96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
import argparse, os, sys, glob
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import numpy as np
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from torch_fidelity import calculate_metrics
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import yaml
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from ldm.modules.evaluate.evaluate_perceptualsim import compute_perceptual_similarity_from_list
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def get_parser():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--logdir",
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type=str,
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nargs="?",
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default="fidelity-evaluation",
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)
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parser.add_argument(
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"--reconstructions",
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type=str,
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help="path to reconstructed images"
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)
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parser.add_argument(
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"--inputs",
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type=str,
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help="path to input images"
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)
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parser.add_argument(
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"--cache_root",
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type=str,
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help="optional, for pre-computed fidelity statistics",
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nargs="?",
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)
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return parser
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if __name__ == "__main__":
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command = " ".join(sys.argv)
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np.random.RandomState(42)
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sys.path.append(os.getcwd())
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parser = get_parser()
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opt, unknown = parser.parse_known_args()
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outdir = os.path.join(opt.logdir, "metrics")
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print(outdir)
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inppath = opt.inputs
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recpath = opt.reconstructions
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results = dict()
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##### fid
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fid_kwargs = {}
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cache_root = None
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if opt.cache_root and os.path.isdir(opt.cache_root):
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print(f'Using cached Inception Features saved under "{cache_root}"')
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fid_kwargs.update({
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'cache_root': cache_root,
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'input2_cache_name': 'input_data',
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'cache': True
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})
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metrics_dict = calculate_metrics(input1=recpath, input2=inppath,
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cuda=True, isc=True, fid=True, kid=True,
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verbose=True, **fid_kwargs)
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results["fidelity"] = metrics_dict
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print(f'Metrics from fidelity: \n {results["fidelity"]}')
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##### sim
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print("Evaluating reconstruction similarity")
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reconstructions = sorted(glob.glob(os.path.join(recpath, "*.png")))
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print(f"num reconstructions found: {len(reconstructions)}")
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inputs = sorted(glob.glob(os.path.join(inppath, "*.png")))
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print(f"num inputs found: {len(inputs)}")
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results["image-sim"] = compute_perceptual_similarity_from_list(
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reconstructions, inputs, take_every_other=False)
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print(f'Results sim: {results["image-sim"]}')
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# info
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results["info"] = {
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"n_examples": len(reconstructions),
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"command": command,
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}
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# write out
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ipath, rpath = map(lambda x: os.path.splitext(x)[0].split(os.sep)[-1], (inppath, recpath))
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resultsfn = f"results_{ipath}-{rpath}.yaml"
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results_file = os.path.join(outdir, resultsfn)
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with open(results_file, 'w') as f:
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yaml.dump(results, f, default_flow_style=False)
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print(results_file)
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print("\ndone.")
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