Reformulate via black
This commit is contained in:
D-X-Y
@@ -12,16 +12,18 @@ import numpy as np
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from typing import List, Text, Dict, Any
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from shutil import copyfile
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from collections import defaultdict
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from copy import deepcopy
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from copy import deepcopy
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from pathlib import Path
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import matplotlib
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import seaborn as sns
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matplotlib.use('agg')
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matplotlib.use("agg")
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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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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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from config_utils import dict2config, load_config
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from log_utils import time_string
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from models import get_cell_based_tiny_net
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@@ -29,387 +31,574 @@ from nats_bench import create
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def visualize_relative_info(api, vis_save_dir, indicator):
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vis_save_dir = vis_save_dir.resolve()
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# print ('{:} start to visualize {:} information'.format(time_string(), api))
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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vis_save_dir = vis_save_dir.resolve()
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# print ('{:} start to visualize {:} information'.format(time_string(), api))
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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cifar010_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar10', indicator)
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cifar100_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar100', indicator)
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imagenet_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('ImageNet16-120', indicator)
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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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cifar010_cache_path = vis_save_dir / "{:}-cache-{:}-info.pth".format("cifar10", indicator)
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cifar100_cache_path = vis_save_dir / "{:}-cache-{:}-info.pth".format("cifar100", indicator)
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imagenet_cache_path = vis_save_dir / "{:}-cache-{:}-info.pth".format("ImageNet16-120", indicator)
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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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print("{:} start to visualize relative ranking".format(time_string()))
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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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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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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 = 200, 1400, 800
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 18, 12
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resnet_scale, resnet_alpha = 120, 0.5
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dpi, width, height = 200, 1400, 800
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 18, 12
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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(30)
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plt.yticks(np.arange(min(indexes), max(indexes), max(indexes)//3), fontsize=LegendFontsize, rotation='vertical')
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plt.xticks(np.arange(min(indexes), max(indexes), max(indexes)//5), fontsize=LegendFontsize)
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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'.format(indicator)).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'.format(indicator)).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(min(indexes), max(indexes))
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plt.ylim(min(indexes), max(indexes))
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# plt.ylabel('y').set_rotation(30)
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plt.yticks(np.arange(min(indexes), max(indexes), max(indexes) // 3), fontsize=LegendFontsize, rotation="vertical")
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plt.xticks(np.arange(min(indexes), max(indexes), max(indexes) // 5), fontsize=LegendFontsize)
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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".format(indicator)).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".format(indicator)).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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def visualize_sss_info(api, dataset, vis_save_dir):
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vis_save_dir = vis_save_dir.resolve()
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print ('{:} start to visualize {:} information'.format(time_string(), dataset))
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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cache_file_path = vis_save_dir / '{:}-cache-sss-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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params, flops, train_accs, valid_accs, test_accs = [], [], [], [], []
