1263 lines
49 KiB
Python
1263 lines
49 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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#####################################################
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# python exps/NAS-Bench-201/visualize.py --api_path $HOME/.torch/NAS-Bench-201-v1_0-e61699.pth
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#####################################################
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import sys, argparse
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from tqdm import tqdm
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from collections import OrderedDict
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import numpy as np
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import torch
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from pathlib import Path
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from collections import defaultdict
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import matplotlib
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import seaborn as sns
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from mpl_toolkits.mplot3d import Axes3D
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matplotlib.use("agg")
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import matplotlib.pyplot as plt
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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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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 nas_201_api import NASBench201API as API
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def calculate_correlation(*vectors):
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matrix = []
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for i, vectori in enumerate(vectors):
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x = []
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for j, vectorj in enumerate(vectors):
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x.append(np.corrcoef(vectori, vectorj)[0, 1])
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matrix.append(x)
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return np.array(matrix)
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def visualize_relative_ranking(vis_save_dir):
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print("\n" + "-" * 100)
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cifar010_cache_path = vis_save_dir / "{:}-cache-info.pth".format("cifar10")
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cifar100_cache_path = vis_save_dir / "{:}-cache-info.pth".format("cifar100")
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imagenet_cache_path = vis_save_dir / "{:}-cache-info.pth".format("ImageNet16-120")
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cifar010_info = torch.load(cifar010_cache_path)
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cifar100_info = torch.load(cifar100_cache_path)
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imagenet_info = torch.load(imagenet_cache_path)
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indexes = list(range(len(cifar010_info["params"])))
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print("{:} start to visualize relative ranking".format(time_string()))
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# maximum accuracy with ResNet-level params 11472
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x_010_accs = [
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cifar010_info["test_accs"][i]
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if cifar010_info["params"][i] <= cifar010_info["params"][11472]
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else -1
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for i in indexes
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]
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x_100_accs = [
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cifar100_info["test_accs"][i]
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if cifar100_info["params"][i] <= cifar100_info["params"][11472]
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else -1
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for i in indexes
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]
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x_img_accs = [
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imagenet_info["test_accs"][i]
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if imagenet_info["params"][i] <= imagenet_info["params"][11472]
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else -1
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for i in indexes
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]
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cifar010_ord_indexes = sorted(indexes, key=lambda i: cifar010_info["test_accs"][i])
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cifar100_ord_indexes = sorted(indexes, key=lambda i: cifar100_info["test_accs"][i])
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imagenet_ord_indexes = sorted(indexes, key=lambda i: imagenet_info["test_accs"][i])
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cifar100_labels, imagenet_labels = [], []
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for idx in cifar010_ord_indexes:
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cifar100_labels.append(cifar100_ord_indexes.index(idx))
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imagenet_labels.append(imagenet_ord_indexes.index(idx))
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print("{:} prepare data done.".format(time_string()))
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dpi, width, height = 300, 2600, 2600
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 18, 18
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resnet_scale, resnet_alpha = 120, 0.5
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xlim(min(indexes), max(indexes))
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plt.ylim(min(indexes), max(indexes))
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# plt.ylabel('y').set_rotation(0)
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plt.yticks(
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np.arange(min(indexes), max(indexes), max(indexes) // 6),
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fontsize=LegendFontsize,
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rotation="vertical",
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)
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plt.xticks(
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np.arange(min(indexes), max(indexes), max(indexes) // 6),
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fontsize=LegendFontsize,
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)
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# ax.scatter(indexes, cifar100_labels, marker='^', s=0.5, c='tab:green', alpha=0.8, label='CIFAR-100')
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# ax.scatter(indexes, imagenet_labels, marker='*', s=0.5, c='tab:red' , alpha=0.8, label='ImageNet-16-120')
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# ax.scatter(indexes, indexes , marker='o', s=0.5, c='tab:blue' , alpha=0.8, label='CIFAR-10')
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ax.scatter(indexes, cifar100_labels, marker="^", s=0.5, c="tab:green", alpha=0.8)
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ax.scatter(indexes, imagenet_labels, marker="*", s=0.5, c="tab:red", alpha=0.8)
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ax.scatter(indexes, indexes, marker="o", s=0.5, c="tab:blue", alpha=0.8)
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ax.scatter([-1], [-1], marker="o", s=100, c="tab:blue", label="CIFAR-10")
