Reformulate via black
This commit is contained in:
D-X-Y
@@ -11,176 +11,194 @@ 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, OrderedDict
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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 nats_bench import create
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from log_utils import time_string
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plt.rcParams.update({
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"text.usetex": True,
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"font.family": "sans-serif",
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"font.sans-serif": ["Helvetica"]})
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plt.rcParams.update({"text.usetex": True, "font.family": "sans-serif", "font.sans-serif": ["Helvetica"]})
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## for Palatino and other serif fonts use:
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plt.rcParams.update({
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"text.usetex": True,
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"font.family": "serif",
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"font.serif": ["Palatino"],
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})
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plt.rcParams.update(
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{
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"text.usetex": True,
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"font.family": "serif",
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"font.serif": ["Palatino"],
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}
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)
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def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
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ss_dir = '{:}-{:}'.format(root_dir, search_space)
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alg2all = OrderedDict()
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# alg2name['REINFORCE'] = 'REINFORCE-0.01'
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# alg2name['RANDOM'] = 'RANDOM'
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# alg2name['BOHB'] = 'BOHB'
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if search_space == 'tss':
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hp = '$\mathcal{H}^{1}$'
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if dataset == 'cifar10':
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suffixes = ['-T1200000', '-T1200000-FULL']
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elif search_space == 'sss':
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hp = '$\mathcal{H}^{2}$'
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if dataset == 'cifar10':
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suffixes = ['-T200000', '-T200000-FULL']
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else:
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raise ValueError('Unkonwn search space: {:}'.format(search_space))
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def fetch_data(root_dir="./output/search", search_space="tss", dataset=None):
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ss_dir = "{:}-{:}".format(root_dir, search_space)
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alg2all = OrderedDict()
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# alg2name['REINFORCE'] = 'REINFORCE-0.01'
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# alg2name['RANDOM'] = 'RANDOM'
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# alg2name['BOHB'] = 'BOHB'
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if search_space == "tss":
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hp = "$\mathcal{H}^{1}$"
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if dataset == "cifar10":
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suffixes = ["-T1200000", "-T1200000-FULL"]
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elif search_space == "sss":
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hp = "$\mathcal{H}^{2}$"
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if dataset == "cifar10":
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suffixes = ["-T200000", "-T200000-FULL"]
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else:
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raise ValueError("Unkonwn search space: {:}".format(search_space))
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alg2all[r'REA ($\mathcal{H}^{0}$)'] = dict(
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path=os.path.join(ss_dir, dataset + suffixes[0], 'R-EA-SS3', 'results.pth'),
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color='b', linestyle='-')
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alg2all[r'REA ({:})'.format(hp)] = dict(
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path=os.path.join(ss_dir, dataset + suffixes[1], 'R-EA-SS3', 'results.pth'),
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color='b', linestyle='--')
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alg2all[r"REA ($\mathcal{H}^{0}$)"] = dict(
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path=os.path.join(ss_dir, dataset + suffixes[0], "R-EA-SS3", "results.pth"), color="b", linestyle="-"
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)
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alg2all[r"REA ({:})".format(hp)] = dict(
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path=os.path.join(ss_dir, dataset + suffixes[1], "R-EA-SS3", "results.pth"), color="b", linestyle="--"
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)
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for alg, xdata in alg2all.items():
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data = torch.load(xdata['path'])
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for index, info in data.items():
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info['time_w_arch'] = [(x, y) for x, y in zip(info['all_total_times'], info['all_archs'])]
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for j, arch in enumerate(info['all_archs']):
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assert arch != -1, 'invalid arch from {:} {:} {:} ({:}, {:})'.format(alg, search_space, dataset, index, j)
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xdata['data'] = data
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return alg2all
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for alg, xdata in alg2all.items():
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data = torch.load(xdata["path"])
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for index, info in data.items():
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info["time_w_arch"] = [(x, y) for x, y in zip(info["all_total_times"], info["all_archs"])]
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for j, arch in enumerate(info["all_archs"]):
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assert arch != -1, "invalid arch from {:} {:} {:} ({:}, {:})".format(
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alg, search_space, dataset, index, j
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)
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xdata["data"] = data
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return alg2all
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def query_performance(api, data, dataset, ticket):
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results, is_size_space = [], api.search_space_name == 'size'
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for i, info in data.items():
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time_w_arch = sorted(info['time_w_arch'], key=lambda x: abs(x[0]-ticket))
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time_a, arch_a = time_w_arch[0]
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time_b, arch_b = time_w_arch[1]
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info_a = api.get_more_info(arch_a, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
