安装和初步使用 nn-Meter

安装和初步使用 nn-Meter

nn-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices
nn-Meter：精准预测深度学习模型在边缘设备上的推理延迟

Li Lyna Zhang, Shihao Han, Jianyu Wei, Ningxin Zheng, Ting Cao, Yuqing Yang, Yunxin Liu

1. nn-Meter

https://github.com/microsoft/nn-Meter

nn-Meter is a novel and efficient system to accurately predict the inference latency of DNN models on diverse edge devices. The key idea is dividing a whole model inference into kernels, i.e., the execution units of fused operators on a device, and conduct kernel-level prediction. We currently evaluate four popular platforms on a large dataset of 26k models. It achieves 99.0% (mobile CPU), 99.1% (mobile Adreno 640 GPU), 99.0% (mobile Adreno 630 GPU), and 83.4% (Intel VPU) prediction accuracy.
为了高效、准确地预测深度神经网络模型在不同边缘设备上的推理延迟，作者提出并开发了一个基于内核的模型推理延迟预测系统 nn-Meter，引入了内核检测，可找出算子融合行为。通过对最有价值的数据进行采样，nn-Meter 有效地建立了内核的延迟预测器。

The current supported hardware and inference frameworks:

Device	Framework	Processor	±10% Accuracy	Hardware name
Pixel4	TFLite v2.1	CortexA76 CPU	99.0%	cortexA76cpu_tflite21
Mi9	TFLite v2.1	Adreno 640 GPU	99.1%	adreno640gpu_tflite21
Pixel3XL	TFLite v2.1	Adreno 630 GPU	99.0%	adreno630gpu_tflite21
Intel Movidius NCS2	OpenVINO2019R2	Myriad VPU	83.4%	myriadvpu_openvino2019r2

Tags
https://github.com/microsoft/nn-Meter/tags

Releases
https://github.com/microsoft/nn-Meter/releases

https://github.com/microsoft/nn-Meter/releases/tag/v1.0-data

adreno630gpu_tflite21.zip
adreno640gpu_tflite21.zip
cortexA76cpu_tflite21.zip
datasets.zip
ir_graphs.zip
myriadvpu_openvino2019r2.zip
onnx_models.zip
pb_models.zip
Source code (zip)
Source code (tar.gz)

https://github.com/microsoft/nn-Meter/releases/tag/v2.0-data

tflite_benchmark_tools_v2.1.zip
tflite_benchmark_tools_v2.7.zip
Source code (zip)
Source code (tar.gz)

nn-Meter Builder
https://github.com/microsoft/nn-Meter/blob/main/docs/builder/overview.md

Neural Network Intelligence，NNI
https://github.com/microsoft/nni
https://www.microsoft.com/en-us/research/project/neural-network-intelligence/
https://nni.readthedocs.io/zh/stable/
https://nni.readthedocs.io/en/stable/

NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments.

26k latency benchmark dataset
https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/datasets.zip

2. Installation

Currently nn-Meter has been tested on Linux and Windows system. Windows 10, Ubuntu 16.04 and 20.04 with python 3.6.10 are tested and supported. Please first install python3 before nn-Meter installation. Then nn-Meter could be installed by running:

pip install nn-meter

pip install nn-meter --default-timeout=1000 -i https://pypi.tuna.tsinghua.edu.cn/simple

nn-meter==2.0 has been released now.

If you want to try latest code, please install nn-Meter from source code. First git clone nn-Meter package to local:

git clone [email protected]:microsoft/nn-Meter.git
cd nn-Meter

Then simply run the following pip install in an environment that has python >= 3.6. The command will complete the automatic installation of all necessary dependencies and nn-Meter.

pip install .

nn-Meter is a latency predictor of models with type of Tensorflow, PyTorch, Onnx, nn-meter IR graph and NNI IR graph (https://github.com/microsoft/nni). To use nn-Meter for specific model type, you also need to install corresponding required packages. The well tested versions are listed below:

Testing Model Type	Requirements
Tensorflow	tensorflow==2.6.0
Torch	torch==1.9.0, torchvision==0.10.0, (alternative) [onnx>=1.9.0, onnx-simplifier==0.3.6] or [nni>=2.4]
Onnx	onnx==1.9.0
nn-Meter IR graph	—
NNI IR graph	nni>=2.4

Please also check the versions of numpy and scikit_learn. The different versions may change the prediction accuracy of kernel predictors.

The stable version of wheel binary package will be released soon.

2.1. python=3.7

conda create --name py37 python=3.7

(base) yongqiang@yongqiang:~$ conda env list
# conda environments:
#
base                  *  /home/yongqiang/miniconda3

(base) yongqiang@yongqiang:~$
(base) yongqiang@yongqiang:~$ conda create --name py37 python=3.7
Collecting package metadata (current_repodata.json): done
Solving environment: unsuccessful attempt using repodata from current_repodata.json, retrying with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: done

==> WARNING: A newer version of conda exists. <==
  current version: 23.7.3
  latest version: 23.10.0

Please update conda by running

    $ conda update -n base -c defaults conda

Or to minimize the number of packages updated during conda update use

     conda install conda=23.10.0

## Package Plan ##

  environment location: /home/yongqiang/miniconda3/envs/py37

  added / updated specs:
    - python=3.7

The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    ca-certificates-2023.08.22 |       h06a4308_0         123 KB  defaults
    certifi-2022.12.7          |   py37h06a4308_0         150 KB  defaults
    openssl-1.1.1w             |       h7f8727e_0         3.7 MB  defaults
    pip-22.3.1                 |   py37h06a4308_0         2.7 MB  defaults
    python-3.7.16              |       h7a1cb2a_0        44.8 MB  defaults
    setuptools-65.6.3          |   py37h06a4308_0         1.1 MB  defaults
    wheel-0.38.4               |   py37h06a4308_0          63 KB  defaults
    ------------------------------------------------------------
                                           Total:        52.7 MB

The following NEW packages will be INSTALLED:

  _libgcc_mutex      anaconda/pkgs/main/linux-64::_libgcc_mutex-0.1-main
  _openmp_mutex      anaconda/pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu
  ca-certificates    anaconda/pkgs/main/linux-64::ca-certificates-2023.08.22-h06a4308_0
  certifi            anaconda/pkgs/main/linux-64::certifi-2022.12.7-py37h06a4308_0
  ld_impl_linux-64   anaconda/pkgs/main/linux-64::ld_impl_linux-64-2.38-h1181459_1
  libffi             anaconda/pkgs/main/linux-64::libffi-3.4.4-h6a678d5_0
  libgcc-ng          anaconda/pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1
  libgomp            anaconda/pkgs/main/linux-64::libgomp-11.2.0-h1234567_1
  libstdcxx-ng       anaconda/pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1
  ncurses            anaconda/pkgs/main/linux-64::ncurses-6.4-h6a678d5_0
  openssl            anaconda/pkgs/main/linux-64::openssl-1.1.1w-h7f8727e_0
  pip                anaconda/pkgs/main/linux-64::pip-22.3.1-py37h06a4308_0
  python             anaconda/pkgs/main/linux-64::python-3.7.16-h7a1cb2a_0
  readline           anaconda/pkgs/main/linux-64::readline-8.2-h5eee18b_0
  setuptools         anaconda/pkgs/main/linux-64::setuptools-65.6.3-py37h06a4308_0
  sqlite             anaconda/pkgs/main/linux-64::sqlite-3.41.2-h5eee18b_0
  tk                 anaconda/pkgs/main/linux-64::tk-8.6.12-h1ccaba5_0
  wheel              anaconda/pkgs/main/linux-64::wheel-0.38.4-py37h06a4308_0
  xz                 anaconda/pkgs/main/linux-64::xz-5.4.2-h5eee18b_0
  zlib               anaconda/pkgs/main/linux-64::zlib-1.2.13-h5eee18b_0

Proceed ([y]/n)? y

Downloading and Extracting Packages

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate py37
#
# To deactivate an active environment, use
#
#     $ conda deactivate

(base) yongqiang@yongqiang:~$
(base) yongqiang@yongqiang:~$ conda env list
# conda environments:
#
base                  *  /home/yongqiang/miniconda3
py37                     /home/yongqiang/miniconda3/envs/py37

(base) yongqiang@yongqiang:~$
(base) yongqiang@yongqiang:~$ conda activate py37
(py37) yongqiang@yongqiang:~$ conda env list
# conda environments:
#
base                     /home/yongqiang/miniconda3
py37                  *  /home/yongqiang/miniconda3/envs/py37

(py37) yongqiang@yongqiang:~$
(py37) yongqiang@yongqiang:~$ python
Python 3.7.16 (default, Jan 17 2023, 22:20:44)
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> exit()
(py37) yongqiang@yongqiang:~$

2.2. nn-meter 2.0

pip install nn-meter --default-timeout=1000 -i https://pypi.tuna.tsinghua.edu.cn/simple

(py37) yongqiang@yongqiang:~$ pip install nn-meter --default-timeout=1000 -i https://pypi.tuna.tsinghua.edu.cn/simple
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Requirement already satisfied: nn-meter in ./miniconda3/envs/py37/lib/python3.7/site-packages (2.0)
Requirement already satisfied: networkx in ./miniconda3/envs/py37/lib/python3.7/site-packages (from nn-meter) (2.6.3)
Requirement already satisfied: scikit-learn in ./miniconda3/envs/py37/lib/python3.7/site-packages (from nn-meter) (1.0.2)
Requirement already satisfied: numpy in ./miniconda3/envs/py37/lib/python3.7/site-packages (from nn-meter) (1.21.6)
Requirement already satisfied: requests in ./miniconda3/envs/py37/lib/python3.7/site-packages (from nn-meter) (2.31.0)
Requirement already satisfied: pandas in ./miniconda3/envs/py37/lib/python3.7/site-packages (from nn-meter) (1.3.5)
Requirement already satisfied: jsonlines in ./miniconda3/envs/py37/lib/python3.7/site-packages (from nn-meter) (3.1.0)
Requirement already satisfied: packaging in ./miniconda3/envs/py37/lib/python3.7/site-packages (from nn-meter) (23.2)
Requirement already satisfied: PyYAML in ./miniconda3/envs/py37/lib/python3.7/site-packages (from nn-meter) (6.0.1)
Requirement already satisfied: protobuf in ./miniconda3/envs/py37/lib/python3.7/site-packages (from nn-meter) (4.24.4)
Requirement already satisfied: tqdm in ./miniconda3/envs/py37/lib/python3.7/site-packages (from nn-meter) (4.66.1)
Requirement already satisfied: attrs>=19.2.0 in ./miniconda3/envs/py37/lib/python3.7/site-packages (from jsonlines->nn-meter) (23.1.0)
Requirement already satisfied: typing-extensions in ./miniconda3/envs/py37/lib/python3.7/site-packages (from jsonlines->nn-meter) (4.7.1)
Requirement already satisfied: python-dateutil>=2.7.3 in ./miniconda3/envs/py37/lib/python3.7/site-packages (from pandas->nn-meter) (2.8.2)
Requirement already satisfied: pytz>=2017.3 in ./miniconda3/envs/py37/lib/python3.7/site-packages (from pandas->nn-meter) (2023.3.post1)
Requirement already satisfied: certifi>=2017.4.17 in ./miniconda3/envs/py37/lib/python3.7/site-packages (from requests->nn-meter) (2022.12.7)
Requirement already satisfied: idna<4,>=2.5 in ./miniconda3/envs/py37/lib/python3.7/site-packages (from requests->nn-meter) (3.4)
Requirement already satisfied: urllib3<3,>=1.21.1 in ./miniconda3/envs/py37/lib/python3.7/site-packages (from requests->nn-meter) (2.0.7)
Requirement already satisfied: charset-normalizer<4,>=2 in ./miniconda3/envs/py37/lib/python3.7/site-packages (from requests->nn-meter) (3.3.2)
Requirement already satisfied: joblib>=0.11 in ./miniconda3/envs/py37/lib/python3.7/site-packages (from scikit-learn->nn-meter) (1.3.2)
Requirement already satisfied: threadpoolctl>=2.0.0 in ./miniconda3/envs/py37/lib/python3.7/site-packages (from scikit-learn->nn-meter) (3.1.0)
Requirement already satisfied: scipy>=1.1.0 in ./miniconda3/envs/py37/lib/python3.7/site-packages (from scikit-learn->nn-meter) (1.7.3)
Requirement already satisfied: importlib-metadata in ./miniconda3/envs/py37/lib/python3.7/site-packages (from attrs>=19.2.0->jsonlines->nn-meter) (6.7.0)
Requirement already satisfied: six>=1.5 in ./miniconda3/envs/py37/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->nn-meter) (1.16.0)
Requirement already satisfied: zipp>=0.5 in ./miniconda3/envs/py37/lib/python3.7/site-packages (from importlib-metadata->attrs>=19.2.0->jsonlines->nn-meter) (3.15.0)
(py37) yongqiang@yongqiang:~$
(py37) yongqiang@yongqiang:~$ conda list | grep scikit-learn
scikit-learn              1.0.2                    pypi_0    pypi
(py37) yongqiang@yongqiang:~$

(py37) yongqiang@yongqiang:~$ conda list | grep nn-meter
nn-meter                  2.0                      pypi_0    pypi
(py37) yongqiang@yongqiang:~$
(py37) yongqiang@yongqiang:~$ nn-meter -h
usage: nn-meter [-h] [-v] [--list-predictors] [--list-backends]
                [--list-kernels] [--list-operators] [--list-testcases]
                {predict,lat_pred,get_ir,create,connect,register,unregister}
                ...

please run "nn-meter {positional argument} --help" to see nn-meter guidance

positional arguments:
  {predict,lat_pred,get_ir,create,connect,register,unregister}
    predict (lat_pred)  apply latency predictor for testing model
    get_ir              specify a model type to convert to nn-meter ir graph
    create              create a workspace folder for nn-Meter builder
    connect             connect to backend
    register            register customized module to nn-Meter, supporting
                        type: predictor, backend, operator, testcase, operator
    unregister          unregister customized module from nn-Meter, supporting
                        type: predictor, backend, operator, testcase, operator

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         increase output verbosity
  --list-predictors     list all supported predictors
  --list-backends       list all supported backends
  --list-kernels        list all supported kernels when building kernel
                        predictors
  --list-operators      list all supported operators when building fusion rule
                        test cases
  --list-testcases      list all supported special test cases when building
                        fusion rule test cases
(py37) yongqiang@yongqiang:~$

2.3. Sample models

/home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/

(py37) yongqiang@yongqiang:~/yongqiang_work/nn-Meter/material/testmodels$ pwd
/home/yongqiang/yongqiang_work/nn-Meter/material/testmodels
(py37) yongqiang@yongqiang:~/yongqiang_work/nn-Meter/material/testmodels$ ls -l
total 18080
-rw-r--r-- 1 yongqiang yongqiang   239602 Nov 12 10:13 mobilenetv3small_0.json
-rw-r--r-- 1 yongqiang yongqiang 10169165 Nov 12 10:13 mobilenetv3small_0.onnx
-rw-r--r-- 1 yongqiang yongqiang  8098911 Nov 12 10:13 mobilenetv3small_0.pb
(py37) yongqiang@yongqiang:~/yongqiang_work/nn-Meter/material/testmodels$

2.4. v1.0-data and v2.0-data

https://github.com/microsoft/nn-Meter/releases/tag/v1.0-data

adreno630gpu_tflite21.zip
adreno640gpu_tflite21.zip
cortexA76cpu_tflite21.zip
datasets.zip
ir_graphs.zip
myriadvpu_openvino2019r2.zip
onnx_models.zip
pb_models.zip
Source code (zip)
Source code (tar.gz)

https://github.com/microsoft/nn-Meter/releases/tag/v2.0-data

tflite_benchmark_tools_v2.1.zip
tflite_benchmark_tools_v2.7.zip
Source code (zip)
Source code (tar.gz)

~/.nn_meter/config

(py37) yongqiang@yongqiang:~/.nn_meter/config$ pwd
/home/yongqiang/.nn_meter/config
(py37) yongqiang@yongqiang:~/.nn_meter/config$
(py37) yongqiang@yongqiang:~/.nn_meter/config$ ls -l
total 8
-rw-r--r-- 1 yongqiang yongqiang 1414 Nov 12 15:04 predictors.yaml
-rw-r--r-- 1 yongqiang yongqiang   44 Nov 12 15:04 settings.yaml
(py37) yongqiang@yongqiang:~/.nn_meter/config$ cat predictors.yaml
- name: cortexA76cpu_tflite21
  version: 1.0
  category: cpu
  kernel_predictors:
    - conv-bn-relu
    - dwconv-bn-relu
    - fc
    - global-avgpool
    - hswish
    - relu
    - se
    - split
    - add
    - addrelu
    - maxpool
    - avgpool
    - bn
    - bnrelu
    - channelshuffle
    - concat
  download: https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/cortexA76cpu_tflite21.zip

- name: adreno640gpu_tflite21
  version: 1.0
  category: gpu
  kernel_predictors:
    - conv-bn-relu
    - dwconv-bn-relu
    - hswish
    - relu
    - se
    - maxpool
    - avgpool
    - bn
    - bnrelu
    - concat
  download: https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/adreno640gpu_tflite21.zip

- name: adreno630gpu_tflite21
  version: 1.0
  category: gpu
  kernel_predictors:
    - conv-bn-relu
    - dwconv-bn-relu
    - hswish
    - relu
    - se
    - maxpool
    - avgpool
    - bn
    - bnrelu
    - concat
  download: https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/adreno630gpu_tflite21.zip

- name: myriadvpu_openvino2019r2
  version: 1.0
  category: vpu
  kernel_predictors:
    - conv-bn-relu
    - dwconv-bn-relu
    - fc
    - hswish
    - relu
    - se
    - addrelu
    - maxpool
    - avgpool
    - bn
    - bnrelu
    - channelshuffle
  download: https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/myriadvpu_openvino2019r2.zip
(py37) yongqiang@yongqiang:~/.nn_meter/config$

data_folder: /home/yongqiang/.nn_meter/data

(py37) yongqiang@yongqiang:~/.nn_meter/config$ cat settings.yaml
data_folder: /home/yongqiang/.nn_meter/data
(py37) yongqiang@yongqiang:~/.nn_meter/config$

nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --tensorflow /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.pb

(py37) yongqiang@yongqiang:~$ nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --tensorflow /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.pb
(nn-Meter) checking local kernel predictors at /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21
(nn-Meter) Download from https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/cortexA76cpu_tflite21.zip ...
  0%|▎                                                      | 868k/376M [14:29<66:56:25, 1.56kiB/s]
  Traceback (most recent call last):
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/urllib3/response.py", line 710, in _error_catcher
    yield
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/urllib3/response.py", line 835, in _raw_read
    raise IncompleteRead(self._fp_bytes_read, self.length_remaining)
urllib3.exceptions.IncompleteRead: IncompleteRead(884149 bytes read, 375517932 more expected)

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/requests/models.py", line 816, in generate
    yield from self.raw.stream(chunk_size, decode_content=True)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/urllib3/response.py", line 936, in stream
    data = self.read(amt=amt, decode_content=decode_content)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/urllib3/response.py", line 907, in read
    data = self._raw_read(amt)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/urllib3/response.py", line 835, in _raw_read
    raise IncompleteRead(self._fp_bytes_read, self.length_remaining)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/contextlib.py", line 130, in __exit__
    self.gen.throw(type, value, traceback)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/urllib3/response.py", line 727, in _error_catcher
    raise ProtocolError(f"Connection broken: {e!r}", e) from e
urllib3.exceptions.ProtocolError: ('Connection broken: IncompleteRead(884149 bytes read, 375517932 more expected)', IncompleteRead(884149 bytes read, 375517932 more expected))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/yongqiang/miniconda3/envs/py37/bin/nn-meter", line 8, in 
    sys.exit(nn_meter_cli())
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/utils/nn_meter_cli/interface.py", line 266, in nn_meter_cli
    args.func(args)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/utils/nn_meter_cli/predictor.py", line 39, in apply_latency_predictor_cli
    predictor = load_latency_predictor(args.predictor, args.predictor_version)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/predictor/nn_meter_predictor.py", line 66, in load_latency_predictor
    kernel_predictors, fusionrule = loading_to_local(pred_info, os.path.join(user_data_folder, 'predictor'))
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/predictor/utils.py", line 30, in loading_to_local
    download_from_url(pred_info["download"], dir)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/utils/utils.py", line 29, in download_from_url
    for data in response.iter_content(block_size):
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/requests/models.py", line 818, in generate
    raise ChunkedEncodingError(e)
requests.exceptions.ChunkedEncodingError: ('Connection broken: IncompleteRead(884149 bytes read, 375517932 more expected)', IncompleteRead(884149 bytes read, 375517932 more expected))
(py37) yongqiang@yongqiang:~$

在线下载 https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/cortexA76cpu_tflite21.zip 失败，可以从 https://github.com/microsoft/nn-Meter/releases/tag/v1.0-data 处离线下载，复制到 data_folder: /home/yongqiang/.nn_meter/data 目录，然后解压，即可使用。

(py37) yongqiang@yongqiang:~/.nn_meter/data/predictor$ chmod a+x cortexA76cpu_tflite21.zip
(py37) yongqiang@yongqiang:~/.nn_meter/data/predictor$ ls -l
total 367584
-rwxr-xr-x 1 yongqiang yongqiang 376402081 Nov 19 21:06 cortexA76cpu_tflite21.zip
(py37) yongqiang@yongqiang:~/.nn_meter/data/predictor$
(py37) yongqiang@yongqiang:~/.nn_meter/data/predictor$ unzip cortexA76cpu_tflite21.zip
Archive:  cortexA76cpu_tflite21.zip
   creating: cortexA76cpu_tflite21/
  inflating: cortexA76cpu_tflite21/add.pkl
  inflating: cortexA76cpu_tflite21/addrelu.pkl
  inflating: cortexA76cpu_tflite21/avgpool.pkl
  inflating: cortexA76cpu_tflite21/bn.pkl
  inflating: cortexA76cpu_tflite21/bnrelu.pkl
  inflating: cortexA76cpu_tflite21/channelshuffle.pkl
  inflating: cortexA76cpu_tflite21/concat.pkl
  inflating: cortexA76cpu_tflite21/conv-bn-relu.pkl
  inflating: cortexA76cpu_tflite21/dwconv-bn-relu.pkl
  inflating: cortexA76cpu_tflite21/fc.pkl
  inflating: cortexA76cpu_tflite21/fusion_rules.json
  inflating: cortexA76cpu_tflite21/global-avgpool.pkl
  inflating: cortexA76cpu_tflite21/hswish.pkl
  inflating: cortexA76cpu_tflite21/maxpool.pkl
  inflating: cortexA76cpu_tflite21/relu.pkl
  inflating: cortexA76cpu_tflite21/se.pkl
  inflating: cortexA76cpu_tflite21/split.pkl
(py37) yongqiang@yongqiang:~/.nn_meter/data/predictor$
(py37) yongqiang@yongqiang:~/.nn_meter/data/predictor$ ls -l
total 367588
drwxr-xr-x 2 yongqiang yongqiang      4096 Jul 23  2021 cortexA76cpu_tflite21
-rwxr-xr-x 1 yongqiang yongqiang 376402081 Nov 19 21:06 cortexA76cpu_tflite21.zip
(py37) yongqiang@yongqiang:~/.nn_meter/data/predictor$
(py37) yongqiang@yongqiang:~/.nn_meter/data/predictor$ cd cortexA76cpu_tflite21/
(py37) yongqiang@yongqiang:~/.nn_meter/data/predictor/cortexA76cpu_tflite21$ ls -l
total 1369956
-rw-r--r-- 1 yongqiang yongqiang  31757462 May 31  2021 add.pkl
-rw-r--r-- 1 yongqiang yongqiang  31757462 May 31  2021 addrelu.pkl
-rw-r--r-- 1 yongqiang yongqiang  77011080 May 31  2021 avgpool.pkl
-rw-r--r-- 1 yongqiang yongqiang  30081902 May 31  2021 bn.pkl
-rw-r--r-- 1 yongqiang yongqiang  31535606 May 31  2021 bnrelu.pkl
-rw-r--r-- 1 yongqiang yongqiang  14827163 May 31  2021 channelshuffle.pkl
-rw-r--r-- 1 yongqiang yongqiang 335113694 May 31  2021 concat.pkl
-rw-r--r-- 1 yongqiang yongqiang 525120135 May 31  2021 conv-bn-relu.pkl
-rw-r--r-- 1 yongqiang yongqiang  82660737 May 31  2021 dwconv-bn-relu.pkl
-rw-r--r-- 1 yongqiang yongqiang  58072608 May 31  2021 fc.pkl
-rw-r--r-- 1 yongqiang yongqiang     16421 Jun  2  2021 fusion_rules.json
-rw-r--r-- 1 yongqiang yongqiang  28156904 May 31  2021 global-avgpool.pkl
-rw-r--r-- 1 yongqiang yongqiang  24177043 May 31  2021 hswish.pkl
-rw-r--r-- 1 yongqiang yongqiang  10680143 May 31  2021 maxpool.pkl
-rw-r--r-- 1 yongqiang yongqiang  31535606 May 31  2021 relu.pkl
-rw-r--r-- 1 yongqiang yongqiang  60680510 May 31  2021 se.pkl
-rw-r--r-- 1 yongqiang yongqiang  29598568 May 31  2021 split.pkl
(py37) yongqiang@yongqiang:~/.nn_meter/data/predictor/cortexA76cpu_tflite21$