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ax.scatter([-1], [-1], marker="^", s=100, c="tab:green", label="CIFAR-100")
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ax.scatter([-1], [-1], marker="*", s=100, c="tab:red", label="ImageNet-16-120")
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plt.grid(zorder=0)
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ax.set_axisbelow(True)
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plt.legend(loc=0, fontsize=LegendFontsize)
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ax.set_xlabel("architecture ranking in CIFAR-10", fontsize=LabelSize)
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ax.set_ylabel("architecture ranking", fontsize=LabelSize)
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save_path = (vis_save_dir / "relative-rank.pdf").resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="pdf")
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save_path = (vis_save_dir / "relative-rank.png").resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
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print("{:} save into {:}".format(time_string(), save_path))
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# calculate correlation
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sns_size = 15
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CoRelMatrix = calculate_correlation(
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cifar010_info["valid_accs"],
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cifar010_info["test_accs"],
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cifar100_info["valid_accs"],
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cifar100_info["test_accs"],
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imagenet_info["valid_accs"],
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imagenet_info["test_accs"],
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)
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fig = plt.figure(figsize=figsize)
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plt.axis("off")
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h = sns.heatmap(
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CoRelMatrix, annot=True, annot_kws={"size": sns_size}, fmt=".3f", linewidths=0.5
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)
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save_path = (vis_save_dir / "co-relation-all.pdf").resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="pdf")
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print("{:} save into {:}".format(time_string(), save_path))
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# calculate correlation
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acc_bars = [92, 93]
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for acc_bar in acc_bars:
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selected_indexes = []
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for i, acc in enumerate(cifar010_info["test_accs"]):
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if acc > acc_bar:
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selected_indexes.append(i)
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print("select {:} architectures".format(len(selected_indexes)))
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cifar010_valid_accs = np.array(cifar010_info["valid_accs"])[selected_indexes]
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cifar010_test_accs = np.array(cifar010_info["test_accs"])[selected_indexes]
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cifar100_valid_accs = np.array(cifar100_info["valid_accs"])[selected_indexes]
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cifar100_test_accs = np.array(cifar100_info["test_accs"])[selected_indexes]
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imagenet_valid_accs = np.array(imagenet_info["valid_accs"])[selected_indexes]
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imagenet_test_accs = np.array(imagenet_info["test_accs"])[selected_indexes]
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CoRelMatrix = calculate_correlation(
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cifar010_valid_accs,
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cifar010_test_accs,
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cifar100_valid_accs,
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cifar100_test_accs,
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imagenet_valid_accs,
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imagenet_test_accs,
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)
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fig = plt.figure(figsize=figsize)
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plt.axis("off")
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h = sns.heatmap(
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CoRelMatrix,
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annot=True,
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annot_kws={"size": sns_size},
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fmt=".3f",
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linewidths=0.5,
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)
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save_path = (
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vis_save_dir / "co-relation-top-{:}.pdf".format(len(selected_indexes))
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).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="pdf")
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print("{:} save into {:}".format(time_string(), save_path))
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plt.close("all")
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def visualize_info(meta_file, dataset, vis_save_dir):
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print("{:} start to visualize {:} information".format(time_string(), dataset))
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cache_file_path = vis_save_dir / "{:}-cache-info.pth".format(dataset)
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if not cache_file_path.exists():
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print("Do not find cache file : {:}".format(cache_file_path))
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nas_bench = API(str(meta_file))
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params, flops, train_accs, valid_accs, test_accs, otest_accs = (
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[],
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[],
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[],
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[],
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[],
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[],
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)
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for index in range(len(nas_bench)):
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info = nas_bench.query_by_index(index, use_12epochs_result=False)
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resx = info.get_comput_costs(dataset)
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flop, param = resx["flops"], resx["params"]
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if dataset == "cifar10":
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res = info.get_metrics("cifar10", "train")
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train_acc = res["accuracy"]
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res = info.get_metrics("cifar10-valid", "x-valid")
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valid_acc = res["accuracy"]
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res = info.get_metrics("cifar10", "ori-test")
