import argparse import random import numpy as np import matplotlib.pyplot as plt import matplotlib as mp import matplotlib matplotlib.use('Agg') from decimal import Decimal from scipy.special import logit, expit from scipy import stats import seaborn as sns ''' font = { 'size' : 18} matplotlib.rc('font', **font) ''' SMALL_SIZE = 10 MEDIUM_SIZE = 12 BIGGER_SIZE = 14 plt.rc('font', size=MEDIUM_SIZE) # controls default text sizes plt.rc('axes', titlesize=BIGGER_SIZE) # fontsize of the axes title plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels plt.rc('xtick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels plt.rc('ytick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels plt.rc('legend', fontsize=MEDIUM_SIZE) # legend fontsize plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title parser = argparse.ArgumentParser(description='NAS Without Training') parser.add_argument('--data_loc', default='../cifardata/', type=str, help='dataset folder') parser.add_argument('--api_loc', default='../NAS-Bench-201-v1_0-e61699.pth', type=str, help='path to API') parser.add_argument('--save_loc', default='results', type=str, help='folder to save results') parser.add_argument('--save_string', default='naswot', type=str, help='prefix of results file') parser.add_argument('--score', default='hook_logdet', type=str, help='the score to evaluate') parser.add_argument('--nasspace', default='nasbench201', type=str, help='the nas search space to use') parser.add_argument('--batch_size', default=128, type=int) parser.add_argument('--repeat', default=1, type=int, help='how often to repeat a single image with a batch') parser.add_argument('--augtype', default='none', type=str, help='which perturbations to use') parser.add_argument('--sigma', default=0.05, type=float, help='noise level if augtype is "gaussnoise"') parser.add_argument('--init', default='', type=str) parser.add_argument('--GPU', default='0', type=str) parser.add_argument('--seed', default=1, type=int) parser.add_argument('--trainval', action='store_true') parser.add_argument('--dropout', action='store_true') parser.add_argument('--dataset', default='cifar10', type=str) parser.add_argument('--maxofn', default=1, type=int, help='score is the max of this many evaluations of the network') parser.add_argument('--n_samples', default=100, type=int) parser.add_argument('--n_runs', default=500, type=int) parser.add_argument('--stem_out_channels', default=16, type=int, help='output channels of stem convolution (nasbench101)') parser.add_argument('--num_stacks', default=3, type=int, help='#stacks of modules (nasbench101)') parser.add_argument('--num_modules_per_stack', default=3, type=int, help='#modules per stack (nasbench101)') parser.add_argument('--num_labels', default=1, type=int, help='#classes (nasbench101)') args = parser.parse_args() print(f'{args.batch_size}') random.seed(args.seed) np.random.seed(args.seed) filename = f'{args.save_loc}/{args.save_string}_{args.score}_{args.nasspace}_{args.dataset}{"_" + args.init + "_" if args.init != "" else args.init}_{"_dropout" if args.dropout else ""}_{args.augtype}_{args.sigma}_{args.repeat}_{args.trainval}_{args.batch_size}_{args.maxofn}_{args.seed}.npy' accfilename = f'{args.save_loc}/{args.save_string}_accs_{args.nasspace}_{args.dataset}_{args.trainval}.npy' from matplotlib.colors import hsv_to_rgb print(filename) scores = np.load(filename) accs = np.load(accfilename) def make_colours_by_hue(h, v=1.): return [hsv_to_rgb((h1 if h1 < 1. else h1-1., s, v)) for h1, s,v in zip(np.linspace(h, h+0.05, 5), np.linspace(1., .6, 5), np.linspace(0.1, 1., 5))] print(f'NETWORK accuracy with highest score {accs[np.argmax(scores)]}') make_colours = lambda cols: [mp.colors.to_rgba(c) for c in cols] oranges = make_colours(['#811F41', '#A92941', '#D15141', '#EF7941', '#F99C4B']) blues = make_colours(['#190C30', '#241147', '#34208C', '#4882FA', '#81BAFC']) print(blues) print(make_colours_by_hue(0.9)) if args.nasspace == 'nasbench101': #colours = blues colours = make_colours_by_hue(0.9) elif 'darts' in