[GitHub] pengzhao-intel commented on a change in pull request #12530: Implement mkldnn convolution fusion and quantization.

[GitBox <gi...@apache.org>]

pengzhao-intel commented on a change in pull request #12530: Implement mkldnn convolution fusion and quantization.
URL: https://github.com/apache/incubator-mxnet/pull/12530#discussion_r220169060
 
 

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 File path: example/quantization/imagenet_gen_qsym_mkldnn.py
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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import argparse
+import os
+import logging
+from common import modelzoo
+import mxnet as mx
+from mxnet.contrib.quantization import *
+from mxnet.base import SymbolHandle, check_call, _LIB, mx_uint, c_str_array
+import ctypes
+
+
+def download_calib_dataset(dataset_url, calib_dataset, logger=None):
+    if logger is not None:
+        logger.info('Downloading calibration dataset from %s to %s' % (dataset_url, calib_dataset))
+    mx.test_utils.download(dataset_url, calib_dataset)
+
+
+def download_model(model_name, logger=None):
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    model_path = os.path.join(dir_path, 'model')
+    if logger is not None:
+        logger.info('Downloading model %s... into path %s' % (model_name, model_path))
+    return modelzoo.download_model(args.model, os.path.join(dir_path, 'model'))
+
+
+def save_symbol(fname, sym, logger=None):
+    if logger is not None:
+        logger.info('Saving symbol into file at %s' % fname)
+    sym.save(fname)
+
+
+def save_params(fname, arg_params, aux_params, logger=None):
+    if logger is not None:
+        logger.info('Saving params into file at %s' % fname)
+    save_dict = {('arg:%s' % k): v.as_in_context(cpu()) for k, v in arg_params.items()}
+    save_dict.update({('aux:%s' % k): v.as_in_context(cpu()) for k, v in aux_params.items()})
+    mx.nd.save(fname, save_dict)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Generate a calibrated quantized model from a FP32 model with MKL-DNN support')
 
 Review comment:
   @eric-haibin-lin it's a good idea. The quantization feature is improved a lot with this PR and we need a clear README. @xinyu-intel please draft a README

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