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for index in range(len(api)):
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cost_info = api.get_cost_info(index, dataset, hp='90')
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params.append(cost_info['params'])
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flops.append(cost_info['flops'])
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# accuracy
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info = api.get_more_info(index, dataset, hp='90', is_random=False)
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train_accs.append(info['train-accuracy'])
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test_accs.append(info['test-accuracy'])
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if dataset == 'cifar10':
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info = api.get_more_info(index, 'cifar10-valid', hp='90', is_random=False)
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valid_accs.append(info['valid-accuracy'])
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else:
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valid_accs.append(info['valid-accuracy'])
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info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_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 = info['params'], info['flops'], info['train_accs'], info['valid_accs'], info['test_accs']
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print ('{:} collect data done.'.format(time_string()))
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vis_save_dir = vis_save_dir.resolve()
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print("{:} start to visualize {:} information".format(time_string(), dataset))
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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cache_file_path = vis_save_dir / "{:}-cache-sss-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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params, flops, train_accs, valid_accs, test_accs = [], [], [], [], []
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for index in range(len(api)):
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cost_info = api.get_cost_info(index, dataset, hp="90")
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params.append(cost_info["params"])
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flops.append(cost_info["flops"])
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# accuracy
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info = api.get_more_info(index, dataset, hp="90", is_random=False)
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train_accs.append(info["train-accuracy"])
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test_accs.append(info["test-accuracy"])
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if dataset == "cifar10":
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info = api.get_more_info(index, "cifar10-valid", hp="90", is_random=False)
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valid_accs.append(info["valid-accuracy"])
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else:
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valid_accs.append(info["valid-accuracy"])
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info = {
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"params": params,
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"flops": flops,
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"train_accs": train_accs,
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"valid_accs": valid_accs,
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"test_accs": test_accs,
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}
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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 = (
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info["params"],
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info["flops"],
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info["train_accs"],
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info["valid_accs"],
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info["test_accs"],
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)
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print("{:} collect data done.".format(time_string()))
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# pyramid = ['8:16:32:48:64', '8:8:16:32:48', '8:8:16:16:32', '8:8:16:16:48', '8:8:16:16:64', '16:16:32:32:64', '32:32:64:64:64']
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pyramid = ['8:16:24:32:40', '8:16:32:48:64', '32:40:48:56:64']
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pyramid_indexes = [api.query_index_by_arch(x) for x in pyramid]
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largest_indexes = [api.query_index_by_arch('64:64:64:64:64')]
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# pyramid = ['8:16:32:48:64', '8:8:16:32:48', '8:8:16:16:32', '8:8:16:16:48', '8:8:16:16:64', '16:16:32:32:64', '32:32:64:64:64']
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pyramid = ["8:16:24:32:40", "8:16:32:48:64", "32:40:48:56:64"]
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pyramid_indexes = [api.query_index_by_arch(x) for x in pyramid]
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largest_indexes = [api.query_index_by_arch("64:64:64:64:64")]
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indexes = list(range(len(params)))
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dpi, width, height = 250, 8500, 1300
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 24, 24
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# resnet_scale, resnet_alpha = 120, 0.5
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xscale, xalpha = 120, 0.8
