387 lines
20 KiB
Python
387 lines
20 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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# python exps/NAS-Bench-102/visualize.py --api_path $HOME/.torch/NAS-Bench-102-v1_0-e61699.pth
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##################################################
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import os, sys, time, argparse, collections
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from tqdm import tqdm
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import numpy as np
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import torch
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import torch.nn as nn
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from pathlib import Path
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from collections import defaultdict
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import matplotlib
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import seaborn as sns
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from mpl_toolkits.mplot3d import Axes3D
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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 log_utils import time_string
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from nas_102_api import NASBench102API as API
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def calculate_correlation(*vectors):
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matrix = []
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for i, vectori in enumerate(vectors):
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x = []
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for j, vectorj in enumerate(vectors):
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x.append( np.corrcoef(vectori, vectorj)[0,1] )
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matrix.append( x )
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return np.array(matrix)
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def visualize_relative_ranking(vis_save_dir):
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print ('\n' + '-'*100)
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cifar010_cache_path = vis_save_dir / '{:}-cache-info.pth'.format('cifar10')
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cifar100_cache_path = vis_save_dir / '{:}-cache-info.pth'.format('cifar100')
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imagenet_cache_path = vis_save_dir / '{:}-cache-info.pth'.format('ImageNet16-120')
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cifar010_info = torch.load(cifar010_cache_path)
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cifar100_info = torch.load(cifar100_cache_path)
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imagenet_info = torch.load(imagenet_cache_path)
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indexes = list(range(len(cifar010_info['params'])))
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print ('{:} start to visualize relative ranking'.format(time_string()))
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# maximum accuracy with ResNet-level params 11472
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x_010_accs = [ cifar010_info['test_accs'][i] if cifar010_info['params'][i] <= cifar010_info['params'][11472] else -1 for i in indexes]
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x_100_accs = [ cifar100_info['test_accs'][i] if cifar100_info['params'][i] <= cifar100_info['params'][11472] else -1 for i in indexes]
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x_img_accs = [ imagenet_info['test_accs'][i] if imagenet_info['params'][i] <= imagenet_info['params'][11472] else -1 for i in indexes]
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cifar010_ord_indexes = sorted(indexes, key=lambda i: cifar010_info['test_accs'][i])
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cifar100_ord_indexes = sorted(indexes, key=lambda i: cifar100_info['test_accs'][i])
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imagenet_ord_indexes = sorted(indexes, key=lambda i: imagenet_info['test_accs'][i])
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cifar100_labels, imagenet_labels = [], []
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for idx in cifar010_ord_indexes:
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cifar100_labels.append( cifar100_ord_indexes.index(idx) )
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imagenet_labels.append( imagenet_ord_indexes.index(idx) )
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print ('{:} prepare data done.'.format(time_string()))
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dpi, width, height = 300, 2600, 2600
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 18, 18
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resnet_scale, resnet_alpha = 120, 0.5
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xlim(min(indexes), max(indexes))
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plt.ylim(min(indexes), max(indexes))
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#plt.ylabel('y').set_rotation(0)
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plt.yticks(np.arange(min(indexes), max(indexes), max(indexes)//6), fontsize=LegendFontsize, rotation='vertical')
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plt.xticks(np.arange(min(indexes), max(indexes), max(indexes)//6), fontsize=LegendFontsize)
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#ax.scatter(indexes, cifar100_labels, marker='^', s=0.5, c='tab:green', alpha=0.8, label='CIFAR-100')
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#ax.scatter(indexes, imagenet_labels, marker='*', s=0.5, c='tab:red' , alpha=0.8, label='ImageNet-16-120')
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#ax.scatter(indexes, indexes , marker='o', s=0.5, c='tab:blue' , alpha=0.8, label='CIFAR-10')
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ax.scatter(indexes, cifar100_labels, marker='^', s=0.5, c='tab:green', alpha=0.8)
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ax.scatter(indexes, imagenet_labels, marker='*', s=0.5, c='tab:red' , alpha=0.8)
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ax.scatter(indexes, indexes , marker='o', s=0.5, c='tab:blue' , alpha=0.8)
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ax.scatter([-1], [-1], marker='o', s=100, c='tab:blue' , label='CIFAR-10')
