nopattern opened a new issue #15482: mx2onnx error about batchnorm URL: https://github.com/apache/incubator-mxnet/issues/15482 ## Description I use mx2onnx onnx_mxnet.export_model to transfer mxnet symbol to onnx . But the moving_mean&moving_var param of Batchnorm is not in the params. So the ## Environment info (Required) ``` ----------Python Info---------- Version : 3.6.8 Compiler : GCC 5.4.0 20160609 Build : ('default', 'May 7 2019 14:58:50') Arch : ('64bit', 'ELF') ------------Pip Info----------- Version : 19.1.1 Directory : /usr/local/lib/python3.6/dist-packages/pip ----------MXNet Info----------- Version : 1.5.0 Directory : /home/deep/workssd/mxnet/incubator-mxnet/python/mxnet Hashtag not found. Not installed from pre-built package. ----------System Info---------- Platform : Linux-4.4.0-148-generic-x86_64-with-Ubuntu-16.04-xenial system : Linux node : MS-7817 release : 4.4.0-148-generic version : #174-Ubuntu SMP Tue May 7 12:20:14 UTC 2019 ----------Hardware Info---------- machine : x86_64 processor : x86_64 Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 4 On-line CPU(s) list: 0-3 Thread(s) per core: 1 Core(s) per socket: 4 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 60 Model name: Intel(R) Core(TM) i5-4590 CPU @ 3.30GHz Stepping: 3 CPU MHz: 3657.070 CPU max MHz: 3700.0000 CPU min MHz: 800.0000 BogoMIPS: 6600.45 Virtualization: VT-x L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 6144K NUMA node0 CPU(s): 0-3 ``` Package used (Python/R/Scala/Julia): (I'm usining Python) ## Build info (Required if built from source) Compiler (gcc): MXNet commit hash: (da4b2a82511df) Build config: # 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. #------------------------------------------------------------------------------- # Template configuration for compiling mxnet # # If you want to change the configuration, please use the following # steps. Assume you are on the root directory of mxnet. First copy the this # file so that any local changes will be ignored by git # # $ cp make/config.mk . # # Next modify the according entries, and then compile by # # $ make # # or build in parallel with 8 threads # # $ make -j8 #------------------------------------------------------------------------------- #--------------------- # choice of compiler #-------------------- ifndef CC export CC = gcc endif ifndef CXX export CXX = g++ endif ifndef NVCC export NVCC = nvcc endif # whether compile with options for MXNet developer DEV = 0 # whether compile with debug DEBUG = 0 # whether to turn on segfault signal handler to log the stack trace USE_SIGNAL_HANDLER = # the additional link flags you want to add ADD_LDFLAGS = # the additional compile flags you want to add ADD_CFLAGS = #--------------------------------------------- # matrix computation libraries for CPU/GPU #--------------------------------------------- # whether use CUDA during compile USE_CUDA = 1 # add the path to CUDA library to link and compile flag # if you have already add them to environment variable, leave it as NONE USE_CUDA_PATH = /usr/local/cuda #USE_CUDA_PATH = NONE # whether to enable CUDA runtime compilation ENABLE_CUDA_RTC = 1 # whether use CuDNN R3 library USE_CUDNN = 1 # whether to use NVTX when profiling USE_NVTX = 0 #whether to use NCCL library USE_NCCL = 0 #add the path to NCCL library USE_NCCL_PATH = NONE # whether use opencv during compilation # you can disable it, however, you will not able to use # imbin iterator USE_OPENCV = 1 # Add OpenCV include path, in which the directory `opencv2` exists USE_OPENCV_INC_PATH = NONE # Add OpenCV shared library path, in which the shared library exists USE_OPENCV_LIB_PATH = NONE #whether use libjpeg-turbo for image decode without OpenCV wrapper USE_LIBJPEG_TURBO = 0 #add the path to libjpeg-turbo library USE_LIBJPEG_TURBO_PATH = NONE # use openmp for parallelization USE_OPENMP = 1 # whether use MKL-DNN library: 0 = disabled, 1 = enabled # if USE_MKLDNN is not defined, MKL-DNN will be enabled by default on x86 Linux. # you can disable it explicity with USE_MKLDNN = 0 USE_MKLDNN = 0 # whether use NNPACK library USE_NNPACK = 0 # choose the version of blas you want to use # can be: mkl, blas, atlas, openblas # in default use atlas for linux while