mirror of
https://github.com/open-mmlab/mmdeploy.git
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4de5bbb461
* add unittests * lint * modify .gitignore, remove useless files * remove emtpy.py and generate it when use * Update according to comments 1. Use tempfile 2. Delete inference test (which will be tested in each codebase) 3. Refine calibration test * update annotation * Add export_info * Reduce data scale, fix assert * update json blank line * add backend check
105 lines
2.7 KiB
Python
105 lines
2.7 KiB
Python
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.apis.tensorrt import (TRTWrapper, create_trt_engine,
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save_trt_engine)
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from mmdeploy.utils.constants import Backend
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onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
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test_img = torch.rand([1, 3, 64, 64])
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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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model = TestModel().eval().cuda()
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@pytest.fixture(autouse=True, scope='module')
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def generate_onnx_file():
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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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def check_backend_avaiable(backend):
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if backend == Backend.TENSORRT:
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from mmdeploy.apis.tensorrt import is_available as trt_available
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if not trt_available():
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pytest.skip(
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'TensorRT is not installed or custom ops are not compiled.')
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if not torch.cuda.is_available():
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pytest.skip('CUDA is not available.')
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def onnx2backend(backend, onnx_file):
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if backend == Backend.TENSORRT:
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backend_file = tempfile.NamedTemporaryFile(suffix='.engine').name
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engine = create_trt_engine(
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onnx_file, {
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'input': {
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'min_shape': [1, 3, 64, 64],
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'opt_shape': [1, 3, 64, 64],
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'max_shape': [1, 3, 64, 64]
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}
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})
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save_trt_engine(engine, backend_file)
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return backend_file
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def create_wrapper(backend, engine_file):
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if backend == Backend.TENSORRT:
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trt_model = TRTWrapper(engine_file)
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return trt_model
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def run_wrapper(backend, wrapper, input):
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if backend == Backend.TENSORRT:
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input = input.cuda()
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results = wrapper({'input': input})['output']
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results = results.detach().cpu()
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return results
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ALL_BACKEND = [Backend.TENSORRT]
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@pytest.mark.parametrize('backend', ALL_BACKEND)
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def test_wrapper(backend):
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check_backend_avaiable(backend)
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model_files = onnx2backend(backend, onnx_file)
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assert model_files is not None
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wrapper = create_wrapper(backend, model_files)
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assert wrapper is not None
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results = run_wrapper(backend, wrapper, test_img)
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assert results is not None
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