2.5. scikit-learn==0.23.1

conda install scikit-learn==0.23.1

(py37) yongqiang@yongqiang:~$ nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --tensorflow /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.pb
(nn-Meter) checking local kernel predictors at /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/dwconv-bn-relu.pkl
/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/sklearn/base.py:338: UserWarning: Trying to unpickle estimator DecisionTreeRegressor from version 0.23.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
  UserWarning,
/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/sklearn/base.py:338: UserWarning: Trying to unpickle estimator RandomForestRegressor from version 0.23.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
  UserWarning,
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/global-avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/concat.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/hswish.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/conv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/channelshuffle.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/split.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/maxpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/fc.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/se.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bnrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/add.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/addrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bn.pkl
(nn-Meter) Start latency prediction ...
(nn-Meter) You have not install the tensorflow package, please install tensorflow==2.7.0 and try again.
Traceback (most recent call last):
  File "/home/yongqiang/miniconda3/envs/py37/bin/nn-meter", line 8, in 
    sys.exit(nn_meter_cli())
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/utils/nn_meter_cli/interface.py", line 266, in nn_meter_cli
    args.func(args)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/utils/nn_meter_cli/predictor.py", line 56, in apply_latency_predictor_cli
    latency = predictor.predict(model, model_type) # in unit of ms
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/predictor/nn_meter_predictor.py", line 106, in predict
    graph = model_file_to_graph(model, model_type, input_shape, apply_nni=apply_nni)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/ir_converter/utils.py", line 42, in model_file_to_graph
    converter = FrozenPbConverter(filename)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/ir_converter/frozenpb_converter/frozenpb_converter.py", line 15, in __init__
    parser = FrozenPbParser(file_name)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/ir_converter/frozenpb_converter/frozenpb_parser.py", line 15, in __init__
    graph = tf.compat.v1.GraphDef()
AttributeError: 'NoneType' object has no attribute 'compat'
(py37) yongqiang@yongqiang:~$
(py37) yongqiang@yongqiang:~$ pip uninstall scikit-learn
Found existing installation: scikit-learn 1.0.2
Uninstalling scikit-learn-1.0.2:
  Would remove:
    /home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/scikit_learn-1.0.2.dist-info/*
    /home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
    /home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/sklearn/*
Proceed (Y/n)? y
  Successfully uninstalled scikit-learn-1.0.2
(py37) yongqiang@yongqiang:~$
(py37) yongqiang@yongqiang:~$ conda install scikit-learn==0.23.1
Collecting package metadata (current_repodata.json): done
...
(py37) yongqiang@yongqiang:~$
(py37) yongqiang@yongqiang:~$ conda list | grep scikit-learn
scikit-learn              0.23.1           py37h423224d_0    defaults
(py37) yongqiang@yongqiang:~$

2.6. tensorflow==2.7.0

pip install tensorflow==2.7.0 --default-timeout=1000 -i https://pypi.tuna.tsinghua.edu.cn/simple

(py37) yongqiang@yongqiang:~$ nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --tensorflow /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.pb
(nn-Meter) checking local kernel predictors at /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/dwconv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/global-avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/concat.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/hswish.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/conv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/channelshuffle.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/split.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/maxpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/fc.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/se.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bnrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/add.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/addrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bn.pkl
(nn-Meter) Start latency prediction ...
(nn-Meter) You have not install the tensorflow package, please install tensorflow==2.7.0 and try again.
Traceback (most recent call last):
  File "/home/yongqiang/miniconda3/envs/py37/bin/nn-meter", line 8, in 
    sys.exit(nn_meter_cli())
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/utils/nn_meter_cli/interface.py", line 266, in nn_meter_cli
    args.func(args)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/utils/nn_meter_cli/predictor.py", line 56, in apply_latency_predictor_cli
    latency = predictor.predict(model, model_type) # in unit of ms
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/predictor/nn_meter_predictor.py", line 106, in predict
    graph = model_file_to_graph(model, model_type, input_shape, apply_nni=apply_nni)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/ir_converter/utils.py", line 42, in model_file_to_graph
    converter = FrozenPbConverter(filename)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/ir_converter/frozenpb_converter/frozenpb_converter.py", line 15, in __init__
    parser = FrozenPbParser(file_name)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/ir_converter/frozenpb_converter/frozenpb_parser.py", line 15, in __init__
    graph = tf.compat.v1.GraphDef()
AttributeError: 'NoneType' object has no attribute 'compat'
(py37) yongqiang@yongqiang:~$
(py37) yongqiang@yongqiang:~$ pip install tensorflow==2.7.0 --default-timeout=1000 -i https://pypi.tuna.tsinghua.edu.cn/simple
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting tensorflow==2.7.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e8/02/d981f022c0232d692db870501c4198cb9de2d6f00b50ee84e58fa755ca84/tensorflow-2.7.0-cp37-cp37m-manylinux2010_x86_64.whl (489.6 MB)
...
(py37) yongqiang@yongqiang:~$

2.7. onnx==1.10.0

pip install onnx==1.10.0 --default-timeout=1000 -i https://pypi.tuna.tsinghua.edu.cn/simple

(py37) yongqiang@yongqiang:~$ nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --onnx /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.onnx
(nn-Meter) checking local kernel predictors at /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/dwconv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/global-avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/concat.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/hswish.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/conv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/channelshuffle.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/split.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/maxpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/fc.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/se.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bnrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/add.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/addrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bn.pkl
(nn-Meter) Start latency prediction ...
(nn-Meter) You have not install the onnx package, please install onnx==1.10.0 and try again.
Traceback (most recent call last):
  File "/home/yongqiang/miniconda3/envs/py37/bin/nn-meter", line 8, in 
    sys.exit(nn_meter_cli())
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/utils/nn_meter_cli/interface.py", line 266, in nn_meter_cli
    args.func(args)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/utils/nn_meter_cli/predictor.py", line 56, in apply_latency_predictor_cli
    latency = predictor.predict(model, model_type) # in unit of ms
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/predictor/nn_meter_predictor.py", line 106, in predict
    graph = model_file_to_graph(model, model_type, input_shape, apply_nni=apply_nni)
  File "/home/yongqiang/miniconda3/envs/py37/lib/python3.7/site-packages/nn_meter/ir_converter/utils.py", line 38, in model_file_to_graph
    model = onnx.load(filename)
AttributeError: 'NoneType' object has no attribute 'load'
(py37) yongqiang@yongqiang:~$
(py37) yongqiang@yongqiang:~$ pip install onnx==1.10.0 --default-timeout=1000 -i https://pypi.tuna.tsinghua.edu.cn/simple
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting onnx==1.10.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/43/79/b274e2a918700012e0dcc0ae68f597110c3e45efbb5a7ce3b8569b3dd299/onnx-1.10.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (12.3 MB)
...
Installing collected packages: onnx
Successfully installed onnx-1.10.0
(py37) yongqiang@yongqiang:~$

3. Usage

3.1. Predict latency of saved CNN model

After installation, a command named nn-meter is enabled. To predict the latency for a CNN model with a predefined predictor in command line, users can run the following commands (sample models can be downloaded here (https://github.com/microsoft/nn-Meter/tree/main/material/testmodels)

# for Tensorflow (*.pb) file
nn-meter predict --predictor <hardware> [--predictor-version <version>] --tensorflow <pb-file_or_folder> 
# Example Usage
nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --tensorflow /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.pb 

# for ONNX (*.onnx) file
nn-meter predict --predictor <hardware> [--predictor-version <version>] --onnx <onnx-file_or_folder>
#Example Usage
nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --onnx /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.onnx 

# for torch model from torchvision model zoo (str)
nn-meter predict --predictor <hardware> [--predictor-version <version>] --torchvision <model-name> <model-name>... 
#Example Usage
nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --torchvision /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenet_v2

# for nn-Meter IR (*.json) file
nn-meter predict --predictor <hardware> [--predictor-version <version>] --nn-meter-ir <json-file_or_folder> 
#Example Usage
nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --nn-meter-ir /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.json 

--predictor-version arguments is optional. When the predictor version is not specified by users, nn-meter will use the latest version of the predictor.

(base) yongqiang@yongqiang:~/yongqiang_work/nn-Meter/material/testmodels$ pwd
/home/yongqiang/yongqiang_work/nn-Meter/material/testmodels
(base) yongqiang@yongqiang:~/yongqiang_work/nn-Meter/material/testmodels$ ls -l
total 18080
-rw-r--r-- 1 yongqiang yongqiang   239602 Nov 12 10:13 mobilenetv3small_0.json
-rw-r--r-- 1 yongqiang yongqiang 10169165 Nov 12 10:13 mobilenetv3small_0.onnx
-rw-r--r-- 1 yongqiang yongqiang  8098911 Nov 12 10:13 mobilenetv3small_0.pb
(base) yongqiang@yongqiang:~/yongqiang_work/nn-Meter/material/testmodels$

(base) yongqiang@yongqiang:~/.nn_meter/config$ ll
total 16
drwxr-xr-x 2 yongqiang yongqiang 4096 Nov 12 15:04 ./
drwxr-xr-x 4 yongqiang yongqiang 4096 Nov 12 18:19 ../
-rw-r--r-- 1 yongqiang yongqiang 1414 Nov 12 15:04 predictors.yaml
-rw-r--r-- 1 yongqiang yongqiang   44 Nov 12 15:04 settings.yaml
(base) yongqiang@yongqiang:~/.nn_meter/config$
(base) yongqiang@yongqiang:~/.nn_meter/config$ cat settings.yaml
data_folder: /home/yongqiang/.nn_meter/data
(base) yongqiang@yongqiang:~/.nn_meter/config$

nn-Meter can support batch mode prediction. To predict latency for multiple models in the same model type once, user should collect all models in one folder and state the folder after --[model-type] liked argument.

It should also be noted that for PyTorch model, nn-meter can only support existing models in torchvision model zoo. The string followed by --torchvision should be exactly one or more string indicating name(s) of some existing torchvision models. To apply latency prediction for torchvision model in command line, onnx and onnx-simplifier packages are required.

nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --tensorflow /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.pb

(py37) yongqiang@yongqiang:~$ nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --tensorflow /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.pb
(nn-Meter) checking local kernel predictors at /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/dwconv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/global-avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/concat.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/hswish.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/conv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/channelshuffle.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/split.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/maxpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/fc.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/se.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bnrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/add.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/addrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bn.pkl
(nn-Meter) Start latency prediction ...
...
(nn-Meter) Predict latency: 12.558942703135 ms
(nn-Meter) [RESULT] predict latency for mobilenetv3small_0.pb: 12.558942703135 ms
(py37) yongqiang@yongqiang:~$

nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --onnx /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.onnx

(py37) yongqiang@yongqiang:~$ nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --onnx /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.onnx
(nn-Meter) checking local kernel predictors at /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/dwconv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/global-avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/concat.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/hswish.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/conv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/channelshuffle.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/split.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/maxpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/fc.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/se.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bnrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/add.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/addrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bn.pkl
(nn-Meter) Start latency prediction ...
(nn-Meter) Predict latency: 11.842191154847175 ms
(nn-Meter) [RESULT] predict latency for mobilenetv3small_0.onnx: 11.842191154847175 ms
(py37) yongqiang@yongqiang:~$

nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --nn-meter-ir /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.json

(py37) yongqiang@yongqiang:~$ nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --nn-meter-ir /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.json
(nn-Meter) checking local kernel predictors at /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/dwconv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/global-avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/concat.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/hswish.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/conv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/channelshuffle.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/split.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/maxpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/fc.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/se.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bnrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/add.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/addrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bn.pkl
(nn-Meter) Start latency prediction ...
(nn-Meter) Predict latency: 12.558942703135 ms
(nn-Meter) [RESULT] predict latency for mobilenetv3small_0.json: 12.558942703135 ms
(py37) yongqiang@yongqiang:~$

Appendix

nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --tensorflow /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.pb

