35 lines
1.4 KiB
Python
35 lines
1.4 KiB
Python
import sys, time, random, argparse
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from copy import deepcopy
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import torchvision.models as models
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from pathlib import Path
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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from utils import weight_watcher
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def main():
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# model = models.vgg19_bn(pretrained=True)
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# _, summary = weight_watcher.analyze(model, alphas=False)
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# for key, value in summary.items():
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# print('{:10s} : {:}'.format(key, value))
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_, summary = weight_watcher.analyze(models.vgg13(pretrained=True), alphas=False)
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print("vgg-13 : {:}".format(summary["lognorm"]))
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_, summary = weight_watcher.analyze(models.vgg13_bn(pretrained=True), alphas=False)
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print("vgg-13-BN : {:}".format(summary["lognorm"]))
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_, summary = weight_watcher.analyze(models.vgg16(pretrained=True), alphas=False)
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print("vgg-16 : {:}".format(summary["lognorm"]))
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_, summary = weight_watcher.analyze(models.vgg16_bn(pretrained=True), alphas=False)
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print("vgg-16-BN : {:}".format(summary["lognorm"]))
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_, summary = weight_watcher.analyze(models.vgg19(pretrained=True), alphas=False)
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print("vgg-19 : {:}".format(summary["lognorm"]))
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_, summary = weight_watcher.analyze(models.vgg19_bn(pretrained=True), alphas=False)
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print("vgg-19-BN : {:}".format(summary["lognorm"]))
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if __name__ == "__main__":
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main()
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