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():
# 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']))
if __name__ == '__main__':
main()
