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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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42
exps/experimental/test-ww.py
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@@ -2,31 +2,33 @@ import sys, time, random, argparse
from copy import deepcopy
import torchvision.models as models
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 utils import weight_watcher
def main():
# model = models.vgg19_bn(pretrained=True)
# _, summary = weight_watcher.analyze(model, alphas=False)
# for key, value in summary.items():
# print('{:10s} : {:}'.format(key, value))
# model = models.vgg19_bn(pretrained=True)
# _, summary = weight_watcher.analyze(model, alphas=False)
# for key, value in summary.items():
# print('{:10s} : {:}'.format(key, value))
_, summary = weight_watcher.analyze(models.vgg13(pretrained=True), alphas=False)
print('vgg-13 : {:}'.format(summary['lognorm']))
_, summary = weight_watcher.analyze(models.vgg13_bn(pretrained=True), alphas=False)
print('vgg-13-BN : {:}'.format(summary['lognorm']))
_, summary = weight_watcher.analyze(models.vgg16(pretrained=True), alphas=False)
print('vgg-16 : {:}'.format(summary['lognorm']))
_, summary = weight_watcher.analyze(models.vgg16_bn(pretrained=True), alphas=False)
print('vgg-16-BN : {:}'.format(summary['lognorm']))
_, summary = weight_watcher.analyze(models.vgg19(pretrained=True), alphas=False)
print('vgg-19 : {:}'.format(summary['lognorm']))
_, summary = weight_watcher.analyze(models.vgg19_bn(pretrained=True), alphas=False)
print('vgg-19-BN : {:}'.format(summary['lognorm']))
_, summary = weight_watcher.analyze(models.vgg13(pretrained=True), alphas=False)
print("vgg-13 : {:}".format(summary["lognorm"]))
_, summary = weight_watcher.analyze(models.vgg13_bn(pretrained=True), alphas=False)
print("vgg-13-BN : {:}".format(summary["lognorm"]))
_, summary = weight_watcher.analyze(models.vgg16(pretrained=True), alphas=False)
print("vgg-16 : {:}".format(summary["lognorm"]))
_, summary = weight_watcher.analyze(models.vgg16_bn(pretrained=True), alphas=False)
print("vgg-16-BN : {:}".format(summary["lognorm"]))
_, summary = weight_watcher.analyze(models.vgg19(pretrained=True), alphas=False)
print("vgg-19 : {:}".format(summary["lognorm"]))
_, summary = weight_watcher.analyze(models.vgg19_bn(pretrained=True), alphas=False)
print("vgg-19-BN : {:}".format(summary["lognorm"]))
if __name__ == '__main__':
main()
if __name__ == "__main__":
main()