Reformulate via black
This commit is contained in:
D-X-Y
@@ -11,170 +11,179 @@ import numpy as np
|
||||
from typing import List, Text, Dict, Any
|
||||
from shutil import copyfile
|
||||
from collections import defaultdict, OrderedDict
|
||||
from copy import deepcopy
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
import matplotlib
|
||||
import seaborn as sns
|
||||
matplotlib.use('agg')
|
||||
|
||||
matplotlib.use("agg")
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.ticker as ticker
|
||||
|
||||
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
|
||||
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
|
||||
lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
|
||||
if str(lib_dir) not in sys.path:
|
||||
sys.path.insert(0, str(lib_dir))
|
||||
from config_utils import dict2config, load_config
|
||||
from nats_bench import create
|
||||
from log_utils import time_string
|
||||
|
||||
|
||||
def get_valid_test_acc(api, arch, dataset):
|
||||
is_size_space = api.search_space_name == 'size'
|
||||
if dataset == 'cifar10':
|
||||
xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
|
||||
test_acc = xinfo['test-accuracy']
|
||||
xinfo = api.get_more_info(arch, dataset='cifar10-valid', hp=90 if is_size_space else 200, is_random=False)
|
||||
valid_acc = xinfo['valid-accuracy']
|
||||
else:
|
||||
xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
|
||||
valid_acc = xinfo['valid-accuracy']
|
||||
test_acc = xinfo['test-accuracy']
|
||||
return valid_acc, test_acc, 'validation = {:.2f}, test = {:.2f}\n'.format(valid_acc, test_acc)
|
||||
is_size_space = api.search_space_name == "size"
|
||||
if dataset == "cifar10":
|
||||
xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
|
||||
test_acc = xinfo["test-accuracy"]
|
||||
xinfo = api.get_more_info(arch, dataset="cifar10-valid", hp=90 if is_size_space else 200, is_random=False)
|
||||
valid_acc = xinfo["valid-accuracy"]
|
||||
else:
|
||||
xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
|
||||
valid_acc = xinfo["valid-accuracy"]
|
||||
test_acc = xinfo["test-accuracy"]
|
||||
return valid_acc, test_acc, "validation = {:.2f}, test = {:.2f}\n".format(valid_acc, test_acc)
|
||||
|
||||
|
||||
def fetch_data(root_dir='./output/search', search_space='tss', dataset=None, suffix='-WARM0.3'):
|
||||
ss_dir = '{:}-{:}'.format(root_dir, search_space)
|
||||
alg2name, alg2path = OrderedDict(), OrderedDict()
|
||||
seeds = [777, 888, 999]
|
||||
print('\n[fetch data] from {:} on {:}'.format(search_space, dataset))
|
||||
if search_space == 'tss':
|
||||
alg2name['GDAS'] = 'gdas-affine0_BN0-None'
|
||||
alg2name['RSPS'] = 'random-affine0_BN0-None'
|
||||
alg2name['DARTS (1st)'] = 'darts-v1-affine0_BN0-None'
|
||||
alg2name['DARTS (2nd)'] = 'darts-v2-affine0_BN0-None'
|
||||
alg2name['ENAS'] = 'enas-affine0_BN0-None'
|
||||
alg2name['SETN'] = 'setn-affine0_BN0-None'
|
||||
else:
|
||||
alg2name['channel-wise interpolation'] = 'tas-affine0_BN0-AWD0.001{:}'.format(suffix)
|
||||
alg2name['masking + Gumbel-Softmax'] = 'mask_gumbel-affine0_BN0-AWD0.001{:}'.format(suffix)
|
||||
alg2name['masking + sampling'] = 'mask_rl-affine0_BN0-AWD0.0{:}'.format(suffix)
|
||||
for alg, name in alg2name.items():
|
||||
alg2path[alg] = os.path.join(ss_dir, dataset, name, 'seed-{:}-last-info.pth')
|
||||
alg2data = OrderedDict()
|
||||
for alg, path in alg2path.items():
|
||||
alg2data[alg], ok_num = [], 0
|
||||
for seed in seeds:
|
||||
xpath = path.format(seed)
|
||||
if os.path.isfile(xpath):
|
||||
ok_num += 1
|
||||
else:
|
||||
print('This is an invalid path : {:}'.format(xpath))
|
||||
continue
|
||||
data = torch.load(xpath, map_location=torch.device('cpu'))
|
||||
try:
|
||||
data = torch.load(data['last_checkpoint'], map_location=torch.device('cpu'))
|
||||
except:
|
||||
xpath = str(data['last_checkpoint']).split('E100-')
|
||||
if len(xpath) == 2 and os.path.isfile(xpath[0] + xpath[1]):
|
||||
xpath = xpath[0] + xpath[1]
|
||||
elif 'fbv2' in str(data['last_checkpoint']):
|
||||
xpath = str(data['last_checkpoint']).replace('fbv2', 'mask_gumbel')
|
||||
elif 'tunas' in str(data['last_checkpoint']):
|
||||
xpath = str(data['last_checkpoint']).replace('tunas', 'mask_rl')
|
||||
else:
|
||||
raise ValueError('Invalid path: {:}'.format(data['last_checkpoint']))
|
||||
data = torch.load(xpath, map_location=torch.device('cpu'))
|
||||
alg2data[alg].append(data['genotypes'])
|
||||
print('This algorithm : {:} has {:} valid ckps.'.format(alg, ok_num))
|
||||
assert ok_num > 0, 'Must have at least 1 valid ckps.'
