mmdeploy/tests/test_pytorch/test_pytorch_functions.py
RunningLeon 4d8ea40f55
Sync v0.7.0 to dev-1.x (#907)
* make -install -> make install (#621)

change `make -install` to `make install`

https://github.com/open-mmlab/mmdeploy/issues/618

* [Fix] fix csharp api detector release result (#620)

* fix csharp api detector release result

* fix wrong count arg of xxx_release_result in c# api

* [Enhancement] Support two-stage rotated detector TensorRT. (#530)

* upload

* add fake_multiclass_nms_rotated

* delete unused code

* align with pytorch

* Update delta_midpointoffset_rbbox_coder.py

* add trt rotated roi align

* add index feature in nms

* not good

* fix index

* add ut

* add benchmark

* move to csrc/mmdeploy

* update unit test

Co-authored-by: zytx121 <592267829@qq.com>

* Reduce mmcls version dependency (#635)

* fix shufflenetv2 with trt (#645)

* fix shufflenetv2 and pspnet

* fix ci

* remove print

* ' -> " (#654)

If there is a variable in the string, single quotes will ignored it, while double quotes will bring the variable into the string after parsing

* ' -> " (#655)

same with https://github.com/open-mmlab/mmdeploy/pull/654

* Support deployment of Segmenter (#587)

* support segmentor with ncnn

* update regression yml

* replace chunk with split to support ts

* update regression yml

* update docs

* fix segmenter ncnn inference failure brought by #477

* add test

* fix test for ncnn and trt

* fix lint

* export nn.linear to Gemm op in onnx for ncnn

* fix ci

* simplify `Expand` (#617)

* Fix typo (#625)

* Add make install in en docs

* Add make install in zh docs

* Fix typo

* Merge and add windows build

Co-authored-by: tripleMu <865626@163.com>

* [Enhancement] Fix ncnn unittest (#626)

* optmize-csp-darknet

* replace floordiv to torch.div

* update csp_darknet default implement

* fix test

* [Enhancement] TensorRT Anchor generator plugin (#646)

* custom trt anchor generator

* add ut

* add docstring, update doc

* Add partition doc and sample code (#599)

* update torch2onnx tool to support onnx partition

* add model partition of yolov3

* add cn doc

* update torch2onnx tool to support onnx partition

* add model partition of yolov3

* add cn doc

* add to index.rst

* resolve comment

* resolve comments

* fix lint

* change caption level in docs

* update docs (#624)

* Add java apis and demos (#563)

* add java classifier detector

* add segmentor

* fix lint

* add ImageRestorer java apis and demo

* remove useless count parameter for Segmentor and Restorer, add PoseDetector

* add RotatedDetection java api and demo

* add Ocr java demo and apis

* remove mmrotate ncnn java api and demo

* fix lint

* sync java api folder after rebase to master

* fix include

* remove record

* fix java apis dir path in cmake

* add java demo readme

* fix lint mdformat

* add test javaapi ci

* fix lint

* fix flake8

* fix test javaapi ci

* refactor readme.md

* fix install opencv for ci

* fix install opencv : add permission

* add all codebases and mmcv install

* add torch

* install mmdeploy

* fix image path

* fix picture path

* fix import ncnn

* fix import ncnn

* add submodule of pybind

* fix pybind submodule

* change download to git clone for submodule

* fix ncnn dir

* fix README error

* simplify the github ci

* fix ci

* fix yapf

* add JNI as required

* fix Capitalize

* fix Capitalize

* fix copyright

* ignore .class changed

* add OpenJDK installation docs

* install target of javaapi

* simplify ci

* add jar

* fix ci

* fix ci

* fix test java command

* debugging what failed

* debugging what failed

* debugging what failed

* add java version info

* install openjdk

* add java env var

* fix export

* fix export

* fix export

* fix export

* fix picture path

* fix picture path

* fix file name

* fix file name

* fix README

* remove java_api strategy

* fix python version

* format task name

* move args position

* extract common utils code

* show image class result

* add detector result

* segmentation result format

* add ImageRestorer result

* add PoseDetection java result format

* fix ci

* stage ocr

* add visualize

* move utils

* fix lint

* fix ocr bugs

* fix ci demo

* fix java classpath for ci

* fix popd

* fix ocr demo text garbled

* fix ci

* fix ci

* fix ci

* fix path of utils ci

* update the circleci config file by adding workflows both for linux, windows and linux-gpu (#368)