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ax.scatter([-1], [-1], marker='^', s=100, c='tab:green', label='CIFAR-100')
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ax.scatter([-1], [-1], marker='*', s=100, c='tab:red' , label='ImageNet-16-120')
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plt.grid(zorder=0)
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ax.set_axisbelow(True)
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plt.legend(loc=0, fontsize=LegendFontsize)
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ax.set_xlabel('architecture ranking in CIFAR-10', fontsize=LabelSize)
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ax.set_ylabel('architecture ranking', fontsize=LabelSize)
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save_path = (vis_save_dir / 'relative-rank.pdf').resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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save_path = (vis_save_dir / 'relative-rank.png').resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print ('{:} save into {:}'.format(time_string(), save_path))
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# calculate correlation
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sns_size = 15
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CoRelMatrix = calculate_correlation(cifar010_info['valid_accs'], cifar010_info['test_accs'], cifar100_info['valid_accs'], cifar100_info['test_accs'], imagenet_info['valid_accs'], imagenet_info['test_accs'])
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fig = plt.figure(figsize=figsize)
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plt.axis('off')
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h = sns.heatmap(CoRelMatrix, annot=True, annot_kws={'size':sns_size}, fmt='.3f', linewidths=0.5)
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save_path = (vis_save_dir / 'co-relation-all.pdf').resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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print ('{:} save into {:}'.format(time_string(), save_path))
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# calculate correlation
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acc_bars = [92, 93]
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for acc_bar in acc_bars:
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selected_indexes = []
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for i, acc in enumerate(cifar010_info['test_accs']):
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if acc > acc_bar: selected_indexes.append( i )
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print ('select {:} architectures'.format(len(selected_indexes)))
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cifar010_valid_accs = np.array(cifar010_info['valid_accs'])[ selected_indexes ]
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cifar010_test_accs = np.array(cifar010_info['test_accs']) [ selected_indexes ]
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cifar100_valid_accs = np.array(cifar100_info['valid_accs'])[ selected_indexes ]
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cifar100_test_accs = np.array(cifar100_info['test_accs']) [ selected_indexes ]
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imagenet_valid_accs = np.array(imagenet_info['valid_accs'])[ selected_indexes ]
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imagenet_test_accs = np.array(imagenet_info['test_accs']) [ selected_indexes ]
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CoRelMatrix = calculate_correlation(cifar010_valid_accs, cifar010_test_accs, cifar100_valid_accs, cifar100_test_accs, imagenet_valid_accs, imagenet_test_accs)
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fig = plt.figure(figsize=figsize)
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plt.axis('off')
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h = sns.heatmap(CoRelMatrix, annot=True, annot_kws={'size':sns_size}, fmt='.3f', linewidths=0.5)
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save_path = (vis_save_dir / 'co-relation-top-{:}.pdf'.format(len(selected_indexes))).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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print ('{:} save into {:}'.format(time_string(), save_path))
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plt.close('all')
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def visualize_info(meta_file, dataset, vis_save_dir):
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print ('{:} start to visualize {:} information'.format(time_string(), dataset))
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cache_file_path = vis_save_dir / '{:}-cache-info.pth'.format(dataset)
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if not cache_file_path.exists():
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print ('Do not find cache file : {:}'.format(cache_file_path))
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nas_bench = API(str(meta_file))
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params, flops, train_accs, valid_accs, test_accs, otest_accs = [], [], [], [], [], []
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for index in range( len(nas_bench) ):
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info = nas_bench.query_by_index(index, use_12epochs_result=False)
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resx = info.get_comput_costs(dataset) ; flop, param = resx['flops'], resx['params']
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if dataset == 'cifar10':
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res = info.get_metrics('cifar10', 'train') ; train_acc = res['accuracy']
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res = info.get_metrics('cifar10-valid', 'x-valid') ; valid_acc = res['accuracy']
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res = info.get_metrics('cifar10', 'ori-test') ; test_acc = res['accuracy']
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res = info.get_metrics('cifar10', 'ori-test') ; otest_acc = res['accuracy']
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else:
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res = info.get_metrics(dataset, 'train') ; train_acc = res['accuracy']
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res = info.get_metrics(dataset, 'x-valid') ; valid_acc = res['accuracy']