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test_acc = res["accuracy"]
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res = info.get_metrics("cifar10", "ori-test")
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otest_acc = res["accuracy"]
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else:
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res = info.get_metrics(dataset, "train")
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train_acc = res["accuracy"]
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res = info.get_metrics(dataset, "x-valid")
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valid_acc = res["accuracy"]
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res = info.get_metrics(dataset, "x-test")
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test_acc = res["accuracy"]
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res = info.get_metrics(dataset, "ori-test")
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otest_acc = res["accuracy"]
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if index == 11472: # resnet
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resnet = {
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"params": param,
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"flops": flop,
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"index": 11472,
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"train_acc": train_acc,
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"valid_acc": valid_acc,
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"test_acc": test_acc,
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"otest_acc": otest_acc,
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}
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flops.append(flop)
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params.append(param)
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train_accs.append(train_acc)
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valid_accs.append(valid_acc)
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test_accs.append(test_acc)
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otest_accs.append(otest_acc)
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# resnet = {'params': 0.559, 'flops': 78.56, 'index': 11472, 'train_acc': 99.99, 'valid_acc': 90.84, 'test_acc': 93.97}
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info = {
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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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"otest_accs": otest_accs,
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}
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info["resnet"] = resnet
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torch.save(info, cache_file_path)
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else:
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print("Find cache file : {:}".format(cache_file_path))
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info = torch.load(cache_file_path)
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params, flops, train_accs, valid_accs, test_accs, otest_accs = (
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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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info["otest_accs"],
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)
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resnet = info["resnet"]
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print("{:} collect data done.".format(time_string()))
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indexes = list(range(len(params)))
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dpi, width, height = 300, 2600, 2600
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figsize = width / float(dpi), height / float(dpi)
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LabelSize, LegendFontsize = 22, 22
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resnet_scale, resnet_alpha = 120, 0.5
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xticks(np.arange(0, 1.6, 0.3), fontsize=LegendFontsize)
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if dataset == "cifar10":
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plt.ylim(50, 100)
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plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize)
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elif dataset == "cifar100":
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plt.ylim(25, 75)
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plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize)
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else:
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plt.ylim(0, 50)
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plt.yticks(np.arange(0, 51, 10), fontsize=LegendFontsize)
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ax.scatter(params, valid_accs, marker="o", s=0.5, c="tab:blue")
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ax.scatter(
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[resnet["params"]],
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[resnet["valid_acc"]],
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marker="*",
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s=resnet_scale,
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c="tab:orange",
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label="resnet",
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alpha=0.4,
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)
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plt.grid(zorder=0)
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ax.set_axisbelow(True)
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plt.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel("#parameters (MB)", fontsize=LabelSize)
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ax.set_ylabel("the validation accuracy (%)", fontsize=LabelSize)
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save_path = (vis_save_dir / "{:}-param-vs-valid.pdf".format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="pdf")
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save_path = (vis_save_dir / "{:}-param-vs-valid.png".format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
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print("{:} save into {:}".format(time_string(), save_path))
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xticks(np.arange(0, 1.6, 0.3), fontsize=LegendFontsize)
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if dataset == "cifar10":
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plt.ylim(50, 100)
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plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize)
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elif dataset == "cifar100":
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plt.ylim(25, 75)
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plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize)
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else:
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plt.ylim(0, 50)
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plt.yticks(np.arange(0, 51, 10), fontsize=LegendFontsize)
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ax.scatter(params, test_accs, marker="o", s=0.5, c="tab:blue")
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ax.scatter(
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[resnet["params"]],
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[resnet["test_acc"]],
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marker="*",
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s=resnet_scale,
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c="tab:orange",