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indexes = list(range(len(params)))
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dpi, width, height = 250, 8500, 1300
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 24, 24
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# resnet_scale, resnet_alpha = 120, 0.5
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xscale, xalpha = 120, 0.8
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fig, axs = plt.subplots(1, 4, figsize=figsize)
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# ax1, ax2, ax3, ax4, ax5 = axs
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for ax in axs:
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for tick in ax.xaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.0f'))
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for tick in ax.yaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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ax1, ax2, ax3, ax4 = axs
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fig, axs = plt.subplots(1, 4, figsize=figsize)
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# ax1, ax2, ax3, ax4, ax5 = axs
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for ax in axs:
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for tick in ax.xaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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ax.yaxis.set_major_formatter(ticker.FormatStrFormatter("%.0f"))
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for tick in ax.yaxis.get_major_ticks():
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tick.label.set_fontsize(LabelSize)
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ax1, ax2, ax3, ax4 = axs
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ax1.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue')
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ax1.scatter([params[x] for x in pyramid_indexes], [train_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
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ax1.scatter([params[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax1.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax1.set_ylabel('train accuracy (%)', fontsize=LabelSize)
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ax1.legend(loc=4, fontsize=LegendFontsize)
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ax1.scatter(params, train_accs, marker="o", s=0.5, c="tab:blue")
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ax1.scatter(
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[params[x] for x in pyramid_indexes],
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[train_accs[x] for x in pyramid_indexes],
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marker="*",
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s=xscale,
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c="tab:orange",
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label="Pyramid Structure",
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alpha=xalpha,
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)
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ax1.scatter(
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[params[x] for x in largest_indexes],
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[train_accs[x] for x in largest_indexes],
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marker="x",
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s=xscale,
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c="tab:green",
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label="Largest Candidate",
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alpha=xalpha,
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)
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ax1.set_xlabel("#parameters (MB)", fontsize=LabelSize)
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ax1.set_ylabel("train accuracy (%)", fontsize=LabelSize)
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ax1.legend(loc=4, fontsize=LegendFontsize)
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ax2.scatter(flops, train_accs, marker='o', s=0.5, c='tab:blue')
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ax2.scatter([flops[x] for x in pyramid_indexes], [train_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
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ax2.scatter([flops[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax2.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
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# ax2.set_ylabel('train accuracy (%)', fontsize=LabelSize)
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ax2.legend(loc=4, fontsize=LegendFontsize)
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ax2.scatter(flops, train_accs, marker="o", s=0.5, c="tab:blue")
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ax2.scatter(
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[flops[x] for x in pyramid_indexes],
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[train_accs[x] for x in pyramid_indexes],
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marker="*",
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s=xscale,
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c="tab:orange",
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label="Pyramid Structure",
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alpha=xalpha,
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)
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ax2.scatter(
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[flops[x] for x in largest_indexes],
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[train_accs[x] for x in largest_indexes],
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marker="x",
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s=xscale,
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c="tab:green",
