init

finetune/tools/misc/publish_model.py

@@ -0,0 +1,50 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import subprocess
+from hashlib import sha256
+
+import torch
+
+BLOCK_SIZE = 128 * 1024
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Process a checkpoint to be published')
+    parser.add_argument('in_file', help='input checkpoint filename')
+    parser.add_argument('out_file', help='output checkpoint filename')
+    args = parser.parse_args()
+    return args
+
+
+def sha256sum(filename: str) -> str:
+    """Compute SHA256 message digest from a file."""
+    hash_func = sha256()
+    byte_array = bytearray(BLOCK_SIZE)
+    memory_view = memoryview(byte_array)
+    with open(filename, 'rb', buffering=0) as file:
+        for block in iter(lambda: file.readinto(memory_view), 0):
+            hash_func.update(memory_view[:block])
+    return hash_func.hexdigest()
+
+
+def process_checkpoint(in_file, out_file):
+    checkpoint = torch.load(in_file, map_location='cpu')
+    # remove optimizer for smaller file size
+    if 'optimizer' in checkpoint:
+        del checkpoint['optimizer']
+    # if it is necessary to remove some sensitive data in checkpoint['meta'],
+    # add the code here.
+    torch.save(checkpoint, out_file)
+    sha = sha256sum(in_file)
+    final_file = out_file.rstrip('.pth') + f'-{sha[:8]}.pth'
+    subprocess.Popen(['mv', out_file, final_file])
+
+
+def main():
+    args = parse_args()
+    process_checkpoint(args.in_file, args.out_file)
+
+
+if __name__ == '__main__':
+    main()