| """ADM FID, preserving the input reference archive.""" |
| import argparse |
| import math |
| import numpy as np |
| import tensorflow.compat.v1 as tf |
| from evaluator import Evaluator |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument('--ref_batch', required=True) |
| parser.add_argument('--sample_batch', required=True) |
| parser.add_argument('--batch_size', type=int, default=64) |
| parser.add_argument('--write-reference-stats', help='Optional NEW cache file; never overwrites the reference') |
| args = parser.parse_args() |
| if args.batch_size < 1: parser.error('batch_size must be positive') |
| config = tf.ConfigProto(allow_soft_placement=True, intra_op_parallelism_threads=8, inter_op_parallelism_threads=8) |
| config.gpu_options.allow_growth = True |
| with tf.Session(config=config) as session: |
| evaluator = Evaluator(session, batch_size=args.batch_size) |
| evaluator.warmup() |
| ref_acts = evaluator.read_activations(args.ref_batch) |
| ref, spatial = evaluator.read_statistics(args.ref_batch, ref_acts) |
| sample_acts = evaluator.read_activations(args.sample_batch) |
| sample, _ = evaluator.read_statistics(args.sample_batch, sample_acts) |
| fid = sample.frechet_distance(ref) |
| if not math.isfinite(fid): raise ValueError(f'Non-finite FID: {fid}') |
| if args.write_reference_stats: |
| with open(args.write_reference_stats, 'xb') as f: |
| np.savez_compressed(f, mu=ref.mu, sigma=ref.sigma, mu_s=spatial.mu, sigma_s=spatial.sigma) |
| print(f'FID_RESULT:{fid}') |
|
|
|
|
| if __name__ == '__main__': main() |
|
|