* update circleci by adding more workflows

* fix test workflow failure on windows platform

* fix docker exec command for SDK unittests

* Fixed tensorrt plugin not found in Windows (#672)

* update introduction.png (#674)

* [Enhancement] Add fuse select assign pass (#589)

* Add fuse select assign pass

* move code to csrc

* add config flag

* remove bool cast

* fix export sdk info of input shape (#667)

* Update get_started.md (#675)

Fix backend model assignment

* Update get_started.md (#676)

Fix backend model assignment

* [Fix] fix clang build (#677)

* fix clang build

* fix ndk build

* fix ndk build

* switch to `std::filesystem` for clang-7 and later

* Deploy the Swin Transformer on TensorRT. (#652)

* resolve conflicts

* update ut and docs

* fix ut

* refine docstring

* add comments and refine UT

* resolve comments

* resolve comments

* update doc

* add roll export

* check backend

* update regression test

* bump version to 0.6.0 (#680)

* bump vertion to 0.6.0

* update version

* pass img_metas while exporting to onnx (#681)

* pass img_metas while exporting to onnx

* remove try-catch in tools for beter debugging

* use get

* fix typo

* [Fix] fix ssd ncnn ut (#692)

* fix ssd ncnn ut

* fix yapf

* fix passing img_metas to pytorch2onnx for mmedit (#700)

* fix passing img_metas for mmdet3d (#707)

* [Fix] Fix android build (#698)

* fix android build

* fix cmake

* fix url link

* fix wrong exit code in pipeline_manager (#715)

* fix exit

* change to general exit errorcode=1

* fix passing wrong backend type (#719)

* Rename onnx2ncnn to mmdeploy_onnx2ncnn (#694)

* improvement(tools/onnx2ncnn.py): rename to mmdeploy_onnx2ncnn

* format(tools/deploy.py): clean code

* fix(init_plugins.py): improve if condition

* fix(CI): update target

* fix(test_onnx2ncnn.py): update desc

* Update init_plugins.py

* [Fix] Fix mmdet ort static shape bug (#687)

* fix shape

* add device

* fix yapf

* fix rewriter for transforms

* reverse image shape

* fix ut of distance2bbox

* fix rewriter name

* fix c4 for torchscript (#724)

* [Enhancement] Standardize C API (#634)

* unify C API naming

* fix demo and move apis/c/* -> apis/c/mmdeploy/*

* fix lint

* fix C# project

* fix Java API

* [Enhancement] Support Slide Vertex TRT (#650)

* reorgnize mmrotate

* fix

* add hbb2obb

* add ut

* fix rotated nms

* update docs

* update benchmark

* update test

* remove ort regression test, remove comment

* Fix get-started rendering issues in readthedocs (#740)

* fix mermaid markdown rendering issue in readthedocs

* fix error in C++ example

* fix error in c++ example in zh_cn get_started doc

* [Fix] set default topk for dump info (#702)

* set default topk for dump info

* remove redundant docstrings

* add ci densenet

* fix classification warnings

* fix mmcls version

* fix logger.warnings

* add version control (#754)

* fix satrn for ORT (#753)

* fix satrn for ORT

* move rewrite into pytorch

* Add inference latency test tool (#665)

* add profile tool

* remove print envs in profile tool

* set cudnn_benchmark to True

* add doc

* update tests

* fix typo

* support test with images from a directory

* update doc

* resolve comments

* [Enhancement] Add CSE ONNX pass (#647)

* Add fuse select assign pass

* move code to csrc

* add config flag

* Add fuse select assign pass

* Add CSE for ONNX

* remove useless code

* Test robot

Just test robot

* Update README.md

Revert

* [Fix] fix yolox point_generator (#758)

* fix yolox point_generator

* add a UT

* resolve comments

* fix comment lines

* limit markdown version (#773)

* [Enhancement] Better index put ONNX export. (#704)

* Add rewriter for tensor setitem

* add version check

* Upgrade Dockerfile to use TensorRT==8.2.4.2 (#706)

* Upgrade TensorRT to 8.2.4.2

* upgrade pytorch&mmcv in CPU Dockerfile

* Delete redundant port example in Docker

* change 160x160-608x608 to 64x64-608x608 for yolov3

* [Fix] reduce log verbosity & improve error reporting (#755)