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res = info.get_metrics(dataset, 'x-test') ; test_acc = res['accuracy']
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res = info.get_metrics(dataset, 'ori-test') ; otest_acc = res['accuracy']
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if index == 11472: # resnet
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resnet = {'params':param, 'flops': flop, 'index': 11472, 'train_acc': train_acc, 'valid_acc': valid_acc, 'test_acc': test_acc, 'otest_acc': otest_acc}
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flops.append( flop )
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params.append( param )
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train_accs.append( train_acc )
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valid_accs.append( valid_acc )
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test_accs.append( test_acc )
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otest_accs.append( otest_acc )
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#resnet = {'params': 0.559, 'flops': 78.56, 'index': 11472, 'train_acc': 99.99, 'valid_acc': 90.84, 'test_acc': 93.97}
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info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_accs, 'otest_accs': otest_accs}
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info['resnet'] = resnet
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torch.save(info, cache_file_path)
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else:
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print ('Find cache file : {:}'.format(cache_file_path))
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info = torch.load(cache_file_path)
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params, flops, train_accs, valid_accs, test_accs, otest_accs = info['params'], info['flops'], info['train_accs'], info['valid_accs'], info['test_accs'], info['otest_accs']
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resnet = info['resnet']
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print ('{:} collect data done.'.format(time_string()))
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indexes = list(range(len(params)))
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dpi, width, height = 300, 2600, 2600
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 22, 22
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resnet_scale, resnet_alpha = 120, 0.5
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xticks(np.arange(0, 1.6, 0.3), fontsize=LegendFontsize)
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if dataset == 'cifar10':
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plt.ylim(50, 100)
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plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize)
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elif dataset == 'cifar100':
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plt.ylim(25, 75)
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plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize)
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else:
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plt.ylim(0, 50)
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plt.yticks(np.arange(0, 51, 10), fontsize=LegendFontsize)
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ax.scatter(params, valid_accs, marker='o', s=0.5, c='tab:blue')
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ax.scatter([resnet['params']], [resnet['valid_acc']], marker='*', s=resnet_scale, c='tab:orange', label='resnet', alpha=0.4)
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plt.grid(zorder=0)
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ax.set_axisbelow(True)
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plt.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax.set_ylabel('the validation accuracy (%)', fontsize=LabelSize)
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save_path = (vis_save_dir / '{:}-param-vs-valid.pdf'.format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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save_path = (vis_save_dir / '{:}-param-vs-valid.png'.format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print ('{:} save into {:}'.format(time_string(), save_path))
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xticks(np.arange(0, 1.6, 0.3), fontsize=LegendFontsize)
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if dataset == 'cifar10':
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plt.ylim(50, 100)
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plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize)
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elif dataset == 'cifar100':
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plt.ylim(25, 75)
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plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize)
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else:
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plt.ylim(0, 50)
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plt.yticks(np.arange(0, 51, 10), fontsize=LegendFontsize)
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ax.scatter(params, test_accs, marker='o', s=0.5, c='tab:blue')
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ax.scatter([resnet['params']], [resnet['test_acc']], marker='*', s=resnet_scale, c='tab:orange', label='resnet', alpha=resnet_alpha)
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plt.grid()
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ax.set_axisbelow(True)
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plt.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax.set_ylabel('the test accuracy (%)', fontsize=LabelSize)
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save_path = (vis_save_dir / '{:}-param-vs-test.pdf'.format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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save_path = (vis_save_dir / '{:}-param-vs-test.png'.format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print ('{:} save into {:}'.format(time_string(), save_path))
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xticks(np.arange(0, 1.6, 0.3), fontsize=LegendFontsize)
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if dataset == 'cifar10':
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plt.ylim(50, 100)