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label="resnet",
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alpha=resnet_alpha,
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)
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plt.grid()
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ax.set_axisbelow(True)
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plt.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel("#parameters (MB)", fontsize=LabelSize)
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ax.set_ylabel("the test accuracy (%)", fontsize=LabelSize)
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save_path = (vis_save_dir / "{:}-param-vs-test.pdf".format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="pdf")
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save_path = (vis_save_dir / "{:}-param-vs-test.png".format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
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print("{:} save into {:}".format(time_string(), save_path))
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xticks(np.arange(0, 1.6, 0.3), fontsize=LegendFontsize)
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if dataset == "cifar10":
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plt.ylim(50, 100)
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plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize)
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elif dataset == "cifar100":
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plt.ylim(20, 100)
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plt.yticks(np.arange(20, 101, 10), fontsize=LegendFontsize)
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else:
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plt.ylim(25, 76)
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plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize)
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ax.scatter(params, train_accs, marker="o", s=0.5, c="tab:blue")
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ax.scatter(
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[resnet["params"]],
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[resnet["train_acc"]],
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marker="*",
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s=resnet_scale,
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c="tab:orange",
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label="resnet",
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alpha=resnet_alpha,
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)
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plt.grid()
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ax.set_axisbelow(True)
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plt.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel("#parameters (MB)", fontsize=LabelSize)
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ax.set_ylabel("the trarining accuracy (%)", fontsize=LabelSize)
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save_path = (vis_save_dir / "{:}-param-vs-train.pdf".format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="pdf")
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save_path = (vis_save_dir / "{:}-param-vs-train.png".format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
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print("{:} save into {:}".format(time_string(), save_path))
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fig = plt.figure(figsize=figsize)
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ax = fig.add_subplot(111)
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plt.xlim(0, max(indexes))
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plt.xticks(
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np.arange(min(indexes), max(indexes), max(indexes) // 5),
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fontsize=LegendFontsize,
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)
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if dataset == "cifar10":
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plt.ylim(50, 100)
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plt.yticks(np.arange(50, 101, 10), fontsize=LegendFontsize)
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elif dataset == "cifar100":
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plt.ylim(25, 75)
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plt.yticks(np.arange(25, 76, 10), fontsize=LegendFontsize)
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else:
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plt.ylim(0, 50)
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plt.yticks(np.arange(0, 51, 10), fontsize=LegendFontsize)
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ax.scatter(indexes, test_accs, marker="o", s=0.5, c="tab:blue")
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ax.scatter(
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[resnet["index"]],
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[resnet["test_acc"]],
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marker="*",
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s=resnet_scale,
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c="tab:orange",
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label="resnet",
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alpha=resnet_alpha,
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)
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plt.grid()
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ax.set_axisbelow(True)
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plt.legend(loc=4, fontsize=LegendFontsize)
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ax.set_xlabel("architecture ID", fontsize=LabelSize)
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ax.set_ylabel("the test accuracy (%)", fontsize=LabelSize)
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save_path = (vis_save_dir / "{:}-test-over-ID.pdf".format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="pdf")
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save_path = (vis_save_dir / "{:}-test-over-ID.png".format(dataset)).resolve()
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fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
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print("{:} save into {:}".format(time_string(), save_path))
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plt.close("all")
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def visualize_rank_over_time(meta_file, vis_save_dir):
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print("\n" + "-" * 150)
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vis_save_dir.mkdir(parents=True, exist_ok=True)
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print(
|
|
"{:} start to visualize rank-over-time into {:}".format(
|
|
time_string(), vis_save_dir
|
|
)
|
|
)
|
|
cache_file_path = vis_save_dir / "rank-over-time-cache-info.pth"
|
|
if not cache_file_path.exists():
|
|
print("Do not find cache file : {:}".format(cache_file_path))
|
|
nas_bench = API(str(meta_file))
|
|
print("{:} load nas_bench done".format(time_string()))
|
|
params, flops, train_accs, valid_accs, test_accs, otest_accs = (
|
|
[],
|
|
[],
|
|
defaultdict(list),
|
|
defaultdict(list),
|
|
defaultdict(list),
|
|
defaultdict(list),
|
|
)
|
|
# for iepoch in range(200): for index in range( len(nas_bench) ):
|
|
for index in tqdm(range(len(nas_bench))):
|
|
info = nas_bench.query_by_index(index, use_12epochs_result=False)
|
|
for iepoch in range(200):
|
|
res = info.get_metrics("cifar10", "train", iepoch)
|
|
train_acc = res["accuracy"]
|
|
res = info.get_metrics("cifar10-valid", "x-valid", iepoch)
|
|
valid_acc = res["accuracy"]
|
|
res = info.get_metrics("cifar10", "ori-test", iepoch)