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label="Largest Candidate",
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alpha=xalpha,
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)
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ax2.set_xlabel("#FLOPs (M)", fontsize=LabelSize)
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# ax2.set_ylabel('train accuracy (%)', fontsize=LabelSize)
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ax2.legend(loc=4, fontsize=LegendFontsize)
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ax3.scatter(params, test_accs, marker='o', s=0.5, c='tab:blue')
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ax3.scatter([params[x] for x in pyramid_indexes], [test_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
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ax3.scatter([params[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
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ax3.set_xlabel('#parameters (MB)', fontsize=LabelSize)
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ax3.set_ylabel('test accuracy (%)', fontsize=LabelSize)
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ax3.legend(loc=4, fontsize=LegendFontsize)
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ax3.scatter(params, test_accs, marker="o", s=0.5, c="tab:blue")
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ax3.scatter(
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[params[x] for x in pyramid_indexes],
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[test_accs[x] for x in pyramid_indexes],
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marker="*",
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s=xscale,
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c="tab:orange",
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label="Pyramid Structure",
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alpha=xalpha,
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)
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ax3.scatter(
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[params[x] for x in largest_indexes],
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[test_accs[x] for x in largest_indexes],
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marker="x",
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s=xscale,
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c="tab:green",
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label="Largest Candidate",
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alpha=xalpha,
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)
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ax3.set_xlabel("#parameters (MB)", fontsize=LabelSize)
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ax3.set_ylabel("test accuracy (%)", fontsize=LabelSize)
|
||||
ax3.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax4.scatter(flops, test_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax4.scatter([flops[x] for x in pyramid_indexes], [test_accs[x] for x in pyramid_indexes], marker='*', s=xscale, c='tab:orange', label='Pyramid Structure', alpha=xalpha)
|
||||
ax4.scatter([flops[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax4.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
|
||||
# ax4.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
ax4.legend(loc=4, fontsize=LegendFontsize)
|
||||
ax4.scatter(flops, test_accs, marker="o", s=0.5, c="tab:blue")
|
||||
ax4.scatter(
|
||||
[flops[x] for x in pyramid_indexes],
|
||||
[test_accs[x] for x in pyramid_indexes],
|
||||
marker="*",
|
||||
s=xscale,
|
||||
c="tab:orange",
|
||||
label="Pyramid Structure",
|
||||
alpha=xalpha,
|
||||
)
|
||||
ax4.scatter(
|
||||
[flops[x] for x in largest_indexes],
|
||||
[test_accs[x] for x in largest_indexes],
|
||||
marker="x",
|
||||
s=xscale,
|
||||
c="tab:green",
|
||||
label="Largest Candidate",
|
||||
alpha=xalpha,
|
||||
)
|
||||
ax4.set_xlabel("#FLOPs (M)", fontsize=LabelSize)
|
||||
# ax4.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
ax4.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
save_path = vis_save_dir / 'sss-{:}.png'.format(dataset.lower())
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
|
||||
print ('{:} save into {:}'.format(time_string(), save_path))
|
||||
plt.close('all')
|
||||
save_path = vis_save_dir / "sss-{:}.png".format(dataset.lower())
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
|
||||
print("{:} save into {:}".format(time_string(), save_path))
|
||||
plt.close("all")
|
||||
|
||||
|
||||
def visualize_tss_info(api, dataset, vis_save_dir):
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
print ('{:} start to visualize {:} information'.format(time_string(), dataset))
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
cache_file_path = vis_save_dir / '{:}-cache-tss-info.pth'.format(dataset)
|
||||
if not cache_file_path.exists():
|
||||
print ('Do not find cache file : {:}'.format(cache_file_path))
|
||||
params, flops, train_accs, valid_accs, test_accs = [], [], [], [], []
|
||||
for index in range(len(api)):
|
||||
cost_info = api.get_cost_info(index, dataset, hp='12')
|
||||
params.append(cost_info['params'])
|
||||
flops.append(cost_info['flops'])
|
||||
# accuracy
|
||||
info = api.get_more_info(index, dataset, hp='200', is_random=False)
|
||||
train_accs.append(info['train-accuracy'])
|
||||
test_accs.append(info['test-accuracy'])
|
||||
if dataset == 'cifar10':
|
||||
info = api.get_more_info(index, 'cifar10-valid', hp='200', is_random=False)
|
||||
valid_accs.append(info['valid-accuracy'])
|
||||
else:
|
||||
valid_accs.append(info['valid-accuracy'])
|
||||
print('')
|
||||
info = {'params': params, 'flops': flops, 'train_accs': train_accs, 'valid_accs': valid_accs, 'test_accs': test_accs}
|
||||
torch.save(info, cache_file_path)
|
||||
else:
|
||||
print ('Find cache file : {:}'.format(cache_file_path))
|
||||
info = torch.load(cache_file_path)
|
||||
