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b32fc41bed
* [refactor][API2.0] Add onnx export and jit trace (#419) * first commit * add async call * add new api onnx export and jit trace * add decorator * fix ci * fix torchscript ci * fix loader * better pipemanager * remove comment, better import * add kwargs * remove comment * better pipeline manager * remove print * [Refactor][API2.0] Api partition calibration (#433) * first commit * add async call * add new api onnx export and jit trace * add decorator * fix ci * fix torchscript ci * fix loader * better pipemanager * remove comment, better import * add partition * move calibration * Better create_calib_table * better deploy * add kwargs * remove comment * better pipeline manager * rename api, remove reduant variable, and misc * [Refactor][API2.0] Api ncnn openvino (#435) * first commit * add async call * add new api onnx export and jit trace * add decorator * fix ci * fix torchscript ci * fix loader * better pipemanager * remove comment, better import * add ncnn api * finish ncnn api * add openvino support * add kwargs * remove comment * better pipeline manager * merge fix * merge util and onnx2ncnn * fix docstring * [Refactor][API2.0] API for TensorRT (#519) * first commit * add async call * add new api onnx export and jit trace * add decorator * fix ci * fix torchscript ci * fix loader * better pipemanager * remove comment, better import * add partition * move calibration * Better create_calib_table * better deploy * add kwargs * remove comment * Add tensorrt API * better pipeline manager * add tensorrt new api * remove print * rename api, remove reduant variable, and misc * add docstring * [Refactor][API2.0] Api ppl other (#528) * first commit * add async call * add new api onnx export and jit trace * add decorator * fix ci * fix torchscript ci * fix loader * better pipemanager * remove comment, better import * add kwargs * Add new APIS for pplnn sdk and misc * remove comment * better pipeline manager * merge fix * update tools/onnx2pplnn.py * rename function
69 lines
1.7 KiB
Python
69 lines
1.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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import tempfile
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import pytest
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import torch
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import torch.nn as nn
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from mmdeploy.backend.ncnn.onnx2ncnn import get_output_model_file
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from mmdeploy.utils import Backend
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from mmdeploy.utils.test import backend_checker
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onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
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test_img = torch.rand([1, 3, 8, 8])
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@pytest.mark.skip(reason='This a not test class but a utility class.')
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class TestModel(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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return x * 0.5
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test_model = TestModel().eval()
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def generate_onnx_file(model):
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with torch.no_grad():
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dynamic_axes = {
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'input': {
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0: 'batch',
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2: 'width',
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3: 'height'
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},
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'output': {
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0: 'batch'
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}
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}
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torch.onnx.export(
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model,
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test_img,
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onnx_file,
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output_names=['output'],
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input_names=['input'],
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keep_initializers_as_inputs=True,
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do_constant_folding=True,
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verbose=False,
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opset_version=11,
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dynamic_axes=dynamic_axes)
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assert osp.exists(onnx_file)
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@backend_checker(Backend.NCNN)
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def test_onnx2ncnn():
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from mmdeploy.apis.ncnn import from_onnx
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model = test_model
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generate_onnx_file(model)
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work_dir, _ = osp.split(onnx_file)
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save_param, save_bin = get_output_model_file(onnx_file, work_dir=work_dir)
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file_name = osp.splitext(onnx_file)[0]
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from_onnx(onnx_file, osp.join(work_dir, file_name))
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assert osp.exists(work_dir)
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assert osp.exists(save_param)
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assert osp.exists(save_bin)
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