(py37) yongqiang@yongqiang:~$ nn-meter predict --predictor cortexA76cpu_tflite21 --predictor-version 1.0 --tensorflow /home/yongqiang/yongqiang_work/nn-Meter/material/testmodels/mobilenetv3small_0.pb
(nn-Meter) checking local kernel predictors at /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/dwconv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/global-avgpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/concat.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/hswish.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/conv-bn-relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/channelshuffle.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/split.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/maxpool.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/fc.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/relu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/se.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bnrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/add.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/addrelu.pkl
(nn-Meter) load predictor /home/yongqiang/.nn_meter/data/predictor/cortexA76cpu_tflite21/bn.pkl
(nn-Meter) Start latency prediction ...
2023-11-20 23:22:53.764300: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/yongqiang/vulkan/1.3.239.0/x86_64/lib
2023-11-20 23:22:53.764628: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
(nn-Meter) Input shape of fc15.fc/weight op is [].
(nn-Meter) Output shape of fc15.fc/weight op is [[1024, 1000]].
(nn-Meter) Input shape of fc15.fc/weight/read op is [].
(nn-Meter) Output shape of fc15.fc/weight/read op is [[1024, 1000]].
(nn-Meter) Input shape of Reshape/shape op is [].
(nn-Meter) Output shape of Reshape/shape op is [[2]].
(nn-Meter) Input shape of Mean/reduction_indices op is [].
(nn-Meter) Output shape of Mean/reduction_indices op is [[2]].
(nn-Meter) Input shape of conv13.1.hswish/mul/y op is [].
(nn-Meter) Output shape of conv13.1.hswish/mul/y op is [[]].
(nn-Meter) Input shape of conv13.1.hswish/add/y op is [].
(nn-Meter) Output shape of conv13.1.hswish/add/y op is [[]].
(nn-Meter) Input shape of conv11.1.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of conv11.1.batchnorm/BatchNorm/moving_variance op is [[1024]].
(nn-Meter) Input shape of conv11.1.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of conv11.1.batchnorm/BatchNorm/moving_variance/read op is [[1024]].
(nn-Meter) Input shape of conv11.1.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of conv11.1.batchnorm/BatchNorm/moving_mean op is [[1024]].
(nn-Meter) Input shape of conv11.1.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of conv11.1.batchnorm/BatchNorm/moving_mean/read op is [[1024]].
(nn-Meter) Input shape of conv11.1.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of conv11.1.batchnorm/BatchNorm/gamma op is [[1024]].
(nn-Meter) Input shape of conv11.1.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of conv11.1.batchnorm/BatchNorm/gamma/read op is [[1024]].
(nn-Meter) Input shape of conv11.1.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of conv11.1.batchnorm/BatchNorm/beta op is [[1024]].
(nn-Meter) Input shape of conv11.1.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of conv11.1.batchnorm/BatchNorm/beta/read op is [[1024]].
(nn-Meter) Input shape of conv13.1.conv/weight op is [].
(nn-Meter) Output shape of conv13.1.conv/weight op is [[1, 1, 96, 1024]].
(nn-Meter) Input shape of conv13.1.conv/weight/read op is [].
(nn-Meter) Output shape of conv13.1.conv/weight/read op is [[1, 1, 96, 1024]].
(nn-Meter) Input shape of layer12.3.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer12.3.batchnorm/BatchNorm/moving_variance op is [[96]].
(nn-Meter) Input shape of layer12.3.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer12.3.batchnorm/BatchNorm/moving_variance/read op is [[96]].
(nn-Meter) Input shape of layer12.3.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer12.3.batchnorm/BatchNorm/moving_mean op is [[96]].
(nn-Meter) Input shape of layer12.3.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer12.3.batchnorm/BatchNorm/moving_mean/read op is [[96]].
(nn-Meter) Input shape of layer12.3.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer12.3.batchnorm/BatchNorm/gamma op is [[96]].
(nn-Meter) Input shape of layer12.3.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer12.3.batchnorm/BatchNorm/gamma/read op is [[96]].
(nn-Meter) Input shape of layer12.3.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer12.3.batchnorm/BatchNorm/beta op is [[96]].
(nn-Meter) Input shape of layer12.3.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer12.3.batchnorm/BatchNorm/beta/read op is [[96]].
(nn-Meter) Input shape of layer12.3.conv/weight op is [].
(nn-Meter) Output shape of layer12.3.conv/weight op is [[1, 1, 576, 96]].
(nn-Meter) Input shape of layer12.3.conv/weight/read op is [].
(nn-Meter) Output shape of layer12.3.conv/weight/read op is [[1, 1, 576, 96]].
(nn-Meter) Input shape of layer12.2.hswish/mul/y op is [].
(nn-Meter) Output shape of layer12.2.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer12.2.hswish/add/y op is [].
(nn-Meter) Output shape of layer12.2.hswish/add/y op is [[]].
(nn-Meter) Input shape of mul_16/y op is [].
(nn-Meter) Output shape of mul_16/y op is [[]].
(nn-Meter) Input shape of Add_13/y op is [].
(nn-Meter) Output shape of Add_13/y op is [[]].
(nn-Meter) Input shape of SE_8/conv2d_17/bias op is [].
(nn-Meter) Output shape of SE_8/conv2d_17/bias op is [[576]].
(nn-Meter) Input shape of SE_8/conv2d_17/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_8/conv2d_17/BiasAdd/ReadVariableOp op is [[576]].
(nn-Meter) Input shape of SE_8/conv2d_17/kernel op is [].
(nn-Meter) Output shape of SE_8/conv2d_17/kernel op is [[1, 1, 144, 576]].
(nn-Meter) Input shape of SE_8/conv2d_17/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_8/conv2d_17/Conv2D/ReadVariableOp op is [[1, 1, 144, 576]].
(nn-Meter) Input shape of SE_8/conv2d_16/bias op is [].
(nn-Meter) Output shape of SE_8/conv2d_16/bias op is [[144]].
(nn-Meter) Input shape of SE_8/conv2d_16/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_8/conv2d_16/BiasAdd/ReadVariableOp op is [[144]].
(nn-Meter) Input shape of SE_8/conv2d_16/kernel op is [].
(nn-Meter) Output shape of SE_8/conv2d_16/kernel op is [[1, 1, 576, 144]].
(nn-Meter) Input shape of SE_8/conv2d_16/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_8/conv2d_16/Conv2D/ReadVariableOp op is [[1, 1, 576, 144]].
(nn-Meter) Input shape of layer12.2.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer12.2.batchnorm/BatchNorm/moving_variance op is [[576]].
(nn-Meter) Input shape of layer12.2.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer12.2.batchnorm/BatchNorm/moving_variance/read op is [[576]].
(nn-Meter) Input shape of layer12.2.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer12.2.batchnorm/BatchNorm/moving_mean op is [[576]].
(nn-Meter) Input shape of layer12.2.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer12.2.batchnorm/BatchNorm/moving_mean/read op is [[576]].
(nn-Meter) Input shape of layer12.2.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer12.2.batchnorm/BatchNorm/gamma op is [[576]].
(nn-Meter) Input shape of layer12.2.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer12.2.batchnorm/BatchNorm/gamma/read op is [[576]].
(nn-Meter) Input shape of layer12.2.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer12.2.batchnorm/BatchNorm/beta op is [[576]].
(nn-Meter) Input shape of layer12.2.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer12.2.batchnorm/BatchNorm/beta/read op is [[576]].
(nn-Meter) Input shape of layer12.2.depconv/weight op is [].
(nn-Meter) Output shape of layer12.2.depconv/weight op is [[5, 5, 576, 1]].
(nn-Meter) Input shape of layer12.2.depconv/weight/read op is [].
(nn-Meter) Output shape of layer12.2.depconv/weight/read op is [[5, 5, 576, 1]].
(nn-Meter) Input shape of layer12.1.hswish/mul/y op is [].
(nn-Meter) Output shape of layer12.1.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer12.1.hswish/add/y op is [].
(nn-Meter) Output shape of layer12.1.hswish/add/y op is [[]].
(nn-Meter) Input shape of layer12.1.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer12.1.batchnorm/BatchNorm/moving_variance op is [[576]].
(nn-Meter) Input shape of layer12.1.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer12.1.batchnorm/BatchNorm/moving_variance/read op is [[576]].
(nn-Meter) Input shape of layer12.1.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer12.1.batchnorm/BatchNorm/moving_mean op is [[576]].
(nn-Meter) Input shape of layer12.1.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer12.1.batchnorm/BatchNorm/moving_mean/read op is [[576]].
(nn-Meter) Input shape of layer12.1.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer12.1.batchnorm/BatchNorm/gamma op is [[576]].
(nn-Meter) Input shape of layer12.1.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer12.1.batchnorm/BatchNorm/gamma/read op is [[576]].
(nn-Meter) Input shape of layer12.1.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer12.1.batchnorm/BatchNorm/beta op is [[576]].
(nn-Meter) Input shape of layer12.1.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer12.1.batchnorm/BatchNorm/beta/read op is [[576]].
(nn-Meter) Input shape of layer12.1.conv/weight op is [].
(nn-Meter) Output shape of layer12.1.conv/weight op is [[1, 1, 96, 576]].
(nn-Meter) Input shape of layer12.1.conv/weight/read op is [].
(nn-Meter) Output shape of layer12.1.conv/weight/read op is [[1, 1, 96, 576]].
(nn-Meter) Input shape of layer11.3.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer11.3.batchnorm/BatchNorm/moving_variance op is [[96]].
(nn-Meter) Input shape of layer11.3.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer11.3.batchnorm/BatchNorm/moving_variance/read op is [[96]].
(nn-Meter) Input shape of layer11.3.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer11.3.batchnorm/BatchNorm/moving_mean op is [[96]].
(nn-Meter) Input shape of layer11.3.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer11.3.batchnorm/BatchNorm/moving_mean/read op is [[96]].
(nn-Meter) Input shape of layer11.3.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer11.3.batchnorm/BatchNorm/gamma op is [[96]].
(nn-Meter) Input shape of layer11.3.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer11.3.batchnorm/BatchNorm/gamma/read op is [[96]].
(nn-Meter) Input shape of layer11.3.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer11.3.batchnorm/BatchNorm/beta op is [[96]].
(nn-Meter) Input shape of layer11.3.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer11.3.batchnorm/BatchNorm/beta/read op is [[96]].
(nn-Meter) Input shape of layer11.3.conv/weight op is [].
(nn-Meter) Output shape of layer11.3.conv/weight op is [[1, 1, 576, 96]].
(nn-Meter) Input shape of layer11.3.conv/weight/read op is [].
(nn-Meter) Output shape of layer11.3.conv/weight/read op is [[1, 1, 576, 96]].
(nn-Meter) Input shape of layer11.2.hswish/mul/y op is [].
(nn-Meter) Output shape of layer11.2.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer11.2.hswish/add/y op is [].
(nn-Meter) Output shape of layer11.2.hswish/add/y op is [[]].
(nn-Meter) Input shape of mul_14/y op is [].
(nn-Meter) Output shape of mul_14/y op is [[]].
(nn-Meter) Input shape of Add_11/y op is [].
(nn-Meter) Output shape of Add_11/y op is [[]].
(nn-Meter) Input shape of SE_7/conv2d_15/bias op is [].
(nn-Meter) Output shape of SE_7/conv2d_15/bias op is [[576]].
(nn-Meter) Input shape of SE_7/conv2d_15/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_7/conv2d_15/BiasAdd/ReadVariableOp op is [[576]].
(nn-Meter) Input shape of SE_7/conv2d_15/kernel op is [].
(nn-Meter) Output shape of SE_7/conv2d_15/kernel op is [[1, 1, 144, 576]].
(nn-Meter) Input shape of SE_7/conv2d_15/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_7/conv2d_15/Conv2D/ReadVariableOp op is [[1, 1, 144, 576]].
(nn-Meter) Input shape of SE_7/conv2d_14/bias op is [].
(nn-Meter) Output shape of SE_7/conv2d_14/bias op is [[144]].
(nn-Meter) Input shape of SE_7/conv2d_14/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_7/conv2d_14/BiasAdd/ReadVariableOp op is [[144]].
(nn-Meter) Input shape of SE_7/conv2d_14/kernel op is [].
(nn-Meter) Output shape of SE_7/conv2d_14/kernel op is [[1, 1, 576, 144]].
(nn-Meter) Input shape of SE_7/conv2d_14/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_7/conv2d_14/Conv2D/ReadVariableOp op is [[1, 1, 576, 144]].
(nn-Meter) Input shape of layer11.2.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer11.2.batchnorm/BatchNorm/moving_variance op is [[576]].
(nn-Meter) Input shape of layer11.2.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer11.2.batchnorm/BatchNorm/moving_variance/read op is [[576]].
(nn-Meter) Input shape of layer11.2.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer11.2.batchnorm/BatchNorm/moving_mean op is [[576]].
(nn-Meter) Input shape of layer11.2.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer11.2.batchnorm/BatchNorm/moving_mean/read op is [[576]].
(nn-Meter) Input shape of layer11.2.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer11.2.batchnorm/BatchNorm/gamma op is [[576]].
(nn-Meter) Input shape of layer11.2.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer11.2.batchnorm/BatchNorm/gamma/read op is [[576]].
(nn-Meter) Input shape of layer11.2.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer11.2.batchnorm/BatchNorm/beta op is [[576]].
(nn-Meter) Input shape of layer11.2.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer11.2.batchnorm/BatchNorm/beta/read op is [[576]].
(nn-Meter) Input shape of layer11.2.depconv/weight op is [].
(nn-Meter) Output shape of layer11.2.depconv/weight op is [[5, 5, 576, 1]].
(nn-Meter) Input shape of layer11.2.depconv/weight/read op is [].
(nn-Meter) Output shape of layer11.2.depconv/weight/read op is [[5, 5, 576, 1]].
(nn-Meter) Input shape of layer11.1.hswish/mul/y op is [].
(nn-Meter) Output shape of layer11.1.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer11.1.hswish/add/y op is [].
(nn-Meter) Output shape of layer11.1.hswish/add/y op is [[]].
(nn-Meter) Input shape of layer11.1.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer11.1.batchnorm/BatchNorm/moving_variance op is [[576]].
(nn-Meter) Input shape of layer11.1.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer11.1.batchnorm/BatchNorm/moving_variance/read op is [[576]].
(nn-Meter) Input shape of layer11.1.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer11.1.batchnorm/BatchNorm/moving_mean op is [[576]].
(nn-Meter) Input shape of layer11.1.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer11.1.batchnorm/BatchNorm/moving_mean/read op is [[576]].
(nn-Meter) Input shape of layer11.1.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer11.1.batchnorm/BatchNorm/gamma op is [[576]].
(nn-Meter) Input shape of layer11.1.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer11.1.batchnorm/BatchNorm/gamma/read op is [[576]].
(nn-Meter) Input shape of layer11.1.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer11.1.batchnorm/BatchNorm/beta op is [[576]].
(nn-Meter) Input shape of layer11.1.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer11.1.batchnorm/BatchNorm/beta/read op is [[576]].
(nn-Meter) Input shape of layer11.1.conv/weight op is [].
(nn-Meter) Output shape of layer11.1.conv/weight op is [[1, 1, 96, 576]].
(nn-Meter) Input shape of layer11.1.conv/weight/read op is [].
(nn-Meter) Output shape of layer11.1.conv/weight/read op is [[1, 1, 96, 576]].
(nn-Meter) Input shape of layer10.3.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer10.3.batchnorm/BatchNorm/moving_variance op is [[96]].
(nn-Meter) Input shape of layer10.3.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer10.3.batchnorm/BatchNorm/moving_variance/read op is [[96]].
(nn-Meter) Input shape of layer10.3.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer10.3.batchnorm/BatchNorm/moving_mean op is [[96]].
(nn-Meter) Input shape of layer10.3.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer10.3.batchnorm/BatchNorm/moving_mean/read op is [[96]].
(nn-Meter) Input shape of layer10.3.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer10.3.batchnorm/BatchNorm/gamma op is [[96]].
(nn-Meter) Input shape of layer10.3.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer10.3.batchnorm/BatchNorm/gamma/read op is [[96]].
(nn-Meter) Input shape of layer10.3.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer10.3.batchnorm/BatchNorm/beta op is [[96]].
(nn-Meter) Input shape of layer10.3.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer10.3.batchnorm/BatchNorm/beta/read op is [[96]].
(nn-Meter) Input shape of layer10.3.conv/weight op is [].
(nn-Meter) Output shape of layer10.3.conv/weight op is [[1, 1, 288, 96]].
(nn-Meter) Input shape of layer10.3.conv/weight/read op is [].
(nn-Meter) Output shape of layer10.3.conv/weight/read op is [[1, 1, 288, 96]].
(nn-Meter) Input shape of layer10.2.hswish/mul/y op is [].
(nn-Meter) Output shape of layer10.2.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer10.2.hswish/add/y op is [].
(nn-Meter) Output shape of layer10.2.hswish/add/y op is [[]].
(nn-Meter) Input shape of mul_12/y op is [].
(nn-Meter) Output shape of mul_12/y op is [[]].
(nn-Meter) Input shape of Add_10/y op is [].
(nn-Meter) Output shape of Add_10/y op is [[]].
(nn-Meter) Input shape of SE_6/conv2d_13/bias op is [].
(nn-Meter) Output shape of SE_6/conv2d_13/bias op is [[288]].
(nn-Meter) Input shape of SE_6/conv2d_13/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_6/conv2d_13/BiasAdd/ReadVariableOp op is [[288]].
(nn-Meter) Input shape of SE_6/conv2d_13/kernel op is [].
(nn-Meter) Output shape of SE_6/conv2d_13/kernel op is [[1, 1, 72, 288]].
(nn-Meter) Input shape of SE_6/conv2d_13/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_6/conv2d_13/Conv2D/ReadVariableOp op is [[1, 1, 72, 288]].
(nn-Meter) Input shape of SE_6/conv2d_12/bias op is [].
(nn-Meter) Output shape of SE_6/conv2d_12/bias op is [[72]].
(nn-Meter) Input shape of SE_6/conv2d_12/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_6/conv2d_12/BiasAdd/ReadVariableOp op is [[72]].
(nn-Meter) Input shape of SE_6/conv2d_12/kernel op is [].
(nn-Meter) Output shape of SE_6/conv2d_12/kernel op is [[1, 1, 288, 72]].
(nn-Meter) Input shape of SE_6/conv2d_12/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_6/conv2d_12/Conv2D/ReadVariableOp op is [[1, 1, 288, 72]].
(nn-Meter) Input shape of layer10.2.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer10.2.batchnorm/BatchNorm/moving_variance op is [[288]].
(nn-Meter) Input shape of layer10.2.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer10.2.batchnorm/BatchNorm/moving_variance/read op is [[288]].
(nn-Meter) Input shape of layer10.2.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer10.2.batchnorm/BatchNorm/moving_mean op is [[288]].
(nn-Meter) Input shape of layer10.2.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer10.2.batchnorm/BatchNorm/moving_mean/read op is [[288]].
(nn-Meter) Input shape of layer10.2.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer10.2.batchnorm/BatchNorm/gamma op is [[288]].
(nn-Meter) Input shape of layer10.2.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer10.2.batchnorm/BatchNorm/gamma/read op is [[288]].
(nn-Meter) Input shape of layer10.2.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer10.2.batchnorm/BatchNorm/beta op is [[288]].
(nn-Meter) Input shape of layer10.2.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer10.2.batchnorm/BatchNorm/beta/read op is [[288]].
(nn-Meter) Input shape of layer10.2.depconv/weight op is [].
(nn-Meter) Output shape of layer10.2.depconv/weight op is [[5, 5, 288, 1]].
(nn-Meter) Input shape of layer10.2.depconv/weight/read op is [].
(nn-Meter) Output shape of layer10.2.depconv/weight/read op is [[5, 5, 288, 1]].
(nn-Meter) Input shape of layer10.1.hswish/mul/y op is [].
(nn-Meter) Output shape of layer10.1.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer10.1.hswish/add/y op is [].
(nn-Meter) Output shape of layer10.1.hswish/add/y op is [[]].
(nn-Meter) Input shape of layer10.1.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer10.1.batchnorm/BatchNorm/moving_variance op is [[288]].
(nn-Meter) Input shape of layer10.1.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer10.1.batchnorm/BatchNorm/moving_variance/read op is [[288]].
(nn-Meter) Input shape of layer10.1.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer10.1.batchnorm/BatchNorm/moving_mean op is [[288]].
(nn-Meter) Input shape of layer10.1.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer10.1.batchnorm/BatchNorm/moving_mean/read op is [[288]].
(nn-Meter) Input shape of layer10.1.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer10.1.batchnorm/BatchNorm/gamma op is [[288]].
(nn-Meter) Input shape of layer10.1.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer10.1.batchnorm/BatchNorm/gamma/read op is [[288]].
(nn-Meter) Input shape of layer10.1.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer10.1.batchnorm/BatchNorm/beta op is [[288]].
(nn-Meter) Input shape of layer10.1.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer10.1.batchnorm/BatchNorm/beta/read op is [[288]].
(nn-Meter) Input shape of layer10.1.conv/weight op is [].
(nn-Meter) Output shape of layer10.1.conv/weight op is [[1, 1, 48, 288]].
(nn-Meter) Input shape of layer10.1.conv/weight/read op is [].
(nn-Meter) Output shape of layer10.1.conv/weight/read op is [[1, 1, 48, 288]].
(nn-Meter) Input shape of layer9.3.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer9.3.batchnorm/BatchNorm/moving_variance op is [[48]].
(nn-Meter) Input shape of layer9.3.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer9.3.batchnorm/BatchNorm/moving_variance/read op is [[48]].
(nn-Meter) Input shape of layer9.3.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer9.3.batchnorm/BatchNorm/moving_mean op is [[48]].
(nn-Meter) Input shape of layer9.3.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer9.3.batchnorm/BatchNorm/moving_mean/read op is [[48]].
(nn-Meter) Input shape of layer9.3.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer9.3.batchnorm/BatchNorm/gamma op is [[48]].
(nn-Meter) Input shape of layer9.3.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer9.3.batchnorm/BatchNorm/gamma/read op is [[48]].