* reduce log verbosity & improve error reporting

* improve error reporting

* [Enhancement] Support latest ppl.nn & ppl.cv (#564)

* support latest ppl.nn

* fix pplnn for model convertor

* fix lint

* update memory policy

* import algo from buffer

* update ppl.cv

* use `ppl.cv==0.7.0`

* document supported ppl.nn version

* skip pplnn dependency when building shared libs

* [Fix][P0] Fix for torch1.12 (#751)

* fix for torch1.12

* add comment

* fix check env (#785)

* [Fix] fix cascade mask rcnn (#787)

* fix cascade mask rcnn

* fix lint

* add regression

* [Feature] Support RoITransRoIHead (#713)

* [Feature] Support RoITransRoIHead

* Add docs

* Add mmrotate models regression test

* Add a draft for test code

* change the argument name

* fix test code

* fix minor change for not class agnostic case

* fix sample for test code

* fix sample for test code

* Add mmrotate in requirements

* Revert "Add mmrotate in requirements"

This reverts commit 043490075e6dbe4a8fb98e94b2b583b91fc5038d.

* [Fix] fix triu (#792)

* fix triu

* triu -> triu_default

* [Enhancement] Install Optimizer by setuptools (#690)

* Add fuse select assign pass

* move code to csrc

* add config flag

* Add fuse select assign pass

* Add CSE for ONNX

* remove useless code

* Install optimizer by setup tools

* fix comment

* [Feature] support MMRotate model with le135 (#788)

* support MMRotate model with le135

* cse before fuse select assign

* remove unused import

* [Fix] Support macOS build (#762)

* fix macOS build

* fix missing

* add option to build & install examples (#822)

* [Fix] Fix setup on non-linux-x64 (#811)

* fix setup

* replace long to int64_t

* [Feature] support build single sdk library (#806)

* build single lib for c api

* update csharp doc & project

* update test build

* fix test build

* fix

* update document for building android sdk (#817)

Co-authored-by: dwSun <dwsunny@icloud.com>

* [Enhancement] support kwargs in SDK python bindings (#794)

* support-kwargs

* make '__call__' as single image inference and add 'batch' API to deal with batch images inference

* fix linting error and typo

* fix lint

* improvement(sdk): add sdk code coverage (#808)

* feat(doc): add CI

* CI(sdk): add sdk coverage

* style(test): code format

* fix(CI): update coverage.info path

* improvement(CI): use internal image

* improvement(CI): push coverage info once

* [Feature] Add C++ API for SDK (#831)

* add C++ API

* unify result type & add examples

* minor fix

* install cxx API headers

* fix Mat, add more examples

* fix monolithic build & fix lint

* install examples correctly

* fix lint

* feat(tools/deploy.py): support snpe (#789)

* fix(tools/deploy.py): support snpe

* improvement(backend/snpe): review advices

* docs(backend/snpe): update build

* docs(backend/snpe): server support specify port

* docs(backend/snpe): update path

* fix(backend/snpe): time counter missing argument

* docs(backend/snpe): add missing argument

* docs(backend/snpe): update download and using

* improvement(snpe_net.cpp): load model with modeldata

* Support setup on environment with no PyTorch (#843)

* support test with multi batch (#829)

* support test with multi batch

* resolve comment

* import algorithm from buffer (#793)

* [Enhancement] build sdk python api in standard-alone manner (#810)

* build sdk python api in standard-alone manner

* enable MMDEPLOY_BUILD_SDK_MONOLITHIC and MMDEPLOY_BUILD_EXAMPLES in prebuild config

* link mmdeploy to python target when monolithic option is on

* checkin README to describe precompiled package build procedure

* use packaging.version.parse(python_version) instead of list(python_version)

* fix according to review results

* rebase master

* rollback cmake.in and apis/python/CMakeLists.txt

* reorganize files in install/example

* let cmake detect visual studio instead of specifying 2019

* rename whl name of precompiled package

* fix according to review results

* Fix SDK backend (#844)

* fix mmpose python api (#852)

* add prebuild package usage docs on windows (#816)

* add prebuild package usage docs on windows

* fix lint

* update

* try fix lint

* add en docs

* update

* update

* udpate faq

* fix typo (#862)

* [Enhancement] Improve get_started documents and bump version to 0.7.0 (#813)