|
||||
return alg2data
|
||||
def fetch_data(root_dir="./output/search", search_space="tss", dataset=None, suffix="-WARM0.3"):
|
||||
ss_dir = "{:}-{:}".format(root_dir, search_space)
|
||||
alg2name, alg2path = OrderedDict(), OrderedDict()
|
||||
seeds = [777, 888, 999]
|
||||
print("\n[fetch data] from {:} on {:}".format(search_space, dataset))
|
||||
if search_space == "tss":
|
||||
alg2name["GDAS"] = "gdas-affine0_BN0-None"
|
||||
alg2name["RSPS"] = "random-affine0_BN0-None"
|
||||
alg2name["DARTS (1st)"] = "darts-v1-affine0_BN0-None"
|
||||
alg2name["DARTS (2nd)"] = "darts-v2-affine0_BN0-None"
|
||||
alg2name["ENAS"] = "enas-affine0_BN0-None"
|
||||
alg2name["SETN"] = "setn-affine0_BN0-None"
|
||||
else:
|
||||
alg2name["channel-wise interpolation"] = "tas-affine0_BN0-AWD0.001{:}".format(suffix)
|
||||
alg2name["masking + Gumbel-Softmax"] = "mask_gumbel-affine0_BN0-AWD0.001{:}".format(suffix)
|
||||
alg2name["masking + sampling"] = "mask_rl-affine0_BN0-AWD0.0{:}".format(suffix)
|
||||
for alg, name in alg2name.items():
|
||||
alg2path[alg] = os.path.join(ss_dir, dataset, name, "seed-{:}-last-info.pth")
|
||||
alg2data = OrderedDict()
|
||||
for alg, path in alg2path.items():
|
||||
alg2data[alg], ok_num = [], 0
|
||||
for seed in seeds:
|
||||
xpath = path.format(seed)
|
||||
if os.path.isfile(xpath):
|
||||
ok_num += 1
|
||||
else:
|
||||
print("This is an invalid path : {:}".format(xpath))
|
||||
continue
|
||||
data = torch.load(xpath, map_location=torch.device("cpu"))
|
||||
try:
|
||||
data = torch.load(data["last_checkpoint"], map_location=torch.device("cpu"))
|
||||
except:
|
||||
xpath = str(data["last_checkpoint"]).split("E100-")
|
||||
if len(xpath) == 2 and os.path.isfile(xpath[0] + xpath[1]):
|
||||
xpath = xpath[0] + xpath[1]
|
||||
elif "fbv2" in str(data["last_checkpoint"]):
|
||||
xpath = str(data["last_checkpoint"]).replace("fbv2", "mask_gumbel")
|
||||
elif "tunas" in str(data["last_checkpoint"]):
|
||||
xpath = str(data["last_checkpoint"]).replace("tunas", "mask_rl")
|
||||
else:
|
||||
raise ValueError("Invalid path: {:}".format(data["last_checkpoint"]))
|
||||
data = torch.load(xpath, map_location=torch.device("cpu"))
|
||||
alg2data[alg].append(data["genotypes"])
|
||||
print("This algorithm : {:} has {:} valid ckps.".format(alg, ok_num))
|
||||
assert ok_num > 0, "Must have at least 1 valid ckps."