(nn-Meter) Input shape of layer9.3.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer9.3.batchnorm/BatchNorm/beta op is [[48]].
(nn-Meter) Input shape of layer9.3.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer9.3.batchnorm/BatchNorm/beta/read op is [[48]].
(nn-Meter) Input shape of layer9.3.conv/weight op is [].
(nn-Meter) Output shape of layer9.3.conv/weight op is [[1, 1, 144, 48]].
(nn-Meter) Input shape of layer9.3.conv/weight/read op is [].
(nn-Meter) Output shape of layer9.3.conv/weight/read op is [[1, 1, 144, 48]].
(nn-Meter) Input shape of layer9.2.hswish/mul/y op is [].
(nn-Meter) Output shape of layer9.2.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer9.2.hswish/add/y op is [].
(nn-Meter) Output shape of layer9.2.hswish/add/y op is [[]].
(nn-Meter) Input shape of mul_10/y op is [].
(nn-Meter) Output shape of mul_10/y op is [[]].
(nn-Meter) Input shape of Add_8/y op is [].
(nn-Meter) Output shape of Add_8/y op is [[]].
(nn-Meter) Input shape of SE_5/conv2d_11/bias op is [].
(nn-Meter) Output shape of SE_5/conv2d_11/bias op is [[144]].
(nn-Meter) Input shape of SE_5/conv2d_11/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_5/conv2d_11/BiasAdd/ReadVariableOp op is [[144]].
(nn-Meter) Input shape of SE_5/conv2d_11/kernel op is [].
(nn-Meter) Output shape of SE_5/conv2d_11/kernel op is [[1, 1, 36, 144]].
(nn-Meter) Input shape of SE_5/conv2d_11/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_5/conv2d_11/Conv2D/ReadVariableOp op is [[1, 1, 36, 144]].
(nn-Meter) Input shape of SE_5/conv2d_10/bias op is [].
(nn-Meter) Output shape of SE_5/conv2d_10/bias op is [[36]].
(nn-Meter) Input shape of SE_5/conv2d_10/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_5/conv2d_10/BiasAdd/ReadVariableOp op is [[36]].
(nn-Meter) Input shape of SE_5/conv2d_10/kernel op is [].
(nn-Meter) Output shape of SE_5/conv2d_10/kernel op is [[1, 1, 144, 36]].
(nn-Meter) Input shape of SE_5/conv2d_10/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_5/conv2d_10/Conv2D/ReadVariableOp op is [[1, 1, 144, 36]].
(nn-Meter) Input shape of layer9.2.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer9.2.batchnorm/BatchNorm/moving_variance op is [[144]].
(nn-Meter) Input shape of layer9.2.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer9.2.batchnorm/BatchNorm/moving_variance/read op is [[144]].
(nn-Meter) Input shape of layer9.2.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer9.2.batchnorm/BatchNorm/moving_mean op is [[144]].
(nn-Meter) Input shape of layer9.2.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer9.2.batchnorm/BatchNorm/moving_mean/read op is [[144]].
(nn-Meter) Input shape of layer9.2.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer9.2.batchnorm/BatchNorm/gamma op is [[144]].
(nn-Meter) Input shape of layer9.2.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer9.2.batchnorm/BatchNorm/gamma/read op is [[144]].
(nn-Meter) Input shape of layer9.2.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer9.2.batchnorm/BatchNorm/beta op is [[144]].
(nn-Meter) Input shape of layer9.2.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer9.2.batchnorm/BatchNorm/beta/read op is [[144]].
(nn-Meter) Input shape of layer9.2.depconv/weight op is [].
(nn-Meter) Output shape of layer9.2.depconv/weight op is [[5, 5, 144, 1]].
(nn-Meter) Input shape of layer9.2.depconv/weight/read op is [].
(nn-Meter) Output shape of layer9.2.depconv/weight/read op is [[5, 5, 144, 1]].
(nn-Meter) Input shape of layer9.1.hswish/mul/y op is [].
(nn-Meter) Output shape of layer9.1.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer9.1.hswish/add/y op is [].
(nn-Meter) Output shape of layer9.1.hswish/add/y op is [[]].
(nn-Meter) Input shape of layer9.1.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer9.1.batchnorm/BatchNorm/moving_variance op is [[144]].
(nn-Meter) Input shape of layer9.1.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer9.1.batchnorm/BatchNorm/moving_variance/read op is [[144]].
(nn-Meter) Input shape of layer9.1.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer9.1.batchnorm/BatchNorm/moving_mean op is [[144]].
(nn-Meter) Input shape of layer9.1.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer9.1.batchnorm/BatchNorm/moving_mean/read op is [[144]].
(nn-Meter) Input shape of layer9.1.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer9.1.batchnorm/BatchNorm/gamma op is [[144]].
(nn-Meter) Input shape of layer9.1.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer9.1.batchnorm/BatchNorm/gamma/read op is [[144]].
(nn-Meter) Input shape of layer9.1.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer9.1.batchnorm/BatchNorm/beta op is [[144]].
(nn-Meter) Input shape of layer9.1.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer9.1.batchnorm/BatchNorm/beta/read op is [[144]].
(nn-Meter) Input shape of layer9.1.conv/weight op is [].
(nn-Meter) Output shape of layer9.1.conv/weight op is [[1, 1, 48, 144]].
(nn-Meter) Input shape of layer9.1.conv/weight/read op is [].
(nn-Meter) Output shape of layer9.1.conv/weight/read op is [[1, 1, 48, 144]].
(nn-Meter) Input shape of layer8.3.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer8.3.batchnorm/BatchNorm/moving_variance op is [[48]].
(nn-Meter) Input shape of layer8.3.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer8.3.batchnorm/BatchNorm/moving_variance/read op is [[48]].
(nn-Meter) Input shape of layer8.3.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer8.3.batchnorm/BatchNorm/moving_mean op is [[48]].
(nn-Meter) Input shape of layer8.3.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer8.3.batchnorm/BatchNorm/moving_mean/read op is [[48]].
(nn-Meter) Input shape of layer8.3.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer8.3.batchnorm/BatchNorm/gamma op is [[48]].
(nn-Meter) Input shape of layer8.3.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer8.3.batchnorm/BatchNorm/gamma/read op is [[48]].
(nn-Meter) Input shape of layer8.3.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer8.3.batchnorm/BatchNorm/beta op is [[48]].
(nn-Meter) Input shape of layer8.3.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer8.3.batchnorm/BatchNorm/beta/read op is [[48]].
(nn-Meter) Input shape of layer8.3.conv/weight op is [].
(nn-Meter) Output shape of layer8.3.conv/weight op is [[1, 1, 120, 48]].
(nn-Meter) Input shape of layer8.3.conv/weight/read op is [].
(nn-Meter) Output shape of layer8.3.conv/weight/read op is [[1, 1, 120, 48]].
(nn-Meter) Input shape of layer8.2.hswish/mul/y op is [].
(nn-Meter) Output shape of layer8.2.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer8.2.hswish/add/y op is [].
(nn-Meter) Output shape of layer8.2.hswish/add/y op is [[]].
(nn-Meter) Input shape of mul_8/y op is [].
(nn-Meter) Output shape of mul_8/y op is [[]].
(nn-Meter) Input shape of Add_7/y op is [].
(nn-Meter) Output shape of Add_7/y op is [[]].
(nn-Meter) Input shape of SE_4/conv2d_9/bias op is [].
(nn-Meter) Output shape of SE_4/conv2d_9/bias op is [[120]].
(nn-Meter) Input shape of SE_4/conv2d_9/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_4/conv2d_9/BiasAdd/ReadVariableOp op is [[120]].
(nn-Meter) Input shape of SE_4/conv2d_9/kernel op is [].
(nn-Meter) Output shape of SE_4/conv2d_9/kernel op is [[1, 1, 30, 120]].
(nn-Meter) Input shape of SE_4/conv2d_9/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_4/conv2d_9/Conv2D/ReadVariableOp op is [[1, 1, 30, 120]].
(nn-Meter) Input shape of SE_4/conv2d_8/bias op is [].
(nn-Meter) Output shape of SE_4/conv2d_8/bias op is [[30]].
(nn-Meter) Input shape of SE_4/conv2d_8/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_4/conv2d_8/BiasAdd/ReadVariableOp op is [[30]].
(nn-Meter) Input shape of SE_4/conv2d_8/kernel op is [].
(nn-Meter) Output shape of SE_4/conv2d_8/kernel op is [[1, 1, 120, 30]].
(nn-Meter) Input shape of SE_4/conv2d_8/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_4/conv2d_8/Conv2D/ReadVariableOp op is [[1, 1, 120, 30]].
(nn-Meter) Input shape of layer8.2.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer8.2.batchnorm/BatchNorm/moving_variance op is [[120]].
(nn-Meter) Input shape of layer8.2.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer8.2.batchnorm/BatchNorm/moving_variance/read op is [[120]].
(nn-Meter) Input shape of layer8.2.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer8.2.batchnorm/BatchNorm/moving_mean op is [[120]].
(nn-Meter) Input shape of layer8.2.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer8.2.batchnorm/BatchNorm/moving_mean/read op is [[120]].
(nn-Meter) Input shape of layer8.2.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer8.2.batchnorm/BatchNorm/gamma op is [[120]].
(nn-Meter) Input shape of layer8.2.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer8.2.batchnorm/BatchNorm/gamma/read op is [[120]].
(nn-Meter) Input shape of layer8.2.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer8.2.batchnorm/BatchNorm/beta op is [[120]].
(nn-Meter) Input shape of layer8.2.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer8.2.batchnorm/BatchNorm/beta/read op is [[120]].
(nn-Meter) Input shape of layer8.2.depconv/weight op is [].
(nn-Meter) Output shape of layer8.2.depconv/weight op is [[5, 5, 120, 1]].
(nn-Meter) Input shape of layer8.2.depconv/weight/read op is [].
(nn-Meter) Output shape of layer8.2.depconv/weight/read op is [[5, 5, 120, 1]].
(nn-Meter) Input shape of layer8.1.hswish/mul/y op is [].
(nn-Meter) Output shape of layer8.1.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer8.1.hswish/add/y op is [].
(nn-Meter) Output shape of layer8.1.hswish/add/y op is [[]].
(nn-Meter) Input shape of layer8.1.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer8.1.batchnorm/BatchNorm/moving_variance op is [[120]].
(nn-Meter) Input shape of layer8.1.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer8.1.batchnorm/BatchNorm/moving_variance/read op is [[120]].
(nn-Meter) Input shape of layer8.1.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer8.1.batchnorm/BatchNorm/moving_mean op is [[120]].
(nn-Meter) Input shape of layer8.1.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer8.1.batchnorm/BatchNorm/moving_mean/read op is [[120]].
(nn-Meter) Input shape of layer8.1.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer8.1.batchnorm/BatchNorm/gamma op is [[120]].
(nn-Meter) Input shape of layer8.1.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer8.1.batchnorm/BatchNorm/gamma/read op is [[120]].
(nn-Meter) Input shape of layer8.1.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer8.1.batchnorm/BatchNorm/beta op is [[120]].
(nn-Meter) Input shape of layer8.1.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer8.1.batchnorm/BatchNorm/beta/read op is [[120]].
(nn-Meter) Input shape of layer8.1.conv/weight op is [].
(nn-Meter) Output shape of layer8.1.conv/weight op is [[1, 1, 40, 120]].
(nn-Meter) Input shape of layer8.1.conv/weight/read op is [].
(nn-Meter) Output shape of layer8.1.conv/weight/read op is [[1, 1, 40, 120]].
(nn-Meter) Input shape of layer7.3.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer7.3.batchnorm/BatchNorm/moving_variance op is [[40]].
(nn-Meter) Input shape of layer7.3.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer7.3.batchnorm/BatchNorm/moving_variance/read op is [[40]].
(nn-Meter) Input shape of layer7.3.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer7.3.batchnorm/BatchNorm/moving_mean op is [[40]].
(nn-Meter) Input shape of layer7.3.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer7.3.batchnorm/BatchNorm/moving_mean/read op is [[40]].
(nn-Meter) Input shape of layer7.3.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer7.3.batchnorm/BatchNorm/gamma op is [[40]].
(nn-Meter) Input shape of layer7.3.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer7.3.batchnorm/BatchNorm/gamma/read op is [[40]].
(nn-Meter) Input shape of layer7.3.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer7.3.batchnorm/BatchNorm/beta op is [[40]].
(nn-Meter) Input shape of layer7.3.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer7.3.batchnorm/BatchNorm/beta/read op is [[40]].
(nn-Meter) Input shape of layer7.3.conv/weight op is [].
(nn-Meter) Output shape of layer7.3.conv/weight op is [[1, 1, 240, 40]].
(nn-Meter) Input shape of layer7.3.conv/weight/read op is [].
(nn-Meter) Output shape of layer7.3.conv/weight/read op is [[1, 1, 240, 40]].
(nn-Meter) Input shape of layer7.2.hswish/mul/y op is [].
(nn-Meter) Output shape of layer7.2.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer7.2.hswish/add/y op is [].
(nn-Meter) Output shape of layer7.2.hswish/add/y op is [[]].
(nn-Meter) Input shape of mul_6/y op is [].
(nn-Meter) Output shape of mul_6/y op is [[]].
(nn-Meter) Input shape of Add_5/y op is [].
(nn-Meter) Output shape of Add_5/y op is [[]].
(nn-Meter) Input shape of SE_3/conv2d_7/bias op is [].
(nn-Meter) Output shape of SE_3/conv2d_7/bias op is [[240]].
(nn-Meter) Input shape of SE_3/conv2d_7/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_3/conv2d_7/BiasAdd/ReadVariableOp op is [[240]].
(nn-Meter) Input shape of SE_3/conv2d_7/kernel op is [].
(nn-Meter) Output shape of SE_3/conv2d_7/kernel op is [[1, 1, 60, 240]].
(nn-Meter) Input shape of SE_3/conv2d_7/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_3/conv2d_7/Conv2D/ReadVariableOp op is [[1, 1, 60, 240]].
(nn-Meter) Input shape of SE_3/conv2d_6/bias op is [].
(nn-Meter) Output shape of SE_3/conv2d_6/bias op is [[60]].
(nn-Meter) Input shape of SE_3/conv2d_6/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_3/conv2d_6/BiasAdd/ReadVariableOp op is [[60]].
(nn-Meter) Input shape of SE_3/conv2d_6/kernel op is [].
(nn-Meter) Output shape of SE_3/conv2d_6/kernel op is [[1, 1, 240, 60]].
(nn-Meter) Input shape of SE_3/conv2d_6/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_3/conv2d_6/Conv2D/ReadVariableOp op is [[1, 1, 240, 60]].
(nn-Meter) Input shape of layer7.2.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer7.2.batchnorm/BatchNorm/moving_variance op is [[240]].
(nn-Meter) Input shape of layer7.2.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer7.2.batchnorm/BatchNorm/moving_variance/read op is [[240]].
(nn-Meter) Input shape of layer7.2.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer7.2.batchnorm/BatchNorm/moving_mean op is [[240]].
(nn-Meter) Input shape of layer7.2.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer7.2.batchnorm/BatchNorm/moving_mean/read op is [[240]].
(nn-Meter) Input shape of layer7.2.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer7.2.batchnorm/BatchNorm/gamma op is [[240]].
(nn-Meter) Input shape of layer7.2.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer7.2.batchnorm/BatchNorm/gamma/read op is [[240]].
(nn-Meter) Input shape of layer7.2.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer7.2.batchnorm/BatchNorm/beta op is [[240]].
(nn-Meter) Input shape of layer7.2.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer7.2.batchnorm/BatchNorm/beta/read op is [[240]].
(nn-Meter) Input shape of layer7.2.depconv/weight op is [].
(nn-Meter) Output shape of layer7.2.depconv/weight op is [[5, 5, 240, 1]].
(nn-Meter) Input shape of layer7.2.depconv/weight/read op is [].
(nn-Meter) Output shape of layer7.2.depconv/weight/read op is [[5, 5, 240, 1]].
(nn-Meter) Input shape of layer7.1.hswish/mul/y op is [].
(nn-Meter) Output shape of layer7.1.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer7.1.hswish/add/y op is [].
(nn-Meter) Output shape of layer7.1.hswish/add/y op is [[]].
(nn-Meter) Input shape of layer7.1.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer7.1.batchnorm/BatchNorm/moving_variance op is [[240]].
(nn-Meter) Input shape of layer7.1.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer7.1.batchnorm/BatchNorm/moving_variance/read op is [[240]].
(nn-Meter) Input shape of layer7.1.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer7.1.batchnorm/BatchNorm/moving_mean op is [[240]].
(nn-Meter) Input shape of layer7.1.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer7.1.batchnorm/BatchNorm/moving_mean/read op is [[240]].
(nn-Meter) Input shape of layer7.1.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer7.1.batchnorm/BatchNorm/gamma op is [[240]].
(nn-Meter) Input shape of layer7.1.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer7.1.batchnorm/BatchNorm/gamma/read op is [[240]].
(nn-Meter) Input shape of layer7.1.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer7.1.batchnorm/BatchNorm/beta op is [[240]].
(nn-Meter) Input shape of layer7.1.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer7.1.batchnorm/BatchNorm/beta/read op is [[240]].
(nn-Meter) Input shape of layer7.1.conv/weight op is [].
(nn-Meter) Output shape of layer7.1.conv/weight op is [[1, 1, 40, 240]].
(nn-Meter) Input shape of layer7.1.conv/weight/read op is [].
(nn-Meter) Output shape of layer7.1.conv/weight/read op is [[1, 1, 40, 240]].
(nn-Meter) Input shape of layer6.3.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer6.3.batchnorm/BatchNorm/moving_variance op is [[40]].
(nn-Meter) Input shape of layer6.3.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer6.3.batchnorm/BatchNorm/moving_variance/read op is [[40]].
(nn-Meter) Input shape of layer6.3.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer6.3.batchnorm/BatchNorm/moving_mean op is [[40]].
(nn-Meter) Input shape of layer6.3.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer6.3.batchnorm/BatchNorm/moving_mean/read op is [[40]].
(nn-Meter) Input shape of layer6.3.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer6.3.batchnorm/BatchNorm/gamma op is [[40]].
(nn-Meter) Input shape of layer6.3.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer6.3.batchnorm/BatchNorm/gamma/read op is [[40]].
(nn-Meter) Input shape of layer6.3.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer6.3.batchnorm/BatchNorm/beta op is [[40]].
(nn-Meter) Input shape of layer6.3.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer6.3.batchnorm/BatchNorm/beta/read op is [[40]].
(nn-Meter) Input shape of layer6.3.conv/weight op is [].
(nn-Meter) Output shape of layer6.3.conv/weight op is [[1, 1, 240, 40]].
(nn-Meter) Input shape of layer6.3.conv/weight/read op is [].
(nn-Meter) Output shape of layer6.3.conv/weight/read op is [[1, 1, 240, 40]].
(nn-Meter) Input shape of layer6.2.hswish/mul/y op is [].
(nn-Meter) Output shape of layer6.2.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer6.2.hswish/add/y op is [].
(nn-Meter) Output shape of layer6.2.hswish/add/y op is [[]].
(nn-Meter) Input shape of mul_4/y op is [].
(nn-Meter) Output shape of mul_4/y op is [[]].
(nn-Meter) Input shape of Add_3/y op is [].
(nn-Meter) Output shape of Add_3/y op is [[]].
(nn-Meter) Input shape of SE_2/conv2d_5/bias op is [].
(nn-Meter) Output shape of SE_2/conv2d_5/bias op is [[240]].
(nn-Meter) Input shape of SE_2/conv2d_5/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_2/conv2d_5/BiasAdd/ReadVariableOp op is [[240]].
(nn-Meter) Input shape of SE_2/conv2d_5/kernel op is [].
(nn-Meter) Output shape of SE_2/conv2d_5/kernel op is [[1, 1, 60, 240]].
(nn-Meter) Input shape of SE_2/conv2d_5/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_2/conv2d_5/Conv2D/ReadVariableOp op is [[1, 1, 60, 240]].
(nn-Meter) Input shape of SE_2/conv2d_4/bias op is [].
(nn-Meter) Output shape of SE_2/conv2d_4/bias op is [[60]].
(nn-Meter) Input shape of SE_2/conv2d_4/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_2/conv2d_4/BiasAdd/ReadVariableOp op is [[60]].
(nn-Meter) Input shape of SE_2/conv2d_4/kernel op is [].
(nn-Meter) Output shape of SE_2/conv2d_4/kernel op is [[1, 1, 240, 60]].
(nn-Meter) Input shape of SE_2/conv2d_4/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_2/conv2d_4/Conv2D/ReadVariableOp op is [[1, 1, 240, 60]].
(nn-Meter) Input shape of layer6.2.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer6.2.batchnorm/BatchNorm/moving_variance op is [[240]].
(nn-Meter) Input shape of layer6.2.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer6.2.batchnorm/BatchNorm/moving_variance/read op is [[240]].
(nn-Meter) Input shape of layer6.2.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer6.2.batchnorm/BatchNorm/moving_mean op is [[240]].
(nn-Meter) Input shape of layer6.2.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer6.2.batchnorm/BatchNorm/moving_mean/read op is [[240]].
(nn-Meter) Input shape of layer6.2.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer6.2.batchnorm/BatchNorm/gamma op is [[240]].
(nn-Meter) Input shape of layer6.2.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer6.2.batchnorm/BatchNorm/gamma/read op is [[240]].
(nn-Meter) Input shape of layer6.2.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer6.2.batchnorm/BatchNorm/beta op is [[240]].
(nn-Meter) Input shape of layer6.2.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer6.2.batchnorm/BatchNorm/beta/read op is [[240]].
(nn-Meter) Input shape of layer6.2.depconv/weight op is [].
(nn-Meter) Output shape of layer6.2.depconv/weight op is [[5, 5, 240, 1]].
(nn-Meter) Input shape of layer6.2.depconv/weight/read op is [].
(nn-Meter) Output shape of layer6.2.depconv/weight/read op is [[5, 5, 240, 1]].
(nn-Meter) Input shape of layer6.1.hswish/mul/y op is [].
(nn-Meter) Output shape of layer6.1.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer6.1.hswish/add/y op is [].
(nn-Meter) Output shape of layer6.1.hswish/add/y op is [[]].
(nn-Meter) Input shape of layer6.1.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer6.1.batchnorm/BatchNorm/moving_variance op is [[240]].
(nn-Meter) Input shape of layer6.1.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer6.1.batchnorm/BatchNorm/moving_variance/read op is [[240]].
(nn-Meter) Input shape of layer6.1.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer6.1.batchnorm/BatchNorm/moving_mean op is [[240]].
(nn-Meter) Input shape of layer6.1.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer6.1.batchnorm/BatchNorm/moving_mean/read op is [[240]].
(nn-Meter) Input shape of layer6.1.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer6.1.batchnorm/BatchNorm/gamma op is [[240]].
(nn-Meter) Input shape of layer6.1.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer6.1.batchnorm/BatchNorm/gamma/read op is [[240]].
(nn-Meter) Input shape of layer6.1.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer6.1.batchnorm/BatchNorm/beta op is [[240]].
(nn-Meter) Input shape of layer6.1.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer6.1.batchnorm/BatchNorm/beta/read op is [[240]].
