mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
c5a87fb1bc
* Fix include and lib paths for onnxruntime. * Fixes for SSD export test * Add onnx2openvino and OpenVINODetector. Test models: ssd, retinanet, fcos, fsaf. * Add support for two-stage models: faster_rcnn, cascade_rcnn * Add doc * Add strip_doc_string for openvino. * Fix openvino preprocess. * Add OpenVINO to test_wrapper.py. * Fix * Add openvino_execute. * Removed preprocessing. * Fix onnxruntime cmake. * Rewrote postprocessing and forward, added docstrings and fixes. * Added device type change to OpenVINOWrapper. * Update forward_of_single_roi_extractor_dynamic_openvino and fix doc. * Update docs. * Add OpenVINODetector and onn2openvino tests. * Add input_info to onnx2openvino. * Add TestOpenVINOExporter and test_single_roi_extractor. * Moved get_input_shape_from_cfg to openvino_utils.py and added test. * Added test_cascade_roi_head. * Add backend.check_env() to tests. * Add OpenVINO to get_rewrite_outputs and to some tests in test_mmdet_models. * Moved test_single_roi_extractor to test_mmdet_models. * Removed TestOpenVINOExporter.
118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
import os
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import os.path as osp
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import tempfile
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import numpy as np
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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.openvino import is_available
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openvino_skip = not 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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def generate_onnx_file(model, export_img, 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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export_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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def get_outputs(pytorch_model, openvino_model_path, input):
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output_pytorch = pytorch_model(input).numpy()
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from mmdeploy.apis.openvino import OpenVINOWrapper
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openvino_model = OpenVINOWrapper(openvino_model_path)
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openvino_output = openvino_model({'input': input})['output']
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return output_pytorch, openvino_output
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@pytest.mark.skipif(openvino_skip, reason='OpenVINO not avaiable')
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def test_onnx2openvino():
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from mmdeploy.apis.openvino import get_output_model_file, onnx2openvino
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pytorch_model = TestModel().eval()
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export_img = torch.rand([1, 3, 8, 8])
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onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
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generate_onnx_file(pytorch_model, export_img, onnx_file)
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input_info = {'input': export_img.shape}
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output_names = ['output']
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openvino_dir = tempfile.TemporaryDirectory().name
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onnx2openvino(input_info, output_names, onnx_file, openvino_dir)
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openvino_model_path = get_output_model_file(onnx_file, openvino_dir)
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assert osp.exists(openvino_model_path), \
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'The file (.xml) for OpenVINO IR has not been created.'
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test_img = torch.rand([1, 3, 16, 16])
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output_pytorch, openvino_output = get_outputs(pytorch_model,
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openvino_model_path,
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test_img)
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assert np.allclose(output_pytorch, openvino_output), \
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'OpenVINO and PyTorch outputs are not the same.'
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@pytest.mark.skipif(openvino_skip, reason='OpenVINO not avaiable')
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def test_can_not_run_onnx2openvino_without_mo():
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current_environ = dict(os.environ)
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os.environ.clear()
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is_error = False
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try:
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from mmdeploy.apis.openvino import onnx2openvino
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onnx2openvino({}, ['output'], 'tmp.onnx', '/tmp')
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except RuntimeError:
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is_error = True
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os.environ.update(current_environ)
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assert is_error, \
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'The onnx2openvino script was launched without checking for MO.'
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@pytest.mark.skipif(openvino_skip, reason='OpenVINO not avaiable')
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def test_get_input_shape_from_cfg():
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from mmdeploy.apis.openvino import get_input_shape_from_cfg
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# Test with default value
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model_cfg = {}
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input_shape = get_input_shape_from_cfg(model_cfg)
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assert input_shape == [1, 3, 800, 1344], \
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'The function returned a different default shape.'
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# Test with config that contains the required data.
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height, width = 800, 1200
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model_cfg = {'test_pipeline': [{}, {'img_scale': (width, height)}]}
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input_shape = get_input_shape_from_cfg(model_cfg)
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assert input_shape == [1, 3, height, width], \
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'The shape in the config does not match the output shape.'
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