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114 lines
4.4 KiB
114 lines
4.4 KiB
#!/usr/bin/env python
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""" Checkpoint Averaging Script
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This script averages all model weights for checkpoints in specified path that match
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the specified filter wildcard. All checkpoints must be from the exact same model.
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For any hope of decent results, the checkpoints should be from the same or child
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(via resumes) training session. This can be viewed as similar to maintaining running
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EMA (exponential moving average) of the model weights or performing SWA (stochastic
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weight averaging), but post-training.
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Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
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"""
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import torch
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import argparse
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import os
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import glob
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import hashlib
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from timm.models.helpers import load_state_dict
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parser = argparse.ArgumentParser(description='PyTorch Checkpoint Averager')
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parser.add_argument('--input', default='', type=str, metavar='PATH',
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help='path to base input folder containing checkpoints')
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parser.add_argument('--filter', default='*.pth.tar', type=str, metavar='WILDCARD',
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help='checkpoint filter (path wildcard)')
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parser.add_argument('--output', default='./averaged.pth', type=str, metavar='PATH',
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help='output filename')
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parser.add_argument('--no-use-ema', dest='no_use_ema', action='store_true',
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help='Force not using ema version of weights (if present)')
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parser.add_argument('--no-sort', dest='no_sort', action='store_true',
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help='Do not sort and select by checkpoint metric, also makes "n" argument irrelevant')
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parser.add_argument('-n', type=int, default=10, metavar='N',
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help='Number of checkpoints to average')
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def checkpoint_metric(checkpoint_path):
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if not checkpoint_path or not os.path.isfile(checkpoint_path):
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return {}
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print("=> Extracting metric from checkpoint '{}'".format(checkpoint_path))
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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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metric = None
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if 'metric' in checkpoint:
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metric = checkpoint['metric']
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return metric
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def main():
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args = parser.parse_args()
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# by default use the EMA weights (if present)
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args.use_ema = not args.no_use_ema
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# by default sort by checkpoint metric (if present) and avg top n checkpoints
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args.sort = not args.no_sort
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if os.path.exists(args.output):
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print("Error: Output filename ({}) already exists.".format(args.output))
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exit(1)
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pattern = args.input
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if not args.input.endswith(os.path.sep) and not args.filter.startswith(os.path.sep):
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pattern += os.path.sep
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pattern += args.filter
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checkpoints = glob.glob(pattern, recursive=True)
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if args.sort:
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checkpoint_metrics = []
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for c in checkpoints:
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metric = checkpoint_metric(c)
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if metric is not None:
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checkpoint_metrics.append((metric, c))
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checkpoint_metrics = list(sorted(checkpoint_metrics))
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checkpoint_metrics = checkpoint_metrics[-args.n:]
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print("Selected checkpoints:")
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[print(m, c) for m, c in checkpoint_metrics]
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avg_checkpoints = [c for m, c in checkpoint_metrics]
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else:
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avg_checkpoints = checkpoints
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print("Selected checkpoints:")
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[print(c) for c in checkpoints]
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avg_state_dict = {}
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avg_counts = {}
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for c in avg_checkpoints:
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new_state_dict = load_state_dict(c, args.use_ema)
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if not new_state_dict:
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print("Error: Checkpoint ({}) doesn't exist".format(args.checkpoint))
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continue
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for k, v in new_state_dict.items():
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if k not in avg_state_dict:
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avg_state_dict[k] = v.clone().to(dtype=torch.float64)
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avg_counts[k] = 1
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else:
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avg_state_dict[k] += v.to(dtype=torch.float64)
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avg_counts[k] += 1
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for k, v in avg_state_dict.items():
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v.div_(avg_counts[k])
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# float32 overflow seems unlikely based on weights seen to date, but who knows
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float32_info = torch.finfo(torch.float32)
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final_state_dict = {}
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for k, v in avg_state_dict.items():
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v = v.clamp(float32_info.min, float32_info.max)
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final_state_dict[k] = v.to(dtype=torch.float32)
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torch.save(final_state_dict, args.output)
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with open(args.output, 'rb') as f:
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sha_hash = hashlib.sha256(f.read()).hexdigest()
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print("=> Saved state_dict to '{}, SHA256: {}'".format(args.output, sha_hash))
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if __name__ == '__main__':
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main()
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