added weight export script for LACE/NoLACE

dnn/torch/osce/export_model_weights.py

+"""
+/* Copyright (c) 2023 Amazon
+   Written by Jan Buethe */
+/*
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions
+   are met:
+
+   - Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+   - Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+   A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
+   OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+   PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+*/
+"""
+
+import os
+import argparse
+import sys
+
+import hashlib
+
+sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange'))
+
+import torch
+import wexchange.torch
+from wexchange.torch import dump_torch_weights
+from models import model_dict
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument('checkpoint', type=str, help='LACE or NoLACE model checkpoint')
+parser.add_argument('output_dir', type=str, help='output folder')
+
+
+# auxiliary functions
+def sha1(filename):
+    BUF_SIZE = 65536
+    sha1 = hashlib.sha1()
+
+    with open(filename, 'rb') as f:
+        while True:
+            data = f.read(BUF_SIZE)
+            if not data:
+                break
+            sha1.update(data)
+
+    return sha1.hexdigest()
+
+def export_name(name):
+    return name.replace('.', '_')
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+
+    checkpoint_path = args.checkpoint
+    outdir = args.output_dir
+    os.makedirs(outdir, exist_ok=True)
+
+    # dump message
+    message = f"Auto generated from checkpoint {os.path.basename(checkpoint_path)} (sha1: {sha1(checkpoint_path)})"
+
+    # create model and load weights
+    checkpoint = torch.load(checkpoint_path, map_location='cpu')
+    model = model_dict[checkpoint['setup']['model']['name']](*checkpoint['setup']['model']['args'], **checkpoint['setup']['model']['kwargs'])
+
+    # CWriter
+    model_name = checkpoint['setup']['model']['name']
+    cwriter = wexchange.c_export.CWriter(os.path.join(outdir, model_name + "_data"), message=message, model_struct_name=model_name.upper())
+
+    # dump numbits_embedding parameters by hand
+    numbits_embedding = model.get_submodule('numbits_embedding')
+    weights = next(iter(numbits_embedding.parameters()))
+    for i, c in enumerate(weights):
+        cwriter.header.write(f"\nNUMBITS_COEF_{i} {float(c.detach())}f")
+    cwriter.header.write("\n\n")
+
+    # dump layers
+    for name, module in model.named_modules():
+        if isinstance(module, torch.nn.Linear) or isinstance(module, torch.nn.Conv1d) \
+            or isinstance(module, torch.nn.ConvTranspose1d) or isinstance(module, torch.nn.Embedding):
+                dump_torch_weights(cwriter, module, name=export_name(name), verbose=True)
+
+    cwriter.close()