93 lines
2.5 KiB
Python
93 lines
2.5 KiB
Python
import os.path as osp
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import tempfile
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import mmcv
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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.apis.tensorrt import is_available
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onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
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engine_file = tempfile.NamedTemporaryFile(suffix='.engine').name
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test_img = torch.rand([1, 3, 8, 8])
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trt_skip = not is_available()
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cuda_skip = not torch.cuda.is_available()
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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().cuda()
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def get_deploy_cfg():
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import tensorrt as trt
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deploy_cfg = mmcv.Config(
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dict(
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backend_config=dict(
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type='tensorrt',
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common_config=dict(
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fp16_mode=False,
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log_level=trt.Logger.INFO,
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max_workspace_size=1 << 30),
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model_inputs=[
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dict(
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input_shapes=dict(
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input=dict(
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min_shape=[1, 3, 8, 8],
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opt_shape=[1, 3, 8, 8],
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max_shape=[1, 3, 8, 8])))
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])))
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return deploy_cfg
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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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@pytest.mark.skipif(trt_skip, reason='TensorRT not avaiable')
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@pytest.mark.skipif(cuda_skip, reason='Cuda not avaiable')
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def test_onnx2tensorrt():
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from mmdeploy.apis.tensorrt import load_trt_engine, onnx2tensorrt
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model = test_model
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generate_onnx_file(model)
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deploy_cfg = get_deploy_cfg()
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work_dir, save_file = osp.split(engine_file)
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onnx2tensorrt(work_dir, save_file, 0, deploy_cfg, onnx_file)
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assert osp.exists(work_dir)
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assert osp.exists(engine_file)
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engine = load_trt_engine(engine_file)
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assert engine is not None
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