params, flops, train_accs, valid_accs, test_accs = info['params'], info['flops'], info['train_accs'], info['valid_accs'], info['test_accs']
|
||||
print ('{:} collect data done.'.format(time_string()))
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
print("{:} start to visualize {:} information".format(time_string(), dataset))
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
cache_file_path = vis_save_dir / "{:}-cache-tss-info.pth".format(dataset)
|
||||
if not cache_file_path.exists():
|
||||
print("Do not find cache file : {:}".format(cache_file_path))
|
||||
params, flops, train_accs, valid_accs, test_accs = [], [], [], [], []
|
||||
for index in range(len(api)):
|
||||
cost_info = api.get_cost_info(index, dataset, hp="12")
|
||||
params.append(cost_info["params"])
|
||||
flops.append(cost_info["flops"])
|
||||
# accuracy
|
||||
info = api.get_more_info(index, dataset, hp="200", is_random=False)
|
||||
train_accs.append(info["train-accuracy"])
|
||||
test_accs.append(info["test-accuracy"])
|
||||
if dataset == "cifar10":
|
||||
info = api.get_more_info(index, "cifar10-valid", hp="200", is_random=False)
|
||||
valid_accs.append(info["valid-accuracy"])
|
||||
else:
|
||||
valid_accs.append(info["valid-accuracy"])
|
||||
print("")
|
||||
info = {
|
||||
"params": params,
|
||||
"flops": flops,
|
||||
"train_accs": train_accs,
|
||||
"valid_accs": valid_accs,
|
||||
"test_accs": test_accs,
|
||||
}
|
||||
torch.save(info, cache_file_path)
|
||||
else:
|
||||
print("Find cache file : {:}".format(cache_file_path))
|
||||
info = torch.load(cache_file_path)
|
||||
params, flops, train_accs, valid_accs, test_accs = (
|
||||
info["params"],
|
||||
info["flops"],
|
||||
info["train_accs"],
|
||||
info["valid_accs"],
|
||||
info["test_accs"],
|
||||
)
|
||||
print("{:} collect data done.".format(time_string()))
|
||||
|
||||
resnet = ['|nor_conv_3x3~0|+|none~0|nor_conv_3x3~1|+|skip_connect~0|none~1|skip_connect~2|']
|
||||
resnet_indexes = [api.query_index_by_arch(x) for x in resnet]
|
||||
largest_indexes = [api.query_index_by_arch('|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|nor_conv_3x3~0|nor_conv_3x3~1|nor_conv_3x3~2|')]
|
||||
resnet = ["|nor_conv_3x3~0|+|none~0|nor_conv_3x3~1|+|skip_connect~0|none~1|skip_connect~2|"]
|
||||
resnet_indexes = [api.query_index_by_arch(x) for x in resnet]
|
||||
largest_indexes = [
|
||||
api.query_index_by_arch(
|
||||
"|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|nor_conv_3x3~0|nor_conv_3x3~1|nor_conv_3x3~2|"
|
||||
)
|
||||
]
|
||||
|
||||
indexes = list(range(len(params)))
|
||||
dpi, width, height = 250, 8500, 1300
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 24, 24
|
||||
# resnet_scale, resnet_alpha = 120, 0.5
|
||||
xscale, xalpha = 120, 0.8
|
||||
indexes = list(range(len(params)))
|
||||
dpi, width, height = 250, 8500, 1300
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 24, 24
|
||||
# resnet_scale, resnet_alpha = 120, 0.5
|
||||
xscale, xalpha = 120, 0.8
|
||||
|
||||
fig, axs = plt.subplots(1, 4, figsize=figsize)
|
||||
for ax in axs:
|
||||
for tick in ax.xaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.0f'))
|
||||
for tick in ax.yaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
ax1, ax2, ax3, ax4 = axs
|
||||
fig, axs = plt.subplots(1, 4, figsize=figsize)
|
||||
for ax in axs:
|
||||
for tick in ax.xaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
ax.yaxis.set_major_formatter(ticker.FormatStrFormatter("%.0f"))
|
||||
for tick in ax.yaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
ax1, ax2, ax3, ax4 = axs
|
||||
|
||||
ax1.scatter(params, train_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax1.scatter([params[x] for x in resnet_indexes] , [train_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
|
||||
ax1.scatter([params[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax1.set_xlabel('#parameters (MB)', fontsize=LabelSize)
|
||||
ax1.set_ylabel('train accuracy (%)', fontsize=LabelSize)
|
||||
ax1.legend(loc=4, fontsize=LegendFontsize)
|
||||
ax1.scatter(params, train_accs, marker="o", s=0.5, c="tab:blue")
|
||||
ax1.scatter(
|
||||
[params[x] for x in resnet_indexes],
|
||||
[train_accs[x] for x in resnet_indexes],
|
||||
marker="*",
|
||||
s=xscale,
|
||||
c="tab:orange",
|
||||
label="ResNet",
|
||||
alpha=xalpha,
|
||||
)
|
||||
ax1.scatter(
|
||||
[params[x] for x in largest_indexes],
|
||||
[train_accs[x] for x in largest_indexes],
|
||||
marker="x",
|
||||
s=xscale,
|
||||
c="tab:green",
|
||||
label="Largest Candidate",
|
||||
alpha=xalpha,
|
||||
)
|
||||
ax1.set_xlabel("#parameters (MB)", fontsize=LabelSize)
|
||||
ax1.set_ylabel("train accuracy (%)", fontsize=LabelSize)
|
||||
ax1.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax2.scatter(flops, train_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax2.scatter([flops[x] for x in resnet_indexes], [train_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
|
||||
ax2.scatter([flops[x] for x in largest_indexes], [train_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax2.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
|
||||
# ax2.set_ylabel('train accuracy (%)', fontsize=LabelSize)
|
||||
ax2.legend(loc=4, fontsize=LegendFontsize)
|
||||
ax2.scatter(flops, train_accs, marker="o", s=0.5, c="tab:blue")
|
||||
ax2.scatter(
|
||||
[flops[x] for x in resnet_indexes],
|
||||
[train_accs[x] for x in resnet_indexes],
|
||||
marker="*",
|
||||
s=xscale,
|
||||
c="tab:orange",
|
||||
label="ResNet",
|
||||
alpha=xalpha,
|
||||
)
|
||||
ax2.scatter(
|
||||
[flops[x] for x in largest_indexes],
|
||||
[train_accs[x] for x in largest_indexes],
|
||||
marker="x",
|
||||
s=xscale,
|
||||
c="tab:green",
|
||||
label="Largest Candidate",
|
||||
alpha=xalpha,
|
||||
)
|
||||
ax2.set_xlabel("#FLOPs (M)", fontsize=LabelSize)
|
||||
# ax2.set_ylabel('train accuracy (%)', fontsize=LabelSize)
|
||||
ax2.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax3.scatter(params, test_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax3.scatter([params[x] for x in resnet_indexes] , [test_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
