118 lines
3.6 KiB
Python
118 lines
3.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from tempfile import NamedTemporaryFile, TemporaryDirectory
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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 mmdeploy.backend.onnxruntime as ort_apis
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from mmdeploy.codebase import import_codebase
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from mmdeploy.utils import Codebase, load_config
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from mmdeploy.utils.test import SwitchBackendWrapper
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try:
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import_codebase(Codebase.MMPOSE)
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except ImportError:
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pytest.skip(
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f'{Codebase.MMPOSE.value} is not installed.', allow_module_level=True)
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from .utils import (generate_datasample, generate_mmpose_deploy_config,
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generate_mmpose_task_processor)
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model_cfg_path = 'tests/test_codebase/test_mmpose/data/model.py'
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model_cfg = load_config(model_cfg_path)[0]
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deploy_cfg = generate_mmpose_deploy_config()
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onnx_file = NamedTemporaryFile(suffix='.onnx').name
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task_processor = generate_mmpose_task_processor()
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img_shape = (192, 256)
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heatmap_shape = (48, 64)
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# mmpose.apis.inference.LoadImage uses opencv, needs float32 in
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# cv2.cvtColor.
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img = np.random.rand(*img_shape, 3).astype(np.float32)
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img_path = 'tests/data/tiger.jpeg'
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num_output_channels = 17
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@pytest.mark.parametrize('imgs', [img, img_path])
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def test_create_input(imgs):
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inputs = task_processor.create_input(imgs, input_shape=img_shape)
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assert isinstance(inputs, tuple) and len(inputs) == 2
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def test_build_pytorch_model():
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from mmpose.models.pose_estimators.base import BasePoseEstimator
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model = task_processor.build_pytorch_model(None)
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assert isinstance(model, BasePoseEstimator)
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@pytest.fixture
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def backend_model():
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from mmdeploy.backend.onnxruntime import ORTWrapper
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ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
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wrapper = SwitchBackendWrapper(ORTWrapper)
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wrapper.set(outputs={
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'output': torch.rand(1, num_output_channels, *heatmap_shape),
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})
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yield task_processor.build_backend_model([''])
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wrapper.recover()
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def test_build_backend_model(backend_model):
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assert isinstance(backend_model, torch.nn.Module)
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def test_visualize():
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datasample = generate_datasample(img.shape[:2])
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output_file = NamedTemporaryFile(suffix='.jpg').name
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task_processor.visualize(
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img, datasample, output_file, show_result=False, window_name='test')
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def test_get_tensor_from_input():
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data = torch.ones(3, 4, 5)
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input_data = {'inputs': data}
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inputs = task_processor.get_tensor_from_input(input_data)
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assert torch.equal(inputs, data)
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def test_get_partition_cfg():
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try:
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_ = task_processor.get_partition_cfg(partition_type='')
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except NotImplementedError:
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pass
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def test_get_model_name():
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model_name = task_processor.get_model_name()
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assert isinstance(model_name, str) and model_name is not None
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def test_build_dataset_and_dataloader():
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from torch.utils.data import DataLoader, Dataset
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val_dataloader = model_cfg['val_dataloader']
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dataset = task_processor.build_dataset(
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dataset_cfg=val_dataloader['dataset'])
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assert isinstance(dataset, Dataset), 'Failed to build dataset'
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dataloader = task_processor.build_dataloader(val_dataloader)
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assert isinstance(dataloader, DataLoader), 'Failed to build dataloader'
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def test_build_test_runner(backend_model):
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from mmdeploy.codebase.base.runner import DeployTestRunner
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temp_dir = TemporaryDirectory().name
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runner = task_processor.build_test_runner(backend_model, temp_dir)
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assert isinstance(runner, DeployTestRunner)
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def test_get_preprocess():
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process = task_processor.get_preprocess()
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assert process is not None
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def test_get_postprocess():
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process = task_processor.get_postprocess()
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assert isinstance(process, dict)
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