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import click
from app.search.params import Params, timestamp
from app.search.search_dense import find_dense_embedding_for_images
@click.command('')
@click.option('-i', '--input', 'opt_fp_in', required=True,
help='Path to input image')
@click.option('-t', '--tag', 'opt_tag', default="inverse_" + timestamp(),
help='Tag this build')
@click.option('-ll', '--feature_layers', 'opt_feature_layers', default="1a,2a,4a,7a",
help='Feature layers used for loss')
@click.option('-s', '--save_progress', 'opt_save_progress', is_flag=True,
help='Save example images every 500 frames')
@click.pass_context
def cli(ctx, opt_fp_in, opt_tag, opt_feature_layers, opt_save_progress):
"""
Search for an image (class vector) in BigGAN using gradient descent
"""
params = Params(opt_fp_in)
opt_feature_layers = opt_feature_layers.split(',')
find_dense_embedding_for_images(params,
opt_tag=opt_tag,
opt_feature_layers=opt_feature_layers,
opt_save_progress=opt_save_progress)
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