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#!/usr/bin/env python3
import os
import shutil
import torch
def get_checkpoint_files(s):
s = s.strip()
if ',' in s:
return [get_checkpoint_files(chunk) for chunk in s.split(',')]
return 'last.ckpt' if s == 'last' else f'{s}.ckpt'
def main(args):
checkpoint_fnames = get_checkpoint_files(args.epochs)
if isinstance(checkpoint_fnames, str):
checkpoint_fnames = [checkpoint_fnames]
assert len(checkpoint_fnames) >= 1
checkpoint_path = os.path.join(args.indir, 'models', checkpoint_fnames[0])
checkpoint = torch.load(checkpoint_path, map_location='cpu')
del checkpoint['optimizer_states']
if len(checkpoint_fnames) > 1:
for fname in checkpoint_fnames[1:]:
print('sum', fname)
sum_tensors_cnt = 0
other_cp = torch.load(os.path.join(args.indir, 'models', fname), map_location='cpu')
for k in checkpoint['state_dict'].keys():
if checkpoint['state_dict'][k].dtype is torch.float:
checkpoint['state_dict'][k].data.add_(other_cp['state_dict'][k].data)
sum_tensors_cnt += 1
print('summed', sum_tensors_cnt, 'tensors')
for k in checkpoint['state_dict'].keys():
if checkpoint['state_dict'][k].dtype is torch.float:
checkpoint['state_dict'][k].data.mul_(1 / float(len(checkpoint_fnames)))
state_dict = checkpoint['state_dict']
if not args.leave_discriminators:
for k in list(state_dict.keys()):
if k.startswith('discriminator.'):
del state_dict[k]
if not args.leave_losses:
for k in list(state_dict.keys()):
if k.startswith('loss_'):
del state_dict[k]
out_checkpoint_path = os.path.join(args.outdir, 'models', 'best.ckpt')
os.makedirs(os.path.dirname(out_checkpoint_path), exist_ok=True)
torch.save(checkpoint, out_checkpoint_path)
shutil.copy2(os.path.join(args.indir, 'config.yaml'),
os.path.join(args.outdir, 'config.yaml'))
if __name__ == '__main__':
import argparse
aparser = argparse.ArgumentParser()
aparser.add_argument('indir',
help='Path to directory with output of training '
'(i.e. directory, which has samples, modules, config.yaml and train.log')
aparser.add_argument('outdir',
help='Where to put minimal checkpoint, which can be consumed by "bin/predict.py"')
aparser.add_argument('--epochs', type=str, default='last',
help='Which checkpoint to take. '
'Can be "last" or integer - number of epoch')
aparser.add_argument('--leave-discriminators', action='store_true',
help='If enabled, the state of discriminators will not be removed from the checkpoint')
aparser.add_argument('--leave-losses', action='store_true',
help='If enabled, weights of nn-based losses (e.g. perceptual) will not be removed')
main(aparser.parse_args())
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