mirror of
https://github.com/open-mmlab/mmdeploy.git
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787ebc2392
* add mmpose code * update * update * add rewrites * test trt * test litehrnet with trt * revert unused change * add docs about mmpose * add docstring and staticmethod * update * update * update docs * fix config name and docs * add pose_detection ut * add pose data * fix lint of model.py * add pose_detection_model ut * fix docs and docstrinf * add test_mmpose_models.py * fix yapf * fix lint * fix create input * support ort ut * fix yapf * fix docs * fix createinput * test ci bug * rm test1.py * fix yapf * fix flake8 * fix yapf * add config and update benchmark * fix table format * update mmpose benchmark * update benchmark for mmpose * run mmpose tests seperately in ci * fix lint * resolve comments * add trt ut config * fix test * fix tests * resolve comments * resolve comments * update tests Co-authored-by: VVsssssk <shenkun@pjlab.org.cn> Co-authored-by: hanrui1sensetime <hanrui1@sensetime.com>
124 lines
4.1 KiB
Python
124 lines
4.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import mmcv
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import numpy as np
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import pytest
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import torch
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from mmdeploy.codebase import import_codebase
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from mmdeploy.core import RewriterContext
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from mmdeploy.utils import Backend, Codebase
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from mmdeploy.utils.test import WrapModel, check_backend, get_rewrite_outputs
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import_codebase(Codebase.MMCLS)
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input = torch.rand(1)
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def get_invertedresudual_model():
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from mmcls.models.backbones.shufflenet_v2 import InvertedResidual
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model = InvertedResidual(16, 16)
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model.requires_grad_(False)
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return model
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def test_baseclassifier_forward():
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from mmcls.models.classifiers import BaseClassifier
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class DummyClassifier(BaseClassifier):
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def __init__(self, init_cfg=None):
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super().__init__(init_cfg=init_cfg)
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def extract_feat(self, imgs):
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pass
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def forward_train(self, imgs):
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return 'train'
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def simple_test(self, img, tmp, **kwargs):
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return 'simple_test'
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model = DummyClassifier().eval()
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model_output = model(input)
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with RewriterContext(
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cfg=mmcv.Config(dict()), backend='onnxruntime'), torch.no_grad():
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backend_output = model(input)
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assert model_output == 'train'
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assert backend_output == 'simple_test'
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def test_cls_head():
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from mmcls.models.heads.cls_head import ClsHead
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model = WrapModel(ClsHead(), 'post_process').eval()
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model_output = model(input)
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with RewriterContext(
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cfg=mmcv.Config(dict()), backend='onnxruntime'), torch.no_grad():
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backend_output = model(input)
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assert list(backend_output.detach().cpu().numpy()) == model_output
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def test_multilabel_cls_head():
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from mmcls.models.heads.multi_label_head import MultiLabelClsHead
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model = WrapModel(MultiLabelClsHead(), 'post_process').eval()
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model_output = model(input)
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with RewriterContext(
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cfg=mmcv.Config(dict()), backend='onnxruntime'), torch.no_grad():
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backend_output = model(input)
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assert list(backend_output.detach().cpu().numpy()) == model_output
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@pytest.mark.parametrize(
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'backend_type',
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[Backend.ONNXRUNTIME, Backend.TENSORRT, Backend.NCNN, Backend.OPENVINO])
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def test_shufflenetv2_backbone__forward(backend_type: Backend):
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check_backend(backend_type, True)
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model = get_invertedresudual_model()
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model.cpu().eval()
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if backend_type.value == 'tensorrt':
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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=backend_type.value,
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common_config=dict(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, 16, 28, 28],
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opt_shape=[1, 16, 28, 28],
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max_shape=[1, 16, 28, 28])))
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]),
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onnx_config=dict(
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input_shape=[28, 28], output_names=['output']),
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codebase_config=dict(type='mmcls', task='Classification')))
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else:
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deploy_cfg = mmcv.Config(
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dict(
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backend_config=dict(type=backend_type.value),
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onnx_config=dict(input_shape=None, output_names=['output']),
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codebase_config=dict(type='mmcls', task='Classification')))
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imgs = torch.rand((1, 16, 28, 28))
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model_outputs = model.forward(imgs)
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wrapped_model = WrapModel(model, 'forward')
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rewrite_inputs = {'x': imgs}
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rewrite_outputs, is_backend_output = get_rewrite_outputs(
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wrapped_model=wrapped_model,
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model_inputs=rewrite_inputs,
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deploy_cfg=deploy_cfg)
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if isinstance(rewrite_outputs, dict):
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rewrite_outputs = rewrite_outputs['output']
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for model_output, rewrite_output in zip(model_outputs, rewrite_outputs):
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model_output = model_output.cpu().numpy()
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if isinstance(rewrite_output, torch.Tensor):
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rewrite_output = rewrite_output.cpu().numpy()
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assert np.allclose(
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model_output, rewrite_output, rtol=1e-03, atol=1e-05)
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