(nn-Meter) Input shape of layer6.1.conv/weight op is [].
(nn-Meter) Output shape of layer6.1.conv/weight op is [[1, 1, 40, 240]].
(nn-Meter) Input shape of layer6.1.conv/weight/read op is [].
(nn-Meter) Output shape of layer6.1.conv/weight/read op is [[1, 1, 40, 240]].
(nn-Meter) Input shape of layer5.3.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer5.3.batchnorm/BatchNorm/moving_variance op is [[40]].
(nn-Meter) Input shape of layer5.3.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer5.3.batchnorm/BatchNorm/moving_variance/read op is [[40]].
(nn-Meter) Input shape of layer5.3.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer5.3.batchnorm/BatchNorm/moving_mean op is [[40]].
(nn-Meter) Input shape of layer5.3.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer5.3.batchnorm/BatchNorm/moving_mean/read op is [[40]].
(nn-Meter) Input shape of layer5.3.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer5.3.batchnorm/BatchNorm/gamma op is [[40]].
(nn-Meter) Input shape of layer5.3.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer5.3.batchnorm/BatchNorm/gamma/read op is [[40]].
(nn-Meter) Input shape of layer5.3.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer5.3.batchnorm/BatchNorm/beta op is [[40]].
(nn-Meter) Input shape of layer5.3.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer5.3.batchnorm/BatchNorm/beta/read op is [[40]].
(nn-Meter) Input shape of layer5.3.conv/weight op is [].
(nn-Meter) Output shape of layer5.3.conv/weight op is [[1, 1, 96, 40]].
(nn-Meter) Input shape of layer5.3.conv/weight/read op is [].
(nn-Meter) Output shape of layer5.3.conv/weight/read op is [[1, 1, 96, 40]].
(nn-Meter) Input shape of layer5.2.hswish/mul/y op is [].
(nn-Meter) Output shape of layer5.2.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer5.2.hswish/add/y op is [].
(nn-Meter) Output shape of layer5.2.hswish/add/y op is [[]].
(nn-Meter) Input shape of mul_2/y op is [].
(nn-Meter) Output shape of mul_2/y op is [[]].
(nn-Meter) Input shape of Add_2/y op is [].
(nn-Meter) Output shape of Add_2/y op is [[]].
(nn-Meter) Input shape of SE_1/conv2d_3/bias op is [].
(nn-Meter) Output shape of SE_1/conv2d_3/bias op is [[96]].
(nn-Meter) Input shape of SE_1/conv2d_3/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_1/conv2d_3/BiasAdd/ReadVariableOp op is [[96]].
(nn-Meter) Input shape of SE_1/conv2d_3/kernel op is [].
(nn-Meter) Output shape of SE_1/conv2d_3/kernel op is [[1, 1, 24, 96]].
(nn-Meter) Input shape of SE_1/conv2d_3/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_1/conv2d_3/Conv2D/ReadVariableOp op is [[1, 1, 24, 96]].
(nn-Meter) Input shape of SE_1/conv2d_2/bias op is [].
(nn-Meter) Output shape of SE_1/conv2d_2/bias op is [[24]].
(nn-Meter) Input shape of SE_1/conv2d_2/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_1/conv2d_2/BiasAdd/ReadVariableOp op is [[24]].
(nn-Meter) Input shape of SE_1/conv2d_2/kernel op is [].
(nn-Meter) Output shape of SE_1/conv2d_2/kernel op is [[1, 1, 96, 24]].
(nn-Meter) Input shape of SE_1/conv2d_2/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE_1/conv2d_2/Conv2D/ReadVariableOp op is [[1, 1, 96, 24]].
(nn-Meter) Input shape of layer5.2.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer5.2.batchnorm/BatchNorm/moving_variance op is [[96]].
(nn-Meter) Input shape of layer5.2.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer5.2.batchnorm/BatchNorm/moving_variance/read op is [[96]].
(nn-Meter) Input shape of layer5.2.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer5.2.batchnorm/BatchNorm/moving_mean op is [[96]].
(nn-Meter) Input shape of layer5.2.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer5.2.batchnorm/BatchNorm/moving_mean/read op is [[96]].
(nn-Meter) Input shape of layer5.2.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer5.2.batchnorm/BatchNorm/gamma op is [[96]].
(nn-Meter) Input shape of layer5.2.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer5.2.batchnorm/BatchNorm/gamma/read op is [[96]].
(nn-Meter) Input shape of layer5.2.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer5.2.batchnorm/BatchNorm/beta op is [[96]].
(nn-Meter) Input shape of layer5.2.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer5.2.batchnorm/BatchNorm/beta/read op is [[96]].
(nn-Meter) Input shape of layer5.2.depconv/weight op is [].
(nn-Meter) Output shape of layer5.2.depconv/weight op is [[5, 5, 96, 1]].
(nn-Meter) Input shape of layer5.2.depconv/weight/read op is [].
(nn-Meter) Output shape of layer5.2.depconv/weight/read op is [[5, 5, 96, 1]].
(nn-Meter) Input shape of layer5.1.hswish/mul/y op is [].
(nn-Meter) Output shape of layer5.1.hswish/mul/y op is [[]].
(nn-Meter) Input shape of layer5.1.hswish/add/y op is [].
(nn-Meter) Output shape of layer5.1.hswish/add/y op is [[]].
(nn-Meter) Input shape of layer5.1.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer5.1.batchnorm/BatchNorm/moving_variance op is [[96]].
(nn-Meter) Input shape of layer5.1.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer5.1.batchnorm/BatchNorm/moving_variance/read op is [[96]].
(nn-Meter) Input shape of layer5.1.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer5.1.batchnorm/BatchNorm/moving_mean op is [[96]].
(nn-Meter) Input shape of layer5.1.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer5.1.batchnorm/BatchNorm/moving_mean/read op is [[96]].
(nn-Meter) Input shape of layer5.1.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer5.1.batchnorm/BatchNorm/gamma op is [[96]].
(nn-Meter) Input shape of layer5.1.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer5.1.batchnorm/BatchNorm/gamma/read op is [[96]].
(nn-Meter) Input shape of layer5.1.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer5.1.batchnorm/BatchNorm/beta op is [[96]].
(nn-Meter) Input shape of layer5.1.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer5.1.batchnorm/BatchNorm/beta/read op is [[96]].
(nn-Meter) Input shape of layer5.1.conv/weight op is [].
(nn-Meter) Output shape of layer5.1.conv/weight op is [[1, 1, 24, 96]].
(nn-Meter) Input shape of layer5.1.conv/weight/read op is [].
(nn-Meter) Output shape of layer5.1.conv/weight/read op is [[1, 1, 24, 96]].
(nn-Meter) Input shape of layer4.3.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer4.3.batchnorm/BatchNorm/moving_variance op is [[24]].
(nn-Meter) Input shape of layer4.3.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer4.3.batchnorm/BatchNorm/moving_variance/read op is [[24]].
(nn-Meter) Input shape of layer4.3.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer4.3.batchnorm/BatchNorm/moving_mean op is [[24]].
(nn-Meter) Input shape of layer4.3.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer4.3.batchnorm/BatchNorm/moving_mean/read op is [[24]].
(nn-Meter) Input shape of layer4.3.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer4.3.batchnorm/BatchNorm/gamma op is [[24]].
(nn-Meter) Input shape of layer4.3.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer4.3.batchnorm/BatchNorm/gamma/read op is [[24]].
(nn-Meter) Input shape of layer4.3.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer4.3.batchnorm/BatchNorm/beta op is [[24]].
(nn-Meter) Input shape of layer4.3.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer4.3.batchnorm/BatchNorm/beta/read op is [[24]].
(nn-Meter) Input shape of layer4.3.conv/weight op is [].
(nn-Meter) Output shape of layer4.3.conv/weight op is [[1, 1, 88, 24]].
(nn-Meter) Input shape of layer4.3.conv/weight/read op is [].
(nn-Meter) Output shape of layer4.3.conv/weight/read op is [[1, 1, 88, 24]].
(nn-Meter) Input shape of layer4.2.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer4.2.batchnorm/BatchNorm/moving_variance op is [[88]].
(nn-Meter) Input shape of layer4.2.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer4.2.batchnorm/BatchNorm/moving_variance/read op is [[88]].
(nn-Meter) Input shape of layer4.2.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer4.2.batchnorm/BatchNorm/moving_mean op is [[88]].
(nn-Meter) Input shape of layer4.2.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer4.2.batchnorm/BatchNorm/moving_mean/read op is [[88]].
(nn-Meter) Input shape of layer4.2.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer4.2.batchnorm/BatchNorm/gamma op is [[88]].
(nn-Meter) Input shape of layer4.2.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer4.2.batchnorm/BatchNorm/gamma/read op is [[88]].
(nn-Meter) Input shape of layer4.2.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer4.2.batchnorm/BatchNorm/beta op is [[88]].
(nn-Meter) Input shape of layer4.2.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer4.2.batchnorm/BatchNorm/beta/read op is [[88]].
(nn-Meter) Input shape of layer4.2.depconv/weight op is [].
(nn-Meter) Output shape of layer4.2.depconv/weight op is [[3, 3, 88, 1]].
(nn-Meter) Input shape of layer4.2.depconv/weight/read op is [].
(nn-Meter) Output shape of layer4.2.depconv/weight/read op is [[3, 3, 88, 1]].
(nn-Meter) Input shape of layer4.1.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer4.1.batchnorm/BatchNorm/moving_variance op is [[88]].
(nn-Meter) Input shape of layer4.1.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer4.1.batchnorm/BatchNorm/moving_variance/read op is [[88]].
(nn-Meter) Input shape of layer4.1.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer4.1.batchnorm/BatchNorm/moving_mean op is [[88]].
(nn-Meter) Input shape of layer4.1.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer4.1.batchnorm/BatchNorm/moving_mean/read op is [[88]].
(nn-Meter) Input shape of layer4.1.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer4.1.batchnorm/BatchNorm/gamma op is [[88]].
(nn-Meter) Input shape of layer4.1.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer4.1.batchnorm/BatchNorm/gamma/read op is [[88]].
(nn-Meter) Input shape of layer4.1.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer4.1.batchnorm/BatchNorm/beta op is [[88]].
(nn-Meter) Input shape of layer4.1.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer4.1.batchnorm/BatchNorm/beta/read op is [[88]].
(nn-Meter) Input shape of layer4.1.conv/weight op is [].
(nn-Meter) Output shape of layer4.1.conv/weight op is [[1, 1, 24, 88]].
(nn-Meter) Input shape of layer4.1.conv/weight/read op is [].
(nn-Meter) Output shape of layer4.1.conv/weight/read op is [[1, 1, 24, 88]].
(nn-Meter) Input shape of layer3.3.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer3.3.batchnorm/BatchNorm/moving_variance op is [[24]].
(nn-Meter) Input shape of layer3.3.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer3.3.batchnorm/BatchNorm/moving_variance/read op is [[24]].
(nn-Meter) Input shape of layer3.3.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer3.3.batchnorm/BatchNorm/moving_mean op is [[24]].
(nn-Meter) Input shape of layer3.3.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer3.3.batchnorm/BatchNorm/moving_mean/read op is [[24]].
(nn-Meter) Input shape of layer3.3.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer3.3.batchnorm/BatchNorm/gamma op is [[24]].
(nn-Meter) Input shape of layer3.3.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer3.3.batchnorm/BatchNorm/gamma/read op is [[24]].
(nn-Meter) Input shape of layer3.3.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer3.3.batchnorm/BatchNorm/beta op is [[24]].
(nn-Meter) Input shape of layer3.3.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer3.3.batchnorm/BatchNorm/beta/read op is [[24]].
(nn-Meter) Input shape of layer3.3.conv/weight op is [].
(nn-Meter) Output shape of layer3.3.conv/weight op is [[1, 1, 72, 24]].
(nn-Meter) Input shape of layer3.3.conv/weight/read op is [].
(nn-Meter) Output shape of layer3.3.conv/weight/read op is [[1, 1, 72, 24]].
(nn-Meter) Input shape of layer3.2.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer3.2.batchnorm/BatchNorm/moving_variance op is [[72]].
(nn-Meter) Input shape of layer3.2.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer3.2.batchnorm/BatchNorm/moving_variance/read op is [[72]].
(nn-Meter) Input shape of layer3.2.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer3.2.batchnorm/BatchNorm/moving_mean op is [[72]].
(nn-Meter) Input shape of layer3.2.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer3.2.batchnorm/BatchNorm/moving_mean/read op is [[72]].
(nn-Meter) Input shape of layer3.2.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer3.2.batchnorm/BatchNorm/gamma op is [[72]].
(nn-Meter) Input shape of layer3.2.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer3.2.batchnorm/BatchNorm/gamma/read op is [[72]].
(nn-Meter) Input shape of layer3.2.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer3.2.batchnorm/BatchNorm/beta op is [[72]].
(nn-Meter) Input shape of layer3.2.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer3.2.batchnorm/BatchNorm/beta/read op is [[72]].
(nn-Meter) Input shape of layer3.2.depconv/weight op is [].
(nn-Meter) Output shape of layer3.2.depconv/weight op is [[3, 3, 72, 1]].
(nn-Meter) Input shape of layer3.2.depconv/weight/read op is [].
(nn-Meter) Output shape of layer3.2.depconv/weight/read op is [[3, 3, 72, 1]].
(nn-Meter) Input shape of layer3.1.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer3.1.batchnorm/BatchNorm/moving_variance op is [[72]].
(nn-Meter) Input shape of layer3.1.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer3.1.batchnorm/BatchNorm/moving_variance/read op is [[72]].
(nn-Meter) Input shape of layer3.1.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer3.1.batchnorm/BatchNorm/moving_mean op is [[72]].
(nn-Meter) Input shape of layer3.1.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer3.1.batchnorm/BatchNorm/moving_mean/read op is [[72]].
(nn-Meter) Input shape of layer3.1.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer3.1.batchnorm/BatchNorm/gamma op is [[72]].
(nn-Meter) Input shape of layer3.1.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer3.1.batchnorm/BatchNorm/gamma/read op is [[72]].
(nn-Meter) Input shape of layer3.1.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer3.1.batchnorm/BatchNorm/beta op is [[72]].
(nn-Meter) Input shape of layer3.1.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer3.1.batchnorm/BatchNorm/beta/read op is [[72]].
(nn-Meter) Input shape of layer3.1.conv/weight op is [].
(nn-Meter) Output shape of layer3.1.conv/weight op is [[1, 1, 16, 72]].
(nn-Meter) Input shape of layer3.1.conv/weight/read op is [].
(nn-Meter) Output shape of layer3.1.conv/weight/read op is [[1, 1, 16, 72]].
(nn-Meter) Input shape of layer2.3.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer2.3.batchnorm/BatchNorm/moving_variance op is [[16]].
(nn-Meter) Input shape of layer2.3.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer2.3.batchnorm/BatchNorm/moving_variance/read op is [[16]].
(nn-Meter) Input shape of layer2.3.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer2.3.batchnorm/BatchNorm/moving_mean op is [[16]].
(nn-Meter) Input shape of layer2.3.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer2.3.batchnorm/BatchNorm/moving_mean/read op is [[16]].
(nn-Meter) Input shape of layer2.3.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer2.3.batchnorm/BatchNorm/gamma op is [[16]].
(nn-Meter) Input shape of layer2.3.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer2.3.batchnorm/BatchNorm/gamma/read op is [[16]].
(nn-Meter) Input shape of layer2.3.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer2.3.batchnorm/BatchNorm/beta op is [[16]].
(nn-Meter) Input shape of layer2.3.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer2.3.batchnorm/BatchNorm/beta/read op is [[16]].
(nn-Meter) Input shape of layer2.3.conv/weight op is [].
(nn-Meter) Output shape of layer2.3.conv/weight op is [[1, 1, 16, 16]].
(nn-Meter) Input shape of layer2.3.conv/weight/read op is [].
(nn-Meter) Output shape of layer2.3.conv/weight/read op is [[1, 1, 16, 16]].
(nn-Meter) Input shape of mul/y op is [].
(nn-Meter) Output shape of mul/y op is [[]].
(nn-Meter) Input shape of Add/y op is [].
(nn-Meter) Output shape of Add/y op is [[]].
(nn-Meter) Input shape of SE/conv2d_1/bias op is [].
(nn-Meter) Output shape of SE/conv2d_1/bias op is [[16]].
(nn-Meter) Input shape of SE/conv2d_1/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE/conv2d_1/BiasAdd/ReadVariableOp op is [[16]].
(nn-Meter) Input shape of SE/conv2d_1/kernel op is [].
(nn-Meter) Output shape of SE/conv2d_1/kernel op is [[1, 1, 4, 16]].
(nn-Meter) Input shape of SE/conv2d_1/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE/conv2d_1/Conv2D/ReadVariableOp op is [[1, 1, 4, 16]].
(nn-Meter) Input shape of SE/conv2d/bias op is [].
(nn-Meter) Output shape of SE/conv2d/bias op is [[4]].
(nn-Meter) Input shape of SE/conv2d/BiasAdd/ReadVariableOp op is [].
(nn-Meter) Output shape of SE/conv2d/BiasAdd/ReadVariableOp op is [[4]].
(nn-Meter) Input shape of SE/conv2d/kernel op is [].
(nn-Meter) Output shape of SE/conv2d/kernel op is [[1, 1, 16, 4]].
(nn-Meter) Input shape of SE/conv2d/Conv2D/ReadVariableOp op is [].
(nn-Meter) Output shape of SE/conv2d/Conv2D/ReadVariableOp op is [[1, 1, 16, 4]].
(nn-Meter) Input shape of layer2.2.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer2.2.batchnorm/BatchNorm/moving_variance op is [[16]].
(nn-Meter) Input shape of layer2.2.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer2.2.batchnorm/BatchNorm/moving_variance/read op is [[16]].
(nn-Meter) Input shape of layer2.2.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer2.2.batchnorm/BatchNorm/moving_mean op is [[16]].
(nn-Meter) Input shape of layer2.2.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer2.2.batchnorm/BatchNorm/moving_mean/read op is [[16]].
(nn-Meter) Input shape of layer2.2.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer2.2.batchnorm/BatchNorm/gamma op is [[16]].
(nn-Meter) Input shape of layer2.2.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer2.2.batchnorm/BatchNorm/gamma/read op is [[16]].
(nn-Meter) Input shape of layer2.2.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer2.2.batchnorm/BatchNorm/beta op is [[16]].
(nn-Meter) Input shape of layer2.2.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer2.2.batchnorm/BatchNorm/beta/read op is [[16]].
(nn-Meter) Input shape of layer2.2.depconv/weight op is [].
(nn-Meter) Output shape of layer2.2.depconv/weight op is [[3, 3, 16, 1]].
(nn-Meter) Input shape of layer2.2.depconv/weight/read op is [].
(nn-Meter) Output shape of layer2.2.depconv/weight/read op is [[3, 3, 16, 1]].
(nn-Meter) Input shape of layer2.1.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of layer2.1.batchnorm/BatchNorm/moving_variance op is [[16]].
(nn-Meter) Input shape of layer2.1.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of layer2.1.batchnorm/BatchNorm/moving_variance/read op is [[16]].
(nn-Meter) Input shape of layer2.1.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of layer2.1.batchnorm/BatchNorm/moving_mean op is [[16]].
(nn-Meter) Input shape of layer2.1.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of layer2.1.batchnorm/BatchNorm/moving_mean/read op is [[16]].
(nn-Meter) Input shape of layer2.1.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of layer2.1.batchnorm/BatchNorm/gamma op is [[16]].
(nn-Meter) Input shape of layer2.1.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of layer2.1.batchnorm/BatchNorm/gamma/read op is [[16]].
(nn-Meter) Input shape of layer2.1.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of layer2.1.batchnorm/BatchNorm/beta op is [[16]].
(nn-Meter) Input shape of layer2.1.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of layer2.1.batchnorm/BatchNorm/beta/read op is [[16]].
(nn-Meter) Input shape of layer2.1.conv/weight op is [].
(nn-Meter) Output shape of layer2.1.conv/weight op is [[1, 1, 16, 16]].
(nn-Meter) Input shape of layer2.1.conv/weight/read op is [].
(nn-Meter) Output shape of layer2.1.conv/weight/read op is [[1, 1, 16, 16]].
(nn-Meter) Input shape of conv1.hswish.hswish/mul/y op is [].
(nn-Meter) Output shape of conv1.hswish.hswish/mul/y op is [[]].
(nn-Meter) Input shape of conv1.hswish.hswish/add/y op is [].
(nn-Meter) Output shape of conv1.hswish.hswish/add/y op is [[]].
(nn-Meter) Input shape of conv1.bn.batchnorm/BatchNorm/moving_variance op is [].
(nn-Meter) Output shape of conv1.bn.batchnorm/BatchNorm/moving_variance op is [[16]].
(nn-Meter) Input shape of conv1.bn.batchnorm/BatchNorm/moving_variance/read op is [].
(nn-Meter) Output shape of conv1.bn.batchnorm/BatchNorm/moving_variance/read op is [[16]].
(nn-Meter) Input shape of conv1.bn.batchnorm/BatchNorm/moving_mean op is [].
(nn-Meter) Output shape of conv1.bn.batchnorm/BatchNorm/moving_mean op is [[16]].
(nn-Meter) Input shape of conv1.bn.batchnorm/BatchNorm/moving_mean/read op is [].
(nn-Meter) Output shape of conv1.bn.batchnorm/BatchNorm/moving_mean/read op is [[16]].
(nn-Meter) Input shape of conv1.bn.batchnorm/BatchNorm/gamma op is [].
(nn-Meter) Output shape of conv1.bn.batchnorm/BatchNorm/gamma op is [[16]].
(nn-Meter) Input shape of conv1.bn.batchnorm/BatchNorm/gamma/read op is [].
(nn-Meter) Output shape of conv1.bn.batchnorm/BatchNorm/gamma/read op is [[16]].
(nn-Meter) Input shape of conv1.bn.batchnorm/BatchNorm/beta op is [].
(nn-Meter) Output shape of conv1.bn.batchnorm/BatchNorm/beta op is [[16]].
(nn-Meter) Input shape of conv1.bn.batchnorm/BatchNorm/beta/read op is [].
(nn-Meter) Output shape of conv1.bn.batchnorm/BatchNorm/beta/read op is [[16]].
(nn-Meter) Input shape of conv1.conv/weight op is [].
(nn-Meter) Output shape of conv1.conv/weight op is [[3, 3, 3, 16]].
(nn-Meter) Input shape of conv1.conv/weight/read op is [].
(nn-Meter) Output shape of conv1.conv/weight/read op is [[3, 3, 3, 16]].
(nn-Meter) Input shape of input_im_0 op is [].
(nn-Meter) Output shape of input_im_0 op is [[1, 224, 224, 3]].
(nn-Meter) Find node conv1.conv/Conv2D with its weight op conv1.conv/weight.
(nn-Meter) Get input shape of conv1.conv/Conv2D from input_im_0, input shape:[1, 224, 224, 3].
(nn-Meter) Get weight shape of conv1.conv/Conv2D from ['conv1.conv/weight'], input shape:[3, 3, 3, 16].
(nn-Meter) Op:conv1.conv/Conv2D, stride:[1, 2, 2, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 224, 224, 3], kernel size: [2, 2], strides: [1, 2, 2, 1], padding: SAME.
(nn-Meter) Input shape of conv1.conv/Conv2D op is [[1, 224, 224, 3]].
(nn-Meter) Output shape of conv1.conv/Conv2D op is [[1, 112, 112, 16]].