|
||||
ax3.scatter([params[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax3.set_xlabel('#parameters (MB)', fontsize=LabelSize)
|
||||
ax3.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
ax3.legend(loc=4, fontsize=LegendFontsize)
|
||||
ax3.scatter(params, test_accs, marker="o", s=0.5, c="tab:blue")
|
||||
ax3.scatter(
|
||||
[params[x] for x in resnet_indexes],
|
||||
[test_accs[x] for x in resnet_indexes],
|
||||
marker="*",
|
||||
s=xscale,
|
||||
c="tab:orange",
|
||||
label="ResNet",
|
||||
alpha=xalpha,
|
||||
)
|
||||
ax3.scatter(
|
||||
[params[x] for x in largest_indexes],
|
||||
[test_accs[x] for x in largest_indexes],
|
||||
marker="x",
|
||||
s=xscale,
|
||||
c="tab:green",
|
||||
label="Largest Candidate",
|
||||
alpha=xalpha,
|
||||
)
|
||||
ax3.set_xlabel("#parameters (MB)", fontsize=LabelSize)
|
||||
ax3.set_ylabel("test accuracy (%)", fontsize=LabelSize)
|
||||
ax3.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
ax4.scatter(flops, test_accs, marker='o', s=0.5, c='tab:blue')
|
||||
ax4.scatter([flops[x] for x in resnet_indexes], [test_accs[x] for x in resnet_indexes], marker='*', s=xscale, c='tab:orange', label='ResNet', alpha=xalpha)
|
||||
ax4.scatter([flops[x] for x in largest_indexes], [test_accs[x] for x in largest_indexes], marker='x', s=xscale, c='tab:green', label='Largest Candidate', alpha=xalpha)
|
||||
ax4.set_xlabel('#FLOPs (M)', fontsize=LabelSize)
|
||||
# ax4.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
ax4.legend(loc=4, fontsize=LegendFontsize)
|
||||
ax4.scatter(flops, test_accs, marker="o", s=0.5, c="tab:blue")
|
||||
ax4.scatter(
|
||||
[flops[x] for x in resnet_indexes],
|
||||
[test_accs[x] for x in resnet_indexes],
|
||||
marker="*",
|
||||
s=xscale,
|
||||
c="tab:orange",
|
||||
label="ResNet",
|
||||
alpha=xalpha,
|
||||
)
|
||||
ax4.scatter(
|
||||
[flops[x] for x in largest_indexes],
|
||||
[test_accs[x] for x in largest_indexes],
|
||||
marker="x",
|
||||
s=xscale,
|
||||
c="tab:green",
|
||||
label="Largest Candidate",
|
||||
alpha=xalpha,
|
||||
)
|
||||
ax4.set_xlabel("#FLOPs (M)", fontsize=LabelSize)
|
||||
# ax4.set_ylabel('test accuracy (%)', fontsize=LabelSize)
|
||||
ax4.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
save_path = vis_save_dir / 'tss-{:}.png'.format(dataset.lower())
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
|
||||
print ('{:} save into {:}'.format(time_string(), save_path))
|
||||
plt.close('all')
|
||||
save_path = vis_save_dir / "tss-{:}.png".format(dataset.lower())
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
|
||||
print("{:} save into {:}".format(time_string(), save_path))
|
||||
plt.close("all")
|
||||
|
||||
|
||||
def visualize_rank_info(api, vis_save_dir, indicator):
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
# print ('{:} start to visualize {:} information'.format(time_string(), api))
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
# print ('{:} start to visualize {:} information'.format(time_string(), api))
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
cifar010_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar10', indicator)
|
||||
cifar100_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar100', indicator)
|
||||
imagenet_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('ImageNet16-120', indicator)
|
||||
cifar010_info = torch.load(cifar010_cache_path)
|
||||
cifar100_info = torch.load(cifar100_cache_path)
|
||||
imagenet_info = torch.load(imagenet_cache_path)
|
||||
indexes = list(range(len(cifar010_info['params'])))
|
||||
cifar010_cache_path = vis_save_dir / "{:}-cache-{:}-info.pth".format("cifar10", indicator)
|
||||
cifar100_cache_path = vis_save_dir / "{:}-cache-{:}-info.pth".format("cifar100", indicator)
|
||||
imagenet_cache_path = vis_save_dir / "{:}-cache-{:}-info.pth".format("ImageNet16-120", indicator)
|
||||
cifar010_info = torch.load(cifar010_cache_path)
|
||||
cifar100_info = torch.load(cifar100_cache_path)
|
||||
imagenet_info = torch.load(imagenet_cache_path)
|
||||
indexes = list(range(len(cifar010_info["params"])))
|
||||
|
||||
print ('{:} start to visualize relative ranking'.format(time_string()))
|
||||
print("{:} start to visualize relative ranking".format(time_string()))
|
||||
|
||||
dpi, width, height = 250, 3800, 1200
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 14, 14
|
||||
dpi, width, height = 250, 3800, 1200
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 14, 14
|
||||
|
||||
fig, axs = plt.subplots(1, 3, figsize=figsize)
|
||||
ax1, ax2, ax3 = axs
|
||||
fig, axs = plt.subplots(1, 3, figsize=figsize)
|
||||
ax1, ax2, ax3 = axs
|
||||
|
||||
def get_labels(info):
|
||||
ord_test_indexes = sorted(indexes, key=lambda i: info['test_accs'][i])
|
||||
ord_valid_indexes = sorted(indexes, key=lambda i: info['valid_accs'][i])
|
||||
labels = []
|
||||
for idx in ord_test_indexes:
|
||||
labels.append(ord_valid_indexes.index(idx))
|
||||
return labels
|
||||
def get_labels(info):
|
||||
ord_test_indexes = sorted(indexes, key=lambda i: info["test_accs"][i])
|
||||
ord_valid_indexes = sorted(indexes, key=lambda i: info["valid_accs"][i])
|
||||
labels = []
|
||||
for idx in ord_test_indexes:
|
||||
labels.append(ord_valid_indexes.index(idx))
|
||||
return labels
|
||||
|
||||
def plot_ax(labels, ax, name):
|
||||
for tick in ax.xaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
for tick in ax.yaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
tick.label.set_rotation(90)
|
||||
ax.set_xlim(min(indexes), max(indexes))
|
||||
ax.set_ylim(min(indexes), max(indexes))
|
||||
ax.yaxis.set_ticks(np.arange(min(indexes), max(indexes), max(indexes)//3))
|
||||
ax.xaxis.set_ticks(np.arange(min(indexes), max(indexes), max(indexes)//5))
|
||||
ax.scatter(indexes, labels , marker='^', s=0.5, c='tab:green', alpha=0.8)
|
||||
ax.scatter(indexes, indexes, marker='o', s=0.5, c='tab:blue' , alpha=0.8)