apple for osx UNAME_S := $(shell uname -s) ifeq ($(UNAME_S), Darwin) USE_BLAS = apple else USE_BLAS = atlas endif # whether use lapack during compilation # only effective when compiled with blas versions openblas/apple/atlas/mkl USE_LAPACK = 1 # path to lapack library in case of a non-standard installation USE_LAPACK_PATH = # add path to intel library, you may need it for MKL, if you did not add the path # to environment variable USE_INTEL_PATH = NONE # If use MKL only for BLAS, choose static link automatically to allow python wrapper ifeq ($(USE_BLAS), mkl) USE_STATIC_MKL = 1 else USE_STATIC_MKL = NONE endif #---------------------------- # Settings for power and arm arch #---------------------------- ARCH := $(shell uname -a) ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64)) USE_SSE=0 USE_F16C=0 else USE_SSE=1 endif #---------------------------- # F16C instruction support for faster arithmetic of fp16 on CPU #---------------------------- # For distributed training with fp16, this helps even if training on GPUs # If left empty, checks CPU support and turns it on. # For cross compilation, please check support for F16C on target device and turn off if necessary. USE_F16C = #---------------------------- # distributed computing #---------------------------- # whether or not to enable multi-machine supporting USE_DIST_KVSTORE = 0 # whether or not allow to read and write HDFS directly. If yes, then hadoop is # required USE_HDFS = 0 # path to libjvm.so. required if USE_HDFS=1 LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server # whether or not allow to read and write AWS S3 directly. If yes, then # libcurl4-openssl-dev is required, it can be installed on Ubuntu by # sudo apt-get install -y libcurl4-openssl-dev USE_S3 = 0 #---------------------------- # performance settings #---------------------------- # Use operator tuning USE_OPERATOR_TUNING = 1 # Use gperftools if found # Disable because of #8968 USE_GPERFTOOLS = 0 # path to gperftools (tcmalloc) library in case of a non-standard installation USE_GPERFTOOLS_PATH = # Link gperftools statically USE_GPERFTOOLS_STATIC = # Use JEMalloc if found, and not using gperftools USE_JEMALLOC = 1 # path to jemalloc library in case of a non-standard installation USE_JEMALLOC_PATH = # Link jemalloc statically USE_JEMALLOC_STATIC = #---------------------------- # additional operators #---------------------------- # path to folders containing projects specific operators that you don't want to put in src/operators EXTRA_OPERATORS = #---------------------------- # other features #---------------------------- # Create C++ interface package USE_CPP_PACKAGE = 0 # Use int64_t type to represent the total number of elements in a tensor # This will cause performance degradation reported in issue #14496 # Set to 1 for large tensor with tensor size greater than INT32_MAX i.e. 2147483647 # Note: the size of each dimension is still bounded by INT32_MAX USE_INT64_TENSOR_SIZE = 0 # Python executable. Needed for cython target PYTHON = python #---------------------------- # plugins #---------------------------- # whether to use caffe integration. This requires installing caffe. # You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH # CAFFE_PATH = $(HOME)/caffe # MXNET_PLUGINS += plugin/caffe/caffe.mk #WARPCTC_PATH = $(HOME)/warp-ctc WARPCTC_PATH = /home/deep/warp-ctc MXNET_PLUGINS += plugin/warpctc/warpctc.mk # whether to use sframe integration. This requires build sframe # g...@github.com:dato-code/SFrame.git # SFRAME_PATH = $(HOME)/SFrame # MXNET_PLUGINS += plugin/sframe/plugin.mk ## Error Message: INFO:root:Converting idx: 0, op: null, name: data INFO:root:Converting idx: 1, op: null, name: first-3x3-conv-conv2d_weight INFO:root:Converting idx: 2, op: Convolution, name: first-3x3-conv-conv2d INFO:root:Converting idx: 3, op: null, name: first-3x3-conv-batchnorm_gamma INFO:root:Converting idx: 4, op: null, name: first-3x3-conv-batchnorm_beta INFO:root:Converting idx: 5, op: null, name: first-3x3-conv-batchnorm_moving_mean Traceback (most recent call last): File "/home/deep/workssd/arm/tvm_app/tune_relay_mobile_gpu.py", line 484, in <module> tune_and_evaluate(tuning_option) File "/home/deep/workssd/arm/tvm_app/tune_relay_mobile_gpu.py", line 