You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
78 lines
3.1 KiB
78 lines
3.1 KiB
#!/usr/bin/env python3
|
|
""" Checkpoint Cleaning Script
|
|
|
|
Takes training checkpoints with GPU tensors, optimizer state, extra dict keys, etc.
|
|
and outputs a CPU tensor checkpoint with only the `state_dict` along with SHA256
|
|
calculation for model zoo compatibility.
|
|
|
|
Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
|
|
"""
|
|
import torch
|
|
import argparse
|
|
import os
|
|
import hashlib
|
|
import shutil
|
|
from collections import OrderedDict
|
|
|
|
from timm.models.helpers import load_state_dict
|
|
from timm.utils import setup_default_logging
|
|
|
|
parser = argparse.ArgumentParser(description='PyTorch Checkpoint Cleaner')
|
|
parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
|
|
help='path to latest checkpoint (default: none)')
|
|
parser.add_argument('--output', default='', type=str, metavar='PATH',
|
|
help='output path')
|
|
parser.add_argument('--use-ema', dest='use_ema', action='store_true',
|
|
help='use ema version of weights if present')
|
|
parser.add_argument('--clean-aux-bn', dest='clean_aux_bn', action='store_true',
|
|
help='remove auxiliary batch norm layers (from SplitBN training) from checkpoint')
|
|
|
|
_TEMP_NAME = './_checkpoint.pth'
|
|
|
|
|
|
def main():
|
|
args = parser.parse_args()
|
|
setup_default_logging()
|
|
|
|
if os.path.exists(args.output):
|
|
print("Error: Output filename ({}) already exists.".format(args.output))
|
|
exit(1)
|
|
|
|
# Load an existing checkpoint to CPU, strip everything but the state_dict and re-save
|
|
if args.checkpoint and os.path.isfile(args.checkpoint):
|
|
print("=> Loading checkpoint '{}'".format(args.checkpoint))
|
|
state_dict = load_state_dict(args.checkpoint, use_ema=args.use_ema)
|
|
new_state_dict = OrderedDict()
|
|
for k, v in state_dict.items():
|
|
if args.clean_aux_bn and 'aux_bn' in k:
|
|
# If all aux_bn keys are removed, the SplitBN layers will end up as normal and
|
|
# load with the unmodified model using BatchNorm2d.
|
|
continue
|
|
name = k[7:] if k.startswith('module.') else k
|
|
new_state_dict[name] = v
|
|
print("=> Loaded state_dict from '{}'".format(args.checkpoint))
|
|
|
|
try:
|
|
torch.save(new_state_dict, _TEMP_NAME, _use_new_zipfile_serialization=False)
|
|
except:
|
|
torch.save(new_state_dict, _TEMP_NAME)
|
|
|
|
with open(_TEMP_NAME, 'rb') as f:
|
|
sha_hash = hashlib.sha256(f.read()).hexdigest()
|
|
|
|
if args.output:
|
|
checkpoint_root, checkpoint_base = os.path.split(args.output)
|
|
checkpoint_base = os.path.splitext(checkpoint_base)[0]
|
|
else:
|
|
checkpoint_root = ''
|
|
checkpoint_base = os.path.splitext(args.checkpoint)[0]
|
|
final_filename = '-'.join([checkpoint_base, sha_hash[:8]]) + '.pth'
|
|
shutil.move(_TEMP_NAME, os.path.join(checkpoint_root, final_filename))
|
|
print("=> Saved state_dict to '{}, SHA256: {}'".format(final_filename, sha_hash))
|
|
else:
|
|
print("Error: Checkpoint ({}) doesn't exist".format(args.checkpoint))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|