|
||||
ax.scatter([-1], [-1], marker='^', s=100, c='tab:green' , label='{:} test'.format(name))
|
||||
ax.scatter([-1], [-1], marker='o', s=100, c='tab:blue' , label='{:} validation'.format(name))
|
||||
ax.legend(loc=4, fontsize=LegendFontsize)
|
||||
ax.set_xlabel('ranking on the {:} validation'.format(name), fontsize=LabelSize)
|
||||
ax.set_ylabel('architecture ranking', fontsize=LabelSize)
|
||||
labels = get_labels(cifar010_info)
|
||||
plot_ax(labels, ax1, 'CIFAR-10')
|
||||
labels = get_labels(cifar100_info)
|
||||
plot_ax(labels, ax2, 'CIFAR-100')
|
||||
labels = get_labels(imagenet_info)
|
||||
plot_ax(labels, ax3, 'ImageNet-16-120')
|
||||
def plot_ax(labels, ax, name):
|
||||
for tick in ax.xaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
for tick in ax.yaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(LabelSize)
|
||||
tick.label.set_rotation(90)
|
||||
ax.set_xlim(min(indexes), max(indexes))
|
||||
ax.set_ylim(min(indexes), max(indexes))
|
||||
ax.yaxis.set_ticks(np.arange(min(indexes), max(indexes), max(indexes) // 3))
|
||||
ax.xaxis.set_ticks(np.arange(min(indexes), max(indexes), max(indexes) // 5))
|
||||
ax.scatter(indexes, labels, marker="^", s=0.5, c="tab:green", alpha=0.8)
|
||||
ax.scatter(indexes, indexes, marker="o", s=0.5, c="tab:blue", alpha=0.8)
|
||||
ax.scatter([-1], [-1], marker="^", s=100, c="tab:green", label="{:} test".format(name))
|
||||
ax.scatter([-1], [-1], marker="o", s=100, c="tab:blue", label="{:} validation".format(name))
|
||||
ax.legend(loc=4, fontsize=LegendFontsize)
|
||||
ax.set_xlabel("ranking on the {:} validation".format(name), fontsize=LabelSize)
|
||||
ax.set_ylabel("architecture ranking", fontsize=LabelSize)
|
||||
|
||||
save_path = (vis_save_dir / '{:}-same-relative-rank.pdf'.format(indicator)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
|
||||
save_path = (vis_save_dir / '{:}-same-relative-rank.png'.format(indicator)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
|
||||
print ('{:} save into {:}'.format(time_string(), save_path))
|
||||
plt.close('all')
|
||||
labels = get_labels(cifar010_info)
|
||||
plot_ax(labels, ax1, "CIFAR-10")
|
||||
labels = get_labels(cifar100_info)
|
||||
plot_ax(labels, ax2, "CIFAR-100")
|
||||
labels = get_labels(imagenet_info)
|
||||
plot_ax(labels, ax3, "ImageNet-16-120")
|
||||
|
||||
save_path = (vis_save_dir / "{:}-same-relative-rank.pdf".format(indicator)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="pdf")
|
||||
save_path = (vis_save_dir / "{:}-same-relative-rank.png".format(indicator)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
|
||||
print("{:} save into {:}".format(time_string(), save_path))
|
||||
plt.close("all")
|
||||
|
||||
|
||||
def compute_kendalltau(vectori, vectorj):
|
||||
# indexes = list(range(len(vectori)))
|
||||
# rank_1 = sorted(indexes, key=lambda i: vectori[i])
|
||||
# rank_2 = sorted(indexes, key=lambda i: vectorj[i])
|
||||
return scipy.stats.kendalltau(vectori, vectorj).correlation
|
||||
# indexes = list(range(len(vectori)))
|
||||
# rank_1 = sorted(indexes, key=lambda i: vectori[i])
|
||||
# rank_2 = sorted(indexes, key=lambda i: vectorj[i])
|
||||
return scipy.stats.kendalltau(vectori, vectorj).correlation
|
||||
|
||||
|
||||
def calculate_correlation(*vectors):
|
||||
matrix = []
|
||||
for i, vectori in enumerate(vectors):
|
||||
x = []
|
||||
for j, vectorj in enumerate(vectors):
|
||||
# x.append(np.corrcoef(vectori, vectorj)[0,1])
|
||||
x.append(compute_kendalltau(vectori, vectorj))
|
||||
matrix.append( x )
|
||||
return np.array(matrix)
|
||||
matrix = []
|
||||
for i, vectori in enumerate(vectors):
|
||||
x = []
|
||||
for j, vectorj in enumerate(vectors):
|
||||
# x.append(np.corrcoef(vectori, vectorj)[0,1])
|
||||
x.append(compute_kendalltau(vectori, vectorj))
|
||||
matrix.append(x)
|
||||
return np.array(matrix)
|
||||
|
||||
|
||||
def visualize_all_rank_info(api, vis_save_dir, indicator):
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
# print ('{:} start to visualize {:} information'.format(time_string(), api))
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
# print ('{:} start to visualize {:} information'.format(time_string(), api))
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
cifar010_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar10', indicator)
|
||||
cifar100_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar100', indicator)
|
||||
imagenet_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('ImageNet16-120', indicator)
|
||||
cifar010_info = torch.load(cifar010_cache_path)
|
||||
cifar100_info = torch.load(cifar100_cache_path)
|
||||
imagenet_info = torch.load(imagenet_cache_path)
|
||||
indexes = list(range(len(cifar010_info['params'])))
|
||||
cifar010_cache_path = vis_save_dir / "{:}-cache-{:}-info.pth".format("cifar10", indicator)
|
||||
cifar100_cache_path = vis_save_dir / "{:}-cache-{:}-info.pth".format("cifar100", indicator)
|
||||
imagenet_cache_path = vis_save_dir / "{:}-cache-{:}-info.pth".format("ImageNet16-120", indicator)
|
||||
cifar010_info = torch.load(cifar010_cache_path)
|
||||
cifar100_info = torch.load(cifar100_cache_path)
|
||||
imagenet_info = torch.load(imagenet_cache_path)
|
||||
indexes = list(range(len(cifar010_info["params"])))
|
||||
|
||||
print ('{:} start to visualize relative ranking'.format(time_string()))
|
||||
|
||||
print("{:} start to visualize relative ranking".format(time_string()))
|
||||
|
||||
dpi, width, height = 250, 3200, 1400
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 14, 14
|
||||
dpi, width, height = 250, 3200, 1400
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 14, 14
|
||||
|
||||
fig, axs = plt.subplots(1, 2, figsize=figsize)
|
||||
ax1, ax2 = axs
|
||||
fig, axs = plt.subplots(1, 2, figsize=figsize)
|
||||
ax1, ax2 = axs
|
||||
|
||||
sns_size, xformat = 15, '.2f'
|
||||
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'])
|
||||
|
||||
