ZhennanQin commented on a change in pull request #12530: Implement mkldnn convolution fusion and quantization. URL: https://github.com/apache/incubator-mxnet/pull/12530#discussion_r220079292
########## File path: example/quantization/imagenet_gen_qsym_mkldnn.py ########## @@ -0,0 +1,213 @@ +# 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: We do want to provide resnet50v1 as example, but we don't know where's to put the pre-trained model and its parameter file. Do you have any suggestion where's to upload them? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