|
|
test_acc = res["accuracy"]
|
|
res = info.get_metrics("cifar10", "ori-test", iepoch)
|
|
otest_acc = res["accuracy"]
|
|
train_accs[iepoch].append(train_acc)
|
|
valid_accs[iepoch].append(valid_acc)
|
|
test_accs[iepoch].append(test_acc)
|
|
otest_accs[iepoch].append(otest_acc)
|
|
if iepoch == 0:
|
|
res = info.get_comput_costs("cifar10")
|
|
flop, param = res["flops"], res["params"]
|
|
flops.append(flop)
|
|
params.append(param)
|
|
info = {
|
|
"params": params,
|
|
"flops": flops,
|
|
"train_accs": train_accs,
|
|
"valid_accs": valid_accs,
|
|
"test_accs": test_accs,
|
|
"otest_accs": otest_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, otest_accs = (
|
|
info["params"],
|
|
info["flops"],
|
|
info["train_accs"],
|
|
info["valid_accs"],
|
|
info["test_accs"],
|
|
info["otest_accs"],
|
|
)
|
|
print("{:} collect data done.".format(time_string()))
|
|
# selected_epochs = [0, 100, 150, 180, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199]
|
|
selected_epochs = list(range(200))
|
|
x_xtests = test_accs[199]
|
|
indexes = list(range(len(x_xtests)))
|
|
ord_idxs = sorted(indexes, key=lambda i: x_xtests[i])
|
|
for sepoch in selected_epochs:
|
|
x_valids = valid_accs[sepoch]
|
|
valid_ord_idxs = sorted(indexes, key=lambda i: x_valids[i])
|
|
valid_ord_lbls = []
|
|
for idx in ord_idxs:
|
|
valid_ord_lbls.append(valid_ord_idxs.index(idx))
|
|
# labeled data
|
|
dpi, width, height = 300, 2600, 2600
|
|
figsize = width / float(dpi), height / float(dpi)
|
|
LabelSize, LegendFontsize = 18, 18
|
|
|
|
fig = plt.figure(figsize=figsize)
|
|
ax = fig.add_subplot(111)
|
|
plt.xlim(min(indexes), max(indexes))
|
|
plt.ylim(min(indexes), max(indexes))
|
|
plt.yticks(
|
|
np.arange(min(indexes), max(indexes), max(indexes) // 6),
|
|
fontsize=LegendFontsize,
|
|
rotation="vertical",
|
|
)
|
|
plt.xticks(
|
|
np.arange(min(indexes), max(indexes), max(indexes) // 6),
|
|
fontsize=LegendFontsize,
|
|
)
|
|
ax.scatter(indexes, valid_ord_lbls, 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="CIFAR-10 validation"
|
|
)
|
|
ax.scatter([-1], [-1], marker="o", s=100, c="tab:blue", label="CIFAR-10 test")
|
|
plt.grid(zorder=0)
|
|
ax.set_axisbelow(True)
|
|
plt.legend(loc="upper left", fontsize=LegendFontsize)
|
|
ax.set_xlabel(
|
|
"architecture ranking in the final test accuracy", fontsize=LabelSize
|
|
)
|
|
ax.set_ylabel("architecture ranking in the validation set", fontsize=LabelSize)
|
|
save_path = (vis_save_dir / "time-{:03d}.pdf".format(sepoch)).resolve()
|
|
fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="pdf")
|
|
save_path = (vis_save_dir / "time-{:03d}.png".format(sepoch)).resolve()
|
|
fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
|
|
print("{:} save into {:}".format(time_string(), save_path))
|
|
plt.close("all")
|
|
|
|
|
|
def write_video(save_dir):
|
|
import cv2
|
|
|
|
video_save_path = save_dir / "time.avi"
|
|
print("{:} start create video for {:}".format(time_string(), video_save_path))
|
|
images = sorted(list(save_dir.glob("time-*.png")))
|
|
ximage = cv2.imread(str(images[0]))
|
|
# shape = (ximage.shape[1], ximage.shape[0])
|
|
shape = (1000, 1000)
|
|
# writer = cv2.VideoWriter(str(video_save_path), cv2.VideoWriter_fourcc(*"MJPG"), 25, shape)
|
|
writer = cv2.VideoWriter(
|
|
str(video_save_path), cv2.VideoWriter_fourcc(*"MJPG"), 5, shape
|
|
)
|
|
for idx, image in enumerate(images):
|
|
ximage = cv2.imread(str(image))
|
|
_image = cv2.resize(ximage, shape)
|
|
writer.write(_image)
|
|
writer.release()
|
|
print("write video [{:} frames] into {:}".format(len(images), video_save_path))
|
|
|
|
|
|
def plot_results_nas_v2(api, dataset_xset_a, dataset_xset_b, root, file_name, y_lims):
|
|
# print ('root-path={:}, dataset={:}, xset={:}'.format(root, dataset, xset))
|
|
print("root-path : {:} and {:}".format(dataset_xset_a, dataset_xset_b))
|
|
checkpoints = [
|
|
"./output/search-cell-nas-bench-201/R-EA-cifar10/results.pth",
|
|
"./output/search-cell-nas-bench-201/REINFORCE-cifar10/results.pth",
|
|
"./output/search-cell-nas-bench-201/RAND-cifar10/results.pth",
|
|
"./output/search-cell-nas-bench-201/BOHB-cifar10/results.pth",
|
|
]
|
|
legends, indexes = ["REA", "REINFORCE", "RANDOM", "BOHB"], None
|
|
All_Accs_A, All_Accs_B = OrderedDict(), OrderedDict()
|
|
for legend, checkpoint in zip(legends, checkpoints):
|
|
all_indexes = torch.load(checkpoint, map_location="cpu")
|
|
accuracies_A, accuracies_B = [], []
|
|
accuracies = []
|
|
for x in all_indexes:
|
|
info = api.arch2infos_full[x]
|
|
metrics = info.get_metrics(
|
|
dataset_xset_a[0], dataset_xset_a[1], None, False
|
|
)
|
|
accuracies_A.append(metrics["accuracy"])
|
|
metrics = info.get_metrics(
|
|
dataset_xset_b[0], dataset_xset_b[1], None, False
|
|
)
|
|
accuracies_B.append(metrics["accuracy"])
|
|
accuracies.append((accuracies_A[-1], accuracies_B[-1]))
|
|
if indexes is None:
|
|
indexes = list(range(len(all_indexes)))
|
|
accuracies = sorted(accuracies)
|
|
All_Accs_A[legend] = [x[0] for x in accuracies]
|
|
All_Accs_B[legend] = [x[1] for x in accuracies]
|
|
|
|
color_set = ["r", "b", "g", "c", "m", "y", "k"]
|
|
dpi, width, height = 300, 3400, 2600
|
|
LabelSize, LegendFontsize = 28, 28
|
|
figsize = width / float(dpi), height / float(dpi)
|
|
fig = plt.figure(figsize=figsize)
|
|
x_axis = np.arange(0, 600)
|
|
plt.xlim(0, max(indexes))
|
|
plt.ylim(y_lims[0], y_lims[1])
|
|
interval_x, interval_y = 100, y_lims[2]
|
|
plt.xticks(np.arange(0, max(indexes), interval_x), fontsize=LegendFontsize)
|
|
plt.yticks(np.arange(y_lims[0], y_lims[1], interval_y), fontsize=LegendFontsize)
|
|
plt.grid()
|
|
plt.xlabel("The index of runs", fontsize=LabelSize)
|
|
plt.ylabel("The accuracy (%)", fontsize=LabelSize)
|
|
|
|
for idx, legend in enumerate(legends):
|
|
plt.plot(
|
|
indexes,
|
|
All_Accs_B[legend],
|
|
color=color_set[idx],
|
|
linestyle="--",
|
|
label="{:}".format(legend),
|
|
lw=1,
|
|
alpha=0.5,
|
|
)
|
|
plt.plot(indexes, All_Accs_A[legend], color=color_set[idx], linestyle="-", lw=1)
|
|
for All_Accs in [All_Accs_A, All_Accs_B]:
|
|
print(
|
|
"{:} : mean = {:}, std = {:} :: {:.2f}$\\pm${:.2f}".format(
|
|
legend,
|
|
np.mean(All_Accs[legend]),
|
|
np.std(All_Accs[legend]),
|
|
np.mean(All_Accs[legend]),
|
|
np.std(All_Accs[legend]),
|
|
)
|
|
)
|
|
plt.legend(loc=4, fontsize=LegendFontsize)
|
|
save_path = root / "{:}".format(file_name)
|
|
print("save figure into {:}\n".format(save_path))
|
|
fig.savefig(str(save_path), dpi=dpi, bbox_inches="tight", format="pdf")
|
|
|
|
|
|
def plot_results_nas(api, dataset, xset, root, file_name, y_lims):
|
|
print("root-path={:}, dataset={:}, xset={:}".format(root, dataset, xset))
|
|
checkpoints = [
|
|
"./output/search-cell-nas-bench-201/R-EA-cifar10/results.pth",
|
|
"./output/search-cell-nas-bench-201/REINFORCE-cifar10/results.pth",
|
|
"./output/search-cell-nas-bench-201/RAND-cifar10/results.pth",
|
|
"./output/search-cell-nas-bench-201/BOHB-cifar10/results.pth",
|
|
]
|
|
legends, indexes = ["REA", "REINFORCE", "RANDOM", "BOHB"], None
|
|
All_Accs = OrderedDict()
|
|
for legend, checkpoint in zip(legends, checkpoints):
|
|
all_indexes = torch.load(checkpoint, map_location="cpu")
|
|
accuracies = []
|
|
for x in all_indexes:
|
|
info = api.arch2infos_full[x]
|
|
metrics = info.get_metrics(dataset, xset, None, False)
|
|
accuracies.append(metrics["accuracy"])
|
|
if indexes is None:
|
|
indexes = list(range(len(all_indexes)))
|
|
All_Accs[legend] = sorted(accuracies)
|
|
|
|
color_set = ["r", "b", "g", "c", "m", "y", "k"]
|
|
dpi, width, height = 300, 3400, 2600
|
|
LabelSize, LegendFontsize = 28, 28
|
|
figsize = width / float(dpi), height / float(dpi)
|
|
fig = plt.figure(figsize=figsize)
|
|
x_axis = np.arange(0, 600)
|
|
plt.xlim(0, max(indexes))
|
|
plt.ylim(y_lims[0], y_lims[1])
|
|
interval_x, interval_y = 100, y_lims[2]
|
|
plt.xticks(np.arange(0, max(indexes), interval_x), fontsize=LegendFontsize)
|
|
