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Reformulate via black

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D-X-Y
2021-03-17 09:25:58 +00:00
parent a9093e41e1
commit f98edea22a
59 changed files with 12289 additions and 8918 deletions
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58
exps/show-dataset.py
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@@ -7,41 +7,47 @@
##############################################################################
import os, sys, time, torch, random, argparse
from typing import List, Text, Dict, Any
from PIL import ImageFile
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from copy import deepcopy
from copy import deepcopy
from pathlib import Path
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 datasets import get_datasets
from nats_bench import create
def show_imagenet_16_120(dataset_dir=None):
if dataset_dir is None:
torch_home_dir = os.environ['TORCH_HOME'] if 'TORCH_HOME' in os.environ else os.path.join(os.environ['HOME'], '.torch')
dataset_dir = os.path.join(torch_home_dir, 'cifar.python', 'ImageNet16')
train_data, valid_data, xshape, class_num = get_datasets('ImageNet16-120', dataset_dir, -1)
split_info = load_config('configs/nas-benchmark/ImageNet16-120-split.txt', None, None)
print('=' * 10 + ' ImageNet-16-120 ' + '=' * 10)
print('Training Data: {:}'.format(train_data))
print('Evaluation Data: {:}'.format(valid_data))
print('Hold-out training: {:} images.'.format(len(split_info.train)))
print('Hold-out valid : {:} images.'.format(len(split_info.valid)))
if dataset_dir is None:
torch_home_dir = (
os.environ["TORCH_HOME"] if "TORCH_HOME" in os.environ else os.path.join(os.environ["HOME"], ".torch")
)
dataset_dir = os.path.join(torch_home_dir, "cifar.python", "ImageNet16")
train_data, valid_data, xshape, class_num = get_datasets("ImageNet16-120", dataset_dir, -1)
split_info = load_config("configs/nas-benchmark/ImageNet16-120-split.txt", None, None)
print("=" * 10 + " ImageNet-16-120 " + "=" * 10)
print("Training Data: {:}".format(train_data))
print("Evaluation Data: {:}".format(valid_data))
print("Hold-out training: {:} images.".format(len(split_info.train)))
print("Hold-out valid : {:} images.".format(len(split_info.valid)))
if __name__ == '__main__':
# show_imagenet_16_120()
api_nats_tss = create(None, 'tss', fast_mode=True, verbose=True)
if __name__ == "__main__":
# show_imagenet_16_120()
api_nats_tss = create(None, "tss", fast_mode=True, verbose=True)
valid_acc_12e = []
test_acc_12e = []
test_acc_200e = []
for index in range(10000):
info = api_nats_tss.get_more_info(index, 'ImageNet16-120', hp='12')
valid_acc_12e.append(info['valid-accuracy']) # the validation accuracy after training the model by 12 epochs
test_acc_12e.append(info['test-accuracy']) # the test accuracy after training the model by 12 epochs
info = api_nats_tss.get_more_info(index, 'ImageNet16-120', hp='200')
test_acc_200e.append(info['test-accuracy']) # the test accuracy after training the model by 200 epochs (which I reported in the paper)
valid_acc_12e = []
test_acc_12e = []
test_acc_200e = []
for index in range(10000):
info = api_nats_tss.get_more_info(index, "ImageNet16-120", hp="12")
valid_acc_12e.append(info["valid-accuracy"]) # the validation accuracy after training the model by 12 epochs
test_acc_12e.append(info["test-accuracy"]) # the test accuracy after training the model by 12 epochs
info = api_nats_tss.get_more_info(index, "ImageNet16-120", hp="200")
test_acc_200e.append(
info["test-accuracy"]
) # the test accuracy after training the model by 200 epochs (which I reported in the paper)