import sys, time, random, argparse from copy import deepcopy import torchvision.models as models from pathlib import Path from xautodl.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()