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import os, sys, numpy, torch, argparse, skimage, json, shutil
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.ticker import MaxNLocator
import matplotlib
def main():
parser = argparse.ArgumentParser(description='ACE optimization utility',
prog='python -m netdissect.aceoptimize')
parser.add_argument('--classname', type=str, default=None,
help='intervention classname')
parser.add_argument('--layer', type=str, default='layer4',
help='layer name')
parser.add_argument('--l2_lambda', type=float, nargs='+',
help='l2 regularizer hyperparameter')
parser.add_argument('--outdir', type=str, default=None,
help='dissection directory')
parser.add_argument('--variant', type=str, default=None,
help='experiment variant')
args = parser.parse_args()
if args.variant is None:
args.variant = 'ace'
run_command(args)
def run_command(args):
fig = Figure(figsize=(4.5,3.5))
FigureCanvas(fig)
ax = fig.add_subplot(111)
for l2_lambda in args.l2_lambda:
variant = args.variant
if l2_lambda != 0.01:
variant += '_reg%g' % l2_lambda
dirname = os.path.join(args.outdir, args.layer, variant, args.classname)
snapshots = os.path.join(dirname, 'snapshots')
try:
dat = [torch.load(os.path.join(snapshots, 'epoch-%d.pth' % i))
for i in range(10)]
except:
print('Missing %s snapshots' % dirname)
return
print('reg %g' % l2_lambda)
for i in range(10):
print(i, dat[i]['avg_loss'],
len((dat[i]['ablation'] == 1).nonzero()))
ax.plot([dat[i]['avg_loss'] for i in range(10)],
label='reg %g' % l2_lambda)
ax.set_title('%s %s' % (args.classname, args.variant))
ax.grid(True)
ax.legend()
ax.set_ylabel('Loss')
ax.set_xlabel('Epochs')
fig.tight_layout()
dirname = os.path.join(args.outdir, args.layer,
args.variant, args.classname)
fig.savefig(os.path.join(dirname, 'loss-plot.png'))
if __name__ == '__main__':
main()
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