args.nasspace: #colours = sns.color_palette("BuGn_r", n_colors=5) colours = make_colours_by_hue(0.0) elif 'pnas' in args.nasspace: #colours = sns.color_palette("PuRd", n_colors=5) colours = make_colours_by_hue(0.1) elif args.nasspace == 'nasbench201': #colours = oranges colours = make_colours_by_hue(0.3) elif 'enas' in args.nasspace: #colours = oranges colours = make_colours_by_hue(0.4) elif 'resnet' in args.nasspace: #colours = sns.color_palette("viridis_r", n_colors=5) colours = make_colours_by_hue(0.5) elif 'amoeba' in args.nasspace: #colours = sns.color_palette("viridis_r", n_colors=5) colours = make_colours_by_hue(0.6) elif 'nasnet' in args.nasspace: #colours = sns.color_palette("viridis_r", n_colors=5) colours = make_colours_by_hue(0.7) elif 'resnext-b' in args.nasspace: #colours = sns.color_palette("viridis_r", n_colors=5) colours = make_colours_by_hue(0.8) else: from zlib import crc32 def bytes_to_float(b): return float(crc32(b) & 0xffffffff) / 2**32 def str_to_float(s, encoding="utf-8"): return bytes_to_float(s.encode(encoding)) #colours = sns.color_palette("Purples_r", n_colors=5) colours = make_colours_by_hue(str_to_float(args.nasspace)) def make_colordict(colours, points): cdict = {'red': [[pt, colour[0], colour[0]] for pt, colour in zip(points, colours)], 'green':[[pt, colour[1], colour[1]] for pt, colour in zip(points, colours)], 'blue':[[pt, colour[2], colour[2]] for pt, colour in zip(points, colours)]} return cdict def make_colormap(dataset, space, colours): if dataset == 'cifar10' and 'resn' in space: points = [0., 0.85, 0.9, 0.95, 1.0, 1.0] colours = [colours[0]] + colours elif dataset == 'cifar10' and 'nds_darts' in space: points = [0., 0.8, 0.85, 0.9, 0.95, 1.0] colours = [colours[0]] + colours elif dataset == 'cifar10' and 'pnas' in space: points = [0., 0.875, 0.9, 0.925, 0.95, 1.0] colours = [colours[0]] + colours elif dataset == 'cifar10': points = [0., 0.6, 0.7, 0.8, 0.9, 1.0] colours = [colours[0]] + colours #cdict = {'red': [[0., colours[0][0], colours[0][0]]] + [[0.1*i + 0.6, colours[i][0], colours[i][0]] for i in range(len(colours))], # 'green':[[0., colours[0][1], colours[0][1]]] + [[0.1*i + 0.6, colours[i][1], colours[i][1]] for i in range(len(colours))], # 'blue':[[0., colours[0][2], colours[0][2]]] + [[0.1*i + 0.6, colours[i][2], colours[i][2]] for i in range(len(colours))]} elif dataset == 'cifar100': points = [0., 0.3, 0.4, 0.5, 0.6, 0.7, 1.0] colours = [colours[0]] + colours + [colours[-1]] #cdict = {'red': [[0., colours[0][0], colours[0][0]]] + [[0.1*i + 0.3, colours[i][0], colours[i][0]] for i in range(len(colours))] + [[1., colours[-1][0], colours[-1][0]]] , # 'green':[[0., colours[0][1], colours[0][1]]] + [[0.1*i + 0.3, colours[i][1], colours[i][1]] for i in range(len(colours))] + [[1., colours[-1][1], colours[-1][1]]] , # 'blue':[[0., colours[0][2], colours[0][2]]] + [[0.1*i + 0.3, colours[i][2], colours[i][2]] for i in range(len(colours))] + [[1., colours[-1][2], colours[-1][2]]] } else: points = [0., 0.1, 0.2, 0.3, 0.4, 1.0] colours = colours + [colours[-1]] #cdict = {'red': [[0.1*i, colours[i][0], colours[i][0]] for i in range(len(colours))] + [[1., colours[-1][0], colours[-1][0]]] , # 'green': [[0.1*i, colours[i][1], colours[i][1]] for i in range(len(colours))] + [[1., colours[-1][1], colours[-1][1]]] , # 'blue': [[0.1*i, colours[i][2], colours[i][2]] for i in range(len(colours))] + [[1., colours[-1][2], colours[-1][2]]] } cdict = make_colordict(colours, points) return cdict cdict = make_colormap(args.dataset, args.nasspace, colours) newcmp = mp.colors.LinearSegmentedColormap('testCmap', segmentdata=cdict, N=256) if args.nasspace == 'nasbench101': accs = accs[:10000] scores = scores[:10000] inds = accs > 0.5 accs = accs[inds] scores = scores[inds] print(accs.shape) elif args.nasspace == 'nds_amoeba' or args.nasspace == 'nds_darts_fix-w-d': print(accs.shape) inds = accs > 15. accs = accs[inds] scores = scores[inds] print(accs.shape) elif args.nasspace == 'nds_darts': inds = accs > 15. from nasspace import get_search_space searchspace = get_search_space(args) accs = accs[inds] scores = scores[inds] print(accs.shape) else: print(accs.shape) inds = accs > 15. accs = accs[inds] scores = scores[inds] print(accs.shape) inds = scores == 0. accs = accs[~inds] scores = scores[~inds] if accs.size > 1000: inds = np.random.choice(accs.size, 1000, replace=False) accs = accs[inds] scores = scores[inds] inds = np.isnan(scores) accs = accs[~inds] scores = scores[~inds] tau, p = stats.kendalltau(accs, scores) if args.nasspace == 'nasbench101': fig, ax = plt.subplots(1, 1, figsize=(5,5)) else: fig, ax = plt.subplots(1, 1, figsize=(5,5)) def scale(x): return 2.**(10*x) - 1. if args.score == 'svd': score_scale = lambda x: 10.0**x else: score_scale = lambda x: x if args.nasspace == 'nonetwork': ax.scatter(scale(accs/100.), score_scale(scores), c=newcmp(accs/100., depths)) else: ax.scatter(scale(accs/100. if args.nasspace == 'nasbench201' or 'nds' in args.nasspace else accs), score_scale(scores), c=newcmp(accs/100. if args.nasspace == 'nasbench201' or 'nds' in args.nasspace else accs)) if args.dataset == 'cifar100': ax.set_xticks([scale(float(a)/100.) for a in [40, 60, 70]]) ax.set_xticklabels([f'{a}' for a in [40, 60, 70]]) elif args.dataset == 'imagenette2': ax.set_xticks([scale(float(a)/100.) for a in [40, 50, 60, 70]]) ax.set_xticklabels([f'{a}' for a in [40, 50, 60, 70]]) elif args.dataset == 'ImageNet16-120': ax.set_xticks([scale(float(a)/100.) for a in [20, 30, 40, 45]]) ax.set_xticklabels([f'{a}' for a in [20, 30, 40, 45]]) elif args.nasspace == 'nasbench101' and args.dataset == 'cifar10': ax.set_xticks([scale(float(a)/100.) for a in [50, 80, 90, 95]]) ax.set_xticklabels([f'{a}' for a in [50, 80, 90, 95]]) elif args.nasspace == 'nasbench201' and args.dataset == 'cifar10' and args.score == 'svd': ax.set_xticks([scale(float(a)/100.) for a in [50, 80, 90, 95]]) ax.set_xticklabels([f'{a}' for a in [50, 80, 90, 95]]) elif 'nds_resne' in args.nasspace and args.dataset == 'cifar10': ax.set_xticks([scale(float(a)/100.) for a in [85, 88, 91, 94]]) ax.set_xticklabels([f'{a}' for a in [85, 88, 91, 94]]) elif args.nasspace == 'nds_darts' and args.dataset == 'cifar10': ax.set_xticks([scale(float(a)/100.) for a in [80, 85, 90, 95]]) ax.set_xticklabels([f'{a}' for a in [80, 85, 90, 95]]) elif args.nasspace == 'nds_pnas' and args.dataset == 'cifar10': ax.set_xticks([scale(float(a)/100.) for a in [90., 91.5, 93, 94.5]]) ax.set_xticklabels([f'{a}' for a in [90., 91.5, 93, 94.5]]) else: ax.set_xticks([scale(float(a)/100.) for a in [50, 80, 90]]) ax.set_xticklabels([f'{a}' for a in [50, 80, 90]]) ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) nasspacenames = { 'nds_resnext-a_in': 'NDS-ResNeXt-A(ImageNet)', 'nds_resnext-b_in': 'NDS-ResNeXt-B(ImageNet)', 'nds_resnext-a': 'NDS-ResNeXt-A(CIFAR10)', 'nds_resnext-b': 'NDS-ResNeXt-B(CIFAR10)', 'nds_nasnet': 'NDS-NASNet(CIFAR10)', 'nds_nasnet_in': 'NDS-NASNet(ImageNet)', 'nds_enas': 'NDS-ENAS(CIFAR10)', 'nds_enas_in': 'NDS-ENAS(ImageNet)', 'nds_amoeba': 'NDS-Amoeba(CIFAR10)', 'nds_amoeba_in': 'NDS-Amoeba(ImageNet)', 'nds_resnet': 'NDS-ResNet(CIFAR10)', 'nds_pnas': 'NDS-PNAS(CIFAR10)', 'nds_pnas_in': 'NDS-PNAS(ImageNet)', 'nds_darts': 'NDS-DARTS(CIFAR10)', 'nds_darts_in': 'NDS-DARTS(ImageNet)', 'nds_darts_fix-w-d': 'NDS-DARTS fixed width/depth (CIFAR10)', 'nds_darts_in_fix-w-d': 'NDS-DARTS fixed width/depth (ImageNet)', 'nds_darts_in': 'NDS-DARTS(ImageNet)', 'nasbench101': 'NAS-Bench-101', 'nasbench201': 'NAS-Bench-201' } ax.set_ylabel('Score') ax.set_xlabel(f'{"Test" if not args.trainval else "Validation"} accuracy') ax.set_title(f'{nasspacenames[args.nasspace]} {args.dataset} \n $\\tau=${tau:.3f}') filename = f'{args.save_loc}/{args.save_string}_{args.score}_{args.nasspace}_{args.dataset}{"_" + args.init + "_" if args.init != "" else args.init}{"_dropout" if args.dropout else ""}_{args.augtype}_{args.sigma}_{args.repeat}_{args.trainval}_{args.batch_size}_{args.maxofn}_{args.seed}' print(filename) plt.tight_layout() plt.savefig(filename + '.pdf') plt.savefig(filename + '.png') plt.show()