plt.yticks(np.arange(y_lims[0], y_lims[1], interval_y), fontsize=LegendFontsize)
|
|
plt.grid()
|
|
plt.xlabel("The index of runs", fontsize=LabelSize)
|
|
plt.ylabel("The accuracy (%)", fontsize=LabelSize)
|
|
|
|
for idx, legend in enumerate(legends):
|
|
plt.plot(
|
|
indexes,
|
|
All_Accs[legend],
|
|
color=color_set[idx],
|
|
linestyle="-",
|
|
label="{:}".format(legend),
|
|
lw=2,
|
|
)
|
|
print(
|
|
"{:} : mean = {:}, std = {:} :: {:.2f}$\\pm${:.2f}".format(
|
|
legend,
|
|
np.mean(All_Accs[legend]),
|
|
np.std(All_Accs[legend]),
|
|
np.mean(All_Accs[legend]),
|
|
np.std(All_Accs[legend]),
|
|
)
|
|
)
|
|
plt.legend(loc=4, fontsize=LegendFontsize)
|
|
save_path = root / "{:}-{:}-{:}".format(dataset, xset, file_name)
|
|
print("save figure into {:}\n".format(save_path))
|
|
fig.savefig(str(save_path), dpi=dpi, bbox_inches="tight", format="pdf")
|
|
|
|
|
|
def just_show(api):
|
|
xtimes = {
|
|
"RSPS": [8082.5, 7794.2, 8144.7],
|
|
"DARTS-V1": [11582.1, 11347.0, 11948.2],
|
|
"DARTS-V2": [35694.7, 36132.7, 35518.0],
|
|
"GDAS": [31334.1, 31478.6, 32016.7],
|
|
"SETN": [33528.8, 33831.5, 35058.3],
|
|
"ENAS": [14340.2, 13817.3, 14018.9],
|
|
}
|
|
for xkey, xlist in xtimes.items():
|
|
xlist = np.array(xlist)
|
|
print("{:4s} : mean-time={:.2f} s".format(xkey, xlist.mean()))
|
|
|
|
xpaths = {
|
|
"RSPS": "output/search-cell-nas-bench-201/RANDOM-NAS-cifar10/checkpoint/",
|
|
"DARTS-V1": "output/search-cell-nas-bench-201/DARTS-V1-cifar10/checkpoint/",
|
|
"DARTS-V2": "output/search-cell-nas-bench-201/DARTS-V2-cifar10/checkpoint/",
|
|
"GDAS": "output/search-cell-nas-bench-201/GDAS-cifar10/checkpoint/",
|
|
"SETN": "output/search-cell-nas-bench-201/SETN-cifar10/checkpoint/",
|
|
"ENAS": "output/search-cell-nas-bench-201/ENAS-cifar10/checkpoint/",
|
|
}
|
|
xseeds = {
|
|
"RSPS": [5349, 59613, 5983],
|
|
"DARTS-V1": [11416, 72873, 81184],
|
|
"DARTS-V2": [43330, 79405, 79423],
|
|
"GDAS": [19677, 884, 95950],
|
|
"SETN": [20518, 61817, 89144],
|
|
"ENAS": [3231, 34238, 96929],
|
|
}
|
|
|
|
def get_accs(xdata, index=-1):
|
|
if index == -1:
|
|
epochs = xdata["epoch"]
|
|
genotype = xdata["genotypes"][epochs - 1]
|
|
index = api.query_index_by_arch(genotype)
|
|
pairs = [
|
|
("cifar10-valid", "x-valid"),
|
|
("cifar10", "ori-test"),
|
|
("cifar100", "x-valid"),
|
|
("cifar100", "x-test"),
|
|
("ImageNet16-120", "x-valid"),
|
|
("ImageNet16-120", "x-test"),
|
|
]
|
|
xresults = []
|
|
for dataset, xset in pairs:
|
|
metrics = api.arch2infos_full[index].get_metrics(dataset, xset, None, False)
|
|
xresults.append(metrics["accuracy"])
|
|
return xresults
|
|
|
|
for xkey in xpaths.keys():
|
|
all_paths = [
|
|
"{:}/seed-{:}-basic.pth".format(xpaths[xkey], seed) for seed in xseeds[xkey]
|
|
]
|
|
all_datas = [torch.load(xpath) for xpath in all_paths]
|
|
accyss = [get_accs(xdatas) for xdatas in all_datas]
|
|
accyss = np.array(accyss)
|
|
print("\nxkey = {:}".format(xkey))
|
|
for i in range(accyss.shape[1]):
|
|
print(
|
|
"---->>>> {:.2f}$\\pm${:.2f}".format(
|
|
accyss[:, i].mean(), accyss[:, i].std()
|
|
)
|
|
)
|
|
|
|
print("\n{:}".format(get_accs(None, 11472))) # resnet
|
|
pairs = [
|
|
("cifar10-valid", "x-valid"),
|
|
("cifar10", "ori-test"),
|
|
("cifar100", "x-valid"),
|
|
("cifar100", "x-test"),
|
|
("ImageNet16-120", "x-valid"),
|
|
("ImageNet16-120", "x-test"),
|
|
]
|
|
for dataset, metric_on_set in pairs:
|
|
arch_index, highest_acc = api.find_best(dataset, metric_on_set)
|
|
print(
|
|
"[{:10s}-{:10s} ::: index={:5d}, accuracy={:.2f}".format(
|
|
dataset, metric_on_set, arch_index, highest_acc
|
|
)
|
|
)
|
|
|
|
|
|
def show_nas_sharing_w(
|
|
api, dataset, subset, vis_save_dir, sufix, file_name, y_lims, x_maxs
|
|
):
|
|
color_set = ["r", "b", "g", "c", "m", "y", "k"]
|
|
dpi, width, height = 300, 3400, 2600
|
|
LabelSize, LegendFontsize = 28, 28
|
|
figsize = width / float(dpi), height / float(dpi)
|
|
fig = plt.figure(figsize=figsize)
|
|
# x_maxs = 250
|
|
plt.xlim(0, x_maxs + 1)
|
|
plt.ylim(y_lims[0], y_lims[1])
|
|
interval_x, interval_y = x_maxs // 5, y_lims[2]
|
|
plt.xticks(np.arange(0, x_maxs + 1, interval_x), fontsize=LegendFontsize)
|
|
plt.yticks(np.arange(y_lims[0], y_lims[1], interval_y), fontsize=LegendFontsize)
|
|
plt.grid()
|
|
plt.xlabel("The searching epoch", fontsize=LabelSize)
|
|
plt.ylabel("The accuracy (%)", fontsize=LabelSize)
|
|
|
|
xpaths = {
|
|
"RSPS": "output/search-cell-nas-bench-201/RANDOM-NAS-cifar10-{:}/checkpoint/".format(
|
|
sufix
|
|
),
|
|
"DARTS-V1": "output/search-cell-nas-bench-201/DARTS-V1-cifar10-{:}/checkpoint/".format(
|
|
sufix
|
|
),
|
|
"DARTS-V2": "output/search-cell-nas-bench-201/DARTS-V2-cifar10-{:}/checkpoint/".format(
|
|
sufix
|
|
),
|
|
"GDAS": "output/search-cell-nas-bench-201/GDAS-cifar10-{:}/checkpoint/".format(
|
|
sufix
|
|
),
|
|
"SETN": "output/search-cell-nas-bench-201/SETN-cifar10-{:}/checkpoint/".format(
|
|
sufix
|
|
),
|
|
"ENAS": "output/search-cell-nas-bench-201/ENAS-cifar10-{:}/checkpoint/".format(
|
|
sufix
|
|
),
|
|
}
|
|
"""
|
|
xseeds = {'RSPS' : [5349, 59613, 5983],
|
|
'DARTS-V1': [11416, 72873, 81184, 28640],
|
|
'DARTS-V2': [43330, 79405, 79423],
|
|
'GDAS' : [19677, 884, 95950],
|
|
'SETN' : [20518, 61817, 89144],
|
|
'ENAS' : [3231, 34238, 96929],
|
|
}
|
|
"""
|
|
xseeds = {
|
|
"RSPS": [23814, 28015, 95809],
|
|
"DARTS-V1": [48349, 80877, 81920],
|
|
"DARTS-V2": [61712, 7941, 87041],
|
|
"GDAS": [72818, 72996, 78877],
|
|
"SETN": [26985, 55206, 95404],
|
|
"ENAS": [21792, 36605, 45029],
|
|
}
|
|
|
|
def get_accs(xdata):
|
|
epochs, xresults = xdata["epoch"], []
|
|
if -1 in xdata["genotypes"]:
|
|
metrics = api.arch2infos_full[
|
|
api.query_index_by_arch(xdata["genotypes"][-1])
|
|
].get_metrics(dataset, subset, None, False)
|
|
else:
|
|
metrics = api.arch2infos_full[api.random()].get_metrics(
|
|
dataset, subset, None, False
|
|
)
|
|
xresults.append(metrics["accuracy"])
|
|
for iepoch in range(epochs):
|
|
genotype = xdata["genotypes"][iepoch]
|
|
index = api.query_index_by_arch(genotype)
|
|
metrics = api.arch2infos_full[index].get_metrics(
|
|
dataset, subset, None, False
|
|
)
|
|
xresults.append(metrics["accuracy"])
|
|
return xresults
|
|
|
|
if x_maxs == 50:
|
|
xox, xxxstrs = "v2", ["DARTS-V1", "DARTS-V2"]
|
|
elif x_maxs == 250:
|
|
xox, xxxstrs = "v1", ["RSPS", "GDAS", "SETN", "ENAS"]
|
|
else:
|
|
raise ValueError("invalid x_maxs={:}".format(x_maxs))
|
|
|
|
for idx, method in enumerate(xxxstrs):
|
|
xkey = method
|
|
all_paths = [
|
|
"{:}/seed-{:}-basic.pth".format(xpaths[xkey], seed) for seed in xseeds[xkey]
|
|
]
|
|
all_datas = [torch.load(xpath, map_location="cpu") for xpath in all_paths]
|
|
accyss = [get_accs(xdatas) for xdatas in all_datas]
|
|
accyss = np.array(accyss)
|
|
epochs = list(range(accyss.shape[1]))
|
|
plt.plot(
|
|
epochs,
|
|
[accyss[:, i].mean() for i in epochs],
|
|
color=color_set[idx],
|
|
linestyle="-",
|
|
label="{:}".format(method),
|
|
lw=2,
|
|
)
|
|
plt.fill_between(
|
|
epochs,
|
|
[accyss[:, i].mean() - accyss[:, i].std() for i in epochs],
|
|
[accyss[:, i].mean() + accyss[:, i].std() for i in epochs],
|
|
alpha=0.2,
|
|
color=color_set[idx],
|
|
)
|
|
# plt.legend(loc=4, fontsize=LegendFontsize)
|
|
plt.legend(loc=0, fontsize=LegendFontsize)
|
|
save_path = vis_save_dir / "{:}.pdf".format(file_name)
|
|
print("save figure into {:}\n".format(save_path))
|
|
fig.savefig(str(save_path), dpi=dpi, bbox_inches="tight", format="pdf")
|
|
|
|
|
|
def show_nas_sharing_w_v2(
|
|
api, data_sub_a, data_sub_b, vis_save_dir, sufix, file_name, y_lims, x_maxs
|
|
):
|
|
color_set = ["r", "b", "g", "c", "m", "y", "k"]
|
|
dpi, width, height = 300, 3400, 2600
|
|
LabelSize, LegendFontsize = 28, 28
|
|
figsize = width / float(dpi), height / float(dpi)
|
|
fig = plt.figure(figsize=figsize)
|
|
# x_maxs = 250
|
|
plt.xlim(0, x_maxs + 1)
|
|
plt.ylim(y_lims[0], y_lims[1])
|
|
interval_x, interval_y = x_maxs // 5, y_lims[2]
|
|
plt.xticks(np.arange(0, x_maxs + 1, interval_x), fontsize=LegendFontsize)
