2020-03-11 08:44:39 +01:00
|
|
|
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))
|
|
|
|
|
|
|
|
|
|
from utils import weight_watcher
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def main():
|
2020-03-13 22:00:54 +01:00
|
|
|
# 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']))
|
2020-03-11 08:44:39 +01:00
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
|
main()
|