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info_b = api.get_more_info(arch_b, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
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accuracy_a, accuracy_b = info_a['test-accuracy'], info_b['test-accuracy']
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interplate = (time_b-ticket) / (time_b-time_a) * accuracy_a + (ticket-time_a) / (time_b-time_a) * accuracy_b
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results.append(interplate)
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# return sum(results) / len(results)
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return np.mean(results), np.std(results)
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results, is_size_space = [], api.search_space_name == "size"
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for i, info in data.items():
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time_w_arch = sorted(info["time_w_arch"], key=lambda x: abs(x[0] - ticket))
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time_a, arch_a = time_w_arch[0]
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time_b, arch_b = time_w_arch[1]
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info_a = api.get_more_info(arch_a, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
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info_b = api.get_more_info(arch_b, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
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accuracy_a, accuracy_b = info_a["test-accuracy"], info_b["test-accuracy"]
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interplate = (time_b - ticket) / (time_b - time_a) * accuracy_a + (ticket - time_a) / (
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time_b - time_a
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) * accuracy_b
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results.append(interplate)
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# return sum(results) / len(results)
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return np.mean(results), np.std(results)
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y_min_s = {('cifar10', 'tss'): 91,
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('cifar10', 'sss'): 91,
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('cifar100', 'tss'): 65,
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('cifar100', 'sss'): 65,
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('ImageNet16-120', 'tss'): 36,
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('ImageNet16-120', 'sss'): 40}
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y_min_s = {
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("cifar10", "tss"): 91,
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("cifar10", "sss"): 91,
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("cifar100", "tss"): 65,
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("cifar100", "sss"): 65,
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("ImageNet16-120", "tss"): 36,
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("ImageNet16-120", "sss"): 40,
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}
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y_max_s = {('cifar10', 'tss'): 94.5,
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('cifar10', 'sss'): 93.5,
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('cifar100', 'tss'): 72.5,
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('cifar100', 'sss'): 70.5,
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('ImageNet16-120', 'tss'): 46,
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('ImageNet16-120', 'sss'): 46}
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y_max_s = {
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("cifar10", "tss"): 94.5,
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("cifar10", "sss"): 93.5,
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("cifar100", "tss"): 72.5,
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("cifar100", "sss"): 70.5,
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("ImageNet16-120", "tss"): 46,
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("ImageNet16-120", "sss"): 46,
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}
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x_axis_s = {('cifar10', 'tss'): 1200000,
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('cifar10', 'sss'): 200000,
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('cifar100', 'tss'): 400,
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('cifar100', 'sss'): 400,
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('ImageNet16-120', 'tss'): 1200,
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('ImageNet16-120', 'sss'): 600}
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x_axis_s = {
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("cifar10", "tss"): 1200000,
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("cifar10", "sss"): 200000,
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("cifar100", "tss"): 400,
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("cifar100", "sss"): 400,
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("ImageNet16-120", "tss"): 1200,
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("ImageNet16-120", "sss"): 600,
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}
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name2label = {'cifar10': 'CIFAR-10',
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'cifar100': 'CIFAR-100',
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'ImageNet16-120': 'ImageNet-16-120'}
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name2label = {"cifar10": "CIFAR-10", "cifar100": "CIFAR-100", "ImageNet16-120": "ImageNet-16-120"}
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spaces2latex = {'tss': r'$\mathcal{S}_{t}$',
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'sss': r'$\mathcal{S}_{s}$',}
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spaces2latex = {
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"tss": r"$\mathcal{S}_{t}$",
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"sss": r"$\mathcal{S}_{s}$",
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}
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# FuncFormatter can be used as a decorator
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@ticker.FuncFormatter
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def major_formatter(x, pos):
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if x == 0:
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return '0'
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else:
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return "{:.2f}e5".format(x/1e5)
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if x == 0:
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return "0"
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else:
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return "{:.2f}e5".format(x / 1e5)
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def visualize_curve(api_dict, vis_save_dir):
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vis_save_dir = vis_save_dir.resolve()
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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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vis_save_dir.mkdir(parents=True, exist_ok=True)
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dpi, width, height = 250, 5000, 2000
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 28, 28
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dpi, width, height = 250, 5000, 2000
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 28, 28
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def sub_plot_fn(ax, search_space, dataset):
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max_time = x_axis_s[(dataset, search_space)]
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alg2data = fetch_data(search_space=search_space, dataset=dataset)
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alg2accuracies = OrderedDict()
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total_tickets = 200