sns.heatmap(CoRelMatrix, annot=True, annot_kws={'size':sns_size}, fmt=xformat, linewidths=0.5, ax=ax1,
|
||||
xticklabels=['C10-V', 'C10-T', 'C100-V', 'C100-T', 'I120-V', 'I120-T'],
|
||||
yticklabels=['C10-V', 'C10-T', 'C100-V', 'C100-T', 'I120-V', 'I120-T'])
|
||||
|
||||
selected_indexes, acc_bar = [], 92
|
||||
for i, acc in enumerate(cifar010_info['test_accs']):
|
||||
if acc > acc_bar: selected_indexes.append( i )
|
||||
cifar010_valid_accs = np.array(cifar010_info['valid_accs'])[ selected_indexes ]
|
||||
cifar010_test_accs = np.array(cifar010_info['test_accs']) [ selected_indexes ]
|
||||
cifar100_valid_accs = np.array(cifar100_info['valid_accs'])[ selected_indexes ]
|
||||
cifar100_test_accs = np.array(cifar100_info['test_accs']) [ selected_indexes ]
|
||||
imagenet_valid_accs = np.array(imagenet_info['valid_accs'])[ selected_indexes ]
|
||||
imagenet_test_accs = np.array(imagenet_info['test_accs']) [ selected_indexes ]
|
||||
CoRelMatrix = calculate_correlation(cifar010_valid_accs, cifar010_test_accs, cifar100_valid_accs, cifar100_test_accs, imagenet_valid_accs, imagenet_test_accs)
|
||||
|
||||
sns.heatmap(CoRelMatrix, annot=True, annot_kws={'size':sns_size}, fmt=xformat, linewidths=0.5, ax=ax2,
|
||||
xticklabels=['C10-V', 'C10-T', 'C100-V', 'C100-T', 'I120-V', 'I120-T'],
|
||||
yticklabels=['C10-V', 'C10-T', 'C100-V', 'C100-T', 'I120-V', 'I120-T'])
|
||||
ax1.set_title('Correlation coefficient over ALL candidates')
|
||||
ax2.set_title('Correlation coefficient over candidates with accuracy > {:}%'.format(acc_bar))
|
||||
save_path = (vis_save_dir / '{:}-all-relative-rank.png'.format(indicator)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
|
||||
print ('{:} save into {:}'.format(time_string(), save_path))
|
||||
plt.close('all')
|
||||
sns_size, xformat = 15, ".2f"
|
||||
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"],
|
||||
)
|
||||
|
||||
sns.heatmap(
|
||||
CoRelMatrix,
|
||||
annot=True,
|
||||
annot_kws={"size": sns_size},
|
||||
fmt=xformat,
|
||||
linewidths=0.5,
|
||||
ax=ax1,
|
||||
xticklabels=["C10-V", "C10-T", "C100-V", "C100-T", "I120-V", "I120-T"],
|
||||
yticklabels=["C10-V", "C10-T", "C100-V", "C100-T", "I120-V", "I120-T"],
|
||||
)
|
||||
|
||||
selected_indexes, acc_bar = [], 92
|
||||
for i, acc in enumerate(cifar010_info["test_accs"]):
|
||||
if acc > acc_bar:
|
||||
selected_indexes.append(i)
|
||||
cifar010_valid_accs = np.array(cifar010_info["valid_accs"])[selected_indexes]
|
||||
cifar010_test_accs = np.array(cifar010_info["test_accs"])[selected_indexes]
|
||||
cifar100_valid_accs = np.array(cifar100_info["valid_accs"])[selected_indexes]
|
||||
cifar100_test_accs = np.array(cifar100_info["test_accs"])[selected_indexes]
|
||||
imagenet_valid_accs = np.array(imagenet_info["valid_accs"])[selected_indexes]
|
||||
imagenet_test_accs = np.array(imagenet_info["test_accs"])[selected_indexes]
|
||||
CoRelMatrix = calculate_correlation(
|
||||
cifar010_valid_accs,
|
||||
cifar010_test_accs,
|
||||
cifar100_valid_accs,
|
||||
cifar100_test_accs,
|
||||
imagenet_valid_accs,
|
||||
imagenet_test_accs,
|
||||
)
|
||||
|
||||
sns.heatmap(
|
||||
CoRelMatrix,
|
||||
annot=True,
|
||||
annot_kws={"size": sns_size},
|
||||
fmt=xformat,
|
||||
linewidths=0.5,
|
||||
ax=ax2,
|
||||
xticklabels=["C10-V", "C10-T", "C100-V", "C100-T", "I120-V", "I120-T"],
|
||||
yticklabels=["C10-V", "C10-T", "C100-V", "C100-T", "I120-V", "I120-T"],
|
||||
)
|
||||
ax1.set_title("Correlation coefficient over ALL candidates")
|
||||
ax2.set_title("Correlation coefficient over candidates with accuracy > {:}%".format(acc_bar))
|
||||
save_path = (vis_save_dir / "{:}-all-relative-rank.png".format(indicator)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
|
||||
print("{:} save into {:}".format(time_string(), save_path))
|
||||
plt.close("all")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='NATS-Bench', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench', help='Folder to save checkpoints and log.')
|
||||
# use for train the model
|
||||
args = parser.parse_args()
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="NATS-Bench", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, default="output/vis-nas-bench", help="Folder to save checkpoints and log."
|
||||
)
|
||||
# use for train the model
|
||||
args = parser.parse_args()
|
||||
|
||||
to_save_dir = Path(args.save_dir)
|
||||
to_save_dir = Path(args.save_dir)
|
||||
|
||||
datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
|
||||
# Figure 3 (a-c)
|
||||
api_tss = create(None, 'tss', verbose=True)
|
||||
for xdata in datasets:
|
||||
visualize_tss_info(api_tss, xdata, to_save_dir)
|
||||
# Figure 3 (d-f)
|
||||
api_sss = create(None, 'size', verbose=True)
|
||||
for xdata in datasets:
|
||||
visualize_sss_info(api_sss, xdata, to_save_dir)
|
||||
datasets = ["cifar10", "cifar100", "ImageNet16-120"]
|
||||
# Figure 3 (a-c)
|
||||
api_tss = create(None, "tss", verbose=True)
|
||||
for xdata in datasets:
|
||||
visualize_tss_info(api_tss, xdata, to_save_dir)
|
||||
# Figure 3 (d-f)
|
||||
api_sss = create(None, "size", verbose=True)
|
||||
for xdata in datasets:
|
||||
visualize_sss_info(api_sss, xdata, to_save_dir)
|
||||
|
||||
# Figure 2
|
||||
visualize_relative_info(None, to_save_dir, 'tss')
|
||||
visualize_relative_info(None, to_save_dir, 'sss')
|
||||
# Figure 2
|
||||
visualize_relative_info(None, to_save_dir, "tss")
|
||||
visualize_relative_info(None, to_save_dir, "sss")
|
||||
|
||||
# Figure 4
|
||||
visualize_rank_info(None, to_save_dir, 'tss')
|
||||
visualize_rank_info(None, to_save_dir, 'sss')
|
||||
# Figure 4
|
||||
visualize_rank_info(None, to_save_dir, "tss")
|
||||
visualize_rank_info(None, to_save_dir, "sss")
|
||||
|
||||
# Figure 5
|
||||
visualize_all_rank_info(None, to_save_dir, 'tss')
|
||||
visualize_all_rank_info(None, to_save_dir, 'sss')
|
||||
# Figure 5
|
||||
visualize_all_rank_info(None, to_save_dir, "tss")
|
||||
visualize_all_rank_info(None, to_save_dir, "sss")
|
||||
|
||||
Reference in New Issue
Block a user