(nn-Meter) Propagate through op conv1.bn.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of conv1.bn.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 112, 112, 16]].
(nn-Meter) Output shape of conv1.bn.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 112, 112, 16]].
(nn-Meter) Input shape of conv1.hswish.hswish/add op is [[1, 112, 112, 16], []].
(nn-Meter) Output shape of conv1.hswish.hswish/add op is [[1, 112, 112, 16]].
(nn-Meter) Propagate through op conv1.hswish.hswish/Relu6.
(nn-Meter) Input shape of conv1.hswish.hswish/Relu6 op is [[1, 112, 112, 16]].
(nn-Meter) Output shape of conv1.hswish.hswish/Relu6 op is [[1, 112, 112, 16]].
(nn-Meter) Input shape of conv1.hswish.hswish/mul op is [[1, 112, 112, 16], []].
(nn-Meter) Output shape of conv1.hswish.hswish/mul op is [[1, 112, 112, 16]].
(nn-Meter) Input shape of conv1.hswish.hswish/mul_1 op is [[1, 112, 112, 16], [1, 112, 112, 16]].
(nn-Meter) Output shape of conv1.hswish.hswish/mul_1 op is [[1, 112, 112, 16]].
(nn-Meter) Find node layer2.1.conv/Conv2D with its weight op layer2.1.conv/weight.
(nn-Meter) Get input shape of layer2.1.conv/Conv2D from conv1.hswish.hswish/mul_1, input shape:[1, 112, 112, 16].
(nn-Meter) Get weight shape of layer2.1.conv/Conv2D from ['layer2.1.conv/weight'], input shape:[1, 1, 16, 16].
(nn-Meter) Op:layer2.1.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 112, 112, 16], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer2.1.conv/Conv2D op is [[1, 112, 112, 16]].
(nn-Meter) Output shape of layer2.1.conv/Conv2D op is [[1, 112, 112, 16]].
(nn-Meter) Propagate through op layer2.1.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer2.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 112, 112, 16]].
(nn-Meter) Output shape of layer2.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 112, 112, 16]].
(nn-Meter) Propagate through op layer2.1.relu/Relu.
(nn-Meter) Input shape of layer2.1.relu/Relu op is [[1, 112, 112, 16]].
(nn-Meter) Output shape of layer2.1.relu/Relu op is [[1, 112, 112, 16]].
(nn-Meter) Find node layer2.2.depconv/depthwise with its weight op layer2.2.depconv/weight.
(nn-Meter) Get input shape of layer2.2.depconv/depthwise from layer2.1.relu/Relu, input shape:[1, 112, 112, 16].
(nn-Meter) Get weight shape of layer2.2.depconv/depthwise from ['layer2.2.depconv/weight'], input shape:[3, 3, 16, 1].
(nn-Meter) Op:layer2.2.depconv/depthwise, stride:[1, 2, 2, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 112, 112, 16], kernel size: [2, 2], strides: [1, 2, 2, 1], padding: SAME.
(nn-Meter) Input shape of layer2.2.depconv/depthwise op is [[1, 112, 112, 16]].
(nn-Meter) Output shape of layer2.2.depconv/depthwise op is [[1, 56, 56, 16]].
(nn-Meter) Propagate through op layer2.2.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer2.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 56, 56, 16]].
(nn-Meter) Output shape of layer2.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 56, 56, 16]].
(nn-Meter) Get input shape of SE/AvgPool from layer2.2.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 56, 56, 16].
(nn-Meter) Op:SE/AvgPool, stride:[1, 1, 1, 1], padding:VALID.
(nn-Meter) Calculating padding shape, input shape: [1, 56, 56, 16], kernel size: [1, 56, 56, 1], strides: [1, 1, 1, 1], padding: VALID.
(nn-Meter) Input shape of SE/AvgPool op is [[1, 56, 56, 16]].
(nn-Meter) Output shape of SE/AvgPool op is [[1, 1, 1, 16]].
(nn-Meter) Find node SE/conv2d/Conv2D with its weight op SE/conv2d/kernel.
(nn-Meter) Get input shape of SE/conv2d/Conv2D from SE/AvgPool, input shape:[1, 1, 1, 16].
(nn-Meter) Get weight shape of SE/conv2d/Conv2D from ['SE/conv2d/kernel'], input shape:[1, 1, 16, 4].
(nn-Meter) Op:SE/conv2d/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 16], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE/conv2d/Conv2D op is [[1, 1, 1, 16]].
(nn-Meter) Output shape of SE/conv2d/Conv2D op is [[1, 1, 1, 4]].
(nn-Meter) Propagate through op SE/conv2d/BiasAdd.
(nn-Meter) Input shape of SE/conv2d/BiasAdd op is [[1, 1, 1, 4]].
(nn-Meter) Output shape of SE/conv2d/BiasAdd op is [[1, 1, 1, 4]].
(nn-Meter) Propagate through op SE/Relu.
(nn-Meter) Input shape of SE/Relu op is [[1, 1, 1, 4]].
(nn-Meter) Output shape of SE/Relu op is [[1, 1, 1, 4]].
(nn-Meter) Find node SE/conv2d_1/Conv2D with its weight op SE/conv2d_1/kernel.
(nn-Meter) Get input shape of SE/conv2d_1/Conv2D from SE/Relu, input shape:[1, 1, 1, 4].
(nn-Meter) Get weight shape of SE/conv2d_1/Conv2D from ['SE/conv2d_1/kernel'], input shape:[1, 1, 4, 16].
(nn-Meter) Op:SE/conv2d_1/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 4], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE/conv2d_1/Conv2D op is [[1, 1, 1, 4]].
(nn-Meter) Output shape of SE/conv2d_1/Conv2D op is [[1, 1, 1, 16]].
(nn-Meter) Propagate through op SE/conv2d_1/BiasAdd.
(nn-Meter) Input shape of SE/conv2d_1/BiasAdd op is [[1, 1, 1, 16]].
(nn-Meter) Output shape of SE/conv2d_1/BiasAdd op is [[1, 1, 1, 16]].
(nn-Meter) Input shape of Add op is [[1, 1, 1, 16], []].
(nn-Meter) Output shape of Add op is [[1, 1, 1, 16]].
(nn-Meter) Propagate through op Relu6.
(nn-Meter) Input shape of Relu6 op is [[1, 1, 1, 16]].
(nn-Meter) Output shape of Relu6 op is [[1, 1, 1, 16]].
(nn-Meter) Input shape of mul op is [[1, 1, 1, 16], []].
(nn-Meter) Output shape of mul op is [[1, 1, 1, 16]].
(nn-Meter) Input shape of mul_1 op is [[1, 56, 56, 16], [1, 56, 56, 16]].
(nn-Meter) Output shape of mul_1 op is [[1, 56, 56, 16]].
(nn-Meter) Propagate through op layer2.2.relu/Relu.
(nn-Meter) Input shape of layer2.2.relu/Relu op is [[1, 56, 56, 16]].
(nn-Meter) Output shape of layer2.2.relu/Relu op is [[1, 56, 56, 16]].
(nn-Meter) Find node layer2.3.conv/Conv2D with its weight op layer2.3.conv/weight.
(nn-Meter) Get input shape of layer2.3.conv/Conv2D from layer2.2.relu/Relu, input shape:[1, 56, 56, 16].
(nn-Meter) Get weight shape of layer2.3.conv/Conv2D from ['layer2.3.conv/weight'], input shape:[1, 1, 16, 16].
(nn-Meter) Op:layer2.3.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 56, 56, 16], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer2.3.conv/Conv2D op is [[1, 56, 56, 16]].
(nn-Meter) Output shape of layer2.3.conv/Conv2D op is [[1, 56, 56, 16]].
(nn-Meter) Propagate through op layer2.3.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer2.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 56, 56, 16]].
(nn-Meter) Output shape of layer2.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 56, 56, 16]].
(nn-Meter) Find node layer3.1.conv/Conv2D with its weight op layer3.1.conv/weight.
(nn-Meter) Get input shape of layer3.1.conv/Conv2D from layer2.3.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 56, 56, 16].
(nn-Meter) Get weight shape of layer3.1.conv/Conv2D from ['layer3.1.conv/weight'], input shape:[1, 1, 16, 72].
(nn-Meter) Op:layer3.1.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 56, 56, 16], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer3.1.conv/Conv2D op is [[1, 56, 56, 16]].
(nn-Meter) Output shape of layer3.1.conv/Conv2D op is [[1, 56, 56, 72]].
(nn-Meter) Propagate through op layer3.1.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer3.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 56, 56, 72]].
(nn-Meter) Output shape of layer3.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 56, 56, 72]].
(nn-Meter) Propagate through op layer3.1.relu/Relu.
(nn-Meter) Input shape of layer3.1.relu/Relu op is [[1, 56, 56, 72]].
(nn-Meter) Output shape of layer3.1.relu/Relu op is [[1, 56, 56, 72]].
(nn-Meter) Find node layer3.2.depconv/depthwise with its weight op layer3.2.depconv/weight.
(nn-Meter) Get input shape of layer3.2.depconv/depthwise from layer3.1.relu/Relu, input shape:[1, 56, 56, 72].
(nn-Meter) Get weight shape of layer3.2.depconv/depthwise from ['layer3.2.depconv/weight'], input shape:[3, 3, 72, 1].
(nn-Meter) Op:layer3.2.depconv/depthwise, stride:[1, 2, 2, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 56, 56, 72], kernel size: [2, 2], strides: [1, 2, 2, 1], padding: SAME.
(nn-Meter) Input shape of layer3.2.depconv/depthwise op is [[1, 56, 56, 72]].
(nn-Meter) Output shape of layer3.2.depconv/depthwise op is [[1, 28, 28, 72]].
(nn-Meter) Propagate through op layer3.2.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer3.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 28, 28, 72]].
(nn-Meter) Output shape of layer3.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 28, 28, 72]].
(nn-Meter) Propagate through op layer3.2.relu/Relu.
(nn-Meter) Input shape of layer3.2.relu/Relu op is [[1, 28, 28, 72]].
(nn-Meter) Output shape of layer3.2.relu/Relu op is [[1, 28, 28, 72]].
(nn-Meter) Find node layer3.3.conv/Conv2D with its weight op layer3.3.conv/weight.
(nn-Meter) Get input shape of layer3.3.conv/Conv2D from layer3.2.relu/Relu, input shape:[1, 28, 28, 72].
(nn-Meter) Get weight shape of layer3.3.conv/Conv2D from ['layer3.3.conv/weight'], input shape:[1, 1, 72, 24].
(nn-Meter) Op:layer3.3.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 28, 28, 72], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer3.3.conv/Conv2D op is [[1, 28, 28, 72]].
(nn-Meter) Output shape of layer3.3.conv/Conv2D op is [[1, 28, 28, 24]].
(nn-Meter) Propagate through op layer3.3.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer3.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 28, 28, 24]].
(nn-Meter) Output shape of layer3.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 28, 28, 24]].
(nn-Meter) Find node layer4.1.conv/Conv2D with its weight op layer4.1.conv/weight.
(nn-Meter) Get input shape of layer4.1.conv/Conv2D from layer3.3.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 28, 28, 24].
(nn-Meter) Get weight shape of layer4.1.conv/Conv2D from ['layer4.1.conv/weight'], input shape:[1, 1, 24, 88].
(nn-Meter) Op:layer4.1.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 28, 28, 24], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer4.1.conv/Conv2D op is [[1, 28, 28, 24]].
(nn-Meter) Output shape of layer4.1.conv/Conv2D op is [[1, 28, 28, 88]].
(nn-Meter) Propagate through op layer4.1.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer4.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 28, 28, 88]].
(nn-Meter) Output shape of layer4.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 28, 28, 88]].
(nn-Meter) Propagate through op layer4.1.relu/Relu.
(nn-Meter) Input shape of layer4.1.relu/Relu op is [[1, 28, 28, 88]].
(nn-Meter) Output shape of layer4.1.relu/Relu op is [[1, 28, 28, 88]].
(nn-Meter) Find node layer4.2.depconv/depthwise with its weight op layer4.2.depconv/weight.
(nn-Meter) Get input shape of layer4.2.depconv/depthwise from layer4.1.relu/Relu, input shape:[1, 28, 28, 88].
(nn-Meter) Get weight shape of layer4.2.depconv/depthwise from ['layer4.2.depconv/weight'], input shape:[3, 3, 88, 1].
(nn-Meter) Op:layer4.2.depconv/depthwise, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 28, 28, 88], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer4.2.depconv/depthwise op is [[1, 28, 28, 88]].
(nn-Meter) Output shape of layer4.2.depconv/depthwise op is [[1, 28, 28, 88]].
(nn-Meter) Propagate through op layer4.2.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer4.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 28, 28, 88]].
(nn-Meter) Output shape of layer4.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 28, 28, 88]].
(nn-Meter) Propagate through op layer4.2.relu/Relu.
(nn-Meter) Input shape of layer4.2.relu/Relu op is [[1, 28, 28, 88]].
(nn-Meter) Output shape of layer4.2.relu/Relu op is [[1, 28, 28, 88]].
(nn-Meter) Find node layer4.3.conv/Conv2D with its weight op layer4.3.conv/weight.
(nn-Meter) Get input shape of layer4.3.conv/Conv2D from layer4.2.relu/Relu, input shape:[1, 28, 28, 88].
(nn-Meter) Get weight shape of layer4.3.conv/Conv2D from ['layer4.3.conv/weight'], input shape:[1, 1, 88, 24].
(nn-Meter) Op:layer4.3.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 28, 28, 88], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer4.3.conv/Conv2D op is [[1, 28, 28, 88]].
(nn-Meter) Output shape of layer4.3.conv/Conv2D op is [[1, 28, 28, 24]].
(nn-Meter) Propagate through op layer4.3.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer4.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 28, 28, 24]].
(nn-Meter) Output shape of layer4.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 28, 28, 24]].
(nn-Meter) Input shape of Add_1 op is [[1, 28, 28, 24], [1, 28, 28, 24]].
(nn-Meter) Output shape of Add_1 op is [[1, 28, 28, 24]].
(nn-Meter) Find node layer5.1.conv/Conv2D with its weight op layer5.1.conv/weight.
(nn-Meter) Get input shape of layer5.1.conv/Conv2D from Add_1, input shape:[1, 28, 28, 24].
(nn-Meter) Get weight shape of layer5.1.conv/Conv2D from ['layer5.1.conv/weight'], input shape:[1, 1, 24, 96].
(nn-Meter) Op:layer5.1.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 28, 28, 24], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer5.1.conv/Conv2D op is [[1, 28, 28, 24]].
(nn-Meter) Output shape of layer5.1.conv/Conv2D op is [[1, 28, 28, 96]].
(nn-Meter) Propagate through op layer5.1.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer5.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 28, 28, 96]].
(nn-Meter) Output shape of layer5.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 28, 28, 96]].
(nn-Meter) Input shape of layer5.1.hswish/add op is [[1, 28, 28, 96], []].
(nn-Meter) Output shape of layer5.1.hswish/add op is [[1, 28, 28, 96]].
(nn-Meter) Propagate through op layer5.1.hswish/Relu6.
(nn-Meter) Input shape of layer5.1.hswish/Relu6 op is [[1, 28, 28, 96]].
(nn-Meter) Output shape of layer5.1.hswish/Relu6 op is [[1, 28, 28, 96]].
(nn-Meter) Input shape of layer5.1.hswish/mul op is [[1, 28, 28, 96], []].
(nn-Meter) Output shape of layer5.1.hswish/mul op is [[1, 28, 28, 96]].
(nn-Meter) Input shape of layer5.1.hswish/mul_1 op is [[1, 28, 28, 96], [1, 28, 28, 96]].
(nn-Meter) Output shape of layer5.1.hswish/mul_1 op is [[1, 28, 28, 96]].
(nn-Meter) Find node layer5.2.depconv/depthwise with its weight op layer5.2.depconv/weight.
(nn-Meter) Get input shape of layer5.2.depconv/depthwise from layer5.1.hswish/mul_1, input shape:[1, 28, 28, 96].
(nn-Meter) Get weight shape of layer5.2.depconv/depthwise from ['layer5.2.depconv/weight'], input shape:[5, 5, 96, 1].
(nn-Meter) Op:layer5.2.depconv/depthwise, stride:[1, 2, 2, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 28, 28, 96], kernel size: [2, 2], strides: [1, 2, 2, 1], padding: SAME.
(nn-Meter) Input shape of layer5.2.depconv/depthwise op is [[1, 28, 28, 96]].
(nn-Meter) Output shape of layer5.2.depconv/depthwise op is [[1, 14, 14, 96]].
(nn-Meter) Propagate through op layer5.2.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer5.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 96]].
(nn-Meter) Output shape of layer5.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 96]].
(nn-Meter) Get input shape of SE_1/AvgPool from layer5.2.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 14, 14, 96].
(nn-Meter) Op:SE_1/AvgPool, stride:[1, 1, 1, 1], padding:VALID.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 96], kernel size: [1, 14, 14, 1], strides: [1, 1, 1, 1], padding: VALID.
(nn-Meter) Input shape of SE_1/AvgPool op is [[1, 14, 14, 96]].
(nn-Meter) Output shape of SE_1/AvgPool op is [[1, 1, 1, 96]].
(nn-Meter) Find node SE_1/conv2d_2/Conv2D with its weight op SE_1/conv2d_2/kernel.
(nn-Meter) Get input shape of SE_1/conv2d_2/Conv2D from SE_1/AvgPool, input shape:[1, 1, 1, 96].
(nn-Meter) Get weight shape of SE_1/conv2d_2/Conv2D from ['SE_1/conv2d_2/kernel'], input shape:[1, 1, 96, 24].
(nn-Meter) Op:SE_1/conv2d_2/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 96], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_1/conv2d_2/Conv2D op is [[1, 1, 1, 96]].
(nn-Meter) Output shape of SE_1/conv2d_2/Conv2D op is [[1, 1, 1, 24]].
(nn-Meter) Propagate through op SE_1/conv2d_2/BiasAdd.
(nn-Meter) Input shape of SE_1/conv2d_2/BiasAdd op is [[1, 1, 1, 24]].
(nn-Meter) Output shape of SE_1/conv2d_2/BiasAdd op is [[1, 1, 1, 24]].
(nn-Meter) Propagate through op SE_1/Relu.
(nn-Meter) Input shape of SE_1/Relu op is [[1, 1, 1, 24]].
(nn-Meter) Output shape of SE_1/Relu op is [[1, 1, 1, 24]].
(nn-Meter) Find node SE_1/conv2d_3/Conv2D with its weight op SE_1/conv2d_3/kernel.
(nn-Meter) Get input shape of SE_1/conv2d_3/Conv2D from SE_1/Relu, input shape:[1, 1, 1, 24].
(nn-Meter) Get weight shape of SE_1/conv2d_3/Conv2D from ['SE_1/conv2d_3/kernel'], input shape:[1, 1, 24, 96].
(nn-Meter) Op:SE_1/conv2d_3/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 24], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_1/conv2d_3/Conv2D op is [[1, 1, 1, 24]].
(nn-Meter) Output shape of SE_1/conv2d_3/Conv2D op is [[1, 1, 1, 96]].
(nn-Meter) Propagate through op SE_1/conv2d_3/BiasAdd.
(nn-Meter) Input shape of SE_1/conv2d_3/BiasAdd op is [[1, 1, 1, 96]].
(nn-Meter) Output shape of SE_1/conv2d_3/BiasAdd op is [[1, 1, 1, 96]].
(nn-Meter) Input shape of Add_2 op is [[1, 1, 1, 96], []].
(nn-Meter) Output shape of Add_2 op is [[1, 1, 1, 96]].
(nn-Meter) Propagate through op Relu6_1.
(nn-Meter) Input shape of Relu6_1 op is [[1, 1, 1, 96]].
(nn-Meter) Output shape of Relu6_1 op is [[1, 1, 1, 96]].
(nn-Meter) Input shape of mul_2 op is [[1, 1, 1, 96], []].
(nn-Meter) Output shape of mul_2 op is [[1, 1, 1, 96]].
(nn-Meter) Input shape of mul_3 op is [[1, 14, 14, 96], [1, 14, 14, 96]].
(nn-Meter) Output shape of mul_3 op is [[1, 14, 14, 96]].
(nn-Meter) Input shape of layer5.2.hswish/add op is [[1, 14, 14, 96], []].
(nn-Meter) Output shape of layer5.2.hswish/add op is [[1, 14, 14, 96]].
(nn-Meter) Propagate through op layer5.2.hswish/Relu6.
(nn-Meter) Input shape of layer5.2.hswish/Relu6 op is [[1, 14, 14, 96]].
(nn-Meter) Output shape of layer5.2.hswish/Relu6 op is [[1, 14, 14, 96]].
(nn-Meter) Input shape of layer5.2.hswish/mul op is [[1, 14, 14, 96], []].
(nn-Meter) Output shape of layer5.2.hswish/mul op is [[1, 14, 14, 96]].
(nn-Meter) Input shape of layer5.2.hswish/mul_1 op is [[1, 14, 14, 96], [1, 14, 14, 96]].
(nn-Meter) Output shape of layer5.2.hswish/mul_1 op is [[1, 14, 14, 96]].
(nn-Meter) Find node layer5.3.conv/Conv2D with its weight op layer5.3.conv/weight.
(nn-Meter) Get input shape of layer5.3.conv/Conv2D from layer5.2.hswish/mul_1, input shape:[1, 14, 14, 96].
(nn-Meter) Get weight shape of layer5.3.conv/Conv2D from ['layer5.3.conv/weight'], input shape:[1, 1, 96, 40].
(nn-Meter) Op:layer5.3.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 96], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer5.3.conv/Conv2D op is [[1, 14, 14, 96]].
(nn-Meter) Output shape of layer5.3.conv/Conv2D op is [[1, 14, 14, 40]].
(nn-Meter) Propagate through op layer5.3.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer5.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 40]].
(nn-Meter) Output shape of layer5.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 40]].
(nn-Meter) Find node layer6.1.conv/Conv2D with its weight op layer6.1.conv/weight.
(nn-Meter) Get input shape of layer6.1.conv/Conv2D from layer5.3.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 14, 14, 40].
(nn-Meter) Get weight shape of layer6.1.conv/Conv2D from ['layer6.1.conv/weight'], input shape:[1, 1, 40, 240].
(nn-Meter) Op:layer6.1.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 40], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer6.1.conv/Conv2D op is [[1, 14, 14, 40]].
(nn-Meter) Output shape of layer6.1.conv/Conv2D op is [[1, 14, 14, 240]].
(nn-Meter) Propagate through op layer6.1.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer6.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of layer6.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 240]].
(nn-Meter) Input shape of layer6.1.hswish/add op is [[1, 14, 14, 240], []].
(nn-Meter) Output shape of layer6.1.hswish/add op is [[1, 14, 14, 240]].
(nn-Meter) Propagate through op layer6.1.hswish/Relu6.
(nn-Meter) Input shape of layer6.1.hswish/Relu6 op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of layer6.1.hswish/Relu6 op is [[1, 14, 14, 240]].
(nn-Meter) Input shape of layer6.1.hswish/mul op is [[1, 14, 14, 240], []].
(nn-Meter) Output shape of layer6.1.hswish/mul op is [[1, 14, 14, 240]].
(nn-Meter) Input shape of layer6.1.hswish/mul_1 op is [[1, 14, 14, 240], [1, 14, 14, 240]].
(nn-Meter) Output shape of layer6.1.hswish/mul_1 op is [[1, 14, 14, 240]].
(nn-Meter) Find node layer6.2.depconv/depthwise with its weight op layer6.2.depconv/weight.
(nn-Meter) Get input shape of layer6.2.depconv/depthwise from layer6.1.hswish/mul_1, input shape:[1, 14, 14, 240].
(nn-Meter) Get weight shape of layer6.2.depconv/depthwise from ['layer6.2.depconv/weight'], input shape:[5, 5, 240, 1].
(nn-Meter) Op:layer6.2.depconv/depthwise, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 240], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer6.2.depconv/depthwise op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of layer6.2.depconv/depthwise op is [[1, 14, 14, 240]].
(nn-Meter) Propagate through op layer6.2.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer6.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of layer6.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 240]].
(nn-Meter) Get input shape of SE_2/AvgPool from layer6.2.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 14, 14, 240].
(nn-Meter) Op:SE_2/AvgPool, stride:[1, 1, 1, 1], padding:VALID.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 240], kernel size: [1, 14, 14, 1], strides: [1, 1, 1, 1], padding: VALID.
(nn-Meter) Input shape of SE_2/AvgPool op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of SE_2/AvgPool op is [[1, 1, 1, 240]].
(nn-Meter) Find node SE_2/conv2d_4/Conv2D with its weight op SE_2/conv2d_4/kernel.
(nn-Meter) Get input shape of SE_2/conv2d_4/Conv2D from SE_2/AvgPool, input shape:[1, 1, 1, 240].
(nn-Meter) Get weight shape of SE_2/conv2d_4/Conv2D from ['SE_2/conv2d_4/kernel'], input shape:[1, 1, 240, 60].
(nn-Meter) Op:SE_2/conv2d_4/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 240], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_2/conv2d_4/Conv2D op is [[1, 1, 1, 240]].
(nn-Meter) Output shape of SE_2/conv2d_4/Conv2D op is [[1, 1, 1, 60]].
(nn-Meter) Propagate through op SE_2/conv2d_4/BiasAdd.
(nn-Meter) Input shape of SE_2/conv2d_4/BiasAdd op is [[1, 1, 1, 60]].
(nn-Meter) Output shape of SE_2/conv2d_4/BiasAdd op is [[1, 1, 1, 60]].
(nn-Meter) Propagate through op SE_2/Relu.
(nn-Meter) Input shape of SE_2/Relu op is [[1, 1, 1, 60]].