* simplify commands in get_started

* add installation commands for Windows

* fix typo

* limit markdown and sphinx_markdown_tables version

* adopt html <details open> tag

* bump mmdeploy version

* bump mmdeploy version

* update get_started

* update get_started

* use python3.8 instead of python3.7

* remove duplicate section

* resolve issue #856

* update according to review results

* add reference to prebuilt_package_windows.md

* fix error when build sdk demos

* fix mmcls

Co-authored-by: Ryan_Huang <44900829+DrRyanHuang@users.noreply.github.com>
Co-authored-by: Chen Xin <xinchen.tju@gmail.com>
Co-authored-by: q.yao <yaoqian@sensetime.com>
Co-authored-by: zytx121 <592267829@qq.com>
Co-authored-by: Li Zhang <lzhang329@gmail.com>
Co-authored-by: tripleMu <gpu@163.com>
Co-authored-by: tripleMu <865626@163.com>
Co-authored-by: hanrui1sensetime <83800577+hanrui1sensetime@users.noreply.github.com>
Co-authored-by: lvhan028 <lvhan_028@163.com>
Co-authored-by: Bryan Glen Suello <11388006+bgsuello@users.noreply.github.com>
Co-authored-by: zambranohally <63218980+zambranohally@users.noreply.github.com>
Co-authored-by: AllentDan <41138331+AllentDan@users.noreply.github.com>
Co-authored-by: tpoisonooo <khj.application@aliyun.com>
Co-authored-by: Hakjin Lee <nijkah@gmail.com>
Co-authored-by: 孙德伟 <5899962+dwSun@users.noreply.github.com>
Co-authored-by: dwSun <dwsunny@icloud.com>
Co-authored-by: Chen Xin <irexyc@gmail.com>
2022-08-19 09:30:13 +08:00