436, in tune_and_evaluate net, params, input_shape, _ = get_network(network, batch_size=1) File "/home/deep/workssd/arm/tvm_app/tune_relay_mobile_gpu.py", line 93, in get_network return get_network_lpr_mb2(name,batch_size) File "/home/deep/workssd/arm/tvm_app/tune_relay_mobile_gpu.py", line 143, in get_network_lpr_mb2 test_onnx() File "/home/deep/workssd/arm/tvm_app/tune_relay_mobile_gpu.py", line 135, in test_onnx converted_model_path = onnx_mxnet.export_model(mx_sym, args, [input_shape], np.float32, onnx_file, True) File "/home/deep/workssd/mxnet/incubator-mxnet/python/mxnet/contrib/onnx/mx2onnx/export_model.py", line 87, in export_model verbose=verbose) File "/home/deep/workssd/mxnet/incubator-mxnet/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py", line 256, in create_onnx_graph_proto idx=idx File "/home/deep/workssd/mxnet/incubator-mxnet/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py", line 92, in convert_layer return convert_func(node, **kwargs) File "/home/deep/workssd/mxnet/incubator-mxnet/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py", line 170, in convert_weights_and_inputs np_arr = weights[name] KeyError: 'first-3x3-conv-batchnorm_moving_mean' Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python3/dist-packages/apport_python_hook.py", line 63, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python3/dist-packages/apport/__init__.py", line 5, in <module> from apport.report import Report File "/usr/lib/python3/dist-packages/apport/report.py", line 30, in <module> import apport.fileutils File "/usr/lib/python3/dist-packages/apport/fileutils.py", line 23, in <module> from apport.packaging_impl import impl as packaging File "/usr/lib/python3/dist-packages/apport/packaging_impl.py", line 23, in <module> import apt File "/usr/lib/python3/dist-packages/apt/__init__.py", line 23, in <module> import apt_pkg ModuleNotFoundError: No module named 'apt_pkg' Original exception was: Traceback (most recent call last): File "/home/deep/workssd/arm/tvm_app/tune_relay_mobile_gpu.py", line 484, in <module> tune_and_evaluate(tuning_option) File "/home/deep/workssd/arm/tvm_app/tune_relay_mobile_gpu.py", line 436, in tune_and_evaluate net, params, input_shape, _ = get_network(network, batch_size=1) File "/home/deep/workssd/arm/tvm_app/tune_relay_mobile_gpu.py", line 93, in get_network return get_network_lpr_mb2(name,batch_size) File "/home/deep/workssd/arm/tvm_app/tune_relay_mobile_gpu.py", line 143, in get_network_lpr_mb2 test_onnx() File "/home/deep/workssd/arm/tvm_app/tune_relay_mobile_gpu.py", line 135, in test_onnx converted_model_path = onnx_mxnet.export_model(mx_sym, args, [input_shape], np.float32, onnx_file, True) File "/home/deep/workssd/mxnet/incubator-mxnet/python/mxnet/contrib/onnx/mx2onnx/export_model.py", line 87, in export_model verbose=verbose) File "/home/deep/workssd/mxnet/incubator-mxnet/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py", line 256, in create_onnx_graph_proto idx=idx File "/home/deep/workssd/mxnet/incubator-mxnet/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py", line 92, in convert_layer return convert_func(node, **kwargs) File "/home/deep/workssd/mxnet/incubator-mxnet/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py", line 170, in convert_weights_and_inputs np_arr = weights[name] KeyError: 'first-3x3-conv-batchnorm_moving_mean' ## Minimum reproducible example `batch_size = 1 input_shape = (batch_size, 3, 512, 512) output_shape = (batch_size, 65520,14) mx_sym, args,auxs = mx.model.load_checkpoint('./model/ssd_mobilenetv2_512', 18) mx_sym = get_symbol('mobilenetv2',512, num_classes=1,nms_thresh=0.5, force_nms=True, nms_topk=400) onnx_file = './mxnet_exported_resnet18.onnx' converted_model_path = onnx_mxnet.export_model(mx_sym, args, [input_shape], np.float32, onnx_file, True)` ## Steps to reproduce (Paste the commands you ran that produced the error.) 1.python3 tran2onnx.py 2. ## What have you tried to solve it? 1.By debugging ,the moving_mean&moving_var of batchnorm is not in params ,so the converter treat it as input which is not real. 2. There should be code to process the moving_mean&moving_var of batchnorm indepently.
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