(nn-Meter) Output shape of SE_2/Relu op is [[1, 1, 1, 60]].
(nn-Meter) Find node SE_2/conv2d_5/Conv2D with its weight op SE_2/conv2d_5/kernel.
(nn-Meter) Get input shape of SE_2/conv2d_5/Conv2D from SE_2/Relu, input shape:[1, 1, 1, 60].
(nn-Meter) Get weight shape of SE_2/conv2d_5/Conv2D from ['SE_2/conv2d_5/kernel'], input shape:[1, 1, 60, 240].
(nn-Meter) Op:SE_2/conv2d_5/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 60], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_2/conv2d_5/Conv2D op is [[1, 1, 1, 60]].
(nn-Meter) Output shape of SE_2/conv2d_5/Conv2D op is [[1, 1, 1, 240]].
(nn-Meter) Propagate through op SE_2/conv2d_5/BiasAdd.
(nn-Meter) Input shape of SE_2/conv2d_5/BiasAdd op is [[1, 1, 1, 240]].
(nn-Meter) Output shape of SE_2/conv2d_5/BiasAdd op is [[1, 1, 1, 240]].
(nn-Meter) Input shape of Add_3 op is [[1, 1, 1, 240], []].
(nn-Meter) Output shape of Add_3 op is [[1, 1, 1, 240]].
(nn-Meter) Propagate through op Relu6_2.
(nn-Meter) Input shape of Relu6_2 op is [[1, 1, 1, 240]].
(nn-Meter) Output shape of Relu6_2 op is [[1, 1, 1, 240]].
(nn-Meter) Input shape of mul_4 op is [[1, 1, 1, 240], []].
(nn-Meter) Output shape of mul_4 op is [[1, 1, 1, 240]].
(nn-Meter) Input shape of mul_5 op is [[1, 14, 14, 240], [1, 14, 14, 240]].
(nn-Meter) Output shape of mul_5 op is [[1, 14, 14, 240]].
(nn-Meter) Input shape of layer6.2.hswish/add op is [[1, 14, 14, 240], []].
(nn-Meter) Output shape of layer6.2.hswish/add op is [[1, 14, 14, 240]].
(nn-Meter) Propagate through op layer6.2.hswish/Relu6.
(nn-Meter) Input shape of layer6.2.hswish/Relu6 op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of layer6.2.hswish/Relu6 op is [[1, 14, 14, 240]].
(nn-Meter) Input shape of layer6.2.hswish/mul op is [[1, 14, 14, 240], []].
(nn-Meter) Output shape of layer6.2.hswish/mul op is [[1, 14, 14, 240]].
(nn-Meter) Input shape of layer6.2.hswish/mul_1 op is [[1, 14, 14, 240], [1, 14, 14, 240]].
(nn-Meter) Output shape of layer6.2.hswish/mul_1 op is [[1, 14, 14, 240]].
(nn-Meter) Find node layer6.3.conv/Conv2D with its weight op layer6.3.conv/weight.
(nn-Meter) Get input shape of layer6.3.conv/Conv2D from layer6.2.hswish/mul_1, input shape:[1, 14, 14, 240].
(nn-Meter) Get weight shape of layer6.3.conv/Conv2D from ['layer6.3.conv/weight'], input shape:[1, 1, 240, 40].
(nn-Meter) Op:layer6.3.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 240], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer6.3.conv/Conv2D op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of layer6.3.conv/Conv2D op is [[1, 14, 14, 40]].
(nn-Meter) Propagate through op layer6.3.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer6.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 40]].
(nn-Meter) Output shape of layer6.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 40]].
(nn-Meter) Input shape of Add_4 op is [[1, 14, 14, 40], [1, 14, 14, 40]].
(nn-Meter) Output shape of Add_4 op is [[1, 14, 14, 40]].
(nn-Meter) Find node layer7.1.conv/Conv2D with its weight op layer7.1.conv/weight.
(nn-Meter) Get input shape of layer7.1.conv/Conv2D from Add_4, input shape:[1, 14, 14, 40].
(nn-Meter) Get weight shape of layer7.1.conv/Conv2D from ['layer7.1.conv/weight'], input shape:[1, 1, 40, 240].
(nn-Meter) Op:layer7.1.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 40], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer7.1.conv/Conv2D op is [[1, 14, 14, 40]].
(nn-Meter) Output shape of layer7.1.conv/Conv2D op is [[1, 14, 14, 240]].
(nn-Meter) Propagate through op layer7.1.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer7.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of layer7.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 240]].
(nn-Meter) Input shape of layer7.1.hswish/add op is [[1, 14, 14, 240], []].
(nn-Meter) Output shape of layer7.1.hswish/add op is [[1, 14, 14, 240]].
(nn-Meter) Propagate through op layer7.1.hswish/Relu6.
(nn-Meter) Input shape of layer7.1.hswish/Relu6 op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of layer7.1.hswish/Relu6 op is [[1, 14, 14, 240]].
(nn-Meter) Input shape of layer7.1.hswish/mul op is [[1, 14, 14, 240], []].
(nn-Meter) Output shape of layer7.1.hswish/mul op is [[1, 14, 14, 240]].
(nn-Meter) Input shape of layer7.1.hswish/mul_1 op is [[1, 14, 14, 240], [1, 14, 14, 240]].
(nn-Meter) Output shape of layer7.1.hswish/mul_1 op is [[1, 14, 14, 240]].
(nn-Meter) Find node layer7.2.depconv/depthwise with its weight op layer7.2.depconv/weight.
(nn-Meter) Get input shape of layer7.2.depconv/depthwise from layer7.1.hswish/mul_1, input shape:[1, 14, 14, 240].
(nn-Meter) Get weight shape of layer7.2.depconv/depthwise from ['layer7.2.depconv/weight'], input shape:[5, 5, 240, 1].
(nn-Meter) Op:layer7.2.depconv/depthwise, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 240], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer7.2.depconv/depthwise op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of layer7.2.depconv/depthwise op is [[1, 14, 14, 240]].
(nn-Meter) Propagate through op layer7.2.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer7.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of layer7.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 240]].
(nn-Meter) Get input shape of SE_3/AvgPool from layer7.2.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 14, 14, 240].
(nn-Meter) Op:SE_3/AvgPool, stride:[1, 1, 1, 1], padding:VALID.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 240], kernel size: [1, 14, 14, 1], strides: [1, 1, 1, 1], padding: VALID.
(nn-Meter) Input shape of SE_3/AvgPool op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of SE_3/AvgPool op is [[1, 1, 1, 240]].
(nn-Meter) Find node SE_3/conv2d_6/Conv2D with its weight op SE_3/conv2d_6/kernel.
(nn-Meter) Get input shape of SE_3/conv2d_6/Conv2D from SE_3/AvgPool, input shape:[1, 1, 1, 240].
(nn-Meter) Get weight shape of SE_3/conv2d_6/Conv2D from ['SE_3/conv2d_6/kernel'], input shape:[1, 1, 240, 60].
(nn-Meter) Op:SE_3/conv2d_6/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 240], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_3/conv2d_6/Conv2D op is [[1, 1, 1, 240]].
(nn-Meter) Output shape of SE_3/conv2d_6/Conv2D op is [[1, 1, 1, 60]].
(nn-Meter) Propagate through op SE_3/conv2d_6/BiasAdd.
(nn-Meter) Input shape of SE_3/conv2d_6/BiasAdd op is [[1, 1, 1, 60]].
(nn-Meter) Output shape of SE_3/conv2d_6/BiasAdd op is [[1, 1, 1, 60]].
(nn-Meter) Propagate through op SE_3/Relu.
(nn-Meter) Input shape of SE_3/Relu op is [[1, 1, 1, 60]].
(nn-Meter) Output shape of SE_3/Relu op is [[1, 1, 1, 60]].
(nn-Meter) Find node SE_3/conv2d_7/Conv2D with its weight op SE_3/conv2d_7/kernel.
(nn-Meter) Get input shape of SE_3/conv2d_7/Conv2D from SE_3/Relu, input shape:[1, 1, 1, 60].
(nn-Meter) Get weight shape of SE_3/conv2d_7/Conv2D from ['SE_3/conv2d_7/kernel'], input shape:[1, 1, 60, 240].
(nn-Meter) Op:SE_3/conv2d_7/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 60], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_3/conv2d_7/Conv2D op is [[1, 1, 1, 60]].
(nn-Meter) Output shape of SE_3/conv2d_7/Conv2D op is [[1, 1, 1, 240]].
(nn-Meter) Propagate through op SE_3/conv2d_7/BiasAdd.
(nn-Meter) Input shape of SE_3/conv2d_7/BiasAdd op is [[1, 1, 1, 240]].
(nn-Meter) Output shape of SE_3/conv2d_7/BiasAdd op is [[1, 1, 1, 240]].
(nn-Meter) Input shape of Add_5 op is [[1, 1, 1, 240], []].
(nn-Meter) Output shape of Add_5 op is [[1, 1, 1, 240]].
(nn-Meter) Propagate through op Relu6_3.
(nn-Meter) Input shape of Relu6_3 op is [[1, 1, 1, 240]].
(nn-Meter) Output shape of Relu6_3 op is [[1, 1, 1, 240]].
(nn-Meter) Input shape of mul_6 op is [[1, 1, 1, 240], []].
(nn-Meter) Output shape of mul_6 op is [[1, 1, 1, 240]].
(nn-Meter) Input shape of mul_7 op is [[1, 14, 14, 240], [1, 14, 14, 240]].
(nn-Meter) Output shape of mul_7 op is [[1, 14, 14, 240]].
(nn-Meter) Input shape of layer7.2.hswish/add op is [[1, 14, 14, 240], []].
(nn-Meter) Output shape of layer7.2.hswish/add op is [[1, 14, 14, 240]].
(nn-Meter) Propagate through op layer7.2.hswish/Relu6.
(nn-Meter) Input shape of layer7.2.hswish/Relu6 op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of layer7.2.hswish/Relu6 op is [[1, 14, 14, 240]].
(nn-Meter) Input shape of layer7.2.hswish/mul op is [[1, 14, 14, 240], []].
(nn-Meter) Output shape of layer7.2.hswish/mul op is [[1, 14, 14, 240]].
(nn-Meter) Input shape of layer7.2.hswish/mul_1 op is [[1, 14, 14, 240], [1, 14, 14, 240]].
(nn-Meter) Output shape of layer7.2.hswish/mul_1 op is [[1, 14, 14, 240]].
(nn-Meter) Find node layer7.3.conv/Conv2D with its weight op layer7.3.conv/weight.
(nn-Meter) Get input shape of layer7.3.conv/Conv2D from layer7.2.hswish/mul_1, input shape:[1, 14, 14, 240].
(nn-Meter) Get weight shape of layer7.3.conv/Conv2D from ['layer7.3.conv/weight'], input shape:[1, 1, 240, 40].
(nn-Meter) Op:layer7.3.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 240], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer7.3.conv/Conv2D op is [[1, 14, 14, 240]].
(nn-Meter) Output shape of layer7.3.conv/Conv2D op is [[1, 14, 14, 40]].
(nn-Meter) Propagate through op layer7.3.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer7.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 40]].
(nn-Meter) Output shape of layer7.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 40]].
(nn-Meter) Input shape of Add_6 op is [[1, 14, 14, 40], [1, 14, 14, 40]].
(nn-Meter) Output shape of Add_6 op is [[1, 14, 14, 40]].
(nn-Meter) Find node layer8.1.conv/Conv2D with its weight op layer8.1.conv/weight.
(nn-Meter) Get input shape of layer8.1.conv/Conv2D from Add_6, input shape:[1, 14, 14, 40].
(nn-Meter) Get weight shape of layer8.1.conv/Conv2D from ['layer8.1.conv/weight'], input shape:[1, 1, 40, 120].
(nn-Meter) Op:layer8.1.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 40], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer8.1.conv/Conv2D op is [[1, 14, 14, 40]].
(nn-Meter) Output shape of layer8.1.conv/Conv2D op is [[1, 14, 14, 120]].
(nn-Meter) Propagate through op layer8.1.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer8.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 120]].
(nn-Meter) Output shape of layer8.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 120]].
(nn-Meter) Input shape of layer8.1.hswish/add op is [[1, 14, 14, 120], []].
(nn-Meter) Output shape of layer8.1.hswish/add op is [[1, 14, 14, 120]].
(nn-Meter) Propagate through op layer8.1.hswish/Relu6.
(nn-Meter) Input shape of layer8.1.hswish/Relu6 op is [[1, 14, 14, 120]].
(nn-Meter) Output shape of layer8.1.hswish/Relu6 op is [[1, 14, 14, 120]].
(nn-Meter) Input shape of layer8.1.hswish/mul op is [[1, 14, 14, 120], []].
(nn-Meter) Output shape of layer8.1.hswish/mul op is [[1, 14, 14, 120]].
(nn-Meter) Input shape of layer8.1.hswish/mul_1 op is [[1, 14, 14, 120], [1, 14, 14, 120]].
(nn-Meter) Output shape of layer8.1.hswish/mul_1 op is [[1, 14, 14, 120]].
(nn-Meter) Find node layer8.2.depconv/depthwise with its weight op layer8.2.depconv/weight.
(nn-Meter) Get input shape of layer8.2.depconv/depthwise from layer8.1.hswish/mul_1, input shape:[1, 14, 14, 120].
(nn-Meter) Get weight shape of layer8.2.depconv/depthwise from ['layer8.2.depconv/weight'], input shape:[5, 5, 120, 1].
(nn-Meter) Op:layer8.2.depconv/depthwise, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 120], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer8.2.depconv/depthwise op is [[1, 14, 14, 120]].
(nn-Meter) Output shape of layer8.2.depconv/depthwise op is [[1, 14, 14, 120]].
(nn-Meter) Propagate through op layer8.2.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer8.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 120]].
(nn-Meter) Output shape of layer8.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 120]].
(nn-Meter) Get input shape of SE_4/AvgPool from layer8.2.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 14, 14, 120].
(nn-Meter) Op:SE_4/AvgPool, stride:[1, 1, 1, 1], padding:VALID.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 120], kernel size: [1, 14, 14, 1], strides: [1, 1, 1, 1], padding: VALID.
(nn-Meter) Input shape of SE_4/AvgPool op is [[1, 14, 14, 120]].
(nn-Meter) Output shape of SE_4/AvgPool op is [[1, 1, 1, 120]].
(nn-Meter) Find node SE_4/conv2d_8/Conv2D with its weight op SE_4/conv2d_8/kernel.
(nn-Meter) Get input shape of SE_4/conv2d_8/Conv2D from SE_4/AvgPool, input shape:[1, 1, 1, 120].
(nn-Meter) Get weight shape of SE_4/conv2d_8/Conv2D from ['SE_4/conv2d_8/kernel'], input shape:[1, 1, 120, 30].
(nn-Meter) Op:SE_4/conv2d_8/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 120], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_4/conv2d_8/Conv2D op is [[1, 1, 1, 120]].
(nn-Meter) Output shape of SE_4/conv2d_8/Conv2D op is [[1, 1, 1, 30]].
(nn-Meter) Propagate through op SE_4/conv2d_8/BiasAdd.
(nn-Meter) Input shape of SE_4/conv2d_8/BiasAdd op is [[1, 1, 1, 30]].
(nn-Meter) Output shape of SE_4/conv2d_8/BiasAdd op is [[1, 1, 1, 30]].
(nn-Meter) Propagate through op SE_4/Relu.
(nn-Meter) Input shape of SE_4/Relu op is [[1, 1, 1, 30]].
(nn-Meter) Output shape of SE_4/Relu op is [[1, 1, 1, 30]].
(nn-Meter) Find node SE_4/conv2d_9/Conv2D with its weight op SE_4/conv2d_9/kernel.
(nn-Meter) Get input shape of SE_4/conv2d_9/Conv2D from SE_4/Relu, input shape:[1, 1, 1, 30].
(nn-Meter) Get weight shape of SE_4/conv2d_9/Conv2D from ['SE_4/conv2d_9/kernel'], input shape:[1, 1, 30, 120].
(nn-Meter) Op:SE_4/conv2d_9/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 30], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_4/conv2d_9/Conv2D op is [[1, 1, 1, 30]].
(nn-Meter) Output shape of SE_4/conv2d_9/Conv2D op is [[1, 1, 1, 120]].
(nn-Meter) Propagate through op SE_4/conv2d_9/BiasAdd.
(nn-Meter) Input shape of SE_4/conv2d_9/BiasAdd op is [[1, 1, 1, 120]].
(nn-Meter) Output shape of SE_4/conv2d_9/BiasAdd op is [[1, 1, 1, 120]].
(nn-Meter) Input shape of Add_7 op is [[1, 1, 1, 120], []].
(nn-Meter) Output shape of Add_7 op is [[1, 1, 1, 120]].
(nn-Meter) Propagate through op Relu6_4.
(nn-Meter) Input shape of Relu6_4 op is [[1, 1, 1, 120]].
(nn-Meter) Output shape of Relu6_4 op is [[1, 1, 1, 120]].
(nn-Meter) Input shape of mul_8 op is [[1, 1, 1, 120], []].
(nn-Meter) Output shape of mul_8 op is [[1, 1, 1, 120]].
(nn-Meter) Input shape of mul_9 op is [[1, 14, 14, 120], [1, 14, 14, 120]].
(nn-Meter) Output shape of mul_9 op is [[1, 14, 14, 120]].
(nn-Meter) Input shape of layer8.2.hswish/add op is [[1, 14, 14, 120], []].
(nn-Meter) Output shape of layer8.2.hswish/add op is [[1, 14, 14, 120]].
(nn-Meter) Propagate through op layer8.2.hswish/Relu6.
(nn-Meter) Input shape of layer8.2.hswish/Relu6 op is [[1, 14, 14, 120]].
(nn-Meter) Output shape of layer8.2.hswish/Relu6 op is [[1, 14, 14, 120]].
(nn-Meter) Input shape of layer8.2.hswish/mul op is [[1, 14, 14, 120], []].
(nn-Meter) Output shape of layer8.2.hswish/mul op is [[1, 14, 14, 120]].
(nn-Meter) Input shape of layer8.2.hswish/mul_1 op is [[1, 14, 14, 120], [1, 14, 14, 120]].
(nn-Meter) Output shape of layer8.2.hswish/mul_1 op is [[1, 14, 14, 120]].
(nn-Meter) Find node layer8.3.conv/Conv2D with its weight op layer8.3.conv/weight.
(nn-Meter) Get input shape of layer8.3.conv/Conv2D from layer8.2.hswish/mul_1, input shape:[1, 14, 14, 120].
(nn-Meter) Get weight shape of layer8.3.conv/Conv2D from ['layer8.3.conv/weight'], input shape:[1, 1, 120, 48].
(nn-Meter) Op:layer8.3.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 120], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer8.3.conv/Conv2D op is [[1, 14, 14, 120]].
(nn-Meter) Output shape of layer8.3.conv/Conv2D op is [[1, 14, 14, 48]].
(nn-Meter) Propagate through op layer8.3.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer8.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 48]].
(nn-Meter) Output shape of layer8.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 48]].
(nn-Meter) Find node layer9.1.conv/Conv2D with its weight op layer9.1.conv/weight.
(nn-Meter) Get input shape of layer9.1.conv/Conv2D from layer8.3.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 14, 14, 48].
(nn-Meter) Get weight shape of layer9.1.conv/Conv2D from ['layer9.1.conv/weight'], input shape:[1, 1, 48, 144].
(nn-Meter) Op:layer9.1.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 48], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer9.1.conv/Conv2D op is [[1, 14, 14, 48]].
(nn-Meter) Output shape of layer9.1.conv/Conv2D op is [[1, 14, 14, 144]].
(nn-Meter) Propagate through op layer9.1.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer9.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 144]].
(nn-Meter) Output shape of layer9.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 144]].
(nn-Meter) Input shape of layer9.1.hswish/add op is [[1, 14, 14, 144], []].
(nn-Meter) Output shape of layer9.1.hswish/add op is [[1, 14, 14, 144]].
(nn-Meter) Propagate through op layer9.1.hswish/Relu6.
(nn-Meter) Input shape of layer9.1.hswish/Relu6 op is [[1, 14, 14, 144]].
(nn-Meter) Output shape of layer9.1.hswish/Relu6 op is [[1, 14, 14, 144]].
(nn-Meter) Input shape of layer9.1.hswish/mul op is [[1, 14, 14, 144], []].
(nn-Meter) Output shape of layer9.1.hswish/mul op is [[1, 14, 14, 144]].
(nn-Meter) Input shape of layer9.1.hswish/mul_1 op is [[1, 14, 14, 144], [1, 14, 14, 144]].
(nn-Meter) Output shape of layer9.1.hswish/mul_1 op is [[1, 14, 14, 144]].
(nn-Meter) Find node layer9.2.depconv/depthwise with its weight op layer9.2.depconv/weight.
(nn-Meter) Get input shape of layer9.2.depconv/depthwise from layer9.1.hswish/mul_1, input shape:[1, 14, 14, 144].
(nn-Meter) Get weight shape of layer9.2.depconv/depthwise from ['layer9.2.depconv/weight'], input shape:[5, 5, 144, 1].
(nn-Meter) Op:layer9.2.depconv/depthwise, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 144], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer9.2.depconv/depthwise op is [[1, 14, 14, 144]].
(nn-Meter) Output shape of layer9.2.depconv/depthwise op is [[1, 14, 14, 144]].
(nn-Meter) Propagate through op layer9.2.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer9.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 144]].
(nn-Meter) Output shape of layer9.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 144]].
(nn-Meter) Get input shape of SE_5/AvgPool from layer9.2.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 14, 14, 144].
(nn-Meter) Op:SE_5/AvgPool, stride:[1, 1, 1, 1], padding:VALID.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 144], kernel size: [1, 14, 14, 1], strides: [1, 1, 1, 1], padding: VALID.
(nn-Meter) Input shape of SE_5/AvgPool op is [[1, 14, 14, 144]].
(nn-Meter) Output shape of SE_5/AvgPool op is [[1, 1, 1, 144]].
(nn-Meter) Find node SE_5/conv2d_10/Conv2D with its weight op SE_5/conv2d_10/kernel.
(nn-Meter) Get input shape of SE_5/conv2d_10/Conv2D from SE_5/AvgPool, input shape:[1, 1, 1, 144].
(nn-Meter) Get weight shape of SE_5/conv2d_10/Conv2D from ['SE_5/conv2d_10/kernel'], input shape:[1, 1, 144, 36].
(nn-Meter) Op:SE_5/conv2d_10/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 144], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_5/conv2d_10/Conv2D op is [[1, 1, 1, 144]].
(nn-Meter) Output shape of SE_5/conv2d_10/Conv2D op is [[1, 1, 1, 36]].
(nn-Meter) Propagate through op SE_5/conv2d_10/BiasAdd.
(nn-Meter) Input shape of SE_5/conv2d_10/BiasAdd op is [[1, 1, 1, 36]].
(nn-Meter) Output shape of SE_5/conv2d_10/BiasAdd op is [[1, 1, 1, 36]].
(nn-Meter) Propagate through op SE_5/Relu.
(nn-Meter) Input shape of SE_5/Relu op is [[1, 1, 1, 36]].
(nn-Meter) Output shape of SE_5/Relu op is [[1, 1, 1, 36]].
(nn-Meter) Find node SE_5/conv2d_11/Conv2D with its weight op SE_5/conv2d_11/kernel.
(nn-Meter) Get input shape of SE_5/conv2d_11/Conv2D from SE_5/Relu, input shape:[1, 1, 1, 36].
(nn-Meter) Get weight shape of SE_5/conv2d_11/Conv2D from ['SE_5/conv2d_11/kernel'], input shape:[1, 1, 36, 144].
(nn-Meter) Op:SE_5/conv2d_11/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 36], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_5/conv2d_11/Conv2D op is [[1, 1, 1, 36]].
(nn-Meter) Output shape of SE_5/conv2d_11/Conv2D op is [[1, 1, 1, 144]].
(nn-Meter) Propagate through op SE_5/conv2d_11/BiasAdd.
(nn-Meter) Input shape of SE_5/conv2d_11/BiasAdd op is [[1, 1, 1, 144]].
(nn-Meter) Output shape of SE_5/conv2d_11/BiasAdd op is [[1, 1, 1, 144]].
(nn-Meter) Input shape of Add_8 op is [[1, 1, 1, 144], []].
(nn-Meter) Output shape of Add_8 op is [[1, 1, 1, 144]].
(nn-Meter) Propagate through op Relu6_5.
(nn-Meter) Input shape of Relu6_5 op is [[1, 1, 1, 144]].
(nn-Meter) Output shape of Relu6_5 op is [[1, 1, 1, 144]].
(nn-Meter) Input shape of mul_10 op is [[1, 1, 1, 144], []].
(nn-Meter) Output shape of mul_10 op is [[1, 1, 1, 144]].
(nn-Meter) Input shape of mul_11 op is [[1, 14, 14, 144], [1, 14, 14, 144]].
(nn-Meter) Output shape of mul_11 op is [[1, 14, 14, 144]].
(nn-Meter) Input shape of layer9.2.hswish/add op is [[1, 14, 14, 144], []].
(nn-Meter) Output shape of layer9.2.hswish/add op is [[1, 14, 14, 144]].
(nn-Meter) Propagate through op layer9.2.hswish/Relu6.
(nn-Meter) Input shape of layer9.2.hswish/Relu6 op is [[1, 14, 14, 144]].
(nn-Meter) Output shape of layer9.2.hswish/Relu6 op is [[1, 14, 14, 144]].
(nn-Meter) Input shape of layer9.2.hswish/mul op is [[1, 14, 14, 144], []].
(nn-Meter) Output shape of layer9.2.hswish/mul op is [[1, 14, 14, 144]].
(nn-Meter) Input shape of layer9.2.hswish/mul_1 op is [[1, 14, 14, 144], [1, 14, 14, 144]].
(nn-Meter) Output shape of layer9.2.hswish/mul_1 op is [[1, 14, 14, 144]].
(nn-Meter) Find node layer9.3.conv/Conv2D with its weight op layer9.3.conv/weight.
(nn-Meter) Get input shape of layer9.3.conv/Conv2D from layer9.2.hswish/mul_1, input shape:[1, 14, 14, 144].
(nn-Meter) Get weight shape of layer9.3.conv/Conv2D from ['layer9.3.conv/weight'], input shape:[1, 1, 144, 48].
(nn-Meter) Op:layer9.3.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 144], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer9.3.conv/Conv2D op is [[1, 14, 14, 144]].
(nn-Meter) Output shape of layer9.3.conv/Conv2D op is [[1, 14, 14, 48]].
(nn-Meter) Propagate through op layer9.3.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer9.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 48]].
(nn-Meter) Output shape of layer9.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 48]].
(nn-Meter) Input shape of Add_9 op is [[1, 14, 14, 48], [1, 14, 14, 48]].
(nn-Meter) Output shape of Add_9 op is [[1, 14, 14, 48]].
(nn-Meter) Find node layer10.1.conv/Conv2D with its weight op layer10.1.conv/weight.
