Reformulate via black
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D-X-Y
@@ -15,10 +15,13 @@ from pathlib import Path
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from collections import OrderedDict
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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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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 log_utils import time_string
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from nats_bench import create
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from models import get_cell_based_tiny_net
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@@ -38,111 +41,125 @@ def tostr(accdict, norms):
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return ' '.join(xstr)
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"""
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def evaluate(api, weight_dir, data: str):
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print('\nEvaluate dataset={:}'.format(data))
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process = psutil.Process(os.getpid())
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norms, accuracies = [], []
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ok, total = 0, 5000
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for idx in range(total):
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arch_index = api.random()
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api.reload(weight_dir, arch_index)
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# compute the weight watcher results
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config = api.get_net_config(arch_index, data)
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net = get_cell_based_tiny_net(config)
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meta_info = api.query_meta_info_by_index(arch_index, hp='200' if api.search_space_name == 'topology' else '90')
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params = meta_info.get_net_param(data, 888 if api.search_space_name == 'topology' else 777)
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with torch.no_grad():
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net.load_state_dict(params)
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_, summary = weight_watcher.analyze(net, alphas=False)
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if 'lognorm' not in summary:
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print("\nEvaluate dataset={:}".format(data))
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process = psutil.Process(os.getpid())
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norms, accuracies = [], []
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ok, total = 0, 5000
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for idx in range(total):
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arch_index = api.random()
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api.reload(weight_dir, arch_index)
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# compute the weight watcher results
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config = api.get_net_config(arch_index, data)
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net = get_cell_based_tiny_net(config)
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meta_info = api.query_meta_info_by_index(arch_index, hp="200" if api.search_space_name == "topology" else "90")
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params = meta_info.get_net_param(data, 888 if api.search_space_name == "topology" else 777)
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with torch.no_grad():
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net.load_state_dict(params)
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_, summary = weight_watcher.analyze(net, alphas=False)
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if "lognorm" not in summary:
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api.clear_params(arch_index, None)
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del net
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continue
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continue
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cur_norm = -summary["lognorm"]
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api.clear_params(arch_index, None)
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del net ; continue
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continue
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cur_norm = -summary['lognorm']
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api.clear_params(arch_index, None)
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if math.isnan(cur_norm):
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del net, meta_info
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continue
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else:
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ok += 1
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norms.append(cur_norm)
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# query the accuracy
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info = meta_info.get_metrics(data, 'ori-test', iepoch=None, is_random=888 if api.search_space_name == 'topology' else 777)
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accuracies.append(info['accuracy'])
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del net, meta_info
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# print the information
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if idx % 20 == 0:
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gc.collect()
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print('{:} {:04d}_{:04d}/{:04d} ({:.2f} MB memory)'.format(time_string(), ok, idx, total, process.memory_info().rss / 1e6))
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return norms, accuracies
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if math.isnan(cur_norm):
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del net, meta_info
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continue
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else:
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ok += 1
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norms.append(cur_norm)
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# query the accuracy
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info = meta_info.get_metrics(
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data, "ori-test", iepoch=None, is_random=888 if api.search_space_name == "topology" else 777
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)
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accuracies.append(info["accuracy"])
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del net, meta_info
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# print the information
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if idx % 20 == 0:
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gc.collect()
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print(
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"{:} {:04d}_{:04d}/{:04d} ({:.2f} MB memory)".format(
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time_string(), ok, idx, total, process.memory_info().rss / 1e6
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)
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)
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return norms, accuracies
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def main(search_space, meta_file: str, weight_dir, save_dir, xdata):
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save_dir.mkdir(parents=True, exist_ok=True)
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api = create(meta_file, search_space, verbose=False)
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datasets = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']
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print(time_string() + ' ' + '='*50)
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for data in datasets:
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hps = api.avaliable_hps
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for hp in hps:
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nums = api.statistics(data, hp=hp)