344 lines
11 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import mmcv
import numpy as np
import pytest
import torch
import torch.nn.functional as F
from packaging.version import parse
from mmdeploy.utils import Backend
from mmdeploy.utils.test import (WrapFunction, backend_checker,
get_rewrite_outputs)
deploy_cfg_ncnn = mmcv.Config(
dict(
onnx_config=dict(input_shape=None),
backend_config=dict(type='ncnn', model_inputs=None, use_vulkan=False),
codebase_config=dict(type='mmdet', task='ObjectDetection')))
def get_trt_config(output_names, shape):
deploy_cfg_tensorrt = mmcv.Config(
dict(
onnx_config=dict(input_shape=None, output_names=output_names),
backend_config=dict(
type='tensorrt',
common_config=dict(
fp16_mode=False, max_workspace_size=1 << 20),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=shape,
opt_shape=shape,
max_shape=shape)))
]),
codebase_config=dict(type='mmdet', task='ObjectDetection')))
return deploy_cfg_tensorrt
@backend_checker(Backend.NCNN)
def test_get_attribute():
def model_func(tensor):
x = tensor.size()
assert isinstance(x[0], int) and not isinstance(x[0], torch.Tensor)
return torch.tensor(x)
input = torch.zeros([1, 2, 3, 4])
wrapped_func = WrapFunction(model_func)
rewrite_outputs, _ = get_rewrite_outputs(
wrapped_func,
model_inputs={'tensor': input},
deploy_cfg=deploy_cfg_ncnn,
run_with_backend=True)
assert rewrite_outputs is not None, 'Got unexpected rewrite '
'outputs: {}'.format(rewrite_outputs)
@backend_checker(Backend.NCNN)
def test_group_norm_ncnn():
input = torch.rand([1, 2, 2, 2])
weight = torch.rand([2])
bias = torch.rand([2])
model_output = F.group_norm(input, 1, weight, bias, 1e-05)
def group_norm_caller(input):
return F.group_norm(input, 1, weight, bias)
wrapped_func = WrapFunction(group_norm_caller)
rewrite_output, _ = get_rewrite_outputs(
wrapped_func,
model_inputs={'input': input},
deploy_cfg=deploy_cfg_ncnn,
run_with_backend=True)
assert np.allclose(model_output, rewrite_output[0], rtol=1e-03, atol=1e-05)
@backend_checker(Backend.NCNN)
def test_chunk_ncnn():
input = torch.rand(1, 16, 16, 16)
model_output = input.chunk(2, dim=1)
def chunk_caller(input):
return input.chunk(2, dim=1)
wrapped_func = WrapFunction(chunk_caller)
rewrite_output, _ = get_rewrite_outputs(
wrapped_func,
model_inputs={'input': input},
deploy_cfg=deploy_cfg_ncnn,
run_with_backend=True)
assert len(model_output) == len(rewrite_output)
for i in range(len(model_output)):
assert np.allclose(
model_output[i], rewrite_output[i], rtol=1e-03, atol=1e-05)
@backend_checker(Backend.NCNN)
def test_interpolate_static():
input = torch.rand([1, 2, 2, 2])
model_output = F.interpolate(input, scale_factor=[2, 2])
def interpolate_caller(*arg, **kwargs):
return F.interpolate(*arg, **kwargs)
wrapped_func = WrapFunction(interpolate_caller, size=[4, 4])
rewrite_output, _ = get_rewrite_outputs(
wrapped_func,
model_inputs={'input': input},
deploy_cfg=deploy_cfg_ncnn,
run_with_backend=True)
assert np.allclose(model_output, rewrite_output[0], rtol=1e-03, atol=1e-05)
@backend_checker(Backend.NCNN)
def test_linear_ncnn():
input = torch.rand([1, 2, 2])
weight = torch.rand([2, 2])
bias = torch.rand([2])
model_output = F.linear(input, weight=weight, bias=bias)
def linear_caller(*arg, **kwargs):
return F.linear(*arg, **kwargs)
wrapped_func = WrapFunction(linear_caller, weight=weight, bias=bias)
rewrite_output, _ = get_rewrite_outputs(
wrapped_func,
model_inputs={'input': input},
deploy_cfg=deploy_cfg_ncnn,
run_with_backend=True)
assert np.allclose(model_output, rewrite_output[0], rtol=1e-03, atol=1e-05)
@backend_checker(Backend.TENSORRT)
def test_repeat_static():
input = torch.rand([1])
def model_func(input):
return torch.Tensor.repeat(input, 4)
wrapped_func = WrapFunction(model_func)
model_output = model_func(input)
deploy_cfg = get_trt_config(['output'], [1])
rewrite_output, is_backend_output = get_rewrite_outputs(
wrapped_func, model_inputs={'input': input}, deploy_cfg=deploy_cfg)
if is_backend_output:
rewrite_output = rewrite_output[0].detach().cpu()
assert np.allclose(
model_output, rewrite_output, rtol=1e-03, atol=1e-05)
else:
assert rewrite_output is not None
@backend_checker(Backend.NCNN)
def test_size_of_tensor_static():
def model_func(input):
x = torch.Tensor.size(input)
assert isinstance(x[0], int) and not isinstance(x[0], torch.Tensor)
return torch.tensor(x)
input = torch.zeros([1, 2, 3, 4])
wrapped_func = WrapFunction(model_func)
rewrite_outputs, _ = get_rewrite_outputs(
wrapped_func,
model_inputs={'input': input},
deploy_cfg=deploy_cfg_ncnn,
run_with_backend=True)