(nn-Meter) Get input shape of layer10.1.conv/Conv2D from Add_9, input shape:[1, 14, 14, 48].
(nn-Meter) Get weight shape of layer10.1.conv/Conv2D from ['layer10.1.conv/weight'], input shape:[1, 1, 48, 288].
(nn-Meter) Op:layer10.1.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 48], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer10.1.conv/Conv2D op is [[1, 14, 14, 48]].
(nn-Meter) Output shape of layer10.1.conv/Conv2D op is [[1, 14, 14, 288]].
(nn-Meter) Propagate through op layer10.1.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer10.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 288]].
(nn-Meter) Output shape of layer10.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 14, 14, 288]].
(nn-Meter) Input shape of layer10.1.hswish/add op is [[1, 14, 14, 288], []].
(nn-Meter) Output shape of layer10.1.hswish/add op is [[1, 14, 14, 288]].
(nn-Meter) Propagate through op layer10.1.hswish/Relu6.
(nn-Meter) Input shape of layer10.1.hswish/Relu6 op is [[1, 14, 14, 288]].
(nn-Meter) Output shape of layer10.1.hswish/Relu6 op is [[1, 14, 14, 288]].
(nn-Meter) Input shape of layer10.1.hswish/mul op is [[1, 14, 14, 288], []].
(nn-Meter) Output shape of layer10.1.hswish/mul op is [[1, 14, 14, 288]].
(nn-Meter) Input shape of layer10.1.hswish/mul_1 op is [[1, 14, 14, 288], [1, 14, 14, 288]].
(nn-Meter) Output shape of layer10.1.hswish/mul_1 op is [[1, 14, 14, 288]].
(nn-Meter) Find node layer10.2.depconv/depthwise with its weight op layer10.2.depconv/weight.
(nn-Meter) Get input shape of layer10.2.depconv/depthwise from layer10.1.hswish/mul_1, input shape:[1, 14, 14, 288].
(nn-Meter) Get weight shape of layer10.2.depconv/depthwise from ['layer10.2.depconv/weight'], input shape:[5, 5, 288, 1].
(nn-Meter) Op:layer10.2.depconv/depthwise, stride:[1, 2, 2, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 14, 14, 288], kernel size: [2, 2], strides: [1, 2, 2, 1], padding: SAME.
(nn-Meter) Input shape of layer10.2.depconv/depthwise op is [[1, 14, 14, 288]].
(nn-Meter) Output shape of layer10.2.depconv/depthwise op is [[1, 7, 7, 288]].
(nn-Meter) Propagate through op layer10.2.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer10.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 288]].
(nn-Meter) Output shape of layer10.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 288]].
(nn-Meter) Get input shape of SE_6/AvgPool from layer10.2.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 7, 7, 288].
(nn-Meter) Op:SE_6/AvgPool, stride:[1, 1, 1, 1], padding:VALID.
(nn-Meter) Calculating padding shape, input shape: [1, 7, 7, 288], kernel size: [1, 7, 7, 1], strides: [1, 1, 1, 1], padding: VALID.
(nn-Meter) Input shape of SE_6/AvgPool op is [[1, 7, 7, 288]].
(nn-Meter) Output shape of SE_6/AvgPool op is [[1, 1, 1, 288]].
(nn-Meter) Find node SE_6/conv2d_12/Conv2D with its weight op SE_6/conv2d_12/kernel.
(nn-Meter) Get input shape of SE_6/conv2d_12/Conv2D from SE_6/AvgPool, input shape:[1, 1, 1, 288].
(nn-Meter) Get weight shape of SE_6/conv2d_12/Conv2D from ['SE_6/conv2d_12/kernel'], input shape:[1, 1, 288, 72].
(nn-Meter) Op:SE_6/conv2d_12/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 288], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_6/conv2d_12/Conv2D op is [[1, 1, 1, 288]].
(nn-Meter) Output shape of SE_6/conv2d_12/Conv2D op is [[1, 1, 1, 72]].
(nn-Meter) Propagate through op SE_6/conv2d_12/BiasAdd.
(nn-Meter) Input shape of SE_6/conv2d_12/BiasAdd op is [[1, 1, 1, 72]].
(nn-Meter) Output shape of SE_6/conv2d_12/BiasAdd op is [[1, 1, 1, 72]].
(nn-Meter) Propagate through op SE_6/Relu.
(nn-Meter) Input shape of SE_6/Relu op is [[1, 1, 1, 72]].
(nn-Meter) Output shape of SE_6/Relu op is [[1, 1, 1, 72]].
(nn-Meter) Find node SE_6/conv2d_13/Conv2D with its weight op SE_6/conv2d_13/kernel.
(nn-Meter) Get input shape of SE_6/conv2d_13/Conv2D from SE_6/Relu, input shape:[1, 1, 1, 72].
(nn-Meter) Get weight shape of SE_6/conv2d_13/Conv2D from ['SE_6/conv2d_13/kernel'], input shape:[1, 1, 72, 288].
(nn-Meter) Op:SE_6/conv2d_13/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 72], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_6/conv2d_13/Conv2D op is [[1, 1, 1, 72]].
(nn-Meter) Output shape of SE_6/conv2d_13/Conv2D op is [[1, 1, 1, 288]].
(nn-Meter) Propagate through op SE_6/conv2d_13/BiasAdd.
(nn-Meter) Input shape of SE_6/conv2d_13/BiasAdd op is [[1, 1, 1, 288]].
(nn-Meter) Output shape of SE_6/conv2d_13/BiasAdd op is [[1, 1, 1, 288]].
(nn-Meter) Input shape of Add_10 op is [[1, 1, 1, 288], []].
(nn-Meter) Output shape of Add_10 op is [[1, 1, 1, 288]].
(nn-Meter) Propagate through op Relu6_6.
(nn-Meter) Input shape of Relu6_6 op is [[1, 1, 1, 288]].
(nn-Meter) Output shape of Relu6_6 op is [[1, 1, 1, 288]].
(nn-Meter) Input shape of mul_12 op is [[1, 1, 1, 288], []].
(nn-Meter) Output shape of mul_12 op is [[1, 1, 1, 288]].
(nn-Meter) Input shape of mul_13 op is [[1, 7, 7, 288], [1, 7, 7, 288]].
(nn-Meter) Output shape of mul_13 op is [[1, 7, 7, 288]].
(nn-Meter) Input shape of layer10.2.hswish/add op is [[1, 7, 7, 288], []].
(nn-Meter) Output shape of layer10.2.hswish/add op is [[1, 7, 7, 288]].
(nn-Meter) Propagate through op layer10.2.hswish/Relu6.
(nn-Meter) Input shape of layer10.2.hswish/Relu6 op is [[1, 7, 7, 288]].
(nn-Meter) Output shape of layer10.2.hswish/Relu6 op is [[1, 7, 7, 288]].
(nn-Meter) Input shape of layer10.2.hswish/mul op is [[1, 7, 7, 288], []].
(nn-Meter) Output shape of layer10.2.hswish/mul op is [[1, 7, 7, 288]].
(nn-Meter) Input shape of layer10.2.hswish/mul_1 op is [[1, 7, 7, 288], [1, 7, 7, 288]].
(nn-Meter) Output shape of layer10.2.hswish/mul_1 op is [[1, 7, 7, 288]].
(nn-Meter) Find node layer10.3.conv/Conv2D with its weight op layer10.3.conv/weight.
(nn-Meter) Get input shape of layer10.3.conv/Conv2D from layer10.2.hswish/mul_1, input shape:[1, 7, 7, 288].
(nn-Meter) Get weight shape of layer10.3.conv/Conv2D from ['layer10.3.conv/weight'], input shape:[1, 1, 288, 96].
(nn-Meter) Op:layer10.3.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 7, 7, 288], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer10.3.conv/Conv2D op is [[1, 7, 7, 288]].
(nn-Meter) Output shape of layer10.3.conv/Conv2D op is [[1, 7, 7, 96]].
(nn-Meter) Propagate through op layer10.3.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer10.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 96]].
(nn-Meter) Output shape of layer10.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 96]].
(nn-Meter) Find node layer11.1.conv/Conv2D with its weight op layer11.1.conv/weight.
(nn-Meter) Get input shape of layer11.1.conv/Conv2D from layer10.3.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 7, 7, 96].
(nn-Meter) Get weight shape of layer11.1.conv/Conv2D from ['layer11.1.conv/weight'], input shape:[1, 1, 96, 576].
(nn-Meter) Op:layer11.1.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 7, 7, 96], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer11.1.conv/Conv2D op is [[1, 7, 7, 96]].
(nn-Meter) Output shape of layer11.1.conv/Conv2D op is [[1, 7, 7, 576]].
(nn-Meter) Propagate through op layer11.1.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer11.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of layer11.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 576]].
(nn-Meter) Input shape of layer11.1.hswish/add op is [[1, 7, 7, 576], []].
(nn-Meter) Output shape of layer11.1.hswish/add op is [[1, 7, 7, 576]].
(nn-Meter) Propagate through op layer11.1.hswish/Relu6.
(nn-Meter) Input shape of layer11.1.hswish/Relu6 op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of layer11.1.hswish/Relu6 op is [[1, 7, 7, 576]].
(nn-Meter) Input shape of layer11.1.hswish/mul op is [[1, 7, 7, 576], []].
(nn-Meter) Output shape of layer11.1.hswish/mul op is [[1, 7, 7, 576]].
(nn-Meter) Input shape of layer11.1.hswish/mul_1 op is [[1, 7, 7, 576], [1, 7, 7, 576]].
(nn-Meter) Output shape of layer11.1.hswish/mul_1 op is [[1, 7, 7, 576]].
(nn-Meter) Find node layer11.2.depconv/depthwise with its weight op layer11.2.depconv/weight.
(nn-Meter) Get input shape of layer11.2.depconv/depthwise from layer11.1.hswish/mul_1, input shape:[1, 7, 7, 576].
(nn-Meter) Get weight shape of layer11.2.depconv/depthwise from ['layer11.2.depconv/weight'], input shape:[5, 5, 576, 1].
(nn-Meter) Op:layer11.2.depconv/depthwise, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 7, 7, 576], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer11.2.depconv/depthwise op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of layer11.2.depconv/depthwise op is [[1, 7, 7, 576]].
(nn-Meter) Propagate through op layer11.2.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer11.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of layer11.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 576]].
(nn-Meter) Get input shape of SE_7/AvgPool from layer11.2.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 7, 7, 576].
(nn-Meter) Op:SE_7/AvgPool, stride:[1, 1, 1, 1], padding:VALID.
(nn-Meter) Calculating padding shape, input shape: [1, 7, 7, 576], kernel size: [1, 7, 7, 1], strides: [1, 1, 1, 1], padding: VALID.
(nn-Meter) Input shape of SE_7/AvgPool op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of SE_7/AvgPool op is [[1, 1, 1, 576]].
(nn-Meter) Find node SE_7/conv2d_14/Conv2D with its weight op SE_7/conv2d_14/kernel.
(nn-Meter) Get input shape of SE_7/conv2d_14/Conv2D from SE_7/AvgPool, input shape:[1, 1, 1, 576].
(nn-Meter) Get weight shape of SE_7/conv2d_14/Conv2D from ['SE_7/conv2d_14/kernel'], input shape:[1, 1, 576, 144].
(nn-Meter) Op:SE_7/conv2d_14/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 576], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_7/conv2d_14/Conv2D op is [[1, 1, 1, 576]].
(nn-Meter) Output shape of SE_7/conv2d_14/Conv2D op is [[1, 1, 1, 144]].
(nn-Meter) Propagate through op SE_7/conv2d_14/BiasAdd.
(nn-Meter) Input shape of SE_7/conv2d_14/BiasAdd op is [[1, 1, 1, 144]].
(nn-Meter) Output shape of SE_7/conv2d_14/BiasAdd op is [[1, 1, 1, 144]].
(nn-Meter) Propagate through op SE_7/Relu.
(nn-Meter) Input shape of SE_7/Relu op is [[1, 1, 1, 144]].
(nn-Meter) Output shape of SE_7/Relu op is [[1, 1, 1, 144]].
(nn-Meter) Find node SE_7/conv2d_15/Conv2D with its weight op SE_7/conv2d_15/kernel.
(nn-Meter) Get input shape of SE_7/conv2d_15/Conv2D from SE_7/Relu, input shape:[1, 1, 1, 144].
(nn-Meter) Get weight shape of SE_7/conv2d_15/Conv2D from ['SE_7/conv2d_15/kernel'], input shape:[1, 1, 144, 576].
(nn-Meter) Op:SE_7/conv2d_15/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 144], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_7/conv2d_15/Conv2D op is [[1, 1, 1, 144]].
(nn-Meter) Output shape of SE_7/conv2d_15/Conv2D op is [[1, 1, 1, 576]].
(nn-Meter) Propagate through op SE_7/conv2d_15/BiasAdd.
(nn-Meter) Input shape of SE_7/conv2d_15/BiasAdd op is [[1, 1, 1, 576]].
(nn-Meter) Output shape of SE_7/conv2d_15/BiasAdd op is [[1, 1, 1, 576]].
(nn-Meter) Input shape of Add_11 op is [[1, 1, 1, 576], []].
(nn-Meter) Output shape of Add_11 op is [[1, 1, 1, 576]].
(nn-Meter) Propagate through op Relu6_7.
(nn-Meter) Input shape of Relu6_7 op is [[1, 1, 1, 576]].
(nn-Meter) Output shape of Relu6_7 op is [[1, 1, 1, 576]].
(nn-Meter) Input shape of mul_14 op is [[1, 1, 1, 576], []].
(nn-Meter) Output shape of mul_14 op is [[1, 1, 1, 576]].
(nn-Meter) Input shape of mul_15 op is [[1, 7, 7, 576], [1, 7, 7, 576]].
(nn-Meter) Output shape of mul_15 op is [[1, 7, 7, 576]].
(nn-Meter) Input shape of layer11.2.hswish/add op is [[1, 7, 7, 576], []].
(nn-Meter) Output shape of layer11.2.hswish/add op is [[1, 7, 7, 576]].
(nn-Meter) Propagate through op layer11.2.hswish/Relu6.
(nn-Meter) Input shape of layer11.2.hswish/Relu6 op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of layer11.2.hswish/Relu6 op is [[1, 7, 7, 576]].
(nn-Meter) Input shape of layer11.2.hswish/mul op is [[1, 7, 7, 576], []].
(nn-Meter) Output shape of layer11.2.hswish/mul op is [[1, 7, 7, 576]].
(nn-Meter) Input shape of layer11.2.hswish/mul_1 op is [[1, 7, 7, 576], [1, 7, 7, 576]].
(nn-Meter) Output shape of layer11.2.hswish/mul_1 op is [[1, 7, 7, 576]].
(nn-Meter) Find node layer11.3.conv/Conv2D with its weight op layer11.3.conv/weight.
(nn-Meter) Get input shape of layer11.3.conv/Conv2D from layer11.2.hswish/mul_1, input shape:[1, 7, 7, 576].
(nn-Meter) Get weight shape of layer11.3.conv/Conv2D from ['layer11.3.conv/weight'], input shape:[1, 1, 576, 96].
(nn-Meter) Op:layer11.3.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 7, 7, 576], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer11.3.conv/Conv2D op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of layer11.3.conv/Conv2D op is [[1, 7, 7, 96]].
(nn-Meter) Propagate through op layer11.3.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer11.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 96]].
(nn-Meter) Output shape of layer11.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 96]].
(nn-Meter) Input shape of Add_12 op is [[1, 7, 7, 96], [1, 7, 7, 96]].
(nn-Meter) Output shape of Add_12 op is [[1, 7, 7, 96]].
(nn-Meter) Find node layer12.1.conv/Conv2D with its weight op layer12.1.conv/weight.
(nn-Meter) Get input shape of layer12.1.conv/Conv2D from Add_12, input shape:[1, 7, 7, 96].
(nn-Meter) Get weight shape of layer12.1.conv/Conv2D from ['layer12.1.conv/weight'], input shape:[1, 1, 96, 576].
(nn-Meter) Op:layer12.1.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 7, 7, 96], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer12.1.conv/Conv2D op is [[1, 7, 7, 96]].
(nn-Meter) Output shape of layer12.1.conv/Conv2D op is [[1, 7, 7, 576]].
(nn-Meter) Propagate through op layer12.1.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer12.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of layer12.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 576]].
(nn-Meter) Input shape of layer12.1.hswish/add op is [[1, 7, 7, 576], []].
(nn-Meter) Output shape of layer12.1.hswish/add op is [[1, 7, 7, 576]].
(nn-Meter) Propagate through op layer12.1.hswish/Relu6.
(nn-Meter) Input shape of layer12.1.hswish/Relu6 op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of layer12.1.hswish/Relu6 op is [[1, 7, 7, 576]].
(nn-Meter) Input shape of layer12.1.hswish/mul op is [[1, 7, 7, 576], []].
(nn-Meter) Output shape of layer12.1.hswish/mul op is [[1, 7, 7, 576]].
(nn-Meter) Input shape of layer12.1.hswish/mul_1 op is [[1, 7, 7, 576], [1, 7, 7, 576]].
(nn-Meter) Output shape of layer12.1.hswish/mul_1 op is [[1, 7, 7, 576]].
(nn-Meter) Find node layer12.2.depconv/depthwise with its weight op layer12.2.depconv/weight.
(nn-Meter) Get input shape of layer12.2.depconv/depthwise from layer12.1.hswish/mul_1, input shape:[1, 7, 7, 576].
(nn-Meter) Get weight shape of layer12.2.depconv/depthwise from ['layer12.2.depconv/weight'], input shape:[5, 5, 576, 1].
(nn-Meter) Op:layer12.2.depconv/depthwise, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 7, 7, 576], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer12.2.depconv/depthwise op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of layer12.2.depconv/depthwise op is [[1, 7, 7, 576]].
(nn-Meter) Propagate through op layer12.2.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer12.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of layer12.2.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 576]].
(nn-Meter) Get input shape of SE_8/AvgPool from layer12.2.batchnorm/BatchNorm/FusedBatchNormV3, input shape:[1, 7, 7, 576].
(nn-Meter) Op:SE_8/AvgPool, stride:[1, 1, 1, 1], padding:VALID.
(nn-Meter) Calculating padding shape, input shape: [1, 7, 7, 576], kernel size: [1, 7, 7, 1], strides: [1, 1, 1, 1], padding: VALID.
(nn-Meter) Input shape of SE_8/AvgPool op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of SE_8/AvgPool op is [[1, 1, 1, 576]].
(nn-Meter) Find node SE_8/conv2d_16/Conv2D with its weight op SE_8/conv2d_16/kernel.
(nn-Meter) Get input shape of SE_8/conv2d_16/Conv2D from SE_8/AvgPool, input shape:[1, 1, 1, 576].
(nn-Meter) Get weight shape of SE_8/conv2d_16/Conv2D from ['SE_8/conv2d_16/kernel'], input shape:[1, 1, 576, 144].
(nn-Meter) Op:SE_8/conv2d_16/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 576], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_8/conv2d_16/Conv2D op is [[1, 1, 1, 576]].
(nn-Meter) Output shape of SE_8/conv2d_16/Conv2D op is [[1, 1, 1, 144]].
(nn-Meter) Propagate through op SE_8/conv2d_16/BiasAdd.
(nn-Meter) Input shape of SE_8/conv2d_16/BiasAdd op is [[1, 1, 1, 144]].
(nn-Meter) Output shape of SE_8/conv2d_16/BiasAdd op is [[1, 1, 1, 144]].
(nn-Meter) Propagate through op SE_8/Relu.
(nn-Meter) Input shape of SE_8/Relu op is [[1, 1, 1, 144]].
(nn-Meter) Output shape of SE_8/Relu op is [[1, 1, 1, 144]].
(nn-Meter) Find node SE_8/conv2d_17/Conv2D with its weight op SE_8/conv2d_17/kernel.
(nn-Meter) Get input shape of SE_8/conv2d_17/Conv2D from SE_8/Relu, input shape:[1, 1, 1, 144].
(nn-Meter) Get weight shape of SE_8/conv2d_17/Conv2D from ['SE_8/conv2d_17/kernel'], input shape:[1, 1, 144, 576].
(nn-Meter) Op:SE_8/conv2d_17/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 1, 1, 144], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of SE_8/conv2d_17/Conv2D op is [[1, 1, 1, 144]].
(nn-Meter) Output shape of SE_8/conv2d_17/Conv2D op is [[1, 1, 1, 576]].
(nn-Meter) Propagate through op SE_8/conv2d_17/BiasAdd.
(nn-Meter) Input shape of SE_8/conv2d_17/BiasAdd op is [[1, 1, 1, 576]].
(nn-Meter) Output shape of SE_8/conv2d_17/BiasAdd op is [[1, 1, 1, 576]].
(nn-Meter) Input shape of Add_13 op is [[1, 1, 1, 576], []].
(nn-Meter) Output shape of Add_13 op is [[1, 1, 1, 576]].
(nn-Meter) Propagate through op Relu6_8.
(nn-Meter) Input shape of Relu6_8 op is [[1, 1, 1, 576]].
(nn-Meter) Output shape of Relu6_8 op is [[1, 1, 1, 576]].
(nn-Meter) Input shape of mul_16 op is [[1, 1, 1, 576], []].
(nn-Meter) Output shape of mul_16 op is [[1, 1, 1, 576]].
(nn-Meter) Input shape of mul_17 op is [[1, 7, 7, 576], [1, 7, 7, 576]].
(nn-Meter) Output shape of mul_17 op is [[1, 7, 7, 576]].
(nn-Meter) Input shape of layer12.2.hswish/add op is [[1, 7, 7, 576], []].
(nn-Meter) Output shape of layer12.2.hswish/add op is [[1, 7, 7, 576]].
(nn-Meter) Propagate through op layer12.2.hswish/Relu6.
(nn-Meter) Input shape of layer12.2.hswish/Relu6 op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of layer12.2.hswish/Relu6 op is [[1, 7, 7, 576]].
(nn-Meter) Input shape of layer12.2.hswish/mul op is [[1, 7, 7, 576], []].
(nn-Meter) Output shape of layer12.2.hswish/mul op is [[1, 7, 7, 576]].
(nn-Meter) Input shape of layer12.2.hswish/mul_1 op is [[1, 7, 7, 576], [1, 7, 7, 576]].
(nn-Meter) Output shape of layer12.2.hswish/mul_1 op is [[1, 7, 7, 576]].
(nn-Meter) Find node layer12.3.conv/Conv2D with its weight op layer12.3.conv/weight.
(nn-Meter) Get input shape of layer12.3.conv/Conv2D from layer12.2.hswish/mul_1, input shape:[1, 7, 7, 576].
(nn-Meter) Get weight shape of layer12.3.conv/Conv2D from ['layer12.3.conv/weight'], input shape:[1, 1, 576, 96].
(nn-Meter) Op:layer12.3.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 7, 7, 576], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of layer12.3.conv/Conv2D op is [[1, 7, 7, 576]].
(nn-Meter) Output shape of layer12.3.conv/Conv2D op is [[1, 7, 7, 96]].
(nn-Meter) Propagate through op layer12.3.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of layer12.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 96]].
(nn-Meter) Output shape of layer12.3.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 96]].
(nn-Meter) Input shape of Add_14 op is [[1, 7, 7, 96], [1, 7, 7, 96]].
(nn-Meter) Output shape of Add_14 op is [[1, 7, 7, 96]].
(nn-Meter) Find node conv13.1.conv/Conv2D with its weight op conv13.1.conv/weight.
(nn-Meter) Get input shape of conv13.1.conv/Conv2D from Add_14, input shape:[1, 7, 7, 96].
(nn-Meter) Get weight shape of conv13.1.conv/Conv2D from ['conv13.1.conv/weight'], input shape:[1, 1, 96, 1024].
(nn-Meter) Op:conv13.1.conv/Conv2D, stride:[1, 1, 1, 1], dilation:[1, 1, 1, 1], padding:SAME.
(nn-Meter) Calculating padding shape, input shape: [1, 7, 7, 96], kernel size: [1, 1], strides: [1, 1, 1, 1], padding: SAME.
(nn-Meter) Input shape of conv13.1.conv/Conv2D op is [[1, 7, 7, 96]].
(nn-Meter) Output shape of conv13.1.conv/Conv2D op is [[1, 7, 7, 1024]].
(nn-Meter) Propagate through op conv11.1.batchnorm/BatchNorm/FusedBatchNormV3.
(nn-Meter) Input shape of conv11.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 1024]].
(nn-Meter) Output shape of conv11.1.batchnorm/BatchNorm/FusedBatchNormV3 op is [[1, 7, 7, 1024]].
(nn-Meter) Input shape of conv13.1.hswish/add op is [[1, 7, 7, 1024], []].
(nn-Meter) Output shape of conv13.1.hswish/add op is [[1, 7, 7, 1024]].
(nn-Meter) Propagate through op conv13.1.hswish/Relu6.
(nn-Meter) Input shape of conv13.1.hswish/Relu6 op is [[1, 7, 7, 1024]].
(nn-Meter) Output shape of conv13.1.hswish/Relu6 op is [[1, 7, 7, 1024]].
(nn-Meter) Input shape of conv13.1.hswish/mul op is [[1, 7, 7, 1024], []].
(nn-Meter) Output shape of conv13.1.hswish/mul op is [[1, 7, 7, 1024]].
(nn-Meter) Input shape of conv13.1.hswish/mul_1 op is [[1, 7, 7, 1024], [1, 7, 7, 1024]].
(nn-Meter) Output shape of conv13.1.hswish/mul_1 op is [[1, 7, 7, 1024]].
(nn-Meter) Get input shape of Mean from conv13.1.hswish/mul_1, input shape:[1, 7, 7, 1024].
(nn-Meter) Get Reduction Indices [1, 2].
(nn-Meter) Input shape of Mean op is [[1, 1024]].
(nn-Meter) Output shape of Mean op is [[1, 1024]].
(nn-Meter) Shape attr find in Reshape op, propagate with normal.
(nn-Meter) Input shape of Reshape op is [[1, 1024]].
(nn-Meter) Output shape of Reshape op is [[-1, 1024]].
(nn-Meter) Find node fc15.fc/MatMul with its weight op fc15.fc/weight.
(nn-Meter) Get weight shape of fc15.fc/MatMul from ['fc15.fc/weight'], input shape:[1024, 1000].
(nn-Meter) Get input shape of fc15.fc/MatMul from Reshape, input shape:[-1, 1024].
(nn-Meter) Input shape of fc15.fc/MatMul op is [[-1, 1024]].
(nn-Meter) Output shape of fc15.fc/MatMul op is [[-1, 1000]].
(nn-Meter) Predict latency: 12.558942703134997 ms
(nn-Meter) [RESULT] predict latency for mobilenetv3small_0.pb: 12.558942703134997 ms
(py37) yongqiang@yongqiang:~$

References

[1] Yongqiang Cheng, https://yongqiang.blog.csdn.net/
[2] microsoft / nn-Meter, https://github.com/microsoft/nn-Meter
[3] nn-meter 2.0, https://pypi.org/project/nn-meter/
[4] nn-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices, https://air.tsinghua.edu.cn/pdf/nn-Meter-Towards-Accurate-Latency-Prediction-of-Deep-Learning-Model-Inference-on-Diverse-Edge-Devices.pdf