|
|
plt.yticks(np.arange(y_lims[0], y_lims[1], interval_y), fontsize=LegendFontsize)
|
|
plt.grid()
|
|
plt.xlabel("The searching epoch", fontsize=LabelSize)
|
|
plt.ylabel("The accuracy (%)", fontsize=LabelSize)
|
|
|
|
xpaths = {
|
|
"RSPS": "output/search-cell-nas-bench-201/RANDOM-NAS-cifar10-{:}/checkpoint/".format(
|
|
sufix
|
|
),
|
|
"DARTS-V1": "output/search-cell-nas-bench-201/DARTS-V1-cifar10-{:}/checkpoint/".format(
|
|
sufix
|
|
),
|
|
"DARTS-V2": "output/search-cell-nas-bench-201/DARTS-V2-cifar10-{:}/checkpoint/".format(
|
|
sufix
|
|
),
|
|
"GDAS": "output/search-cell-nas-bench-201/GDAS-cifar10-{:}/checkpoint/".format(
|
|
sufix
|
|
),
|
|
"SETN": "output/search-cell-nas-bench-201/SETN-cifar10-{:}/checkpoint/".format(
|
|
sufix
|
|
),
|
|
"ENAS": "output/search-cell-nas-bench-201/ENAS-cifar10-{:}/checkpoint/".format(
|
|
sufix
|
|
),
|
|
}
|
|
"""
|
|
xseeds = {'RSPS' : [5349, 59613, 5983],
|
|
'DARTS-V1': [11416, 72873, 81184, 28640],
|
|
'DARTS-V2': [43330, 79405, 79423],
|
|
'GDAS' : [19677, 884, 95950],
|
|
'SETN' : [20518, 61817, 89144],
|
|
'ENAS' : [3231, 34238, 96929],
|
|
}
|
|
"""
|
|
xseeds = {
|
|
"RSPS": [23814, 28015, 95809],
|
|
"DARTS-V1": [48349, 80877, 81920],
|
|
"DARTS-V2": [61712, 7941, 87041],
|
|
"GDAS": [72818, 72996, 78877],
|
|
"SETN": [26985, 55206, 95404],
|
|
"ENAS": [21792, 36605, 45029],
|
|
}
|
|
|
|
def get_accs(xdata, dataset, subset):
|
|
epochs, xresults = xdata["epoch"], []
|
|
if -1 in xdata["genotypes"]:
|
|
metrics = api.arch2infos_full[
|
|
api.query_index_by_arch(xdata["genotypes"][-1])
|
|
].get_metrics(dataset, subset, None, False)
|
|
else:
|
|
metrics = api.arch2infos_full[api.random()].get_metrics(
|
|
dataset, subset, None, False
|
|
)
|
|
xresults.append(metrics["accuracy"])
|
|
for iepoch in range(epochs):
|
|
genotype = xdata["genotypes"][iepoch]
|
|
index = api.query_index_by_arch(genotype)
|
|
metrics = api.arch2infos_full[index].get_metrics(
|
|
dataset, subset, None, False
|
|
)
|
|
xresults.append(metrics["accuracy"])
|
|
return xresults
|
|
|
|
if x_maxs == 50:
|
|
xox, xxxstrs = "v2", ["DARTS-V1", "DARTS-V2"]
|
|
elif x_maxs == 250:
|
|
xox, xxxstrs = "v1", ["RSPS", "GDAS", "SETN", "ENAS"]
|
|
else:
|
|
raise ValueError("invalid x_maxs={:}".format(x_maxs))
|
|
|
|
for idx, method in enumerate(xxxstrs):
|
|
xkey = method
|
|
all_paths = [
|
|
"{:}/seed-{:}-basic.pth".format(xpaths[xkey], seed) for seed in xseeds[xkey]
|
|
]
|
|
all_datas = [torch.load(xpath, map_location="cpu") for xpath in all_paths]
|
|
accyss_A = np.array(
|
|
[get_accs(xdatas, data_sub_a[0], data_sub_a[1]) for xdatas in all_datas]
|
|
)
|
|
accyss_B = np.array(
|
|
[get_accs(xdatas, data_sub_b[0], data_sub_b[1]) for xdatas in all_datas]
|
|
)
|
|
epochs = list(range(accyss_A.shape[1]))
|
|
for j, accyss in enumerate([accyss_A, accyss_B]):
|
|
if x_maxs == 50:
|
|
color, line = color_set[idx * 2 + j], "-" if j == 0 else "--"
|
|
elif x_maxs == 250:
|
|
color, line = color_set[idx], "-" if j == 0 else "--"
|
|
else:
|
|
raise ValueError("invalid x-maxs={:}".format(x_maxs))
|
|
plt.plot(
|
|
epochs,
|
|
[accyss[:, i].mean() for i in epochs],
|
|
color=color,
|
|
linestyle=line,
|
|
label="{:} ({:})".format(method, "VALID" if j == 0 else "TEST"),
|
|
lw=2,
|
|
alpha=0.9,
|
|
)
|
|
plt.fill_between(
|
|
epochs,
|
|
[accyss[:, i].mean() - accyss[:, i].std() for i in epochs],
|
|
[accyss[:, i].mean() + accyss[:, i].std() for i in epochs],
|
|
alpha=0.2,
|
|
color=color,
|
|
)
|
|
setname = data_sub_a if j == 0 else data_sub_b
|
|
print(
|
|
"{:} -- {:} ---- {:.2f}$\\pm${:.2f}".format(
|
|
method, setname, accyss[:, -1].mean(), accyss[:, -1].std()
|
|
)
|
|
)
|
|
# plt.legend(loc=4, fontsize=LegendFontsize)
|
|
plt.legend(loc=0, fontsize=LegendFontsize)
|
|
save_path = vis_save_dir / "{:}-{:}".format(xox, file_name)
|
|
print("save figure into {:}\n".format(save_path))
|
|
fig.savefig(str(save_path), dpi=dpi, bbox_inches="tight", format="pdf")
|
|
|
|
|
|
def show_reinforce(api, root, dataset, xset, file_name, y_lims):
|
|
print("root-path={:}, dataset={:}, xset={:}".format(root, dataset, xset))
|
|
LRs = ["0.01", "0.02", "0.1", "0.2", "0.5"]
|
|
checkpoints = [
|
|
"./output/search-cell-nas-bench-201/REINFORCE-cifar10-{:}/results.pth".format(x)
|
|
for x in LRs
|
|
]
|
|
acc_lr_dict, indexes = {}, None
|
|
for lr, checkpoint in zip(LRs, checkpoints):
|
|
all_indexes, accuracies = torch.load(checkpoint, map_location="cpu"), []
|
|
for x in all_indexes:
|
|
info = api.arch2infos_full[x]
|
|
metrics = info.get_metrics(dataset, xset, None, False)
|
|
accuracies.append(metrics["accuracy"])
|
|
if indexes is None:
|
|
indexes = list(range(len(accuracies)))
|
|
acc_lr_dict[lr] = np.array(sorted(accuracies))
|
|
print(
|
|
"LR={:.3f}, mean={:}, std={:}".format(
|
|
float(lr), acc_lr_dict[lr].mean(), acc_lr_dict[lr].std()
|
|
)
|
|
)
|
|
|
|
color_set = ["r", "b", "g", "c", "m", "y", "k"]
|
|
dpi, width, height = 300, 3400, 2600
|
|
LabelSize, LegendFontsize = 28, 22
|
|
figsize = width / float(dpi), height / float(dpi)
|
|
fig = plt.figure(figsize=figsize)
|
|
x_axis = np.arange(0, 600)
|
|
plt.xlim(0, max(indexes))
|
|
plt.ylim(y_lims[0], y_lims[1])
|
|
interval_x, interval_y = 100, y_lims[2]
|
|
plt.xticks(np.arange(0, max(indexes), interval_x), fontsize=LegendFontsize)
|
|
plt.yticks(np.arange(y_lims[0], y_lims[1], interval_y), fontsize=LegendFontsize)
|
|
plt.grid()
|
|
plt.xlabel("The index of runs", fontsize=LabelSize)
|
|
plt.ylabel("The accuracy (%)", fontsize=LabelSize)
|
|
|
|
for idx, LR in enumerate(LRs):
|
|
legend = "LR={:.2f}".format(float(LR))
|
|
# color, linestyle = color_set[idx // 2], '-' if idx % 2 == 0 else '-.'
|
|
color, linestyle = color_set[idx], "-"
|
|
plt.plot(
|
|
indexes,
|
|
acc_lr_dict[LR],
|
|
color=color,
|
|
linestyle=linestyle,
|
|
label=legend,
|
|
lw=2,
|
|
alpha=0.8,
|
|
)
|
|
print(
|
|
"{:} : mean = {:}, std = {:} :: {:.2f}$\\pm${:.2f}".format(
|
|
legend,
|
|
np.mean(acc_lr_dict[LR]),
|
|
np.std(acc_lr_dict[LR]),
|
|
np.mean(acc_lr_dict[LR]),
|
|
np.std(acc_lr_dict[LR]),
|
|
)
|
|
)
|
|
plt.legend(loc=4, fontsize=LegendFontsize)
|
|
save_path = root / "{:}-{:}-{:}.pdf".format(dataset, xset, file_name)
|
|
print("save figure into {:}\n".format(save_path))
|
|
fig.savefig(str(save_path), dpi=dpi, bbox_inches="tight", format="pdf")
|
|
|
|
|
|
def show_rea(api, root, dataset, xset, file_name, y_lims):
|
|
print("root-path={:}, dataset={:}, xset={:}".format(root, dataset, xset))
|
|
SSs = [3, 5, 10]
|
|
checkpoints = [
|
|
"./output/search-cell-nas-bench-201/R-EA-cifar10-SS{:}/results.pth".format(x)
|
|
for x in SSs
|
|
]
|
|
acc_ss_dict, indexes = {}, None
|
|
for ss, checkpoint in zip(SSs, checkpoints):
|
|
all_indexes, accuracies = torch.load(checkpoint, map_location="cpu"), []
|
|
for x in all_indexes:
|
|
info = api.arch2infos_full[x]
|
|
metrics = info.get_metrics(dataset, xset, None, False)
|
|
accuracies.append(metrics["accuracy"])
|
|
if indexes is None:
|
|
indexes = list(range(len(accuracies)))
|
|
acc_ss_dict[ss] = np.array(sorted(accuracies))
|
|
print(
|
|
"Sample-Size={:2d}, mean={:}, std={:}".format(
|
|
ss, acc_ss_dict[ss].mean(), acc_ss_dict[ss].std()
|
|
)
|
|
)
|
|
|
|
color_set = ["r", "b", "g", "c", "m", "y", "k"]
|
|
dpi, width, height = 300, 3400, 2600
|
|
LabelSize, LegendFontsize = 28, 22
|
|
figsize = width / float(dpi), height / float(dpi)
|
|
fig = plt.figure(figsize=figsize)
|
|
x_axis = np.arange(0, 600)
|
|
plt.xlim(0, max(indexes))
|
|
plt.ylim(y_lims[0], y_lims[1])
|
|
interval_x, interval_y = 100, y_lims[2]
|
|
plt.xticks(np.arange(0, max(indexes), interval_x), fontsize=LegendFontsize)
|
|
plt.yticks(np.arange(y_lims[0], y_lims[1], interval_y), fontsize=LegendFontsize)
|
|
plt.grid()
|
|
plt.xlabel("The index of runs", fontsize=LabelSize)
|
|
plt.ylabel("The accuracy (%)", fontsize=LabelSize)
|
|
|
|
for idx, ss in enumerate(SSs):
|
|
legend = "sample-size={:2d}".format(ss)