|
||||
return alg2data
|
||||
|
||||
|
||||
y_min_s = {('cifar10', 'tss'): 90,
|
||||
('cifar10', 'sss'): 92,
|
||||
('cifar100', 'tss'): 65,
|
||||
('cifar100', 'sss'): 65,
|
||||
('ImageNet16-120', 'tss'): 36,
|
||||
('ImageNet16-120', 'sss'): 40}
|
||||
y_min_s = {
|
||||
("cifar10", "tss"): 90,
|
||||
("cifar10", "sss"): 92,
|
||||
("cifar100", "tss"): 65,
|
||||
("cifar100", "sss"): 65,
|
||||
("ImageNet16-120", "tss"): 36,
|
||||
("ImageNet16-120", "sss"): 40,
|
||||
}
|
||||
|
||||
y_max_s = {('cifar10', 'tss'): 94.5,
|
||||
('cifar10', 'sss'): 93.3,
|
||||
('cifar100', 'tss'): 72,
|
||||
('cifar100', 'sss'): 70,
|
||||
('ImageNet16-120', 'tss'): 44,
|
||||
('ImageNet16-120', 'sss'): 46}
|
||||
y_max_s = {
|
||||
("cifar10", "tss"): 94.5,
|
||||
("cifar10", "sss"): 93.3,
|
||||
("cifar100", "tss"): 72,
|
||||
("cifar100", "sss"): 70,
|
||||
("ImageNet16-120", "tss"): 44,
|
||||
("ImageNet16-120", "sss"): 46,
|
||||
}
|
||||
|
||||
name2label = {'cifar10': 'CIFAR-10',
|
||||
'cifar100': 'CIFAR-100',
|
||||
'ImageNet16-120': 'ImageNet-16-120'}
|
||||
name2label = {"cifar10": "CIFAR-10", "cifar100": "CIFAR-100", "ImageNet16-120": "ImageNet-16-120"}
|
||||
|
||||
name2suffix = {("sss", "warm"): "-WARM0.3", ("sss", "none"): "-WARMNone", ("tss", "none"): None, ("tss", None): None}
|
||||
|
||||
name2suffix = {('sss', 'warm'): '-WARM0.3',
|
||||
('sss', 'none'): '-WARMNone',
|
||||
('tss', 'none') : None,
|
||||
('tss', None) : None}
|
||||
|
||||
def visualize_curve(api, vis_save_dir, search_space, suffix):
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
vis_save_dir = vis_save_dir.resolve()
|
||||
vis_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
dpi, width, height = 250, 5200, 1400
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 16, 16
|
||||
dpi, width, height = 250, 5200, 1400
|
||||
figsize = width / float(dpi), height / float(dpi)
|
||||
LabelSize, LegendFontsize = 16, 16
|
||||
|
||||
def sub_plot_fn(ax, dataset):
|
||||
print('{:} plot {:10s}'.format(time_string(), dataset))
|
||||
alg2data = fetch_data(search_space=search_space, dataset=dataset, suffix=name2suffix[(search_space, suffix)])
|
||||
alg2accuracies = OrderedDict()
|
||||
epochs = 100
|
||||
colors = ['b', 'g', 'c', 'm', 'y', 'r']
|
||||
ax.set_xlim(0, epochs)
|
||||
# ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)])
|
||||
for idx, (alg, data) in enumerate(alg2data.items()):
|
||||
xs, accuracies = [], []
|
||||
for iepoch in range(epochs + 1):
|
||||
try:
|
||||
structures, accs = [_[iepoch-1] for _ in data], []
|
||||
except:
|
||||
raise ValueError('This alg {:} on {:} has invalid checkpoints.'.format(alg, dataset))
|
||||
for structure in structures:
|
||||
info = api.get_more_info(structure, dataset=dataset, hp=90 if api.search_space_name == 'size' else 200, is_random=False)
|
||||
accs.append(info['test-accuracy'])
|
||||
accuracies.append(sum(accs)/len(accs))
|
||||
xs.append(iepoch)
|
||||
alg2accuracies[alg] = accuracies
|
||||
ax.plot(xs, accuracies, c=colors[idx], label='{:}'.format(alg))
|
||||
ax.set_xlabel('The searching epoch', fontsize=LabelSize)
|
||||
ax.set_ylabel('Test accuracy on {:}'.format(name2label[dataset]), fontsize=LabelSize)
|
||||
ax.set_title('Searching results on {:}'.format(name2label[dataset]), fontsize=LabelSize+4)
|
||||
structures, valid_accs, test_accs = [_[epochs-1] for _ in data], [], []
|
||||
print('{:} plot alg : {:} -- final {:} architectures.'.format(time_string(), alg, len(structures)))
|
||||
for arch in structures:
|
||||
valid_acc, test_acc, _ = get_valid_test_acc(api, arch, dataset)
|
||||
test_accs.append(test_acc)
|
||||
valid_accs.append(valid_acc)
|
||||
print('{:} plot alg : {:} -- validation: {:.2f}$\pm${:.2f} -- test: {:.2f}$\pm${:.2f}'.format(
|
||||
time_string(), alg, np.mean(valid_accs), np.std(valid_accs), np.mean(test_accs), np.std(test_accs)))
|
||||
ax.legend(loc=4, fontsize=LegendFontsize)
|
||||
def sub_plot_fn(ax, dataset):
|
||||
print("{:} plot {:10s}".format(time_string(), dataset))
|
||||
alg2data = fetch_data(search_space=search_space, dataset=dataset, suffix=name2suffix[(search_space, suffix)])