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plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize)
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elif dataset == 'cifar100':
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plt.ylim(20, 100)
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plt.yticks(np.arange(20, 101, 10), fontsize=LegendFontsize)
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else:
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plt.ylim(25, 76)
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plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize)
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ax.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue')
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ax.scatter([resnet['params']], [resnet['train_acc']], marker='*', s=resnet_scale, c='tab:orange', label='resnet', alpha=resnet_alpha)
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plt.grid()
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ax.set_axisbelow(True)
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plt.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax.set_ylabel('the trarining accuracy (%)', fontsize=LabelSize)
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save_path = (vis_save_dir / '{:}-param-vs-train.pdf'.format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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save_path = (vis_save_dir / '{:}-param-vs-train.png'.format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print ('{:} save into {:}'.format(time_string(), save_path))
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xlim(0, max(indexes))
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plt.xticks(np.arange(min(indexes), max(indexes), max(indexes)//5), fontsize=LegendFontsize)
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if dataset == 'cifar10':
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plt.ylim(50, 100)
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plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize)
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elif dataset == 'cifar100':
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plt.ylim(25, 75)
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plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize)
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else:
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plt.ylim(0, 50)
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plt.yticks(np.arange(0, 51, 10), fontsize=LegendFontsize)
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ax.scatter(indexes, test_accs, marker='o', s=0.5, c='tab:blue')
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ax.scatter([resnet['index']], [resnet['test_acc']], marker='*', s=resnet_scale, c='tab:orange', label='resnet', alpha=resnet_alpha)
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plt.grid()
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ax.set_axisbelow(True)
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plt.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel('architecture ID', fontsize=LabelSize)
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ax.set_ylabel('the test accuracy (%)', fontsize=LabelSize)
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save_path = (vis_save_dir / '{:}-test-over-ID.pdf'.format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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save_path = (vis_save_dir / '{:}-test-over-ID.png'.format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print ('{:} save into {:}'.format(time_string(), save_path))
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plt.close('all')
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def visualize_rank_over_time(meta_file, vis_save_dir):
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print ('\n' + '-'*150)
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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print ('{:} start to visualize rank-over-time into {:}'.format(time_string(), vis_save_dir))
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cache_file_path = vis_save_dir / 'rank-over-time-cache-info.pth'
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if not cache_file_path.exists():
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print ('Do not find cache file : {:}'.format(cache_file_path))
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nas_bench = API(str(meta_file))
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print ('{:} load nas_bench done'.format(time_string()))
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params, flops, train_accs, valid_accs, test_accs, otest_accs = [], [], defaultdict(list), defaultdict(list), defaultdict(list), defaultdict(list)
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#for iepoch in range(200): for index in range( len(nas_bench) ):
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for index in tqdm(range(len(nas_bench))):
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info = nas_bench.query_by_index(index, use_12epochs_result=False)
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for iepoch in range(200):
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res = info.get_metrics('cifar10' , 'train' , iepoch) ; train_acc = res['accuracy']
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res = info.get_metrics('cifar10-valid', 'x-valid' , iepoch) ; valid_acc = res['accuracy']
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res = info.get_metrics('cifar10' , 'ori-test', iepoch) ; test_acc = res['accuracy']
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res = info.get_metrics('cifar10' , 'ori-test', iepoch) ; otest_acc = res['accuracy']
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train_accs[iepoch].append( train_acc )
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valid_accs[iepoch].append( valid_acc )
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test_accs [iepoch].append( test_acc )
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otest_accs[iepoch].append( otest_acc )
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if iepoch == 0:
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res = info.get_comput_costs('cifar10') ; flop, param = res['flops'], res['params']
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flops.append( flop )