assert rewrite_outputs is not None, 'Got unexpected rewrite '
'outputs: {}'.format(rewrite_outputs)
class TestTopk:
input = torch.rand(1, 5, 5, 5)
@backend_checker(Backend.NCNN)
@pytest.mark.parametrize('k', [1, 3, 4])
@pytest.mark.parametrize('dim', [1, 2, 3])
def test_topk_ncnn(self, dim, k):
model_output = torch.Tensor.topk(TestTopk.input, k, dim).values
def model_func(input):
x = input.topk(k, dim)
return x.indices, x.values
wrapped_func = WrapFunction(model_func)
# mmdeploy.pytorch.functions.topk.topk_dynamic
output, _ = get_rewrite_outputs(
wrapped_func,
model_inputs={'input': TestTopk.input},
deploy_cfg=deploy_cfg_ncnn,
run_with_backend=True)
assert np.allclose(model_output, output[0], rtol=1e-03, atol=1e-05)
@backend_checker(Backend.TENSORRT)
@pytest.mark.parametrize('k', [1, 3, 4])
@pytest.mark.parametrize('dim', [1, 2, 3])
def test_topk_tensorrt(self, dim, k):
model_output = torch.Tensor.topk(TestTopk.input, k, dim).values
def model_func(input):
x = input.topk(k, dim)
return x.indices, x.values
wrapped_func = WrapFunction(model_func)
# mmdeploy.pytorch.functions.topk.topk_static
deploy_cfg_tensorrt = get_trt_config(['indices', 'values'],
[1, 5, 5, 5])
output, is_backend_output = get_rewrite_outputs(
wrapped_func,
model_inputs={'input': TestTopk.input},
deploy_cfg=deploy_cfg_tensorrt)
if is_backend_output:
output = output[1].detach().cpu()
assert np.allclose(model_output, output, rtol=1e-03, atol=1e-05)
else:
assert output is not None
@backend_checker(Backend.TENSORRT)
@pytest.mark.parametrize('shape', [[2, 2], [4, 2], [2, 4], [2, 4, 2]])
@pytest.mark.parametrize('diagonal', [0, 1, -1])
def test_triu_trt(shape, diagonal):
input = torch.rand(shape)
model_output = torch.triu(input=input, diagonal=diagonal)
def triu_caller(*arg, **kwargs):
return torch.triu(*arg, **kwargs)
wrapped_func = WrapFunction(triu_caller, diagonal=diagonal)
rewrite_outputs, is_backend_output = get_rewrite_outputs(
wrapped_func,
model_inputs={'input': input},
deploy_cfg=get_trt_config(['output'], shape=shape),
run_with_backend=True)
if is_backend_output:
rewrite_outputs = rewrite_outputs[0].detach().cpu()
assert np.allclose(
model_output, rewrite_outputs, rtol=1e-03, atol=1e-05)
else:
assert rewrite_outputs is not None
@backend_checker(Backend.NCNN)
@pytest.mark.parametrize(
'input',
[torch.rand(1, 16, 16), torch.rand(1, 3, 16, 16)])
@pytest.mark.parametrize('dim', [1, 2])
def test_normalize_ncnn(input, dim):
import mmdeploy.apis.ncnn as ncnn_apis
from mmdeploy.utils.test import get_onnx_model
def norm_func(input, dim):
return F.normalize(input, p=2, dim=dim)
wrapped_func = WrapFunction(norm_func, dim=dim)
model_inputs = {'input': input}
ir_file_path = get_onnx_model(wrapped_func, model_inputs, deploy_cfg_ncnn)
assert osp.exists(ir_file_path)
ncnn_files_prefix = osp.splitext(ir_file_path)[0]
ncnn_apis.from_onnx(ir_file_path, ncnn_files_prefix)
param_path, bin_path = ncnn_apis.get_output_model_file(ir_file_path)
assert osp.exists(param_path)
assert osp.exists(bin_path)
@backend_checker(Backend.ONNXRUNTIME)
@pytest.mark.parametrize(
'input',
[torch.rand(1, 16, 16), torch.rand(1, 3, 16, 16)])
def test_masked_fill_onnxruntime(input):
mask = input > 0
value = float('-inf')
def masked_fill_caller(*arg, **kwargs):
return torch.masked_fill(*arg, **kwargs)
deploy_cfg_ort = mmcv.Config(
dict(
onnx_config=dict(input_shape=None),
backend_config=dict(type='onnxruntime'),
codebase_config=dict(type='mmdet', task='ObjectDetection')))
wrapped_func = WrapFunction(masked_fill_caller, mask=mask, value=value)
rewrite_output, _ = get_rewrite_outputs(
wrapped_func,
model_inputs={'input': input},
deploy_cfg=deploy_cfg_ort,
run_with_backend=True)
assert rewrite_output is not None
@backend_checker(Backend.ONNXRUNTIME)
@pytest.mark.skipif(
parse(torch.__version__) < parse('1.9.0'), reason='requires torch>1.8.0')
@pytest.mark.parametrize('x', [torch.rand(1, 3, 16, 16)])
@pytest.mark.parametrize('y', [torch.rand(1, 3, 4, 4)])
def test_tensor_setitem(x, y):
import onnx
from mmdeploy.utils.test import get_onnx_model
def setitem_slice(x, y):
H, W = y.shape[2:]
x[:, :, 2:H + 2, 2:W + 2] = y
return x
wrapped_func = WrapFunction(setitem_slice)
model_inputs = {'x': x, 'y': y}
deploy_cfg = mmcv.Config(
dict(
onnx_config=dict(input_shape=None),
backend_config=dict(type='onnxruntime'),
codebase_config=dict(type='mmdet', task='ObjectDetection')))
ir_file_path = get_onnx_model(wrapped_func, model_inputs, deploy_cfg)
onnx_model = onnx.load(ir_file_path)
nodes = onnx_model.graph.node
for node in nodes:
assert node.op_type != 'ScatterND'