|
||||
alg2accuracies = OrderedDict()
|
||||
epochs = 100
|
||||
colors = ["b", "g", "c", "m", "y", "r"]
|
||||
ax.set_xlim(0, epochs)
|
||||
# ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)])
|
||||
for idx, (alg, data) in enumerate(alg2data.items()):
|
||||
xs, accuracies = [], []
|
||||
for iepoch in range(epochs + 1):
|
||||
try:
|
||||
structures, accs = [_[iepoch - 1] for _ in data], []
|
||||
except:
|
||||
raise ValueError("This alg {:} on {:} has invalid checkpoints.".format(alg, dataset))
|
||||
for structure in structures:
|
||||
info = api.get_more_info(
|
||||
structure, dataset=dataset, hp=90 if api.search_space_name == "size" else 200, is_random=False
|
||||
)
|
||||
accs.append(info["test-accuracy"])
|
||||
accuracies.append(sum(accs) / len(accs))
|
||||
xs.append(iepoch)
|
||||
alg2accuracies[alg] = accuracies
|
||||
ax.plot(xs, accuracies, c=colors[idx], label="{:}".format(alg))
|
||||
ax.set_xlabel("The searching epoch", fontsize=LabelSize)
|
||||
ax.set_ylabel("Test accuracy on {:}".format(name2label[dataset]), fontsize=LabelSize)
|
||||
ax.set_title("Searching results on {:}".format(name2label[dataset]), fontsize=LabelSize + 4)
|
||||
structures, valid_accs, test_accs = [_[epochs - 1] for _ in data], [], []
|
||||
print("{:} plot alg : {:} -- final {:} architectures.".format(time_string(), alg, len(structures)))
|
||||
for arch in structures:
|
||||
valid_acc, test_acc, _ = get_valid_test_acc(api, arch, dataset)
|
||||
test_accs.append(test_acc)
|
||||
valid_accs.append(valid_acc)
|
||||
print(
|
||||
"{:} plot alg : {:} -- validation: {:.2f}$\pm${:.2f} -- test: {:.2f}$\pm${:.2f}".format(
|
||||
time_string(), alg, np.mean(valid_accs), np.std(valid_accs), np.mean(test_accs), np.std(test_accs)
|
||||
)
|
||||
)
|
||||
ax.legend(loc=4, fontsize=LegendFontsize)
|
||||
|
||||
fig, axs = plt.subplots(1, 3, figsize=figsize)
|
||||
datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
|
||||
for dataset, ax in zip(datasets, axs):
|
||||
sub_plot_fn(ax, dataset)
|
||||
print('sub-plot {:} on {:} done.'.format(dataset, search_space))
|
||||
save_path = (vis_save_dir / '{:}-ws-{:}-curve.png'.format(search_space, suffix)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
|
||||
print ('{:} save into {:}'.format(time_string(), save_path))
|
||||
plt.close('all')
|
||||
fig, axs = plt.subplots(1, 3, figsize=figsize)
|
||||
datasets = ["cifar10", "cifar100", "ImageNet16-120"]
|
||||
for dataset, ax in zip(datasets, axs):
|
||||
sub_plot_fn(ax, dataset)
|
||||
print("sub-plot {:} on {:} done.".format(dataset, search_space))
|
||||
save_path = (vis_save_dir / "{:}-ws-{:}-curve.png".format(search_space, suffix)).resolve()
|
||||
fig.savefig(save_path, dpi=dpi, bbox_inches="tight", format="png")
|
||||
print("{:} save into {:}".format(time_string(), save_path))
|
||||
plt.close("all")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='NATS-Bench', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench/nas-algos', help='Folder to save checkpoints and log.')
|
||||
args = parser.parse_args()
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="NATS-Bench", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, default="output/vis-nas-bench/nas-algos", help="Folder to save checkpoints and log."
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
save_dir = Path(args.save_dir)
|
||||
save_dir = Path(args.save_dir)
|
||||
|
||||
api_tss = create(None, 'tss', fast_mode=True, verbose=False)
|
||||
visualize_curve(api_tss, save_dir, 'tss', None)
|
||||
api_tss = create(None, "tss", fast_mode=True, verbose=False)
|
||||
visualize_curve(api_tss, save_dir, "tss", None)
|
||||
|
||||
api_sss = create(None, 'sss', fast_mode=True, verbose=False)
|
||||
visualize_curve(api_sss, save_dir, 'sss', 'warm')
|
||||
visualize_curve(api_sss, save_dir, 'sss', 'none')
|
||||
api_sss = create(None, "sss", fast_mode=True, verbose=False)
|
||||
visualize_curve(api_sss, save_dir, "sss", "warm")
|
||||
visualize_curve(api_sss, save_dir, "sss", "none")
|
||||
|
||||
Reference in New Issue
Block a user