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total = sum([k*v for k, v in nums.items()])
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print('Using {:3s} epochs, trained on {:20s} : {:} trials in total ({:}).'.format(hp, data, total, nums))
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print(time_string() + ' ' + '='*50)
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save_dir.mkdir(parents=True, exist_ok=True)
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api = create(meta_file, search_space, verbose=False)
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datasets = ["cifar10-valid", "cifar10", "cifar100", "ImageNet16-120"]
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print(time_string() + " " + "=" * 50)
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for data in datasets:
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hps = api.avaliable_hps
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for hp in hps:
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nums = api.statistics(data, hp=hp)
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total = sum([k * v for k, v in nums.items()])
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print("Using {:3s} epochs, trained on {:20s} : {:} trials in total ({:}).".format(hp, data, total, nums))
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print(time_string() + " " + "=" * 50)
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norms, accuracies = evaluate(api, weight_dir, xdata)
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norms, accuracies = evaluate(api, weight_dir, xdata)
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indexes = list(range(len(norms)))
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norm_indexes = sorted(indexes, key=lambda i: norms[i])
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accy_indexes = sorted(indexes, key=lambda i: accuracies[i])
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labels = []
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for index in norm_indexes:
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labels.append(accy_indexes.index(index))
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indexes = list(range(len(norms)))
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norm_indexes = sorted(indexes, key=lambda i: norms[i])
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accy_indexes = sorted(indexes, key=lambda i: accuracies[i])
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labels = []
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for index in norm_indexes:
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labels.append(accy_indexes.index(index))
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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, 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='Test accuracy')
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ax.scatter([-1], [-1], marker='*', s=100, c='tab:red' , label='Weight watcher')
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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 sorted by the test accuracy ', fontsize=LabelSize)
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ax.set_ylabel('architecture ranking computed by weight watcher', fontsize=LabelSize)
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save_path = (save_dir / '{:}-{:}-test-ww.pdf'.format(search_space, xdata)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='pdf')
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save_path = (save_dir / '{:}-{:}-test-ww.png'.format(search_space, xdata)).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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print('{:} finish this test.'.format(time_string()))
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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, 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="Test accuracy")
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ax.scatter([-1], [-1], marker="*", s=100, c="tab:red", label="Weight watcher")
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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 sorted by the test accuracy ", fontsize=LabelSize)
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ax.set_ylabel("architecture ranking computed by weight watcher", fontsize=LabelSize)
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save_path = (save_dir / "{:}-{:}-test-ww.pdf".format(search_space, xdata)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="pdf")
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save_path = (save_dir / "{:}-{:}-test-ww.png".format(search_space, xdata)).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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print("{:} finish this test.".format(time_string()))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("Analysis of NAS-Bench-201")
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parser.add_argument('--save_dir', type=str, default='./output/vis-nas-bench/', help='The base-name of folder to save checkpoints and log.')
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parser.add_argument('--search_space', type=str, default=None, choices=['tss', 'sss'], help='The search space.')
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parser.add_argument('--base_path', type=str, default=None, help='The path to the NAS-Bench-201 benchmark file and weight dir.')
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parser.add_argument('--dataset' , type=str, default=None, help='.')
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args = parser.parse_args()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Analysis of NAS-Bench-201")
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parser.add_argument(
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"--save_dir",
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type=str,
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default="./output/vis-nas-bench/",
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help="The base-name of folder to save checkpoints and log.",
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)
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parser.add_argument("--search_space", type=str, default=None, choices=["tss", "sss"], help="The search space.")
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parser.add_argument(
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"--base_path", type=str, default=None, help="The path to the NAS-Bench-201 benchmark file and weight dir."
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)
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parser.add_argument("--dataset", type=str, default=None, help=".")
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args = parser.parse_args()
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save_dir = Path(args.save_dir)
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save_dir.mkdir(parents=True, exist_ok=True)
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meta_file = Path(args.base_path + '.pth')
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weight_dir = Path(args.base_path + '-full')
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assert meta_file.exists(), 'invalid path for api : {:}'.format(meta_file)
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assert weight_dir.exists() and weight_dir.is_dir(), 'invalid path for weight dir : {:}'.format(weight_dir)
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main(args.search_space, str(meta_file), weight_dir, save_dir, args.dataset)
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save_dir = Path(args.save_dir)
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save_dir.mkdir(parents=True, exist_ok=True)
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meta_file = Path(args.base_path + ".pth")
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weight_dir = Path(args.base_path + "-full")
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assert meta_file.exists(), "invalid path for api : {:}".format(meta_file)
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assert weight_dir.exists() and weight_dir.is_dir(), "invalid path for weight dir : {:}".format(weight_dir)
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main(args.search_space, str(meta_file), weight_dir, save_dir, args.dataset)
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