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time_tickets = [float(i) / total_tickets * int(max_time) for i in range(total_tickets)]
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ax.set_xlim(0, x_axis_s[(dataset, search_space)])
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ax.set_ylim(y_min_s[(dataset, search_space)],
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y_max_s[(dataset, search_space)])
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for tick in ax.get_xticklabels():
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tick.set_rotation(25)
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tick.set_fontsize(LabelSize - 6)
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for tick in ax.get_yticklabels():
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tick.set_fontsize(LabelSize - 6)
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ax.xaxis.set_major_formatter(major_formatter)
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for idx, (alg, xdata) in enumerate(alg2data.items()):
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accuracies = []
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for ticket in time_tickets:
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# import pdb; pdb.set_trace()
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accuracy, accuracy_std = query_performance(
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api_dict[search_space], xdata['data'], dataset, ticket)
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accuracies.append(accuracy)
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# print('{:} plot alg : {:10s}, final accuracy = {:.2f}$\pm${:.2f}'.format(time_string(), alg, accuracy, accuracy_std))
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print('{:} plot alg : {:10s} on {:}'.format(time_string(), alg, search_space))
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alg2accuracies[alg] = accuracies
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ax.plot(time_tickets, accuracies, c=xdata['color'], linestyle=xdata['linestyle'], label='{:}'.format(alg))
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ax.set_xlabel('Estimated wall-clock time', fontsize=LabelSize)
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ax.set_ylabel('Test accuracy', fontsize=LabelSize)
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ax.set_title(r'Results on {:} over {:}'.format(name2label[dataset], spaces2latex[search_space]),
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fontsize=LabelSize)
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ax.legend(loc=4, fontsize=LegendFontsize)
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def sub_plot_fn(ax, search_space, dataset):
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max_time = x_axis_s[(dataset, search_space)]
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alg2data = fetch_data(search_space=search_space, dataset=dataset)
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alg2accuracies = OrderedDict()
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total_tickets = 200
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time_tickets = [float(i) / total_tickets * int(max_time) for i in range(total_tickets)]
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ax.set_xlim(0, x_axis_s[(dataset, search_space)])
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ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)])
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for tick in ax.get_xticklabels():
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tick.set_rotation(25)
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tick.set_fontsize(LabelSize - 6)
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for tick in ax.get_yticklabels():
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tick.set_fontsize(LabelSize - 6)
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ax.xaxis.set_major_formatter(major_formatter)
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for idx, (alg, xdata) in enumerate(alg2data.items()):
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accuracies = []
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for ticket in time_tickets:
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# import pdb; pdb.set_trace()
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accuracy, accuracy_std = query_performance(api_dict[search_space], xdata["data"], dataset, ticket)
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accuracies.append(accuracy)
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# print('{:} plot alg : {:10s}, final accuracy = {:.2f}$\pm${:.2f}'.format(time_string(), alg, accuracy, accuracy_std))
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print("{:} plot alg : {:10s} on {:}".format(time_string(), alg, search_space))
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alg2accuracies[alg] = accuracies
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ax.plot(time_tickets, accuracies, c=xdata["color"], linestyle=xdata["linestyle"], label="{:}".format(alg))
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ax.set_xlabel("Estimated wall-clock time", fontsize=LabelSize)
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ax.set_ylabel("Test accuracy", fontsize=LabelSize)
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ax.set_title(
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r"Results on {:} over {:}".format(name2label[dataset], spaces2latex[search_space]), fontsize=LabelSize
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)
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ax.legend(loc=4, fontsize=LegendFontsize)
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fig, axs = plt.subplots(1, 2, figsize=figsize)
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sub_plot_fn(axs[0], 'tss', 'cifar10')
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sub_plot_fn(axs[1], 'sss', 'cifar10')
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save_path = (vis_save_dir / 'full-curve.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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plt.close('all')
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fig, axs = plt.subplots(1, 2, figsize=figsize)
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sub_plot_fn(axs[0], "tss", "cifar10")
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sub_plot_fn(axs[1], "sss", "cifar10")
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save_path = (vis_save_dir / "full-curve.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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plt.close("all")
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench/nas-algos-vs-h', help='Folder to save checkpoints and log.')
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args = parser.parse_args()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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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/nas-algos-vs-h",
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help="Folder to save checkpoints and log.",
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)
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args = parser.parse_args()
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save_dir = Path(args.save_dir)
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save_dir = Path(args.save_dir)
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api_tss = create(None, 'tss', fast_mode=True, verbose=False)
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api_sss = create(None, 'sss', fast_mode=True, verbose=False)
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visualize_curve(dict(tss=api_tss, sss=api_sss), save_dir)
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api_tss = create(None, "tss", fast_mode=True, verbose=False)
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api_sss = create(None, "sss", fast_mode=True, verbose=False)
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visualize_curve(dict(tss=api_tss, sss=api_sss), save_dir)
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