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params.append( param )
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info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_accs, 'otest_accs': otest_accs}
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torch.save(info, cache_file_path)
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else:
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print ('Find cache file : {:}'.format(cache_file_path))
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info = torch.load(cache_file_path)
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params, flops, train_accs, valid_accs, test_accs, otest_accs = info['params'], info['flops'], info['train_accs'], info['valid_accs'], info['test_accs'], info['otest_accs']
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print ('{:} collect data done.'.format(time_string()))
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#selected_epochs = [0, 100, 150, 180, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199]
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selected_epochs = list( range(200) )
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x_xtests = test_accs[199]
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indexes = list(range(len(x_xtests)))
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ord_idxs = sorted(indexes, key=lambda i: x_xtests[i])
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for sepoch in selected_epochs:
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x_valids = valid_accs[sepoch]
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valid_ord_idxs = sorted(indexes, key=lambda i: x_valids[i])
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valid_ord_lbls = []
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for idx in ord_idxs:
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valid_ord_lbls.append( valid_ord_idxs.index(idx) )
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# labeled data
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dpi, width, height = 300, 2600, 2600
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 18, 18
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xlim(min(indexes), max(indexes))
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plt.ylim(min(indexes), max(indexes))
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plt.yticks(np.arange(min(indexes), max(indexes), max(indexes)//6), fontsize=LegendFontsize, rotation='vertical')
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plt.xticks(np.arange(min(indexes), max(indexes), max(indexes)//6), fontsize=LegendFontsize)
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ax.scatter(indexes, valid_ord_lbls, marker='^', s=0.5, c='tab:green', alpha=0.8)
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ax.scatter(indexes, indexes , marker='o', s=0.5, c='tab:blue' , alpha=0.8)
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ax.scatter([-1], [-1], marker='^', s=100, c='tab:green', label='CIFAR-10 validation')
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ax.scatter([-1], [-1], marker='o', s=100, c='tab:blue' , label='CIFAR-10 test')
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plt.grid(zorder=0)
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ax.set_axisbelow(True)
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plt.legend(loc='upper left', fontsize=LegendFontsize)
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ax.set_xlabel('architecture ranking in the final test accuracy', fontsize=LabelSize)
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ax.set_ylabel('architecture ranking in the validation set', fontsize=LabelSize)
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save_path = (vis_save_dir / 'time-{:03d}.pdf'.format(sepoch)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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save_path = (vis_save_dir / 'time-{:03d}.png'.format(sepoch)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
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print ('{:} save into {:}'.format(time_string(), save_path))
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plt.close('all')
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|
|
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def write_video(save_dir):
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import cv2
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video_save_path = save_dir / 'time.avi'
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print ('{:} start create video for {:}'.format(time_string(), video_save_path))
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images = sorted( list( save_dir.glob('time-*.png') ) )
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ximage = cv2.imread(str(images[0]))
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#shape = (ximage.shape[1], ximage.shape[0])
|
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shape = (1000, 1000)
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#writer = cv2.VideoWriter(str(video_save_path), cv2.VideoWriter_fourcc(*"MJPG"), 25, shape)
|
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writer = cv2.VideoWriter(str(video_save_path), cv2.VideoWriter_fourcc(*"MJPG"), 5, shape)
|
|
for idx, image in enumerate(images):
|
|
ximage = cv2.imread(str(image))
|
|
_image = cv2.resize(ximage, shape)
|
|
writer.write(_image)
|
|
writer.release()
|
|
print ('write video [{:} frames] into {:}'.format(len(images), video_save_path))
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
parser = argparse.ArgumentParser(description='NAS-Bench-102', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
|
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.')
|
|
parser.add_argument('--api_path', type=str, default=None, help='The path to the NAS-Bench-102 benchmark file.')
|
|
args = parser.parse_args()
|
|
|
|
vis_save_dir = Path(args.save_dir)
|
|
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
|
meta_file = Path(args.api_path)
|
|
assert meta_file.exists(), 'invalid path for api : {:}'.format(meta_file)
|
|
#visualize_rank_over_time(str(meta_file), vis_save_dir / 'over-time')
|
|
#write_video(vis_save_dir / 'over-time')
|
|
#visualize_info(str(meta_file), 'cifar10' , vis_save_dir)
|
|
#visualize_info(str(meta_file), 'cifar100', vis_save_dir)
|
|
#visualize_info(str(meta_file), 'ImageNet16-120', vis_save_dir)
|
|
#visualize_relative_ranking(vis_save_dir)
|