d9db64a48
commit 05ee34e355
2 changed files with 40 additions and 37 deletions
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graph_dit/exp_201/main.py
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@ -2,44 +2,45 @@
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
# 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')
# 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)
# source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
# api = API(source)
@ -50,8 +51,10 @@ percentages = []
len_201 = 15625
for i in range(len_201):
percentage = get_nasbench201_idx_score(i, train_loader, searchspace, args, device)
percentages.append(percentage)
# percentage = get_nasbench201_idx_score(i, train_loader, searchspace, args, device)
results = api.query_by_index(i, 'cifar10')
result = results[111].get_eval('ori-test')
percentages.append(result)
# 定义10%区间
bins = [i for i in range(0, 101, 10)]