|
|
# color, linestyle = color_set[idx // 2], '-' if idx % 2 == 0 else '-.'
|
|
color, linestyle = color_set[idx], "-"
|
|
plt.plot(
|
|
indexes,
|
|
acc_ss_dict[ss],
|
|
color=color,
|
|
linestyle=linestyle,
|
|
label=legend,
|
|
lw=2,
|
|
alpha=0.8,
|
|
)
|
|
print(
|
|
"{:} : mean = {:}, std = {:} :: {:.2f}$\\pm${:.2f}".format(
|
|
legend,
|
|
np.mean(acc_ss_dict[ss]),
|
|
np.std(acc_ss_dict[ss]),
|
|
np.mean(acc_ss_dict[ss]),
|
|
np.std(acc_ss_dict[ss]),
|
|
)
|
|
)
|
|
plt.legend(loc=4, fontsize=LegendFontsize)
|
|
save_path = root / "{:}-{:}-{:}.pdf".format(dataset, xset, file_name)
|
|
print("save figure into {:}\n".format(save_path))
|
|
fig.savefig(str(save_path), dpi=dpi, bbox_inches="tight", format="pdf")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
parser = argparse.ArgumentParser(
|
|
description="NAS-Bench-201",
|
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
|
)
|
|
parser.add_argument(
|
|
"--save_dir",
|
|
type=str,
|
|
default="./output/search-cell-nas-bench-201/visuals",
|
|
help="The base-name of folder to save checkpoints and log.",
|
|
)
|
|
parser.add_argument(
|
|
"--api_path",
|
|
type=str,
|
|
default=None,
|
|
help="The path to the NAS-Bench-201 benchmark file.",
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
vis_save_dir = Path(args.save_dir)
|
|
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
|
meta_file = Path(args.api_path)
|
|
assert meta_file.exists(), "invalid path for api : {:}".format(meta_file)
|
|
# visualize_rank_over_time(str(meta_file), vis_save_dir / 'over-time')
|
|
# write_video(vis_save_dir / 'over-time')
|
|
# visualize_info(str(meta_file), 'cifar10' , vis_save_dir)
|
|
# visualize_info(str(meta_file), 'cifar100', vis_save_dir)
|
|
# visualize_info(str(meta_file), 'ImageNet16-120', vis_save_dir)
|
|
# visualize_relative_ranking(vis_save_dir)
|
|
|
|
api = API(args.api_path)
|
|
# show_reinforce(api, vis_save_dir, 'cifar10-valid' , 'x-valid', 'REINFORCE-CIFAR-10', (85, 92, 2))
|
|
# show_rea (api, vis_save_dir, 'cifar10-valid' , 'x-valid', 'REA-CIFAR-10', (88, 92, 1))
|
|
|
|
# plot_results_nas_v2(api, ('cifar10-valid' , 'x-valid'), ('cifar10' , 'ori-test'), vis_save_dir, 'nas-com-v2-cifar010.pdf', (85,95, 1))
|
|
# plot_results_nas_v2(api, ('cifar100' , 'x-valid'), ('cifar100' , 'x-test' ), vis_save_dir, 'nas-com-v2-cifar100.pdf', (60,75, 3))
|
|
# plot_results_nas_v2(api, ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test' ), vis_save_dir, 'nas-com-v2-imagenet.pdf', (35,48, 2))
|
|
|
|
show_nas_sharing_w_v2(
|
|
api,
|
|
("cifar10-valid", "x-valid"),
|
|
("cifar10", "ori-test"),
|
|
vis_save_dir,
|
|
"BN0",
|
|
"BN0-DARTS-CIFAR010.pdf",
|
|
(0, 100, 10),
|
|
50,
|
|
)
|
|
show_nas_sharing_w_v2(
|
|
api,
|
|
("cifar100", "x-valid"),
|
|
("cifar100", "x-test"),
|
|
vis_save_dir,
|
|
"BN0",
|
|
"BN0-DARTS-CIFAR100.pdf",
|
|
(0, 100, 10),
|
|
50,
|
|
)
|
|
show_nas_sharing_w_v2(
|
|
api,
|
|
("ImageNet16-120", "x-valid"),
|
|
("ImageNet16-120", "x-test"),
|
|
vis_save_dir,
|
|
"BN0",
|
|
"BN0-DARTS-ImageNet.pdf",
|
|
(0, 100, 10),
|
|
50,
|
|
)
|
|
|
|
show_nas_sharing_w_v2(
|
|
api,
|
|
("cifar10-valid", "x-valid"),
|
|
("cifar10", "ori-test"),
|
|
vis_save_dir,
|
|
"BN0",
|
|
"BN0-OTHER-CIFAR010.pdf",
|
|
(0, 100, 10),
|
|
250,
|
|
)
|
|
show_nas_sharing_w_v2(
|
|
api,
|
|
("cifar100", "x-valid"),
|
|
("cifar100", "x-test"),
|
|
vis_save_dir,
|
|
"BN0",
|
|
"BN0-OTHER-CIFAR100.pdf",
|
|
(0, 100, 10),
|
|
250,
|
|
)
|
|
show_nas_sharing_w_v2(
|
|
api,
|
|
("ImageNet16-120", "x-valid"),
|
|
("ImageNet16-120", "x-test"),
|
|
vis_save_dir,
|
|
"BN0",
|
|
"BN0-OTHER-ImageNet.pdf",
|
|
(0, 100, 10),
|
|
250,
|
|
)
|
|
|
|
show_nas_sharing_w(
|
|
api,
|
|
"cifar10-valid",
|
|
"x-valid",
|
|
vis_save_dir,
|
|
"BN0",
|
|
"BN0-XX-CIFAR010-VALID.pdf",
|
|
(0, 100, 10),
|
|
250,
|
|
)
|
|
show_nas_sharing_w(
|
|
api,
|
|
"cifar10",
|
|
"ori-test",
|
|
vis_save_dir,
|
|
"BN0",
|
|
"BN0-XX-CIFAR010-TEST.pdf",
|
|
(0, 100, 10),
|
|
250,
|
|
)
|
|
"""
|
|
for x_maxs in [50, 250]:
|
|
show_nas_sharing_w(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
|
|
show_nas_sharing_w(api, 'cifar10' , 'ori-test', vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
|
|
show_nas_sharing_w(api, 'cifar100' , 'x-valid' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
|
|
show_nas_sharing_w(api, 'cifar100' , 'x-test' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
|
|
show_nas_sharing_w(api, 'ImageNet16-120', 'x-valid' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
|
|
show_nas_sharing_w(api, 'ImageNet16-120', 'x-test' , vis_save_dir, 'nas-plot.pdf', (0, 100,10), x_maxs)
|
|
|
|
show_nas_sharing_w_v2(api, ('cifar10-valid' , 'x-valid'), ('cifar10' , 'ori-test') , vis_save_dir, 'DARTS-CIFAR010.pdf', (0, 100,10), 50)
|
|
just_show(api)
|
|
plot_results_nas(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-com.pdf', (85,95, 1))
|
|
plot_results_nas(api, 'cifar10' , 'ori-test', vis_save_dir, 'nas-com.pdf', (85,95, 1))
|
|
plot_results_nas(api, 'cifar100' , 'x-valid' , vis_save_dir, 'nas-com.pdf', (55,75, 3))
|
|
plot_results_nas(api, 'cifar100' , 'x-test' , vis_save_dir, 'nas-com.pdf', (55,75, 3))
|
|
plot_results_nas(api, 'ImageNet16-120', 'x-valid' , vis_save_dir, 'nas-com.pdf', (35,50, 3))
|
|
plot_results_nas(api, 'ImageNet16-120', 'x-test' , vis_save_dir, 'nas-com.pdf', (35,50, 3))
|
|
"""
|