mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
d4828c7836
* add mmdet unittests * remove redundant img meta info * import wrapper from backend util * force add wrapper * use smaller nuance * add seed everything * add create input part and some inference part unittests * fix lint * skip ppl * remove pyppl * add dataset files * import ncnn_ext inside ncnn warpper * use cpu device to create input * add pssd and ptsd unittests * clear mmdet unittests * wrap function to enable rewrite * refine codes and resolve comments * move mmdet inside test func * remove redundant line * test visualize in mmdeploy.apis * use less memory * resolve comments * fix ci * move pytest.skip inside test function and use 3 channel input
105 lines
3.1 KiB
Python
105 lines
3.1 KiB
Python
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.apis.utils import (build_dataloader, build_dataset, create_input,
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get_tensor_from_input)
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from mmdeploy.utils.constants import Codebase, Task
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def test_create_input():
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task = Task.OBJECT_DETECTION
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test = dict(pipeline=[{
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'type': 'LoadImageFromWebcam'
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}, {
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'type':
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'MultiScaleFlipAug',
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'img_scale': [32, 32],
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'flip':
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False,
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'transforms': [{
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'type': 'Resize',
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'keep_ratio': True
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}, {
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'type': 'RandomFlip'
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}, {
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'type': 'Normalize',
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'mean': [123.675, 116.28, 103.53],
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'std': [58.395, 57.12, 57.375],
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'to_rgb': True
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}, {
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'type': 'Pad',
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'size_divisor': 32
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}, {
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'type': 'DefaultFormatBundle'
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}, {
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'type': 'Collect',
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'keys': ['img']
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}]
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}])
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data = dict(test=test)
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model_cfg = mmcv.Config(dict(data=data))
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imgs = [np.random.rand(32, 32, 3)]
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inputs = create_input(
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Codebase.MMDET,
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task,
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model_cfg,
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imgs,
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input_shape=(32, 32),
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device='cpu')
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assert inputs is not None, 'Failed to create input'
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@pytest.mark.parametrize('input_data', [{'img': [torch.ones(3, 4, 5)]}])
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def test_get_tensor_from_input(input_data):
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inputs = get_tensor_from_input(Codebase.MMDET, input_data)
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assert inputs is not None, 'Failed to get tensor from input'
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def test_build_dataset():
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data = dict(
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test={
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'type': 'CocoDataset',
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'ann_file': 'tests/test_mmdet/data/coco_sample.json',
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'img_prefix': 'tests/test_mmdet/data/imgs/',
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'pipeline': [
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{
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'type': 'LoadImageFromFile'
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},
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]
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})
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dataset_cfg = mmcv.Config(dict(data=data))
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dataset = build_dataset(
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Codebase.MMDET, dataset_cfg=dataset_cfg, dataset_type='test')
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assert dataset is not None, 'Failed to build dataset'
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dataloader = build_dataloader(Codebase.MMDET, dataset, 1, 1)
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assert dataloader is not None, 'Failed to build dataloader'
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def test_clip_bboxes():
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from mmdeploy.mmdet.export import clip_bboxes
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x1 = torch.rand(3, 2) * 224
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y1 = torch.rand(3, 2) * 224
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x2 = x1 * 2
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y2 = y1 * 2
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outs = clip_bboxes(x1, y1, x2, y2, [224, 224])
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for out in outs:
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assert int(out.max()) <= 224
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def test_pad_with_value():
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from mmdeploy.mmdet.export import pad_with_value
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x = torch.rand(3, 2)
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padded_x = pad_with_value(x, pad_dim=1, pad_size=4, pad_value=0)
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assert np.allclose(
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padded_x.shape, torch.Size([3, 6]), rtol=1e-03, atol=1e-05)
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assert np.allclose(padded_x.sum(), x.sum(), rtol=1e-03, atol=1e-05)
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@pytest.mark.parametrize('partition_type', ['single_stage', 'two_stage'])
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def test_get_partition_cfg(partition_type):
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from mmdeploy.mmdet.export import get_partition_cfg
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partition_cfg = get_partition_cfg(partition_type=partition_type)
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assert partition_cfg is not None
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